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***Draft***

**National Polar-orbiting Operational Environmental Satellite System
\[NPOESS\]**

**Preparatory Project \[NPP\]**

**NPP**

**Calibration and Product Validation**

**Plan**

**December 30, 2001**

**Updates for OMPS: May 2003**

NATIONAL POLAR-ORBITING OPERATIONAL\
ENVIRONMENTAL SATELLITE SYSTEM (NPOESS)

INTEGRATED PROGRAM OFFICE

and the

NATIONAL AERONAUTICS AND SPACE ADMINISTRATION

**Calibration and Product Validation Plan**

Prepared by: NPP Calibration / Validation Team

Reviewed by:
\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_

Stephen A. Mango, NPOESS IPO Date

NPOESS Project Scientist

\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_

Robert E. Murphy, NASA GSFC Date

GSFC Project Scientist

Approved by:
\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_

John D. Cunningham, NPOESS IPO Date

System Program Director, NPOESS

\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_

Phil Sabelhouse, NASA GSFC Date

Associate Director of Flight Projects for EOS-G

**List of Contributors**

**[Name Area of Contribution/Responsibility]{.underline}**

Gail Bingham CrIS Sensor, Characterization and Calibration

Ralph Bennartz MW Cal/Val

Wayne Esaias Visible Calibration, Ocean EDRs

Larry Flynn OMPS

Mitch Goldberg CrIS, Product QA

Andy Heidinger Atmospheric/Cloud EDRs

Ernest Hilsenrath OMPS

John Hornstein OMPS

R. O. Knuteson Field Experiment, IR Radiative Transfer

Louis Kouvaris CrIMSS and CDRs

Allen Larar CrIMSS Chair

Chuck McClain Data management

Steve Mango Government Team Co-Chair

Paul Menzel VIIRS Chair

Peter Minnett VIIRS SDR, SST

Dan Mooney CrIS Sensor

Jeff Morisette Validation, VIIRS Land

Bob Murphy Government Team Co-Chair

Hassan Ouaidrari VIIRS Cal/Land, NPP Cal/Val Executive Secretary

Steve Platnick VIIRS Atmosphere

Jeff Privette VIIRS Land EDRs

Gail Reichert NPP Direct Broadcast

Hank Revercomb CrIS EDRs, Sensor Design, Characterization & Calibration

Joe Rice NIST Calibration and Verification

David Roy Data Issues (QA)

Dave Staelin ATMS, MW Radiative Transfer, ATMS Products

Bill Smith Product Grouping, CrIS Sensor/CrIS/ATMS EDRs

Dave Starr Cal/Val Management Issues

Larrabee Strow CrIS EDRs, Forward Model, Characterization & Calibration

Joel Susskind CrIMSS and CDRs

Les Thompson Sensor Design

Dave Tobin CrIS SDR, Field Experiment, IR Radiative Transfer

Fuzhong Weng Microwave Sounding, Sounding Products

Jack Xiong VIIRS Characterization and Calibration, Imagery EDR

**[List of Contents]{.underline}**

1 Executive Summary [8](#executive-summary)

1.1 The Government Team [8](#the-government-team)

1.1.1 IPO Activities [9](#ipo-activities)

1.1.2 NASA Activities [9](#nasa-activities)

1.2 NPP Calibration Validation Activities
[10](#npp-calibration-validation-activities)

1.2.1 Sensor Test Data [10](#sensor-test-data)

1.2.2 Simulated data [10](#simulated-data)

1.2.3 Aircraft Validation Data [10](#aircraft-validation-data)

1.2.4 Other Sensors Validation Data [11](#other-sensors-validation-data)

1.2.5 Coordinated Measurement Campaigns
[11](#coordinated-measurement-campaigns)

1.2.6 Data Processing [11](#data-processing)

1.3 Phasing of Activities and Evolution of This Plan
[12](#phasing-of-activities-and-evolution-of-this-plan)

2 Introduction [14](#introduction)

2.1 Bridging the EOS and NPOESS Eras
[16](#bridging-the-eos-and-npoess-eras)

2.1.1 Maintaining Continuity of Data Records
[16](#maintaining-continuity-of-data-records)

2.1.2 NPP Bridging Mission [16](#npp-bridging-mission)

2.1.3 NASA Project Science Office (PSO)
[18](#nasa-project-science-office-pso)

2.1.4 Integrated Program Office (IPO)
[18](#integrated-program-office-ipo)

2.2 Overview of NPP Sensors [19](#overview-of-npp-sensors)

2.2.1 VIIRS [19](#viirs)

2.2.2 CrIS [19](#cris)

2.2.3 ATMS [19](#atms)

2.3 Overview of Calibration/Validation Efforts
[20](#overview-of-calibrationvalidation-efforts)

2.3.1 Pre-launch Test Data [22](#pre-launch-test-data)

2.3.2 Ground Validation Network [23](#ground-validation-network)

2.3.3 Field Experiments [23](#field-experiments)

2.3.4 Intercomparison with Other Satellite Sensors
[24](#intercomparison-with-other-satellite-sensors)

2.4 Getting Ready for NPP [24](#getting-ready-for-npp)

2.4.1 NPP Calibration and Validation Program Management
[24](#npp-calibration-and-validation-program-management)

2.4.2 Calibration and Validation Team Responsibilities
[25](#calibration-and-validation-team-responsibilities)

2.4.3 Timeline of Major Activities [25](#timeline-of-major-activities)

2.4.4 Participants [25](#participants)

3 NPP Product Summary [26](#npp-product-summary)

3.1 NPP Product Generation [26](#npp-product-generation)

3.1.1 IDPS Operational Products [26](#idps-operational-products)

3.1.2 SDS Climate Research Products [27](#sds-climate-research-products)

3.2 Description of Level 1 Products (SDRs/Level 1B)
[27](#description-of-sdrslevel-1b-products)

3.3 Description of Level 2 (EDR/CDRs) Products
[28](#description-of-level-2-edrcdrs-products)

3.4 Summary of EDRs/CDRs Performance Requirements
[31](#summary-of-edrscdrs-performance-requirements)

4 Instrument Pre-launch Characterization and Calibration
[33](#instrument-pre-launch-characterization-and-calibration)

4.1 Common Issues for Instrument Characterization and Calibration
[33](#common-issues-for-instrument-characterization-and-calibration)

4.2 VIIRS Pre-Launch Characterization and Calibration
[35](#viirs-pre-launch-characterization-and-calibration)

4.2.1 VIIRS Instrument Characterization
[36](#viirs-instrument-characterization)

4.2.2 On-Board Calibrator Characterization
[37](#on-board-calibrator-characterization)

4.3 CrIS Pre-launch Characterization and Calibration
[38](#cris-pre-launch-characterization-and-calibration)

4.3.1 CrIS Radiometric Calibration [38](#cris-radiometric-calibration)

4.3.2 CrIS Noise Performance Verification
[39](#cris-noise-performance-verification)

4.3.3 CrIS Spectral Calibration [39](#cris-spectral-calibration)

4.3.4 Additional CrIS Characterization
[40](#additional-cris-characterization)

4.4 ATMS Pre-Launch Characterization and Calibration
[40](#atms-pre-launch-characterization-and-calibration)

4.4.1 ATMS Temperature Sensitivity (NEDT)
[40](#atms-temperature-sensitivity-nedt)

4.4.2 ATMS Bandpass Characteristics [40](#atms-bandpass-characteristics)

4.4.3 ATMS System Linearity [40](#atms-system-linearity)

4.4.4 ATMS Calibration [41](#atms-calibration)

4.4.5 ATMS Antenna Pattern Measurements
[41](#atms-antenna-pattern-measurements)

4.4.6 ATMS Polarization Angle Alignment
[41](#atms-polarization-angle-alignment)

4.5 Verification of NPOESS Calibration/Validation Standards Using NIST
Traceability
[45](#verification-of-npoess-calibrationvalidation-standards-using-nist-traceability)

4.5.1 Plan Overview [45](#plan-overview)

4.5.2 Summary of the Intercomparison Activities
[46](#summary-of-the-intercomparison-activities)

5 Level 1 Product Post-launch Validation
[49](#level-1-product-post-launch-validation)

5.1 Radiative Transfer Models Calculations
[50](#radiative-transfer-models-calculations)

5.1.1 VIIRS Radiative Transfer (Visible to Infrared)
[51](#viirs-radiative-transfer-visible-to-infrared)

5.1.2 CrIS Radiative Transfer (Infrared)
[52](#cris-radiative-transfer-infrared)

5.1.3 ATMS Radiative Transfer Model (Microwave)
[54](#atms-radiative-transfer-model-microwave)

5.2 Radiance Validation [62](#radiance-validation)

5.2.1 Visible, Infrared and Microwave Radiance Validation
[62](#visible-infrared-and-microwave-radiance-validation)

5.2.2 Approaches for Routine Radiance Validation
[63](#approaches-for-routine-radiance-validation)

5.2.3 Approaches Planned for Radiance Validation
[64](#approaches-planned-for-radiance-validation)

5.3 Spatial co-registration validation
[70](#spatial-co-registration-validation)

5.3.1 Instrument and spacecraft alignment data verification
[71](#instrument-and-spacecraft-alignment-data-verification)

5.3.2 Band-to-Band Registration (BBR) verification
[71](#band-to-band-registration-bbr-verification)

5.3.3 Instrument-to-Instrument Registration
[71](#instrument-to-instrument-registration-in-addition-to-pre-launch-spatial-characterization-that-includes-the-determination-of-band-to-band-registration-it-is-essential-to-validate-and-monitor-on-orbit-variation-of-bbr-and-its-long-term-stability.-for-sensors-with-multi-focal-plane-assemblies-this-also-includes-the-co-registration-among-different-focal-planes.-for-comparison-of-science-products-derived-from-different-sensors-sensor-to-sensor-co-registration-is-also-needed.-most-likely-this-can-only-be-done-from-on-orbit-observations.)

5.3.4 Sensor Navigation Validation [72](#sensor-navigation-validation)

6 Level 2 and Higher Product Post-launch Validation
[73](#level-2-and-higher-product-post-launch-validation)

6.1 Validation of Groups of NPP Products
[74](#validation-of-groups-of-npp-products)

6.1.1 Surface Validation Sites [76](#surface-validation-sites)

6.1.2 Airborne Validation Platforms [77](#airborne-validation-platforms)

6.1.3 Satellite Sensor-to-Sensor Cross-Validation
[77](#satellite-sensor-to-sensor-cross-validation)

6.2 Activities Supporting NPP Products Validation
[77](#activities-supporting-npp-products-validation)

6.3 Routine Validation Approaches [78](#routine-validation-approaches)

6.3.1 Product Validation Using NWP Analysis
[78](#product-validation-using-nwp-analysis)

6.3.2 Product Validation Using Operational Radiosondes
[79](#product-validation-using-operational-radiosondes)

6.3.3 Regeneration of Products Using Ancillary Data
[79](#regeneration-of-products-using-ancillary-data)

6.3.4 Product Validation Using Sun-photometer Networks (AERONET)
[79](#product-validation-using-sun-photometer-networks-aeronet)

6.3.5 Product Validation Using Ship Cruises and Buoys
[79](#product-validation-using-ship-cruises-and-buoys)

6.3.6 Product Validation Using Tower Data (EOS Validation Core Sites)
[80](#product-validation-using-tower-data-eos-validation-core-sites)

6.4 Planned Validation Approaches for NPP EDR Operational Products
[80](#planned-validation-approaches-for-npp-edr-operational-products)

6.4.1 Validation Approaches for Atmospheric Sounding Products
[80](#validation-approaches-for-atmospheric-sounding-products)

6.4.2 Validation Approaches for Aerosol Products
[82](#validation-approaches-for-aerosol-products)

6.4.3 Validation Approaches for Cloud Products
[83](#validation-approaches-for-cloud-products)

6.4.4 Validation Approaches for Land Products
[83](#validation-approaches-for-land-products)

6.4.5 Validation Approaches for Ocean Products
[86](#validation-approaches-for-ocean-products)

6.4.6 Validation Approaches for Snow/Ice Products
[87](#validation-approaches-for-snowice-products)

6.5 Planned Validation Approaches for NPP CDR products
[89](#planned-validation-approaches-for-other-npp-edr-and-cdr-products)

6.5.1 Atmospheric Sounding Profile Validation
[89](#atmospheric-sounding-profile-validation)

6.5.2 Validation Approaches for Aerosol Products
[92](#validation-approaches-for-aerosol-products-1)

6.5.3 Validation Approaches for Cloud Products
[93](#validation-approaches-for-cloud-products-1)

6.5.4 Validation Approaches for Land Products
[93](#validation-approaches-for-land-products-1)

6.5.5 Validation Approaches for Ocean Products
[94](#validation-approaches-for-ocean-products-1)

7 Data Processing Support for Calibration, Quality Assessment and
Validation
[95](#data-processing-support-for-calibration-quality-assessment-and-validation)

7.1 Calibration, Quality Assessment and Validation
[95](#calibration-quality-assessment-and-validation)

7.2 NPP Data Approach [96](#npp-data-approach)

7.3 Production System Support for Calibration, Quality Assessment, and
Validation
[97](#production-system-support-for-calibration-quality-assessment-and-validation)

7.3.1 Interface Data Processing Segment (IDPS)
[97](#interface-data-processing-segment-idps)

7.3.2 Science Data Segment (SDS) [97](#science-data-segment-sds)

7.3.3 Direct Broadcast System [98](#direct-broadcast-system)

7.4 Archive and Distribution System Support for Calibration, Quality
Assessment, and Validation
[98](#archive-and-distribution-system-support-for-calibration-quality-assessment-and-validation)

7.4.1 Archive & Distribution Segment (ADS)
[99](#archive-distribution-segment-ads)

7.4.2 SDS archive [99](#sds-archive)

7.4.3 ADS and SDS Archive Requirements
[99](#ads-and-sds-archive-requirements)

7.5 Validation Data Set Needs and Management
[100](#validation-data-set-needs-and-management)

7.6 Examples of Validation Data Supporting QA System
[101](#examples-of-validation-data-supporting-qa-system)

**[Appendices]{.underline}**

Appendix A: Overall Approach to Calibration and Validation
[103](#__RefHeading___Toc534619217)

Appendix B: EDR and CDR Performance Requirements
[113](#__RefHeading___Toc534619218)

Appendix C: Verification to NIST Standards
[132](#__RefHeading___Toc534619219)

Appendix D: VIIRS Instrument Characterization and Calibration Tests
[143](#__RefHeading___Toc534619220)

Appendix E: CrIS Instrument Characterization and Calibration Tests
[150](#__RefHeading___Toc534619221)

Appendix F: ATMS Instrument Characterization and Calibration Tests
[152](#__RefHeading___Toc534619222)

Appendix G: Surface Based Networks / Field Campaigns Relevant to NPP
Cal/Val [156](#__RefHeading___Toc534619223)

Appendix H: EDR/CDRs Validation Specifics
[189](#__RefHeading___Toc534619224)

Appendix I: Matrix of Who, What, When and How Much (from Co-Chairs)
[217](#__RefHeading___Toc534619225)

Appendix J: Definitions [218](#__RefHeading___Toc534619226)

Appendix K: References [222](#__RefHeading___Toc534619227)

Appendix L: Acronyms [224](#__RefHeading___Toc534619228)

**[List of Figures]{.underline}**

Figure 2‑1: Agency Responsibility for NPP Segments and Data Flow
[15](#_Ref519068341)

Figure 4‑1: Instrument Characterization Occurs at all Levels of Assembly
[34](#__RefHeading___Toc534619230)

Figure 5‑1: High Level Schematic of the Radiance Validation Process
[49](#__RefHeading___Toc534619231)

Figure 6‑1: High Level Schematic of the EDR/CDRs Validation Process
[73](#__RefHeading___Toc534619232)

**[List of Tables]{.underline}**

Table 2‑1: Data Set Processing Levels [17](#__RefHeading___Toc534619233)

Table 3‑1: SDR/Level 1B information required for NPP sensors
[28](#__RefHeading___Toc534619234)

Table 3‑2: IDPS EDRs, Product Group, & Primary Associated Instruments
[30](#__RefHeading___Toc534619235)

Table 3‑3: Potential CDRs, Product Group, and Primary Associated
Instruments [31](#__RefHeading___Toc534619236)

Table 4‑1: Characterization and Calibration of the VIIRS Instrument
[37](#__RefHeading___Toc534619237)

Table 4‑2: Plan of the Intercomparison Activities during the NPP
Pre-Launch Phase [48](#__RefHeading___Toc534619238)

Table 4‑3: Participants in NIST Traceability Verification Activities
[48](#__RefHeading___Toc534619239)

Table 6‑1: NPP Validation Strategies [74](#__RefHeading___Toc534619240)

Table 6‑2: Validation Strategies for NPP EDR/CDRs
[75](#__RefHeading___Toc534619241)

Table 6‑3: NPP Ground Validation Sites
[76](#__RefHeading___Toc534619242)

# Executive Summary

The calibration and product validation for the NPOESS Preparatory
Project (NPP) focuses on (1) verification of instrument pre-launch
characterization and post-launch calibration, (2) participation in the
assessment of operational and science algorithms, (3) validation of
operational and science products, and (4) participation in the
evaluation of overall system performance.

#  {#section .unnumbered}

This plan describes the structure and plans of the Government Team's
contributions to the characterization, calibration and validation of the
sensors, algorithms and data products of the NPOESS Preparatory Project
(NPP). The Government Team consists of two coordinated activities; one
funded and managed by the IPO Cal/Val Team, which is made up of the
Science and Engineering Team from the IPO Internal Government Studies
(IGS) Program and the IPO Operational Algorithm Teams (OATs). The second
is NASA Cal/Val Team activities, conducted by the Global Change Science
Team (GCST) and the NPP Calibration Support Team (NCST).

These activities will be, in turn, coordinated with those of the IPO's
NPOESS Engineering and Manufacturing Development (EMD) contractor. The
EMD contractor, called also the NPOESS Shared System Performance
Responsibility (SSPR) contractor, has responsibility to develop the
sensors, algorithms and data production systems, and to assure the
quality of the resultant Environmental Data Records (EDRs). The SSPR
contractor program will include the activities of the Sensor/Algorithm
Subcontractors.

The Government Team's role is to provide government expertise where
appropriate and to assess the final results on behalf of the U.S.
government. The results of the Government Team's Calibration and
Validation of Products activities described in this plan will provide
support to the IPO in its evaluation of the SSPR performance and provide
any suggestions for consideration of system and algorithm improvements.

For the purposes of this plan, the term "Government Team" refers to any
or all of the groups listed above, e.g. IGS, OATs, GCST and NCST. A
later version of this plan will detail the specific activities to be
conducted by the specific groups.

## The Government Team

In order to accomplish its activities, the Government Team will
participate in the performance verification of the NPP sensors. This
requires timely access to contractor test plans, calibration algorithms
and research and operational codes prior to the conduct of tests, and
timely access to full test results. It is envisioned that the Government
Team will be an active participant in the SSPR Contractor's Integrated
Product Teams (IPTs).

The Government Team has prepared this NPP Calibration and Product
Validation Plan for defining their role in the NPP
Calibration/Validation Process. This Plan will be made available to the
SSPR contractor prior to the submission of the Contractor's
Calibration/Validation Plan. The SSPR Contractor's Plan will define
their role in a shared NPP Calibration/Validation effort. This SSPR
Contractor's Plan will serve as the basis for a portion of an acceptance
test procedure for the Products produced in the system provided by the
SSPR Contractor.

This plan is specific to the NPP mission and it addresses only those
sensors (VIIRS, CrIS, ATMS, and OMPS) that are on NPP and their specific
EDRs. The activities conducted under this plan will serve as pathfinder
to the subsequent task of characterizing and calibrating the full suite
of sensors and validating the full suite of EDRs during the NPOESS era.

The IPO and NASA activities will be separately funded and managed. The
IPO activities will seek to validate the end-to-end system performance
against the explicit requirements of the NPOESS as detailed in the
Integrated Operational Requirements Document (IORD) and the specific
details of the sensor and algorithm specifications. The NASA activities
will seek to validate the system performance for certain data products
for the purposes of global change research. The NASA activities will
place greater emphasis on longer term, consistently processed data sets
utilizing optimal ancillary data. The IPO activities emphasis will be on
validating the operational products that are produced with more rapid
data delivery and necessarily involves high-speed availability of
ancillary data and high-performance execution of
state-of-the-art-science algorithms. The NASA activities will contribute
to the Government Team efforts to conduct the overall calibration and
validation of the NPP instruments.

### IPO Activities

The IPO will manage the NPP Calibration/Validation of RDR, SDR and EDR
Products. The IPO Calibration/Validation Team consists of scientists and
engineers of the NPOESS Internal Government Studies (IGS) and the NPOESS
Operational Algorithms Teams (OATs). The IPO Calibration/Validation Team
activities are funded by the IPO and managed by the IPO Chief Scientist.
Participants include scientists and engineers from universities,
government laboratories and centers and federally funded research and
development centers. Key government participants are drawn from the
NPOESS user agencies (DoD, DOC/NOAA and NASA) and the DOC/National
Institute of Standards & Technology (NIST). Part of the NASA
contribution is through this mechanism. The IPO airborne risk-reduction
and Calibration/Validation program serves as a resource for both the IPO
supported and NASA supported activities.

### NASA Activities

The independently managed NASA activity utilizing NPP is primarily
within the Global Change Science Team (GCST) which consists of a
competed science team supported by NASA internally funded project
activities. Key government participants are drawn from the NASA Goddard
Space Flight Center, the Langley Research Center, and the Jet Propulsion
Laboratory. The NASA airborne science program serves as a resource for
both the IPO supported and NASA supported activities. A NASA Research
Announcement (NRA) will be used to solicit proposals for specific
activities (including Calibration/Validation) by university, government,
and corporate investigators.

The Project Scientist will manage an NPP Calibration Support Team
(NCST), to develop research quality Level 1B data products for VIIRS,
CrIS, ATMS, and OMPS. NCST will work with the government partners and
the contractors to assure that the sensors are fully characterized and
calibrated during the pre-launch and post-launch phases. NCST will
conduct coordinated analyses of the data and instrument trending during
the NPP mission, and share these results with the IPO and the sensor
vendors in a timely manner.

## NPP Calibration Validation Activities

The IPO will oversee the overall, operational generation of NPOESS
products at the Interface Data Processing Segment (IDPS); the management
of agency product evolution will be the responsibility of the NESDIS
Product Oversight Panels, the DoD Product Panels and the IPO Integrated
Product Team. Operational calibration, validation, and evolution of
NPOESS products will rely heavily on the procedures and approaches
demonstrated in the execution of this NPP Calibration and Validation
Plan.

NASA will oversee the validation of research products and global change
data sets generated by the GCST in the Science Data Segment (SDS). The
results of the validation activities by the GCST will be incorporated to
provide guidance to the IPO in its evaluation of the EMD performance and
to suggest and/or provide algorithm improvements.

Pre-launch activities focus on development of validation procedures,
preliminary validation of new algorithms (and radiative transfer models)
using existing space-borne and airborne sensors, verification and
characterization of instrument performance over the ranges of operation,
and estimation of the precision, accuracy, and overall uncertainty of
the derived products. An essential feature of the plan is NIST
traceability. Post-launch emphasis is on sensor calibration and
validation of data products, leading to algorithm refinement.

Validation will be conducted using independent means to assess
uncertainties of geophysical data products derived from instrument
system outputs. This is generally approached by direct comparison with
independent correlative measurements from ground-based networks,
comprehensive test sites, and field campaigns, along with comparisons
with independent satellite retrieval products from instruments on the
same and different platforms. It is essential to have an integrated
strategy for validation, including contributions from airborne field
campaigns, surface networks, as well as satellites.

### Sensor Test Data

The Government Team will work closely with the vendors during the
pre-launch testing and characterization to assure that the post-launch
instrument performance is understood, and that sensor radiances are
correctly assimilated and tested.

### Simulated data

The Government Team will work closely with the vendors to assure that
the sensors' radiance and EDRs are correctly simulated, and performance
of EDRs' algorithms and quality of products are carefully tested using
simulated data.

### Aircraft Validation Data

Aircraft data is important to the program both before and after launch.
Before launch, it provides the means to demonstrate expected product
performance and to establish algorithm approaches that will work in the
presence of actual environmental conditions. After launch, it is a major
part of system validation. The NAST, Scanning HIS, MAS, PSR, APMIR,
MASTER and AVIRIS aircraft instruments are key components for performing
product validation.

### Other Sensors Validation Data

Similarly, data from precursor sensors are used to test algorithms in
the pre-launch phase and to validate the data products from the NPP
sensors. MODIS serves as the source of test data for VIIRS algorithms.
AIRS will serve as the source of test data for CrIS while AMSU/HSB will
serve as the source for ATMS. Validation will be done against these
sensors plus OLS and AVHRR for VIIRS and HIRS/AMSU and possibly
IASI/AMSU for CrIS/ATMS. SOLSE/LORE, POAM III, SAGE II and III, HALOE,
TOMS, SBUV/2, GOME, OMI, HRDLS, MLS SCIAMACHY, and balloon ozonesondes
will serve as sources of test data for OMPS.

### Coordinated Measurement Campaigns

It is important for the Government Team to plan for the necessary data
gathering and data analysis which can suggest instrument processing
adjustments and algorithm evolution that will foster the maximum
utilization of NPP data. This will be an intensive effort after CrIS,
VIIRS, ATMS, and OMPS launch on NPP and continue throughout the life of
NPP and after that through the NPOESS Era.

IPO and NASA began conducting missions with the NAST, S-HIS, and MAS
instruments in 1997 and will continue such missions throughout the
remainder of this decade. Significant missions already conducted include
the SAFARI mission at EOS sites in South Africa, and a joint water vapor
experiment with the DOE centered around the Atmospheric Radiation
Measurement (ARM) site in Oklahoma. NOAA has been and will continue to
conduct Calibration/Validation of the operational polar orbiting visible
and infrared imagers and the infrared and microwave sounders throughout
the decade; DoD has been and will continue to conduct
Calibration/Validation of the operational polar orbiting microwave
imagers and sounders and visible/infrared imagers throughout the decade;
inter-calibration of the ongoing series of POES and DMSP sensors and the
associated sounding and imaging products is a high priority for these
efforts.

Major expenses such as ship and aircraft deployments and other field
campaigns will be jointly funded and managed. In addition, many of these
activities will benefit from other agency programs. Key among those are
the ongoing activities of the ARM-CART program of DOE, the LTER program
of NSF, the operational Calibration/Validation programs of NOAA, DoD and
IPO, programs such as NASA's AERONET and MOBY future field campaigns,
and the existing networks of balloon borne ozonesondes.

### Data Processing

NPP data will be processed at two facilities: 1. Interface Data
Processing Segment (IDPS), requiring operational capabilities; and 2.
Science Data Segment (SDS) for climate research purposes.

***IDPS (Operational Processing)***

The data from NPP instruments will be processed by the Interface Data
Processing Segment (IDPS) and delivered to the users at the operational
facilities in the form of Raw Data Records (RDRs), Sensor Data Records
(SDRs), and Environmental Data Records (EDRs). Participants in the IPO
Calibration/Validation Team will have access to these data either from
direct broadcast, from arrangements with the operational facilities at
NESDIS and AFWA, or from the NOAA Long term Archive.

***SDS (Climate Research Processing)***

The NASA Science Data Segment (SDS) will provide a production facility
with reprocessing capabilities of NPP data, using algorithms developed
by the GCST. Their data products are identified as Level 1B and Climate
Data Records (CDRs) since they will be optimized for climate studies.
The SDS will also support access to RDRs, and selected SDRs and EDRs
processed at IDPS to support validation activities by the GCST teams.
The SDS will maintain a store of all mission data for the life of the
mission, except SDRs and EDRs.

## Phasing of Activities and Evolution of This Plan

This plan is expected to evolve. The activities detailed here are
already underway and they will continue through to the end of the NPP
mission. Post-launch activities will peak in the year following the
launch of NPP assuming full delivery of all data products is underway by
launch plus six (6) months. Sustaining and intermittent
Calibration/Validation activities will continue throughout the NPP
mission and should overlap the first NPOESS satellite system
Calibration/Validation efforts. This plan will be updated periodically.

As other government-sponsored participants are identified, this plan
will be expanded to include them by incorporation of, or reference, to
their independent plans. A separate plan will be developed to reflect
the coordination of the government-sponsored activities with those
detailed here.

The plan introduces the NPP and the instruments to be flown, describes
the associated products, details pre-launch characterization and
calibration efforts using NIST traceability, lists the validation
approaches for the level 1 and 2 products (including ancillary data from
ground based, airborne, and other satellite systems), and recommends
data processing support necessary for quality assessment. Appendices
include Environmental Data Record (EDR) performance requirements, lists
of characterization and calibration tests, summaries of field
experiments and ground networks involved in the NPP Cal/Val, and various
science supporting references.

In summary, the NPP Cal/Val Plan:

> \* Identifies efforts necessary for verification of pre-launch and
> post-launch instrument characterization and calibration.
>
> \* Defines NPP products (CDRs will be defined after NRA selection), as
> well as the testing and evaluation necessary to ensure product
> quality.
>
> \* Defines validation approaches and validation data sets

\* Identifies types of field experiments that will be needed to support
NPP Cal/Val efforts as well as NOAA and DoD familiarization, product
development, and test with advanced NWP models.

\* Identifies procedures for user evaluation and feedback.

> \* Identifies the necessary linkages between NOAA, DoD and NASA
> organizational elements using NPP data, products (RDRs, SDRs, EDRs and
> CDRs), and services
>
> \* Provides a product management structure for the Government Team,
> stresses linkages to the NESDIS Product Oversight Panels and the DoD
> Product Panels, and identifies the need for the multi-agency technical
> advisory committee, the IPO Integrated Product Team (IPT).
>
> \* Identifies resources that are needed to carry out this plan.

# Introduction 

The National Polar-orbiting Operational Environmental Satellite System
(NPOESS) is a joint DOC/NOAA, DoD and NASA program merging the current
POES & DMSP systems into a common system of polar satellites with the
goal of providing meteorological, atmospheric, oceanographic,
terrestrial, climate, space environment and other environmental data
products operationally.

In order to achieve these goals, these programs must produce accurate
and precise long-time series of radiometric measurement data from
multiple instruments on multiple platforms. Understanding and correctly
interpreting these data require the ability to separate geophysical
variability from instrument response changes in the observed signal
during the missions. This requires a detailed pre-launch, system-level
instrument characterization, as well as extensive in-flight calibration
and validation activities.

The NPP defines a program to implement and demonstrate a satellite
platform, proto-flight instruments, ground data system, command and
control system, and algorithms for EDRs and CDRs. It is a bridge between
NASA EOS era science measurements and the start of NPOESS full
operational capabilities. NPP provides a linkage between EOS
instrumentation and the NPOESS series of instruments. NPP strives to use
equipment and procedures developed for EOS instrumentation and POES/DMSP
instrumentation for both pre-launch and post-launch testing.

NPP is a joint agency program. The Table 2-1 and Figure 2-1 show in
words and graphics the division of responsibilities between IPO and
NASA.

**Table 2-1:** NPP Division of Responsibilities between IPO and NASA

**[IPO]{.underline}**

-   Joint Program Management

-   VIIRS Instrument/Algorithms

-   CrIS Instrument/Algorithms

-   OMPS Instrument/Algorithms

-   Command, Communications, and Control Segment

-   Interface Data Processing Segment -- for RDR, SDR and EDR Production

-   Mission Management and Satellite Operations

-   Manage NPP cal/val of RDRs, SDRs and EDRs

-   Science Support (IGS)

**[NASA]{.underline}**

-   Joint Program Management

-   Mission System Engineering, Integration and Test

-   ATMS Instrument/Algorithms

-   Spacecraft and Subsystem/instrument Integration to the S/C

-   Launch Vehicle and Associated Activities

-   Science Data Segment -- for Global Change Science Initiative

-   Manage NPP cal/val of Level 1B and CDRs

-   Science Support (NRA)

![](media/image2.wmf){width="5.597222222222222in"
height="4.377083333333333in"}

[]{#_Ref519068341 .anchor}

Figure 2‑1: Agency Responsibility for NPP Segments and Data Flow

A description of NPP and NPOESS missions may be found at:

<http://jointmission.gsfc.nasa.gov/>

[http://npoesslib.ipo.noaa.gov](http://npoesslib.ipo.noaa.gov/)

This NPP Calibration and Product Validation Plan document provides a
roadmap for the validation of Level 1A and 1B Products, RDRs and SDRs
(calibrated/navigated radiances) and Level 2 and higher Products (EDRs
and CDRs). The general strategy and specific implementation plans for
validating VIIRS, CrIMSS and OMPS products are presented. CrIMSS, the
Cross-track Infrared/Microwave Sounding Suite is the combination of the
key component instruments, CrIS, and ATMS.

The elements of NPP Cal / Val Plan include:

-   Pre-launch participation, consultation, and recommendation regarding
    instrument design, test plans, and performance verification

-   Participation in the formal review processes

-   Contribution to post-launch instrument characterization and trending
    analysis

-   Timely access to test data and access to instrument contractor
    calibration algorithms

-   Development of research quality Level 1 products and validation
    during the mission

-   Support of calibration and validation of instrument radiance (Level
    1, RDRs and SDRs), EDRs and CDRs.

-   Coordination within the IPO (DOC/NOAA, DoD and NASA) and with the
    external scientific community to assess the use of research
    algorithms in the operational system to produce EDRs with improved
    geophysical parameters.

## Bridging the EOS and NPOESS Eras

### Maintaining Continuity of Data Records

NPP sustains some of the measurement series initiated under the EOS
Terra and Aqua missions and the POES/DMSP missions. These measurement
series are being considered for continuation operationally in the NPOESS
missions. As such it is essential that direct comparisons be made
between EOS and NPP instruments. Furthermore, MODIS and VIIRS, CrIS and
AIRS, and ATMS and AMSU/HSB should have a minimum of six months overlap
in operations (i.e., after activation of instruments) to assure that at
least some aspects of seasonal variation are characterized. To allow the
measurement of long-term trends the OMPS ozone total column measurements
should overlap with EOS-Aura/OMI and with GOME, the OMPS nadir
measurement of the ozone profile should overlap with POES/SBUV/2, and
the OMPS Limb Profiler measurements should overlap with SAGE III,
EOS-Aura/MLS and HRDLS.

### NPP Bridging Mission 

This NPP Calibration/Validation plan defines the government team
contribution to a combined government-contractor effort to verify NPP
sensor and algorithm performance pre-launch and details NPP test
activities directed at evaluation and verification of NPP products
post-launch. The NPP Calibration/Validation must satisfy both IPO
(Operational System) and NASA EOS (Climate Change Research)
requirements. The IPO and NASA requirements have been combined in this
joint NPP plan. To clarify between operational and climate research
products, distinctive names were chosen; the IPO RDR product corresponds
to what NASA characterizes as Level 1A product; the IPO SDR to NASA
Level 1B product, and the IPO EDR to NASA CDR or Level 2 product (Table
2.2). IPO RDRs, SDRs and EDRs are produced at the IDPS, and NASA Level
1A, Level 1B and CDRs are produced at SDS. Coordination within the IPO,
NASA and with the external scientific community will be conducted to
assess the use of research algorithms in the operational system to
produce EDRs with improved geophysical parameters.

[]{#__RefHeading___Toc534619233 .anchor}Table 2‑1: Data Set Processing
Levels

+------------------+-------------------+------------------------------+
| **Data Level:**  |                   | **Description**              |
+------------------+-------------------+------------------------------+
| **NASA**         | IPO               |                              |
+------------------+-------------------+------------------------------+
| Level 1A:        | RDRs:             | Reconstructed, unprocessed   |
|                  |                   | instrument or payload data   |
| High quality     | High quality      | at full resolution, time     |
| research         | operational       | referenced, and annotated    |
| products derived | products derived  | with ancillary information,  |
| at SDS           | at IDPS           | including radiometric and    |
|                  |                   | geometric calibration        |
|                  |                   | coefficients and             |
|                  |                   | geo-referencing parameters   |
|                  |                   | (i.e. platform ephemeris and |
|                  |                   | orientation) computed and    |
|                  |                   | appended but not applied to  |
|                  |                   | the level 0 data             |
+------------------+-------------------+------------------------------+
| Level 1B:        | SDRs:             | Level 1A data that have been |
|                  |                   | processed to sensor units.   |
| High quality     | High quality      |                              |
| research         | operational       |                              |
| products derived | products derived  |                              |
| at SDS           | at IDPS           |                              |
+------------------+-------------------+------------------------------+
| Level 2, CDRs:   | EDRs:             | Geophysical variables        |
|                  |                   | derived at the same          |
| High quality     | High quality      | resolution and location as   |
| research         | operational       | the Level 1 source data      |
| products derived | products derived  |                              |
| at SDS           | at IDPS           |                              |
+------------------+-------------------+------------------------------+

Remote sensing of the earth now has science records that represent
decades of continuous observation of the atmosphere, oceans, and land. A
common objective of NASA and the IPO for NPP is to provide a
continuation of this long-term data record. NPP/NASA science team
supports the development of research quality algorithms consistent with
continuation of the Earth Science Enterprise (ESE) objectives. The
advanced instrumentation of NPP offers new opportunities for remote
sensing research and innovations in data analysis and information
extraction. In addition, NPP science is a bridge between EOS research
and the science that will be supported by NPOESS. Data continuity of EOS
era instruments requires that the NPP instruments be calibrated
following EOS-type approaches and cross-calibration on-orbit with the
corresponding EOS instruments. The objective is to establish a
multi-decade data record.

NPP will be implemented in a joint program environment that provides
opportunities for the participation of the research community. The
VIIRS, CrIS, ATMS, and OMPS instrument contractors have responsibility
for instrument development and performance verification, and EDR
algorithm development and validation. SDR algorithms are being developed
by the ATMS instrument contractor. The ATMS SDR products will be used by
the CrIS contractor with the CrIS SDRs to develop the combined CrIMMS
sounding EDRs. In addition, the IPO has selected a Shared System
Performance Responsibility (SSPR) contractor who will oversee and manage
NPP instrument design and fabrication, including instrument
characterization and calibration, and share the responsibility with the
IPO for the RDR/SDR/EDR product validation.

The Government Team will participate in this characterization and
calibration and the validation of products. This joint plan addresses
the validation components of that work, as well as the calibration and
validation activities related to sensor design, fabrication, and test,
plus the validation of the algorithms.

The NASA and IPO science teams both have an interest in insuring high
quality products that will support the NASA and NOAA climate missions.
High quality research Level 1B and CDR products will be generated to
support the NASA research climate mission and high quality EDRs, SDRs
and EDRs will be generated to support the NOAA operational climate
mission.

In order to provide the calibration and operational product validation
the IPO will continue to support some of the present IPO science team
for calibration/validation and open the team to new investigators at
least two years prior to the NPP planned launch.

NASA's selection of EDRs for validation will support Level 1B and CDR
algorithm development, and seeks the reduction of the uncertainties for
selected EDRs. Prior to the launch of NPP, NASA will invite the research
community by a NASA Research Announcement (NRA) to propose
investigations for developing state-of-the-art algorithms for CDRs and
to validate selected EDRs on behalf of the global change research
program. .

### NASA Project Science Office (PSO)

The NASA Project Science Office (PSO) will represent the NASA research
community in their effort to contribute to the development of NPP
instruments that bridge EOS measurements into the NPOESS era through
Level 1B and CDR research and development. The PSO will identify and
analyze the feasible approaches for ensuring the calibration of NPP
instruments to the same scale and/or cross-calibrated with the
corresponding EOS instruments. The PSO will help to define the
capabilities of the Science Data Segment (see Figure 2-1) and will
provide guidance and recommendations to the requirements and design
based on interaction with the Investigators' Teams and the larger
climate research community.

The PSO works with the Science Data Segment (SDS) on product planning
and reprocessing, and approves the calibration Look-Up-Tables (LUTs)
updates, derived by the SDS Climate Calibration System (CCS). The PSO
provides ongoing operations support, including recommendation for
calibration table changes, reprocessing requests, and satellite
operations planning via the SDS.

### Integrated Program Office (IPO)

The Integrated Program Office will manage the calibration and validation
of the RDR, SDR and EDR products for the operational meteorological,
atmospheric, oceanographic, terrestrial and climate missions. The IPO
will coordinate the government science and operational user community in
their effort to assist the vendors in developing and testing the NPP
instruments and to independently validate the performance of the
instruments and assess the utility of the products. This will involve
science team activities within the IPO Internal Government Studies and
the IPO Operational Algorithm Teams. The IPO will help to define the
capabilities of the Interface Data Processing Segment (see Figure 2-1)
which will produce the RDR, SDR and EDR Products and the IPO will
oversee the command, control, and communications and the Mission
Management Center for the NPP.

## Overview of NPP Sensors

The data from the four NPP instruments, VIIRS, CrIS, ATMS, and OMPS,
will be processed in the IDPS and will be available to the SDS, ADS and
users in the form of Raw Data Records (RDRs), Sensor Data Records
(SDRs), and Environmental Data Records (EDRs). A brief description of
each instrument/sensor is provided below.

### VIIRS

The Visible Infrared Imaging Radiometer Suite (VIIRS) features a modular
design with 22 spectral bands between 0.4 and 12 microns, nadir
resolution of 370 or 740 meters (depending on spectral band), advanced
focal plane detector technology, mature visible and infrared calibration
systems, three stage passive radiative cooler, and heritage careful risk
reduction measures; it represents a significant advance in operational
polar orbiting imagers. VIIRS will be capable of producing enhanced
products that include (a) cloud detection, (b) aerosol concentration and
optical properties during the day, (c) cloud optical thickness, cloud
thermodynamic phase, and cloud top temperature, (d) vegetation and land
surface cover, (f) snow and sea-ice cover, (g) sea, land and ice surface
temperature, (h) ocean leaving spectral radiances and color, (i)
chlorophyll concentration, and (j) high-quality, moderate resolution
imagery. VIIRS provides primary measurements for a variety of other
geophysical parameters as listed in the Integrated Operational
Requirements Document (IORD).

### CrIS

The Cross track Infrared Sounder (CrIS) is a Michelson interferometer
infrared sounder that is designed to measure with high resolution and
high spectral accuracy the emission of infrared radiation from the
atmosphere in three bands in the spectral range from 3.9 to 15.4 microns
(650 -- 2550 cm^-1^). The core of the instrument is a Fourier transform
spectrometer that measures in one sweep the spectral features of the
atmosphere with high spectral resolution and throughput. The
spectrometer transforms the incoming spectral radiance, i.e. the
spectrum, into a modulated signal, the interferogram, where all infrared
wavenumbers in the band of interest are present. The output from the
spectrometer consists of one interferogram for each observed scene. The
CrIS instrument observes the ground with an Instantaneous Field Of View
(FOV) of 14 km at nadir from an altitude of 833 km. The Field Of Regard
is composed of 3x3 FOVs (each FOV is simultaneously observed by a
separate detector). The CrIS sensor provides cross-track measurements of
scene radiance to permit the calculation of the vertical distribution of
temperature and moisture in the Earth\'s atmosphere. It also provides
supporting measurements for a variety of other geophysical parameters as
listed in the IORD. CrIS data will be analyzed together with that of the
collocated microwave cross-track sounder, ATMS.

### ATMS

The Advanced Technology Microwave Sounder (ATMS) has 22 spectral
channels, including windows near 23, 31, 89, 166 GHz, and several
channels within the 50-60 GHz oxygen band and across the 183 GHz water
vapor absorption line (13 and 5 channels respectively). ATMS has more
tropospheric channels and scans more widely (no equatorial gaps between
orbits) than its predecessor, the Advance Microwave Sounder Unit-A
(AMSU-A) and the AMSU-B (or either of the nearly equivalent Microwave
Humidity Sounder (MHS) and Humidity Sounder Brazil (HSB) instruments).
The ATMS nadir resolution is 33 km for the 50-60 GHz oxygen band, and 15
km for all channels above 150 GHz, thus enhancing hurricane, humidity,
and precipitation monitoring. All channels are sampled on a 15-km grid
down-track and (near nadir) at 7.5 km cross-track, permitting the 77-km
nadir resolution of the 23.8- and 32.4-GHz channels to be sharpened to
the equivalent of \~50-km resolution, and the 33-km of the 50-GHz
channels to be sharpened to the equivalent of \~25-km resolution.

**2.2.4 OMPS**

The Ozone Mapper and Profiling Suite (OMPS) measures the ozone total
column and vertical profile by measuring ozone absorption features in
the spectrum of sunlight scattered into the instrument's field of view
by the Earth's surface, clouds, or atmosphere. Because they require
scattered sunlight, these measurements are possible only on the day side
of each orbit. OMPS also includes special processing of CrIS
measurements of upward emission from the 9.6 m band of ozone, to provide
day/night measurements of the ozone total column. The OMPS hardware
consists of a Nadir Sensor and a Limb Profiler. The Nadir Sensor
measures the ozone total column using an improved version of the
well-proven TOMS instrument, and also measures the ozone profile, at
altitudes above about 22 km and with coarse vertical resolution, using
an improved version of the well-proven SBUV/2 instrument. These
measurements will extend the long-term TOMS and SBUV/2 time series,
which are crucial for monitoring ozone trends. The OMPS Limb Profiler
measures the vertical distribution of ozone between the tropopause and
60 km with good (about 3 km) vertical resolution. Since the measurement
requires corrections for stratospheric aerosols and clouds, the Limb
Profiler will also provide extinction profiles for stratospheric
aerosols and clouds. The Limb Profiler is based on the SME and
SOLSE/LORE heritage. The Nadir Total Column measurement measures
wavelengths between 308 and 380 nm (near UV), the Nadir Profile
measurement umeasures wavelengths between 250 and 310 nm (near UV), and
the Limb Profiler measures wavelengths between 290 and 1000 nm (near UV,
visible, near IR). The Nadir Sensor and the Limb Profiler use diffusers
for solar calibrations.

## Overview of Calibration/Validation Efforts

NPP provides an opportunity to validate approximately 28 of the 55 EDRs
to be provided operationally by NPOESS. The NPP validation program
should ensure that those EDRs, and their associated algorithms, are (1)
robust over all expected environmental conditions, and (2) provide
values of known certainty, before the first launch of the mid-morning
NPOESS satellite.

In order to validate NPP data products, it is necessary to validate
atmospheric, land, and ocean parameters under a wide variety of
atmospheric conditions, solar illuminations, viewing angles, and
ecosystems worldwide. The Government Team will use several validation
techniques to develop uncertainty information on its products. These
include:

> \(i\) Analysis of pre-launch instrument characterization and
> calibration data,
>
> \(ii\) Coordination and collocation with higher resolution aircraft
> data,
>
> \(iii\) Intercomparisons with ground-based and aircraft in-situ
> observations,
>
> \(iv\) Intercomparisons with other space-based instruments, (e.g.,
> MODIS, ASTER, MISR, AIRS, IASI, GIFTS, AMSU, HSB, MHS, SSMI, SSMIS,
> AMSR, GLI, AATSR, MERIS, OMI, GOME, HRDLS, MLS, POAM III, SAGE III,
> ILAS-2, SBUV/2)
>
> \(v\) Intercomparison with model data (e.g., NCEP, ECMWF,...)
>
> \(vi\) Analysis of trends over time and consistency across boundaries
> (e.g., land versus ocean, day versus night, seasonal variation).

The NPP (VIIRS, CrIS, ATMS, and OMPS) calibration and product validation
plans and efforts benefit greatly from the validation efforts and
infrastructure of several existing programs. These include the NASA EOS
AIRS/AMSU/HSB, MODI S and ASTER programs, the NPOESS
Calibration/Validation Program including the NPOESS Airborne Sounder
Testbed (NAST) project, the DoD SSM/I and SSM/IS programs, the DOE
Atmospheric Radiation Measurement (ARM) program, the NASA New Millennium
Program Earth Observing 3 (NMP EO-3) Geostationary Imaging Fourier
Transform Spectrometer (GIFTS), the NOAA Advanced Baseline Sounder (ABS)
program for the Geostationary Operational Environmental Satellite
(GOES), and the EUMETSAT METOP program introducing the Infrared
Atmospheric Sounding Interferometer (IASI). Where applicable and
possible, the NPP validation efforts will draw from these existing
programs.

While the infrastructures for many of these programs are, or will be, in
place for NPP calibration and product validation, additional resources
may be required for implementation and analysis of the validation.
Specifically, arrangement and funding of aircraft campaigns involving
the NAST-I, S-HIS, NAST-M, PSR, APMIR, WINDRAD, MAS, and/or LASE and
other special field campaigns (DOE ARM CART site, EOS sites, Polar AERI,
and ship cruises) are required.

For the VIIRS, the pre-launch calibration efforts and the post-launch
validation will be largely based on the MODIS, as well as AVHRR and OLS
experiences. Spectral response characterization, polarization, response
versus scan (RVS), detector linearity, blackbody calibration, radiance
determination and other sensor issues will be studied and compared to
reference data. Reflective and emissive band calibrations will rely on
on-board calibration systems, which are based on the solar diffuser and
blackbody references. Intercomparisons with MODIS, GLI, AVHRR, OLS,
MERIS and other sensors are planned, as well as aircraft campaigns with
the MAS, NAST-I and S-HIS instruments. Over the open ocean, buoys, ship
cruise data, and dropsondes are planned for calibration purposes.

For the CrIS, the pre-launch calibration efforts and the post-launch
validation will be largely based on the AIRS, HIRS, NAST, and S-HIS
experiences. However, the understanding and characterization of the
portion of the process of producing SDRs from RDRs for the CrIS which is
an FTIR type instrument will use mainly the FTIR instrument experiences
for NAST-I, S-HIS and possibly IASI. This is due to the fundamentally
different method of measuring infrared radiant energy with an FTIR
system such as CrIS rather than with a grating spectrometer such as
AIRS. Detector linearity, truncation due to finite dynamic range,
analog-to-digital conversion, modulation of the interferogram due to
scan noise, and other potential error sources will be characterized and
the corresponding algorithm considerations must be incorporated.
Determination of blackbody reference radiance data will be studied.
Intercomparisons with AIRS, GIFTS, and IASI are planned, and aircraft
campaigns with the NAST-I and S-HIS. Radiosonde and dropsonde data will
play a significant role in the product evaluations.

For ATMS, the pre-launch calibration and post-launch validation efforts
will be similar to those detailed for AMSU in the AIRS/AMSU calibration
validation plan. This is sensible because the two instruments are so
similar in frequency coverage and other specifications. Thus the Cal/Val
plan for NPP/ATMS is similar to those prepared by NOAA for POES, and by
NASA for AMSU/HSB on Aqua. The principal calibration standards will be
derived from microwave radiances predicted from (1) CrIS on NPP, (2)
RAOBs in clear still air, (3) underflying high altitude microwave
sensors such as NAST-M and/or PSR, and (4) microwave spectrometers on
other satellites, such as AMSU on NOAA-15 and NOAA-16.

The pre-launch calibration and post-launch validation of OMPS will be
based on different precedents for each of the different types of
measurements made by OMPS. For the nadir measurement of the ozone total
column the Cal/Val efforts will be based on experience with the TOMS
series and with GOME and OMI. For the nadir measurement of the ozone
profile the Cal/Val efforts will be based on experience with the long
series of SBUV and SBUV/2 instruments. For the limb profile measurement
the efforts will be based on experience with the two sets of SOLSE/LORE
instruments, and the validation efforts will also incorporate experience
with POAM II and III, SAGE II and III and other ozone profilers. For
these three measurements particular emphasis will be given to spectral
and radiometric response, focal plane characterization, and
characterization of the solar diffusers. The forward models for the two
Nadir Sensor measurements have largely been validated via the extensive
experience with TOMS and SBUV/2, although additional checks will be made
for NPP. Validation of the Limb Profiler's forward model will be an
important component of the Cal/Val activities for that instrument. For
the day/night measurement of the ozone total column via OMPS-associated
special processing of CrIS radiances, a significant part of the Cal/Val
will be accomplished by the CrIS team, including validation of the
forward model, but the ozone total columns deduced from the CrIS data
must be validated. The CrIS-based dayside ozone columns can be validated
along with the Nadir Sensor measurements of the ozone total column, but
the CrIS-based nightside measurements of the ozone total column will
have to be based on data from ozonesondes and from space-based emission
measurements of the ozone profile (MLS, HRDLS, ILAS-2), corrected for
the fact that those latter measurements do not include the roughly 10%
of the ozone column that lies below the tropopause.

### Pre-launch Test Data

Pre-launch instrument characterization will be the responsibility of the
instrument vendors for the NPP. The Government Team team will work
closely with the vendors during the pre-launch testing and
characterization to assure that the post-launch instrument performance
is understood and radiances are correctly assimilated (See Section 4 for
details on this effort).

***Necessary Resources***

The validation of NPP measurements will require utilization of data from
many resources, some of which are supported by agencies or countries
outside of the NPP domain. Some of the resources are extant now, but may
not be at the time they will be needed for the NPP activities; thus it
is incumbent on the Government Team to endeavor to ensure the continued
existence of these vital assets in the NPP era. In addition to the
sensors and instrument networks, there is also the need to nurture the
expertise in the scientific community required to make the appropriate
contributions to the validation exercise.

***NIST Traceability***

The ability to trace the validation data to appropriate national
measurement standards is of fundamental importance to the validation
campaign. In addition to the primary NIST reference standards, there are
others that are recognized secondary standards, traceable to NIST, that
are used to calibrate sensors used in the field. These include the
Spherical Integrating Spheres at GSFC, for calibrating visible
radiometers, and the Water-Bath Black Body Calibration Targets at
RSMAS-University of Miami (www.rsmas.miami.edu/ir2001) and at
APL-University of Washington for calibrating infrared radiometers.
Secondary standard thermometers are also in use at these laboratories,
and elsewhere, such as SSEC-University of Wisconsin-Madison.
NIST-traceability for the BRDF and BTRF of the solar-calibration
diffusers for OMPS will also be established.\]

### Ground Validation Network

In order to validate global atmospheric and surface properties derived
from NPP satellite data, a reasonable sampling of the global variability
of these products is necessary. Given that each NPP product may vary
widely in space and time, most of the difficulty in validating the
global products arises from limited sampling of the range of values
encountered in each product. Hence, the NPP validation strategy includes
not only focussed field campaigns in specific locations and under
specific environmental conditions, but also a long time-series of
selected measurements from a select distribution of ground validation
sites. The primary surface validation sites promoted for use by NPP are
those currently also being used by EOS, POES and DMSP. This enables
continuity in the validation data used to assess both the EOS and NPP
products. Required site/network instrumentation and measurements are
specific to each discipline (atmosphere, land, ocean, cryosphere,
clouds, and aerosols). Such networks include, for example, AERONET, the
ARM CART sites, the EOS Land Validation Core sites, the international
radiosonde network, and the MOBY sites. Additional information regarding
the ground validation sites is given later in this section and in
Appendix G.

### Field Experiments 

To supplement the routine observations taken at the various surface
sites and to extend the range of observation variability, the NPP
validation will benefit from additional key observations collected
during intermittent field campaigns. These campaigns can take many forms
and include both pre- and post-launch experiments, aimed at both
algorithm and product validation. As with many recent campaigns, most of
the experiments should be conducted in the context of larger science
objectives, while also leveraging the campaign for NPP satellite
validation. The experiments should provide a larger geographical extent
to the routine, on-going observations made at the surface networks, and
also cover a larger range of conditions (surface types, temperature,
moisture, air mass, clouds, etc...) not observed at the routine sites.
Other experiments with specific, targeted validation goals are also
envisioned. These campaigns will often involve high altitude aircraft
based sensors, as well as profiling aircraft, ship based cruises, and
additional surface based sensors. The IPO developed NAST suite of
aircraft sensors and similar sensors including S-HIS and MAS (for
example) which provide NPP-like radiometric observations, will be used.
These and other remote passive sensing, active sensing, and in-situ
observations can provide the proper spatial and temporal context needed
for satellite validation. The higher spectral and spatial resolution
data can be averaged spectrally and spatially to simulate NPP
measurements during overpass events. Some additional information on
field campaigns is given in Appendix G.

### Intercomparison with Other Satellite Sensors 

A significant part of the NPP Cal/Val effort will involve
intercomparison of radiances and derived products with EOS, POES, DMSP,
METOP, ENVISAT, GOES, EO-3 and other available satellite sensors.
Maintenance of long-term data sets and continuity of data quality is a
mandate for the EOS-NPP-NPOESS series of sensors. These intercomparisons
are necessary both for Calibration/Validation efforts and climate
studies (See Appendix A for outlines of many of these intercomparisons
using NPP validation sites).

Advanced polar and geostationary satellites likely to be in orbit during
the NPP mission (e.g., METOP, ESSPs, EO-3, ENVISAT, etc.) will carry
sensors with comparable, or better, spectral and spatial resolution to
those sensors to be carried on the NPP. The products of these sensors
will provide an important cross-validation of the NPP geophysical
products. This is particularly important with regard to METOP and EO-3;
products from these platforms and their operational successors are
intended for use in combination with NPOESS products to provide a global
high spatial and temporal resolution data set for climate research and
operational weather forecast applications.

Well-characterized products of operational satellites (e.g., POES, GOES,
and Meteosat) will also provide valuable satellite cross validation
data.

## Getting Ready for NPP

### NPP Calibration and Validation Program Management

Key to the NPP validation program will be a management structure to
ensure appropriate pre-flight instrument testing and characterization,
verification of calibration approaches and values, monitoring and
assessment of EDRs and intermediate products over the range of expected
conditions, independent measurement and comparison of EDR and
intermediate product values, and an approach for reporting and mediating
discrepancies, errors and biases. Given the high cost of global product
validation, NPP Cal/Val program management must also identify key
community resources (e.g., marine buoy programs, EOS validation sites,
ARM CART sites, balloon radiosonde, ozonesonde and dustsonde sites)
required for NPP validation. In some cases, the management office must
advocate and perhaps negotiate continuity of such resources.

The NPP Cal/Val program management should provide additional resources
that may be required for implementation and analysis of the calibration
validation effort. Specifically, arrangement and funding of field
campaigns or ocean cruises experiments, involving ground instruments
and/or aircraft should be coordinated.

The NPP Cal/Val program management should develop and actively manage a
team of investigators to meet its goals. Clearly, the team must include
a wide range of expertise. Participating agencies, or other groups or
institutions (e.g., CEOS), may choose to conduct NPP validation
activities. In such cases, the NPP Cal/Val program management must
strive to make such efforts compatible and complementary to activities
of the Government Team.

The Government Team must interact with the SSPR contractor, ideally
including the algorithm developers. Such interaction may minimally
include SSPR review or observations of government field campaigns or
monitoring networks to identify missing measurements or inappropriate
approaches, and regular workshops to reveal and discuss government
results and remediation strategies, and open access to SSPR monitoring
results and government-procured field, network and satellite data.

### Calibration and Validation Team Responsibilities

In order to validate NPP data products, it is necessary to validate
atmospheric, land, and ocean parameters under a wide variety of
atmospheric conditions, solar illumination and viewing angles, and over
a wide variety of ecosystems worldwide. The Government Team will use a
wide variety of validation techniques to develop uncertainty information
on its products.

### Timeline of Major Activities

To be added

### Participants

To be added

# NPP Product Summary 

The NPP mission provides remotely sensed land, ocean and atmospheric
measurements that support research into long-term change in the global
climate and serves as a risk reduction precursor for the NPOESS.

The NPP operational products, RDRs, SDRs and EDRs, for VIIRS, CrIS,
ATMS, and OMPS will be produced using the Interface Data Processing
Segment (IDPS) resources. Research algorithm Level 1, Level 2 (CDRs) and
higher products will be produced at the Science Data Segment (SDS). More
detail on the data processing and product generation is provided in
section 7 of this document.

Level 1A (RDR) and Level 1B (SDR) products are used in three ways:

> \(1\) They are processed to produce products of Level 2 and higher
> (EDRs and CDRs), and are also used in diagnosing defects in those
> higher-level products.
>
> \(2\) They are used directly in some approaches to numerical weather
> forecasting. The weather forecasting community expects improved
> forecasts to result from directly assimilating radiances whenever
> possible instead of assimilating profiles produced using the
> atmospheric inversion processes. The direct assimilation of radiances
> can take into account all the other meteorological observations over a
> larger area being assimilated in the specification of the atmospheric
> state variable.
>
> \(3\) They contribute to a permanent archive of Level 1 products to be
> used for climate and engineering applications requiring the direct use
> of radiances, including system design (DoD, NASA, NOAA) and
> backgrounds (DoD). The Level 1A (RDR) archive also enables
> retrospective reprocessing, to improve the products of Levels 1B (SDR)
> and higher.

## NPP Product Generation

### IDPS Operational Products

The Interface Data Processing Segment (IDPS) receives raw instrument
data and telemetry from ground stations supporting the NPP mission. The
IDPS removes Raw Data Records (RDRs) from the data stream, then
processes RDRs into Sensor Data Records (SDRs) and ultimately
Environmental Data Records (EDRs). During the NPP era, the IDPS will
supply RDRs, SDRs and EDRs to two meteorological Centrals for evaluation
or use in environmental applications. These two Centrals are National
Environmental Satellite, Data, and Information Service (NESDIS) and the
Air Force Weather Agency (AFWA).

The IDPS also transmits RDRs, SDRs, and EDRs to the Archive and
Distribution Segment (ADS) for archiving and access/distribution to the
broader user community and forwards RDRs to the Science Data Segment
(SDS). The SDS will interface with the IDPS installed in the NESDIS
facility to establish the necessary data flows.

In addition, the IDPS provides routine instrument calibration and
monitors the performance of data processing algorithms employed in the
generation of environmental data and products.

### SDS Climate Research Products

The SDS contains five functional elements:

1.  Climate Data Management Service (CDMS),

2.  Climate Calibration Service (CCS),

3.  Climate Analysis and Research Service (CARS)

4.  Distributed CDR Algorithm Validation Service (DCAVS)

5.  Climate Mission Storage (CMS)

The SDS receives RDRs from the IDPS and employs algorithms sponsored by
NASA to create Level 1A/B data from RDRs. The SDS provides Level 1A and
Level 1B data to a small, competitively selected Science Team, the
members of which are responsible for generating Level 2/3 science
products called Climate Data Records (CDRs). Because the SDS stores RDRs
over the life of the NPP mission and beyond, the SDS can reprocess RDRs
and subsequently regenerate CDRs. The SDS forwards Level 1B data and
CDRs to the ADS.

The SDS also generates and distributes science-quality instrument
calibration parameters internally for application in SDS processing and
externally for consideration and potential application in IDPS
processing.

## Description of SDRs/Level 1B Products

VIIRS, CrIS, ATMS, and OMPS Sensor Data Records (SDRs) are full
resolution sensor data that are time referenced, Earth located, and
calibrated by applying the ancillary information, including radiometric
and geometric calibration coefficients and geo-referencing parameters
such as platform ephemeris. These data are processed to sensor units
(e.g., radiances). Calibration, ephemeris, and other ancillary data
necessary to convert the sensor data back to sensor raw data (counts)
are included.

For all NPP instruments (VIIRS, CrIS, ATMS, and OMPS), the operational
SDR products from IDPS and Level 1B research product from SDS should, at
a minimum, consist of the following information (Table 3.1):

[]{#__RefHeading___Toc534619234 .anchor}Table 3‑2: SDR/Level 1B
information required for NPP sensors

  -----------------------------------------------------------------------
  Spacecraft ID tag

  Sensor ID or serial number

  Flight software version number

  Orbit number

  Beginning Julian day and time tag

  Ending Julian day and time tag

  Ascending Node Julian day and time tag

  Spectral radiance in all channels

  Signal levels from all visible detectors.

  Geolocation: geodetic latitude and longitude for each sample

  Time tag information -- beginning of scan time

  Scan index
  -----------------------------------------------------------------------

## Description of Level 2 (EDR/CDRs) Products 

The NPP Mission will produce a series of Environmental Data Records
(EDRs) which are a subset of the NPOESS EDRs. All 28 NPP EDRs will be
produced at the IDPS and a subset of these, and potentially other
products, will be produced as Climate Data Records (CDRs) at the SDS.
These EDRs and CDRs may have different requirements. In the case of
operational products, emphasis will be on generating products with a
more rapid data delivery that necessarily involves high-speed
availability of ancillary data and high-performance execution of the
sensor contractors' state-of-the-art science algorithms for civilian and
military applications. For climate research products, the requirement of
timeliness can be relaxed, thereby allowing for the implementation of
complex algorithms using diverse ancillary data. As understanding of
sensor calibration issues and radiative transfer from the Earth and
Atmosphere improves, algorithms can be improved, and products can be
generated via reprocessing.

NPP and climate research monitoring may have different product
attributes in their requirements. NPP EDRs requirements were defined by
the three NPOESS agencies, NOAA, DoD and NASA considering all of their
operational missions. These EDR requirements can be found in the
Integrated Operational Requirements Document II (IORD II). Appendix B
provides the IORD tables of requirements for each of the NPP EDRs. NPP
EDRs can be broken into two categories: primary and secondary. Primary
EDRs are those EDR attributes for which a sensor contractor has been
assigned primary sensor and algorithm development responsibility (NPP
mission will provide 4 Primary EDRs: Atmospheric Moisture and
Temperature Profiles, Imagery and Sea Surface Temperature). Secondary
EDRs are those EDR attributes for which the sensor may provide data as a
secondary input to an EDR algorithm assigned as a primary EDR (all
non-primary EDRs).

Since the CDRs will be produced by a future science team, selected by an
open, peer-reviewed process through a NASA Research Announcement (NRA),
CDR requirements will not be provided at this time in this document.

The NPP EDRs/CDRs can be categorized into six (6) groups, plus an
Imagery EDR (Table 3-2 and Table 3-3):

(1) Atmospheric sounding,

(2) Aerosol,

(3) Cloud,

(4) Land,

(5) Ocean,

(6) Snow and ice.

Table 3-2 provides the comprehensive list of 28 EDRs and clear column
radiances that will be produced in the IDPS during the NPP era. Detailed
requirements for these EDRs are provided in Appendix B. Section 3.4
provides a short description of the specific requirements adopted by
NPP/NPOESS for the EDR products. It should be noted that many products
will be derived from a combination of radiance measurements from the
CrIS, VIIRS, ATMS and OMPS. Here we note which instrument is primary in
the determination of each product.

Table 3-3 provides a list of CDRs that have great potential to be
selected as CDRs during the NRA process. However, other products might
be included or selected during this process. This list will evolve
during this document development, and will be completed after the GCST
selection some time in mid-2002. Here again, most of these products will
be derived from the combination of radiance measurements provided by the
CrIS/VIIRS/ATMS suite of instruments.

[]{#__RefHeading___Toc534619235 .anchor}Table 3‑3: IDPS EDRs, Product
Group, & Primary Associated Instruments

  ------------------------------------------ --------------- -------------
  **Name of Product**                        **Group**       **Type**

  **Imagery** \*                             Imagery         EDR

  **Atmospheric Vertical Moisture Profile**  Atm. Sounding   EDR
  \*                                                         

  **Atmospheric Vertical Temperature         Atm. Sounding   EDR
  Profile** \*                                               

  Pressure Vertical Profile                  Atm. Sounding   EDR

  Clear Column Radiances                     Atm. Sounding   SDR

  Precipitable Water                         Atmosphere      EDR

  Suspended Matter                           Atmosphere      EDR

  Aerosol Optical Thickness                  Aerosol         EDR

  Aerosol Particle Size                      Aerosol         EDR

  Cloud Base Height                          Cloud           EDR

  Cloud Cover/Layers                         Cloud           EDR

  Cloud Effective Particle Size              Cloud           EDR

  Cloud Optical Thickness/Transmittance      Cloud           EDR

  Cloud Top Height                           Cloud           EDR

  Cloud Top Pressure                         Cloud           EDR

  Cloud Top Temperature                      Cloud           EDR

  Active Fires                               Land            Application

  Albedo (Surface)                           Land            EDR

  Land Surface Temperature                   Land            EDR

  Soil Moisture                              Land            EDR

  Surface Type                               Land            EDR

  Vegetation Index                           Land            EDR

  **Sea Surface Temperature** \*             Ocean           EDR

  Ocean Color and Chlorophyll                Ocean           EDR

  Net Heat Flux                              Ocean           EDR

  Sea Ice Characterization                   Snow and Ice    EDR

  Ice Surface Temperature                    Snow and Ice    EDR

  Snow Cover and Depth                       Snow and Ice    EDR

  Ozone Column                               Atmosphere      EDR

  Ozone Profile                              Atmosphere      EDR
  ------------------------------------------ --------------- -------------

  --- ------------- -- --------- ---- ------------- ------------------------
      CrIS/ATMS        VIIRS          OMPS          \* NPP primary EDRs

  --- ------------- -- --------- ---- ------------- ------------------------

[]{#__RefHeading___Toc534619236 .anchor}Table 3‑4: Potential CDRs,
Product Group, and Primary Associated Instruments

  ------------------------------------- --------------- -----------------
  **Name of Product**                   **Group**       **Type**

  Clear Column Radiance (CrIS)          Atm. Sounding   CDR (TBD)

  Ozone                                 Atm. Sounding   CDR (TBD)

  Precipitation Rate                    Atm. Sounding   CDR (TBD)

  Trace Gasses                          Atm. Sounding   CDR (TBD)

  Cloud Ice Water Path                  Cloud           CDR (TBD)

  Cloud Liquid Water                    Cloud           CDR (TBD)

  Atmospherically Corrected Reflectance Land            CDR (TBD)

  Active Fire                           Land            CDR (TBD)

  LAI/FPAR                              Land            CDR (TBD)

  Sea Surface Temperature               Ocean           CDR (TBD)

  Ocean Color (Water Leaving Radiance)  Ocean           CDR (TBD)
  ------------------------------------- --------------- -----------------

  --- ----------- --- --------- --- --------------------------------------
      CrIS/ATMS       VIIRS         Not yet Assigned to VIIRS or CrIS/ATMS

  --- ----------- --- --------- --- --------------------------------------

## Summary of EDRs/CDRs Performance Requirements

The NPP will produce a series of EDRs that is a subset of the NPOESS
EDRs. The following environmental and climate data record (EDR/CDR)
requirements define the environmental data to be derived from the NPP
data stream and delivered to users to meet mission needs. EDRs and CDRs
requirements are listed in the Appendix B (CDRs requirements will be
available after NRA selection), including attribute thresholds which
characterize satellite sensor data requirements.

EDRs requirements (Appendix B) are specified with a general definition
of required data content, the units for the reported data, and a set of
attributes that fall into four categories:

(1) those that further define data content in a precise, quantitative
    manner,

(2) those that define the quality of the data to be provided,

(3) those that define the reporting frequency for the EDR, and

(4) timeliness of EDR delivery to users.

Attributes addressing data content are:

(1) horizontal and vertical cell size,

(2) horizontal and vertical reporting interval, and

(3) horizontal and vertical coverage.

Attributes addressing data quality are:

(1) measurement uncertainty,

(2) measurement accuracy,

(3) measurement precision,

(4) long term stability, and

(5) mapping uncertainty.

All of these attributes apply to data products, not to sensor
performance characteristics, and are defined in the Appendix J
(Definitions). The product attributes' performance flows to the sensor
and algorithm performance specifications. The EDR requirements format
(Appendix B) addresses the data content attributes first, then the data
quality attributes, and finally the reporting frequency attributes. The
latency requirements are not applicable for the NPP.

Seven EDRs, Imagery, SST, soil moisture, cloud base height, pressure,
snow cover and depth and precipitable water, are not required to satisfy
the "cloudy/all weather" attribute because the required sensors are not
flown on NPP (required sensors will be available for the NPOESS system).
Three EDRs, aerosol optical thickness, aerosol particle size and net
heat flux are not required to meet the attribute thresholds because the
required sensors are not flown on NPP (required sensors will be
available for the NPOESS system).

# Instrument Pre-launch Characterization and Calibration 

## Common Issues for Instrument Characterization and Calibration

Characterization describes quantitatively how a system or subsystem
responds under the range of conditions to be encountered in use.
Calibration describes how to quantitatively convert sensor units (RDRs)
to scientific units (e.g. radiances in SDRs). Characterizations are
needed both for producing calibrations and for redesign and diagnostic
purposes. Characterizations and calibrations must be sufficiently
accurate to enable the SDR processing to produce high quality
radiance-based EDRs/CDRs.

The Government Team will aid in evaluating the instrument vendor
pre-launch characterization and calibration plans and performance; the
Government Team will also perform independent checks of the instrument
vendor measurements and analyses. The Government Team participates in
the design, test, and performance verification of each of the
instruments. They have timely access to test data. All data requests
will be made through the respective government instrument developers in
advance of test execution. The Government Team has access to instrument
contractor calibration algorithms and research code. This provides a
starting point for IDPS and SDS implementation of improved quality SDRs
and Level 1B products. The instrument developers will enable this
exchange of information. Figure 4-1 shows how characterizations and
calibrations of subsystems flow into those of more complete systems.

![](media/image3.wmf)

[]{#__RefHeading___Toc534619230 .anchor}Figure 4‑2: Instrument
Characterization Occurs at all Levels of Assembly

The following ingredients of characterization and calibration apply to
all NPP instruments:

-   **Spectral response** including center wavelength or frequency,
    bandpass, channel location on focal plane (if applicable),
    out-of-band response, and spectral cross-talk.

-   **Spatial response** including response to point and edge sources,
    pixel boundary locations (if applicable), image deformation on the
    focal plane (if applicable), out-of-field responses (including stray
    light), and alignment.

-   **Radiometric response** including radiance responsivity in
    appropriate units, linearity, dynamic range, noise, cross-talk, and
    dead, marginal, and hot pixels.

-   **Polarization tests** that quantify the response to polarized
    radiation. Even when the radiation incident on the instrument is
    unpolarized, reflections from spacecraft/payload surface may
    introduce polarization.

-   **The spectral, spatial, radiometric, and polarimetric tests**
    should include both staring mode and scanning mode views, if
    applicable.

-   **Analog-to-digital converters** and other electronic components
    critical to the spectral, spatial, radiometric, and polarimetric
    response should be characterized.

-   **Environmental stability characterization** including response to
    changes in operating temperatures and spatial gradients of
    temperature, response to space radiation, response to saturation,
    and response to spacecraft power variation. Thermal vacuum testing
    should characterize the effect of operating temperature on spectral,
    spatial, and radiometric characterizations and calibrations of the
    instrument. Thermal performance tests shall incorporate the same
    spatial gradients of temperature expected on orbit.

-   **On-board sources and calibration systems** (emissive and
    reflective targets, transmissive diffusers) require characterization
    over the range of expected viewing angles and external illumination.
    BRDF and polarization should be measured, and the NIST-traceability
    of BRDF scales should be experimentally verified as described in
    Section 4.5. Radiance, spectral content, and spatial distribution
    function should be characterized for self-emitting sources used
    within instruments, including the effects of thermal expansion on
    the measured signals.

-   **GSE test targets and sources** should be characterized fully prior
    to use with the instrument(s). Independent verification of source
    characteristics is recommended, and particularly NIST-traceability
    of radiance scales as established by such sources should be verified
    as described in Section 4.5.

-   **Both ambient and thermal vacuum testing** of the instrument should
    be performed. Optical stimulus is to be provided and instrument
    response characterized while the instrument is in thermal vacuum
    test prior to delivery to the spacecraft. Data should be taken at
    coldest, intermediate, and hottest environments. Response data
    should be collected at the spacecraft level of assembly.

An essential part of these calibration and characterization activities
is the use of instruments having accurate measurement scales. While test
instrumentation used by instrument vendors is often claimed to be NIST
traceable, experience with radiometric measurements indicates that an
experimental verification of NIST traceability of vendor radiometric
scales at economical points in the test process is necessary in order to
establish confidence in the high accuracy products required by NPP. The
IPO has enlisted NIST assistance to perform these radiometric
traceability verifications as summarized in Section 4.5 and detailed in
Appendix C. The activities under the NIST traceability verification plan
are cost-effective and proven from heritage on other programs.

One final aspect of the pre-launch characterization and calibration plan
applicable to all instruments is the development, use, and maintenance
of detailed and accurate math models. These models will be used for a
variety of purposes:

-   Define and iterate with systems engineering requirements and error
    flow-downs.

-   Define critical functions for test hardware and establish
    tolerances.

-   Review existing documentation on math models developed in support of
    error budgets of vendor calibration sources.

-   Predict results prior to testing based on as-measured parameters.

-   Analyze laboratory measurements.

-   Predict sensor performance based on these measurements.

-   Define calibration coefficients for the instrument database.

-   Act as a resource to monitor, simulate, and troubleshoot changes in
    calibration once in orbit.

-   Utilize for on-orbit operational purposes later in the program as
    appropriate.

## VIIRS Pre-Launch Characterization and Calibration

VIIRS calibration involves measuring pre-launch and post-launch the
response of the instrument subsystems to controlled, well-characterized
stimuli. Pre-launch instrument characterization should be performed
under environmental conditions (thermal vacuum tests), which simulate
on-orbit conditions as closely as possible.

Absolute radiometric calibration and associated uncertainties /
instabilities will be verified by analysis, modeling, and / or
simulation. The process of satisfying the radiometric calibration
requirements against both uniform and structured backgrounds will be
accomplished through instrument characterization using NIST traceable
standards. The radiance levels applied to the calibration process will
be based on flow-down requirements for the EDRs based on measuring
top-of-the-atmosphere radiance levels.

The characterization and calibration process will follow well-defined
protocols and guidelines established within NASA, NOAA and DoD and
detailed by the MODIS, AVHRR, and OLS calibration and characterization
teams. The VIIRS sensor calibration will incorporate onboard
calibration, including a Solar Diffuser (SD) and thermal blackbody. The
in-flight calibration process will also include solar, deep space, and
lunar radiometric calibration opportunities.

More details on VIIRS characterization and calibration are presented in
Appendix D; a short summary follows.

### VIIRS Instrument Characterization

Instrument characterization enables the determination of the
quantitative effect of the performance of the subsystem on the overall
instrument system-level performance. Table 4-1 presents VIIRS parameters
to be characterized and calibrated, using MODIS-heritage test equipment
at SBRS. It includes:

-   **Radiometric Response** - At the component level of assembly, the
    response of focal plane elements and subassemblies are characterized
    for linearity, dynamic range, noise, various cross-talk sources,
    mirror scan angle variation (RVS), and dead and marginal performing
    detector channels. At the subsystem level of assembly, tests with
    digital electronics determine ADC characteristics and anomalies. At
    the instrument level of assembly, response is characterized with
    static and active scans. Tests include pathological target cases
    (cloud over ocean and littoral waters).

-   **Spectral Response** - Optical element, subassembly, and instrument
    level spectral characterization includes center wavelength,
    band-pass, out-of-band response, cross-talk from other spectral
    regions, spurious response to near field sources (warm optical path
    seen by cold focal plane), signal level dependent out-of-band
    response, others as identified.

-   **Spatial Response** - Knowledge of point source and edge response,
    center of pixel position, edge of pixel position, relative alignment
    of spectral bands each to the other during static and active scan,
    knowledge of the line-of-sight, variations with field-of-regard,
    errors at aggregation switching points, and others identified later.

-   **Stability** -- Changes with time and environmental conditions must
    be characterized. The stability of the instrument as it passes from
    night to day and back must be understood. The VIIRS may have a
    different power profile day and night; the transient associated with
    each must be understood.

Special care should be taken to decide which characterization test
should be performed in the ambient environment or in both the ambient
and thermal vacuum environments.

[]{#__RefHeading___Toc534619237 .anchor}Table 4‑5: Characterization and
Calibration of the VIIRS Instrument

  -------------------------- ---------------- ----------------------------
  **Test Parameter**         **Test           **Optical Stimuli or
                             Environment**    Comparison Radiometer**

  **Radiometric                               
  Characterization &                          
  Calibration**                               

  Linearity of the detector  Amb Lab, T/V     Radiance level changes
  and repeatability                           

  Polarization               Amb Lab          Polarization Source Assembly

  Signal-to-Noise Ratio      Amb Lab and T/V  Spherical Integrating Source

  Dynamic Range              Amb Lab and T/V  Spherical Integrating
                                              Source, Blackbody
                                              Calibration Source

  Focal Plan Temperature     T/V              Spherical Integrating Source

  Spectral Response                           

  Relative Spectral Response Amb Lab and T/V  Spectral Measurement
                                              Assembly

  Out-of-band response       Amb Lab          Spectral Measurement
                                              Assembly

  Spatial Response                            

  Response Versus Scan       Amb Lab          Spherical Integrating Source
                                              and Blackbody Calibration
                                              Source

  MTF/point spread function  Amb Lab and T/V  Integrated Alignment
                                              Collimator

  Near field scatter         Amb Lab and T/V  Scatter Calibration
                                              Measurement Assembly

  Far field scatter          Amb Lab          Quartz halogen source

  Ghosting                   Amb Lab          Scatter Calibration
                                              Measurement Assembly

  Electronic cross talk      Amb Lab          Spectral Measurement
                                              Assembly

  Optical cross talk         Amb Lab          Spectral Measurement
                                              Assembly and Integrated
                                              Alignment Collimator

  Pointing knowledge         Amb Lab          Integrated Alignment
                                              Collimator

  Alignment change           Amb Lab          Integrated Alignment
                                              Collimator

  Spectral band registration Amb Lab and T/V  Integrated Alignment
                                              Collimator +Reticles

  Stability                                   

  Short-Term Pre-flight      Amb Lab          Spherical Integrating Source
                                              +monitoring radiometer

  Short Term on-orbit        On-orbit         Solar Diffuser / Solar
                                              Diffuser Stability Monitor,
                                              Moon (experimental)

  Long-term                  On-orbit         Solar Diffuser / Solar
                                              Diffuser Stability Monitor,
                                              Moon (experimental)
  -------------------------- ---------------- ----------------------------

### On-Board Calibrator Characterization

In addition to these tests, the pre-launch and on-board calibrators
should be characterized to ensure the required on-orbit calibration
performance. Specifically, the BRDF of the Solar Diffuser should be
analyzed, and its stability well understood. The NIST-traceability of
the BRDF scale of SBRS should be experimentally verified as described in
Section 4.5. The Solar Diffuser Stability Monitor (SDSM) and the
Blackbody Calibrator should be characterized assuming operational
conditions.

## CrIS Pre-launch Characterization and Calibration

The basic radiometric and spectral performance of the CrIS over the life
of the mission is the focus of these efforts. The requirements for
continued long-term EDRs/CDRs puts special emphasis on production of
consistent SDRs which depend on a complete understanding of the
instrument radiometric, spectral, and spatial characteristics determined
pre-launch. This can only be accomplished by normalization of observed
radiances for all FOVs to a common wavenumber scale and instrument line
shape, making the SDR product equivalent to a Level 1C product. This
normalized SDR product, when performed up-front in the processing, will
allow relatively inexpensive re-processing of EDR/CDRs to form
consistent long-term data sets. Careful definition of the frequency
scale and instrument line shape are not only important for SDR
production but are also used directly in the EDR/CDR production via the
forward radiative transfer model. A stable SDR is also essential for all
users of CrIS, especially the numerical weather prediction centers, who
directly assimilate the SDRs into their models. These users cannot cope
with any significant variation in the SDR frequency scale or instrument
line shape and consequently pre-launch testing must provide a detailed
understanding of how these parameters could drift over time.

The fundamental approaches for prelaunch radiometric testing of the CrIS
Fourier Transform Spectrometer (FTS) are similar to those for any IR
radiometer, but special spectral testing and detailed analysis
differences are necessary. Because profile retrievals are sensitive to
characterization accuracy of the instrument spectral calibration, and
because of the relatively short history of spaceborne high-spectral
resolution radiometry, the importance of the spectral part of the CrIS
calibration effort is emphasized. The Government Team-led effort will
review CrIS vendor (ITT) test plans for completeness and will make use
of test data collected by ITT. Coordinated applications of NIST sensor
and source standards are also briefly addressed here.

### CrIS Radiometric Calibration

The following general activities will be performed to confirm absolute
radiometric accuracy and reproducibility.

Blackbody reference checks are mandatory. As discussed in Section 4.5, a
NIST maintained FTIR transfer radiometer system (the Fourier-transform
Thermal-Infrared Transfer Radiometer, FTXR) will be used to verify the
radiance from the external blackbody standard used in CrIS testing.
While this FTIR system may be no more accurate than the CrIS blackbody
reference itself, the system will provide transfer observations for
comparing the standards used for CrIS and VIIRS. Any observed
differences will be explored to reduce absolute errors. The NIST
radiometric calibration of the FTXR will consist of laboratory
characterizations and calibrations using the NIST radiometric
infrastructure described in Appendix C.

CrIS measurements of an external blackbody will be used to verify CrIS
end-to-end absolute accuracy and reproducibility. The blackbody will be
operated at a range of temperatures spanning atmospheric conditions and
the test will be repeated over the range of instrument temperatures
expected in flight. This test will be used for:

-   verification of non-linearity corrections derived from separate
    tests (if similar tests are used, the verification needs to be from
    independent data),

-   verification that the observed responsivity (signal level) is
    consistent with component level optical and detector
    characteristics, and

-   verification of interferometric phase stability and check that the
    imaginary part of the calibrated spectrum is not influenced by any
    artifacts, except pure noise.

Any unexpected behavior must be evaluated.

### CrIS Noise Performance Verification

The end-to-end noise performance will be evaluated by deriving the Noise
Equivalent Spectral Radiance (NESR). This characterization should be
performed as a function of scene temperature using the same data
collected for radiometric calibration (4.3.1). Analyses to be performed
include:

-   determination of the NESR from all sources by taking the standard
    deviation of calibrated spectral radiances for a stable blackbody
    source and instrument,

-   estimation, by separate analysis, of random noise (uncorrelated with
    wavenumber) and interferometric noise, and

-   identification of any spectrally localized noise sources indicative
    of EMI.

Any significant differences from expectations should be resolved.

The noise characteristics as a function of scene temperature are
required for CDR as well as EDR production, and must be provided in
addition to the SDRs.

### CrIS Spectral Calibration

CrIS spectral calibration specifies the shape of the instrument spectral
line-shape and wavenumber scale. Under perfect instrument alignment, the
Fourier transforms of the 9 FOVs in each focal plane will produce 3
distinct channel wavenumber scales. In addition, the instrument
line-shape function spectral width will vary slowly with wavenumber.
These effects arise from the fundamental linkage between spatial and
spectral response in an interferometer. Fortunately, with a FTS (that
ties the spectral characteristics to a reference laser and to a small
number of geometrical parameters) the spectral characterization of a
small number of channels per band is sufficient to calibrate all channel
spectral characteristics.

The goal of spectral calibration is to (1) directly measure several CrIS
instrument line shapes in each band, and (2) establish the CrIS absolute
wavenumber scale. These two quantities are integrated into the
EDR/CDR/assimilation forward model to allow regression of the observed
radiances to computed radiances in order to retrieve the atmospheric
state.

The required spectral characterization and calibration refinements can
be obtained from measuring well-known gas transmittances. A nominal
approach is described in Appendix E. A similar test was performed during
EOS-AIRS pre-launch testing and was highly successful. Comparison of the
gas transmittance spectrum with easily computed simulated spectra allows
retrieval of the wavenumber scale and verification of the expected
instrument line-shape function for each of the 3x3 FOVs.

### Additional CrIS Characterization

In addition, pre-launch characterization of the CrIS instrument must
address several issues, as discussed in the introduction to Section 4.1.
These include (1) spatial response, (2) stability of radiances with
varying instrument temperature, (3) cross-talk (which should be a very
minor concern), and (4) effects of polarization on the radiometry. In
addition, care should be taken to examine the CrIS interferograms for
any channeling due to unwanted interference between optical elements.
Since there is a connection between spectral and spatial response in a
FTIR, analysis of calibration results should determine if these two
calibration parameters are compatible.

Complete communication of the calibration results to users outside of
the hardware vendor and SSPR is essential to the success of NPP.

## ATMS Pre-Launch Characterization and Calibration

The pre-launch characterization and calibration of ATMS will rely
heavily on the thermal vacuum calibration program, supplemented by more
thorough measurements at ambient pressure and multiple temperatures, as
discussed below. Some measurements should be performed on all flight
instruments, and some only on an engineering unit or a single flight
unit (see Appendix F for more details).

### ATMS Temperature Sensitivity (NEDT)

Temperature sensitivities, referred to the antenna aperture, will be
determined for all flight instruments (1) for antenna viewing 300K
target, (2) under conditions similar to in-flight operation, and (3)
with enough measurements so that more would not alter the results by
more than 0.01K rms.

### ATMS Bandpass Characteristics

The bandpass characteristics for each channel should be measured and
documented over the extreme operational temperature range to be
encountered in space; two or three temperatures would normally suffice,
and thermal vacuum would normally not be required. The accuracy should
be sufficient to ensure that no indicated brightness temperatures would
depart by more than 0.2 K from that expected for any reasonable
atmospheric profile solely as a result of incorrect or incomplete
pre-launch bandpass characterization.

### ATMS System Linearity

The amplifiers and detectors in sensitive radiometers often exhibit
non-linearities that threaten calibration accuracy at antenna
temperatures removed from those of the two calibration loads. Tests
should be performed to ensure that such compensated non-linearities
after compensation will introduce less than 0.1K calibration error under
the most challenging plausible combinations of antenna (radiometric) and
instrument temperature.

### ATMS Calibration

ATMS is calibrated every scan cycle in space using cold space and an
unheated blackbody load. Calibration errors in ATMS-like prior
instruments have generally been dominated by: bandpass variations,
non-linearities, unknown blackbody emissivities below unity, temperature
gradients within the calibration blackbody, errors in blackbody
temperature sensors, variations of instrument response with calibration
switch position (in ATMS this is the position of the scanning mirror),
and angle- and situation-dependent contributions to antenna temperature
due to the Earth/space boundary, spacecraft, sun, and moon. Most of
these potential error sources can be measured and compensated pre-launch
using thermal vacuum calibration tests, laboratory measurements, and
antenna range measurements. Each of these sources of calibration error
should be measured so that uncompensated contributions to calibration
error from each are less than \~0.1 K rms, a level that helps ensure
cumulative errors less than \~0.5 K rms.

### ATMS Antenna Pattern Measurements

Accurate antenna patterns are needed to (1) facilitate the image
sharpening made possible by Nyquist sampling, and (2) assess and correct
the scan-angle dependent sidelobe contributions to brightness
temperature error. The uncompensated brightness temperature error, to
the extent possible, should always be less than 0.1K. These errors are
most critical for channels 52.8-58 GHz and are most serious when the
sidelobes have significant amplitude and large-scale structure near the
planetary limb. The patterns for at least one flight unit should be
measured at least at the center frequency of each channel. The
sensitivity of these antenna pattern measurements should permit
accuracies of 2 dB rms at absolute antenna gains 20 dB below isotropic,
which implies a dynamic range of at least 65 dB (TBR) for the narrow
beams, essentially free of antenna-range-wall and surface-reflection
effects. The rms accuracy of the absolute pattern measurement should
otherwise generally be no worse than the less restrictive of 3 percent
in absolute gain or 0.5 dB (TBR), and the rms precision should be
one-fifth of that.

### ATMS Polarization Angle Alignment 

ATMS has several channels with temperature weighting functions peaking
in the lower troposphere or below the surface. These measurements are
affected by surface emissivity. Over oceans, the emissivity varies with
view angle and polarization. ATMS measures an angle-dependent
combination of vertical and horizontal polarization. A small
misalignment of the polarization angle would therefore result in an
asymmetric radiance across the scan lines. Thus, the Government Team
needs to monitor the ATMS pre-launch calibration procedure including the
polarization angle alignment. It is recommended that the angle be
accurate to a few tenths of a degree (goal).

**4.5 OMPS Pre-Launch Characterization and Calibration**

The pre-launch charcaterization and calibration of the OMPS hardware
will place particular emphasis on the focal plane arrays (FPAs), system
and sub-system Modulation Transfoer Functions (MTFs), signal-to-noise
raitios (SNRs), stray light, the solar diffusers, and the effects of
temperature variations along the obrit, space radiation and
optically-effective contaminant films. Characterization and calibration
will occur at several levels of assembly, namely, for components,
telescopes, spectromters, and complete sensors. See Appendix GNEW for
further details.

Before discussing the characterization of particular systems and
subsytems it is convenient to discuss three types of perturbations of
OMPS, namely temperature variations, space radiation, and
photopolymerized hydrocarbon contaminant films, since each affects the
pre-launch characterization activities for multiple systems and
subsystems.

Variations of hardware temperatures and temperature gradients along the
orbit will cause time dependent changes in the positions and sizes of
optical elements. This could produce a time dependent spectral
registration, and a time dependent sharpness of focus and associated
time dependent MTF and SNRs. For example, the weekly solar calibrations
of the spectral and radiometric characteristics of each OMPS sensor
occur when OMPS is transitioning from the day side to the night side of
an orbit, and is therefore still fairly warm. The Earth views which will
be calibrated using the solar data take place at various positions along
the day side of the orbit, and include observations when OMPS has just
emerged from the night side, and is fairly cool. Other observations
occur the solar zenith angle at the satellite is minimal, and still
others occur where the spacecraft is about to leave the day side and
enter the night side. Temperatures and temperature gradients within the
instrument will differ between the solar and Earth viewing observations,
and also between different Earth viewing observations. Spectral
registrations, MTFs and SNRs will therefore also differ. Modeling
indicates that the induced optical variations will be negligible, but
models are always simplifications and prudence requires that the
variations be characterized by means of measurements.

Space radiation is another concern. OMPS will encounter energetic
electrons when its host spacecraft passes through the Auroral Oval and
energetic protons when its host passes through the South Atlantic
Anomaly. The South Atlantic Anomaly is a region where the Earth's
magnetic field is particularly weak. It occupies a large area over the
Atlantic Ocean northeast of Brazil, and currently is growing because the
Earth's magnetic field presently is weakening. Focal plane array pixels
that receive space radiation will temporarily provide false readings.
The readings from some pixels will be aggregated on board the
spacecraft, and additional aggregation will occur during processing on
the ground, to reduce the data rate on the downlink and the processing
burden on the ground. Aggregation will dilute the effects of the
spurious readings from the pixels affected by space radiation.
Cross-talk during read-out of the FPA, e.g., due to charge-transfer
inefficiency, will spread around the spurious readings. Almost all
pixels affected by space radiation will eventually "anneal" and
thereafter provide correct readings. The time required for annealing
depends on the temperature of the focal plane array. These facts drive
several of the characterization requirements below.

The UV and shortwave visible optical properties of solat diffusers and
other upstream optical elements in OMPS can be significantly affected by
the time dependent build up of a hydrocarbon contaminant film, produced
by outgased hydrocarbons that condense on optical elements and are then
photopolymerized by the intense solar UV incident on the spacecraft, and
possibly also by space radiation. This increases the difficulty of
measuring long term trends. The optical effects are roughly proportional
to the time integrated solar exposure, so the film's optical effects on
the diffusers (but not on other optical elements) will be estimated by
occasionally using an alternative diffuser that is exposed to sunlight
much less frequently than the working diffuser. These facts drive some
of the characterization requirements below for the diffusers and for
other forward optical elements.

The characterization of the subsystems of OMPS will be discussed roughly
in the order in which they are encountered by incoming light and by the
resulting transduced e;lectrical signal.

**4.5.1 Characterization of Solar Diffusers**

The Nadir Sensor uses reflective solar diffusers for calibration, and
the Limb Profiler uses transmissive diffusers. Although in a sense the
diffusers are part of the Nadir and Limb telescope assemblies, they are
treated separately here because they are not used when the telescopes
are viewing the Earth, i.e., most of the time. Pre-launch
characterization of the diffusers will measure their spectral
reflectance or transmittance, and the angular dependence (Bi-directional
Reflectance Distribution Function BDRDF) and Bi-directional
Transmittance Distribution Function (BTDF)) of radiation reflected or
transmitted through them. In particular, the spectral and radiometric
throughput and angular optical proerties of each working diffuser and
each corresponding infrequently-used comparison diffuser must be known
well enough that the effects of substitution one for the other can be
correced for with sufficient accuracy and precision. Seaonal variations
in the Earth-Sun distance will cause seasonal variations in the
temperature of OMPS during the solar calibrations. The corresponding
differences in the amounts of thermal expansion will cause seasonal
variations in the location of the part of the reflective diffuser seen
by the Nadir Sensor's telescope. This variation will be characterized by
modeling and/or measurement. \[Is that worth doing?\] Analysis and
laboratory measurements on spare diffusers will characterize the effect
on the diffuser's overall throughput and angular optical properties of
the effect of exposure to space radiation. Analysis and laboratory
measurements will also characterize the effect on the diffusers' overall
throughput and angular properties of a photopolymerized thin film of
contaminant hydrocarbons produced by intense sunlight acting on
condensed hydrocarbons outgassed by the spacecraft and its payloads.
\[Is that worth the expense, or will the effects change so slowly that
they will be calibrated out?\].

**4.5.2 Characterization of Telescopes**

OMPS has two reflective telescopes: one for the Limb Sensor and one for
the Nadir Sensor. The Nadir telescope ends with a beam splitter that
feeds light to the spectrometers for the Nadir Total Column and Nadir
Profiler. The telescopes contain transmissive as well as reflective
optical elements, so some spectral variation in the telescopes' optical
properties can be expected. The MTF of each telescope will be
characterized as a function of wavelength. The effect on the spectral
MTF of the changes of the telescopes' temperatures along the orbit will
be characterized. {Measurement, or modeling?\] Ghosting and scattered
stray light and the polarization properties of the telescopes will be
characterized. For the Nadir telescope the characterizations should span
the rather wide range of directions viewed for the Nadir Total Column
and Nadir Profile measurements. The fields of view of the Nadir Total
Column and Nadir Profile light paths within the Nadir telescope will be
characterized, including the slight along-track offset between them. For
the Limb telescope the characterizations will be performed for the
high-gain (upper stratosphere and above) and low gain (lower
stratosphere) parts of all three vertical images, i.e., for six images
in all. The cross-track horizontal field of view and the vertical field
of view will also be characterized for each of the six images. For both
telescopes, modeling and/or measurements on spares will be used to
characterize the effect of time dependent on-orbit contaminant films on
forward optical elements.

**4.5.3 Characterization of Spectrometers**

OMPS contains three spectrometers: one for the Limb Sensor, and one each
for the Nadir Total Column and the Nadir Profile measurements. A prism
is the dispersive element in the Limb spectrometer, while each of the
Nadir spectrometers uses a grating as its dispersive element. The
spectral resolution and the spectral-radiometric and polarization
throughput of each of the three spectrometers will be characterized. The
spectrometer's effect on the MTF at the focal plane arrays will be
characterized. The variation of each spectrometers' optical properties
will be characterized over the range of specrrometer temperature
expected on orbit. Each spectromter's contribution to scattered light
and ghosted stray light at the FPA will be characterized. Also, tests on
spares will measure the resistance of the Nadir Sensor's two gratings to
delamination.

**4.5.4 Characterization of Packaged Focal Plane Arrays**

The focal plane arrays for the Nadir Total Column, Nadir Profile and
Limb Profile spectrometers are three nominally identical CCDs.
Characterization of these arrays will map the pixel-to-pixel variations
in spectral responsivity/quantum efficiency across each array, including
the identification of dead and hot pixels. Well depths, charge-transfer
efficiency, dark current and noise will also be characterized. The
effects of space radiation (electrons from the Auroral Oval as well as
protons from the South Atlantic Anomaly) will be characterized using
poorer quality copies of the CCDs; in particular the time for radiation
damage to anneal away will be characterized. The linearity of each CCD
will be characterized as a function of irradiance and exposure time;
nonlinearities are expected to be dominated by the pre-amps in the
read-out circuitry. The number of effective bits provided by the ADC
will be characterized as a function of sampling rate. The windows and
filters of the CCD packages will be characterized. In particular,
ghosting and scattering contributions to stray light by the windows in
front of the CCDs will be characterized, as well as the spectral
transmittance of the filters on the windows of the CCDs, The adhesion
amd defects of the filters will also be characterized, including
worm-holes in the filters. Life testing?

**4.5.5 Other Optical Elements**

The optical properties (spectral transmittance, polarization properties)
of the depolarizers in both the Nadir and Limb Sensors and of the beam
splitter in the Nadir Sensor will be characterized to improve the
accuracy of ray tracing models of the optics. Characterization of slits
will include slit width, the smoothness and parallelism of the jaws,
deformation under expected changing thermal conditions, and their
contribution to stray light. The contribution of each optical element to
ghosting and to diffusely scattered stray light will be characterized,
including any possible ghosting by the lens in front of the Nadir
Profiler's CCD.

**4.5.6 Non-optical elements**

Each CCD is backed by a thermo-electric cooler (TEC). The cooling
efficiency of the TECs will be characterized as a function of how hard
they are run, and their degradation over time will be estimated. Heating
by the heaters will be characterized, and theiur degradation over time
will be estimated. The motors are of a new design, and therefore need to
be characterized, including the linearity of the stepper motor,
repeatability, and lifetime. Timing by the Main Electronics Boxes (MEBs)
timing will be characterized, including temperature dependence, timing
jitter and time offsets.

**4.5.7 Characterization at the Sensor Level (End-to-End Sensor
Characterization)**

The Nadir Sensor and the Limb Sensor as a whole consists of a telescope,
associated spectrometers, and the relevant electronics in a MEB. The
signals fed to the MEB are digital and therefore cannot be degraded by
the MEB except by being aggregated, which will alter the spectral
resolution and possibly (inaderrtantly) the MTF from what the telescope
and spectrometers alone would have given, and can dilute spurious pixel
values caused by radiation damage. The SNRs, MTFs and spectral
resolution of each sensor will be characterized. Cross talk (optical and
electrical) will also be characterized, including both out of band and
out of field signals, since these types of cross talk can be affected by
aggregation.

\[ Note that the numbers of all 4.x sections after this one must be
incremented. For example, the next section should become 4.6 instead of
4.5. Word won't let me make that change, but someone else may know how
to do it. Otherwise replacement headings will have to be made by hand.\]

## Verification of NPOESS Calibration/Validation Standards Using NIST Traceability

### Plan Overview

This section presents a plan for making NIST-traceable verifications of
the uncertainty of the radiometric reference standards that are relevant
for NPP. There are two general classes of such standards: those used for
calibration of the space flight instruments themselves, and those used
for calibration of the instruments deployed in validation field
campaigns. This plan addresses both of these. For radiance, the
verification method is to deploy NIST portable transfer radiometers and
sources at flight instrument calibration facilities and at validation
field instrument intercomparisons. The NIST transfer radiometers are
calibrated at NIST against the NIST radiance reference standards before
and after deployment to each intercomparison. For reflectance, the
verification method is through a round-robin in which participating
laboratories, including the reflectance reference facility at NIST,
measure the same set of diffuse reflectance panels and their results are
intercompared. These methods follow the basic scheme that evolved from
the NIST cal/val verification program with NASA EOS. The data from the
intercomparisons are analyzed by NIST to determine the level of
agreement, and this level of agreement is compared with the uncertainty
required. Where scales agree to within the required uncertainty, the
calibration standards can be said to have been verified. In cases where
scales do not agree to within the required uncertainty, attempts are
made to discover and correct the source of the disagreement.

There are five basic types of intercomparison activities that form the
overall verification plan. These are summarized in the next section, and
more detailed descriptions can be found in Appendix C. Appendix C also
contains a substantial amount of material that describes the NIST
radiometric measurement infrastructure as it relates to the absolute
calibration of instruments used in these intercomparisons. The NIST
traceable Reflectance Standards, the NIST traceable Radiance Standards,
and the NIST traceable Standards under development are described.

The result of performing the activities in this plan will be continuity
of measurement scales with other programs, assurance of the precision,
accuracy and uncertainty of the EDRs, identification of systematic
effects, and ties to a common measurement reference as maintained by
NIST. Although this plan concentrates only on the NPP phase of the
NPOESS program, it is planned that similar verification activities will
continue for the duration of the NPOESS program.

### Summary of the Intercomparison Activities

A summary of the plan in terms of activity type, dates, participants,
and expected uncertainties, is given in Table 4-2. There are five types
of intercomparison activities:

-   **Type A**: BRDF round-robin of diffuser plates. This will be done
    once for the NPP VIIRS to verify the BRDF scale at Raytheon SBRS.

-   **Type B**. Intercomparison of lamp-illuminated integrating
    spheres/plaques and radiometers. This will be done on a yearly basis
    for MAS and once for NPP VIIRS to verify the SBRS SIS spectral
    radiance scale at Raytheon SBRS.

-   **Type C**. Chamber deployment of NIST thermal-IR spectroradiometer
    to view chamber calibration sources in-situ. This will be done once
    for NPP VIIRS to verify the BCS and SVT scales at the VIIRS
    calibration chamber at Raytheon SBRS, and once for NPP CrIS to
    verify the external calibration source blackbody at the CrIS
    calibration chamber at ITT.

-   **Type D**. Thermal-infrared intercomparison of blackbodies and
    radiometers (at ambient temperature and pressure). This will be done
    on a yearly basis for NAST-I, S-HIS, and MAS, linked to particular
    field campaigns.

-   Type E: Intercomparison with Portable Laser-Illuminated Integrating
    Sphere Source. This will be done once for NPP to update stray-light
    corrections in the MOBY spectrographs prior to use of MOBY for VIIRS
    ocean-color EDR validation.

More details of these types of activities are presented in Appendix
C**.**

Note that the dates for the activity that involves the flight instrument
calibration chambers (Type C) needs to be linked to chamber availability
at Raytheon SBRS and ITT. NIST experience with this activity is that
these two vendors are quite willing to cooperate and that there will be
no impact on flight instrument schedule since the activity can be
performed after the flight-instrument removal from the calibration
chamber.

[]{#__RefHeading___Toc534619238 .anchor}Table 4‑6: Plan of the
Intercomparison Activities during the NPP Pre-Launch Phase

  ---------------- --------- -------- -------- ------- ------- -------- --------
                   VIIRS     VIIRS    CrIS     MAS     MAS     NAST-I   MOBY

  Type             A         C        C        B       D       D        E

  Dates            FY03      FY04     FY04     FY 04,  FY 04,  FY 04,   FY05
                                               05, 06  05, 06  05, 06   

  Duration         4 months  2 weeks  2 weeks  1 week  1 week  1 week   2 weeks

  Participants\*   1a, 2     1e, 3    1e, 4    1b, 1c, 1e, 1f, 1e,1f, 6 1g, 7
                                               1d, 5   5                

  Uncertainties    1.4%      0.1 K    0.1 K    1%      0.1 K   0.1 K    \<1%

  Anticipated      1.5%      0.2 K    0.2 K    2%-4%   0.1 K   0.1 K    TBD
  Agreement with                                                        
  NIST                                                                  
  ---------------- --------- -------- -------- ------- ------- -------- --------

\* These references are in the table 4-3 below.

[]{#__RefHeading___Toc534619239 .anchor}Table 4‑7: Participants in NIST
Traceability Verification Activities

+-----------------------------------------------------------------------+
| 1.  NIST Optical Technology Division                                  |
|                                                                       |
| ```{=html}                                                            |
| <!-- -->                                                              |
| ```                                                                   |
| a.  BRDF measurement facility.                                        |
|                                                                       |
| b.  Portable lamp-illuminated integrating sphere source.              |
|                                                                       |
| c.  Portable Vis/NIR spectroradiometer.                               |
|                                                                       |
| d.  Portable SWIR spectroradiometer.                                  |
|                                                                       |
| e.  Portable Thermal-IR FTIR spectroradiometer (FTXR).                |
|                                                                       |
| f.  Portable water bath black body.                                   |
|                                                                       |
| g.  Travelling-SIRCUS                                                 |
+-----------------------------------------------------------------------+
| 2.  VIIRS calibration personnel working on diffuser panel BRDF        |
|     measurements (Raytheon SBRS Team).                                |
+-----------------------------------------------------------------------+
| 3.  VIIRS pre-launch radiometric chamber calibration personnel        |
|     (Raytheon SBRS Team).                                             |
+-----------------------------------------------------------------------+
| 4.  CrIS pre-launch radiometric chamber calibration personnel (ITT    |
|     Team).                                                            |
+-----------------------------------------------------------------------+
| 5.  MAS radiometric calibration personnel (NASA Ames and/or NASA      |
|     Goddard for reflective channels, Univ. of Wisconsin for           |
|     thermal-IR).                                                      |
+-----------------------------------------------------------------------+
| 6.  NAST-I radiometric calibration personnel (NASA Langley, Univ. of  |
|     Wisconsin, Utah State Univ., Lincoln Laboratory, Harvard Univ.).  |
+-----------------------------------------------------------------------+
| 7.  S-HIS radiometric calibration personnel (University of            |
|     Wisconsin).                                                       |
+-----------------------------------------------------------------------+
| 8.  MOBY team.                                                        |
+-----------------------------------------------------------------------+
| 9.  NPOESS IPO Shared System Performance Responsibility \[SSPR\]      |
|     Integration Contractor Calibration/Validation oversight team (as  |
|     needed).                                                          |
+-----------------------------------------------------------------------+

**\[Add OMPS to Tables 4-2 and 4-3 NIST traceability relevant to OMPS,
since Integrating spheres and spectral measurements will be used in
characterizing it.\]**

# Level 1 Product Post-launch Validation

Level 1 (RDR and SDR) products validation is the process of assessing by
independent means the accuracy of the radiance derived from each
instrument. Validation establishes the accuracy or confidence levels
associated with each Level 1 algorithm over the range of scene
conditions.

These Level 1 Products need to be independently validated, both because
they are valuable products in their own right (for direct input to
numerical models and for climate applications) and to allow their
suitability for supporting level 2 products to be evaluated

Figure 5.1 illustrates some of the elements associated with the
validation process.

Validation of satellite data, both on a global and a regional scale, are
usually conducted in several ways using multiple (independent)
measurement approaches. The methods include:

-   satellite versus satellite comparison

-   satellite versus aircraft under-flight comparison

-   satellite versus numerical weather analysis comparison

-   satellite versus in-situ ground, sun-photometer, radiosonde, buoy,
    and ship data comparison

![](media/image4.wmf)

[]{#__RefHeading___Toc534619231 .anchor}Figure 5‑3: High Level Schematic
of the Radiance Validation Process

Field campaigns and aircraft under-flights are resources that need to be
coordinated by the Government Team with the enabling NASA and IPO
authorities as well as the participating scientists.

Satellite-versus-satellite comparisons depend on overlap of orbital
operations and similar equatorial crossing times. Opportunities to
collect data from instruments on separate spacecraft will be identified
and pursued as appropriate.

Access to radiosonde and buoy data is important. The NOAA networks allow
rapid and free access to the data. Other international and national
networks do not provide similar data access by external scientists.
Using the non-NOAA networks for validation requires special
consideration. Other resources like the NASA operated AERONET may
require additional funding to acquire the data.

It is important to recognize that validation is an on-going
activity/process for the mission duration including intense initial
post-launch activities as well as intermittent and sustaining activities
throughout the remainder of the mission.

Finally, it is important to note that for instrument/algorithm
combinations designed to measure profiles rather than surfaces (that is,
for sounders such as CrIS, ATMS and OMPS, as contrasted to VIIRS),
validation of the forward model is one of the most essential steps in
the validation. The performance of current atmospheric profiling systems
is typically limited by errors in the forward models rather than by the
inversion algorithms (Rodgers, 2000).

##  Radiative Transfer Models Calculations

The two fundamentally distinct applications of forward models are (1)
radiance validation, and (2) atmospheric state, cloud, and surface
property retrievals.

Radiance validation will be accomplished by comparing radiative transfer
model calculations using independent truth fields to the direct radiance
observations. When comparing observed spectra to those calculated from
independent atmospheric state data the key role of the radiative
transfer model is obvious. However, radiative transfer models are also
important to account for the effects of different observing altitudes
and view angles for validation comparisons between satellite observed
radiances and radiances observed using aircraft instruments.

For atmospheric state retrieval, the quality of the model directly
affects the quality of the retrieved products. The retrieval is most
often posed as the solution of an integral equation in which the kernel
is provided by radiative transfer models. It is well known that this
ill-posed inversion process is critically dependent on observing errors
(instrument noise, spectral response uncertainties and calibration
errors), and of course, forward model errors have the same effect. In
fact, for the present state of the art in atmospheric remote sensing
forward model errors are generally larger than calibration errors.

### VIIRS Radiative Transfer (Visible to Infrared)

The radiative transfer model used for VIIRS retrievals must accurately
calculate the effects of multiple scattering in the atmosphere. This is
particularly true for cloudy conditions, but the effects of aerosols
under clear skies are also significant. Scattering is the dominant
effect in the shortwave and absorption dominates in the longwave, but
both must be considered for accurate radiance simulations. It is not
sufficient to represent visible and infrared radiative transfer with
no-absorption and no-scattering approximations.

The specification of cloud optical properties in forward models is
therefore critical. Parameterizations of optical properties based on Mie
theory are generally adequate for the representation of scattering in
liquid clouds. However, Mie theory is not appropriate for ice clouds,
and existing broadband models for hexagonal crystals have limited
applicability. Parameterizations of the optical properties for a variety
of particle \"habits\" have recently been developed for the shortwave
and are now being incorporated into radiative transfer models.
Parameterizations for the longwave need to be developed.

Even with improved cloud optical property parameterizations, however,
cloud property retrievals will still suffer from the lack of a priori
information on particle shape and vertical distribution. It is not
likely that satellite data alone can be used to obtain information on
particle shape, size, vertical distribution, and optical thickness,
though some possibilities are being explored. Aircraft measurements of
cloud microphysical and bulk optical properties are needed for the
validation of forward models and satellite retrievals. It is possible
that relationships between cloud temperature, water content, and the
distribution of particle shapes will be discovered and employed in
retrievals of cloud properties. Existing aircraft data needs further
analysis, and considerably more data for a variety of atmospheric
conditions need to be collected. Field campaigns in tropical,
mid-latitude, and polar regions should be used to supplement
ground-based cloud lidar and radar measurements.

Surface bidirectional reflectivity plays an important role in clear sky
shortwave radiative transfer. Until recently forward models did not
include surface bidirectional reflectance functions (BRDF), and their
availability is still very limited. This situation is expected to change
in the near future, as empirical BRDFs for many surface types are being
compiled from MODIS data. Additionally, surface BRDF measurements made
during recent and future field experiments can be used to validate those
based on MODIS data. Bidirectional emissivity is important for longwave
radiative transfer, but few field measurements are available. Laboratory
measurements are useful, but limited in scope.

There are several radiative transfer models available that use spherical
harmonics, discrete ordinates, and successive orders of scattering in
different combinations. There is general agreement in the results, but
as computational efficiency is increased accuracy sometimes suffers. A
good balance must be achieved. One standard of performance is the
MODTRAN transmittance, radiance, and flux model that covers the visible
to infrared part of the spectrum. DISORT, SHDOM, SHARM, 6S are also
popular.

### CrIS Radiative Transfer (Infrared)

The CrIS retrieval products are generated by minimizing the observed
minus computed radiances. This places the fast forward radiative
transfer model, which generates the computed radiances, on the same
footing as the observed radiances. Consequently, validation of the
forward model is as important as validation of the instrument radiances.

The accuracy of the forward model is determined by the accuracies of its
three main components, (1) the fundamental atmospheric spectroscopy that
forms the core of the forward model, (2) the CrIS instrument model, or
more specifically the CrIS instrument spectral line shape, and (3) the
parameterization algorithm that converts monochromatic atmospheric
transmittances into transmittances at the resolution of the CrIS
radiances. In the past, component (3) has often received the most
attention by retrieval system developers since the speed of the fast
forward model was of primary importance, and because they did not have
the expertise to improve the fundamental spectroscopy. During the last
10 years the fundamental atmospheric spectroscopy has improved
tremendously based on a combination of new laboratory spectroscopy,
theory, and intensive field campaigns/sites.

These improvements in spectroscopy are now approaching the estimated
accuracy of the radiances measured by high-spectral resolution sounders
and should remove the need to "tune" (or "bias correct") the computed
radiances in order to force them to agree with radiances computed from
other so-called truth such as radiosondes and/or NWP models. This is
presently a common practice with HIRS on TOVS, and if continued in the
EOS-AIRS and NPOESS era will severely limit the utility of the sounder
products since they become tied to the accuracy of the radiosonde
measurements and NWP models. We are now entering the era where the
forward models may be significantly more accurate, in an absolute sense,
than any other measurements of the atmospheric state for the time/space
scales that a satellite sounder can sense. (For example, this is
unquestionably true for upper atmospheric moisture, even for HIRS.) If
we abandon the practice of "tuning" that forces the satellite retrievals
to match the radiosonde measurements then the validation of the fast
forward model becomes as important as the validation of the instrument
radiances.

The high spectral resolution aircraft instruments developed by the IPO
for NPOESS validation (NAST and Scanning HIS) are key tools for this
work. Although significant progress has been made in recent years from
data collected by aircraft and ground-based FTS instruments, important
issues still remain. The data from AIRS onboard the NASA EOS Aqua
spacecraft will also play a very significant role in validating forward
models and ultimately understanding the information content in the
radiances that is not present in NWP models. A significant focus on this
effort is needed to make sure that the best possible models are
available at NPP launch.

#### CrIS Validation Issues and Approaches

Validation of the CrIS fast forward model can be organized by the three
main components outlined above. Forward model validation requires a
common definition of the CrIS radiance product (SDR/Level 1B). This also
impacts a variety of other validation efforts that require computation
of CrIS radiances using various atmospheric truth data sets.

#### Fundamental Spectroscopy

The fundamental spectroscopy in the CrIS forward model is derived from
monochromatic line-by-line radiative transfer algorithms that are
nominally under continuous development by several groups. These include,
for example, AER's LBLRTM and UMBC's kCARTA codes. At present, it is
uncertain which line-by-line algorithm will be used in which segment of
NPP. Moreover, the NWP centers have severe constraints on the form of
their fast forward radiative transfer algorithms, and they will likely
develop their own forward models based on some existing line-by-line
algorithm. This suggests a dangerous situation where multiple users take
the same radiances, but use different forward models resulting in
different retrievals. These differences can arise not only due to
differing spectroscopy but also due to different instrument line-shape
models (and/or optional apodization approaches), and different
parameterization approaches. A solution to this problem is discussed
below.

There are a number of on-going laboratory studies, and future
ground/aircraft campaigns that promise to improve the fundamental
atmospheric spectroscopy in time for NPP. These "pre-flight" validation
activities, funded by various sources including the IPO, need to be
integrated into the line-by-line radiative transfer model(s) that are
used to generate the CrIS forward model(s). At present there is no
identifiable effort to ensure that new forward model validation results
are inserted into the forward model used in the retrievals.

#### CrIS Instrument Line Shape Model

An accurate instrument model is key to production of an accurate forward
model. This issue is complicated by the fact that retrieval groups may
wish to use different additional apodizations in their forward models
that make validation problematic. It is recommended to agree upon a
single apodization, so that the forward model validation across agencies
will be much easier. Ultimately, the instrument apodization model used
for forward model development is validated by ensuring that this model
also accurately reproduces the CrIS gas transmittances measured during
pre-launch calibration.

#### CrIS Parameterization Model

The desire for high-speed forward models has always forced the use of
semi-empirical parameterizations of the atmospheric transmittances.
Validation of these parameterizations can primarily be performed
internally by the algorithm developer. However, these parameterizations
result from regressions using statistical sets of atmospheric profiles
that may not be optimal for the retrieval system. Care should be taken
to ensure that poor forward model performance under certain atmospheric
conditions is not a result of a poor choice of regression profiles
during fast forward model development.

Generally a wide variety of trace gas constituents have their profiles
fixed within a fast forward model. Some of these gases can vary slowly
over time. Validation of the parameterization should also include
validation that these fixed values for various atmospheric gases are
correct.

#### Pre-launch Activities

Pre-launch validation will draw upon activities within the AIRS and IASI
science teams. These include use of surface, radiosonde, and aircraft
data at NPP validation sites to simulate the CrIS spectral radiances.
These activities should lead to improved radiative transfer formulation,
and better performance stability of the products at global scale.
Pre-launch activity will also include software preparation for
validation data processing and analysis.

#### Post-launch activities

Post-launch validation activities will focus on the comparison of CrIS
observations over NPP validation sites with radiances calculated using
the forward model. Inputs for the forward model will come from ground
instruments, radiosondes, as well as aircraft data. Performance accuracy
will be budgeted for each site and in different climate regimes

The Government Team will strive to resolve any discrepancies observed
between CrIS and the forward model calculations, and improvements should
be proposed.

### ATMS Radiative Transfer Model (Microwave)

Validation of a microwave radiative transfer model is an important
component in the overall NPP cal/val activities and will begin during
the pre-launch period. With the collocated data sets including
atmospheric, surface, and satellite radiances, the radiance calculated
from the model can be directly compared with the ATMS measurements and
the error can be budgeted for each channel. In addition, the instrument
performance such as antenna pointing angle and the polarization angle
alignment can be assessed.

The most challenging issues and most of the uncertainty in microwave
forward modeling are caused by surface emissivity; atmospheric
scattering from clouds and precipitation; and spatial inhomogeneity. To
simulate the surface emissivity, the Government Team will identify
various emissivity models and assess their accuracies under various
environmental conditions. Presently, the emissivity over oceans can be
derived with fairly good accuracy (errors less than 1%) for the ATMS
frequencies below 90 GHz using an empirical model. However, the
simulation of the emissivitty at higher ATMS frequencies is yet to be
determined. While the comprehensive microwave land emissivity models are
being developed, the errors for the surface models need to be assessed
at various ATMS channels.

The particle scattering from clouds and precipitation must be taken into
account in ATMS radiance validation. To use the ATMS measurements for
the direct radiance assimilation and climate applications, Government
Team must validate the radiance accuracy under all weather conditions
and assess the various schemes for computing atmospheric optical
parameters, which are utilized in the radiative transfer models. In
addition, the radiative transfer scheme including polarization and
scattering should be evaluated by comparing the results with various
bench-mark tests published in the literature.

The accuracy of any validation of the NPP science data products is
dependent on precise characterization of both the atmospheric and
surface states during the satellite observation. For ATMS validation
cases, the uncertainties in knowledge of parameters such as temperature,
water vapor and atmospheric hydrometeor profiles are crucial for the
validation of the forward model, observed radiances, and science
products.

#### Pre-launch Activities

Validation of the microwave forward models must be accomplished. The
microwave emissivity significantly varies according to the surface
types. Over oceans, the emissivity can be accurately estimated at most
of ATMS frequencies using an empirical model (Wisler and Hollinger 1977)
or a two-scale model (Yueh, 1997). Over land, the microwave emissivity
model has been recently developed to predict the emissivity spectrum
(Weng et al., 2001). Both models would be crucial for the ATMS radiance
computation, especially for the channels "seeing" the surfaces. To
validate the ATMS radiance at the cloudy and precipitation conditions,
the microwave forward model can be generalized to include the scattering
contributions. An improved description of the scattering properties of
ice particles has been developed by Bennartz and Petty (2001). This
approach leads to a consistent description of scattering over the entire
frequency range of the ATMS. A fast polarimetric radiative transfer
model (FASTPORT) has been recently developed by NESDIS scientists (Liu
and Weng, 2001) and was proved to be very accurate, compared to the
other sophisticated RT models. The Government Team will also evaluate
other microwave forward models for the ATMS applications.

Validation of the microwave radiative transfer model is part of the
overall NPP cal/val activities and will begin before launch. One element
of this effort will utilize co-located clear-air satellite and aircraft
infrared and microwave data sets to predict radiances for ATMS and other
instruments that can be compared with observations to yield estimated
potential errors for each channel of each participating instrument. This
"round robin" calibration method is in lieu of a direct NIST involvement
in the microwave region (see Section 4.5.2).. By relating these
discovered errors to the limited degrees of freedom in the physical
models and laboratory transmittance measurements, it is expected that
all transmittance models can be evaluated and improved.

Because microwave and infrared spectrometers utilize single blackbody
calibration loads across many frequencies, the calibration errors of
channels with very different transmittances are generally highly
correlated. Moreover, the information of adjacent pixels in both
aircraft and satellite data sets are also often highly correlated, so
they may be averaged, yielding the potential for discovering, through
the round-robin technique, some transmittance-model-based radiance
discrepancies of as little as 0.01-0.1K. Since it is unlikely that all
microwave or IR frequencies would be offset in transmittance such that a
single calibration offset (say 1K) could duplicate a typical
multi-atmosphere ensemble of discovered error spectra, this
hard-to-reduce systematic calibration error may not be limiting. This
round-robin cross-calibration method should be tested for the first time
during CY 2002 using NAST and satellites of opportunity.

#### Post-Launch Activities

To the extent that the transmittance models can not be improved or
validated as suggested above, reliance must be placed on the absolute
accuracies of the various calibration systems and protocols. Microwave
calibration of NPP is discussed in Section 4.4

Two other challenging sources of uncertainty in field-based microwave
transmittance tests are microwave surface emissivity and atmospheric
scattering from clouds and precipitation. Clear-air tests at
surface-blind frequencies should be primary for validating microwave
clear-air transmittances, with emphasis on testing diverse atmospheric
temperature and humidity profiles. For frequencies where surface
emissivity limits ATMS transmittance validation accuracies, up-looking
microwave observations offer an alternative. Unfortunately atmospheric
water vapor can become dominant, so stable atmosphere and very precise
collateral water-vapor profile determinations exactly coincident in time
and space with transmittance observations are required. Similarly,
nadir-viewing transmittance tests would require precise surface
emissivity models validated under various environmental conditions.

In order to achieve more accurate retrievals of cloud parameter, the
scattering properties of clouds and precipitation are needed to
reconcile ATMS observations with atmospheric models. Aircraft
observations at relevant ATMS frequencies are required over diverse
clouds for this purpose because only aircraft instruments can resolve
the complex structure of clouds (\<\~3 km) to the degree necessary to
permit horizontally uniform models to be utilized.

**5.1.4 OMPS Radiative Transfer (Near UV to IR)**

This section covers the validation of the radiances and reflectances
calculated by the OMPS forward models, in contrast to section 5.2, which
will cover the validation of the radiances measured by the OMPS hardware
and calibration algorithms, i.e., the SDRs. The radiances pertain to the
physical entrance aperture of the instrument, and are given by a
radiative transfer code, but the forward model also includes a model of
how the instrument (spectral slit functions, vertical modulation
transfer functions) converts the incoming radiances into electronically
registered signals (expressed for convenience as effective radiances).
Both parts of the forward model require validation, so the present
section also discusses the validation of the instrument models contained
in the forward models.

The Nadir Total Column and Nadir Profile measurements are based on
radiances in the ultraviolet: 250 to 310 nm for the Nadir Profile
measurement, and 308 to 380 nm for the Nadir Total Column measurement.
The radiative transfer code for both is contained in the TOMRAD forward
model, developed by the NASA Goddard Space Flight Center. TOMRAD has
been used for decades to analyze TOMS, SBUV and SBUV/2 data. \[Has
TOMRAD been validated explicitly, or only implicitly, i.e., via the
validation of TOMS and SBUV and SBUV/2 products based on it?\] \[Is
TOMRAD a radiative transfer code only, or also a forward model?\]

The Limb Profile measurement is based on radiances between 290 and 1000
nm, i.e., in the near UV, visible and near IR spectral regions. The
radiative transfer code and forward model for the Limb Profiler was
developed by B. Herman and D. Flittner (Univ. of Arizona) for
SOLSE/LORE, and has been adapted for OMPS by J. Larsen (Raytheon). The
SOLSE/LORE forward model, unlike TOMRAD, has not been applied to huge
amounts of on-orbit data whose processed results for ozone were then
compared to independent measurements. Validation of the Limb Profiler's
forward model therefore receives special emphasis here.

The forward model (including the radiative transfer code) used in
determining the ozone total column from CrIS measurements of radiances
at 9.6 m (long-wave infrared) was developed by AER. \[Degree of overlap
with the CrIS forward model?\] To the extent that it is based on the
forward model used for CrIS its validation was treated earlier, in
section 5.1.2, but to the extent that it does not overlap with the CrIS
forward model its radiance calculations and instrument model will need
its own validation for NPP.

Specific aspects of the OMPS forward models that require validation are
discussed in the next X subsections.

**5.1.4.1 Multiple Scattering**

The OMPS Nadir Sensor and Limb Profiler all measure the spectral imprint
of ozone on sunlight that has been scattered toward OMPS by the Earth's
surface, clouds, or atmosphere. Multiple scattering in the troposphere
and lower stratosphere contributes significantly to the received
scattered sunlight for nadir views and for some limb-viewing lines of
sight. In particular, clouds, aerosols and reflective surfaces that are
near but not on a direct refracted line of sight can contribute to the
radiance from that line of sight. Algorithms for calculating multiple
scattering at relevant wavelengths are fairly mature and accurate, but
are simplified for greater computational speed, especially where the
Earth's curvature must be taken into account and when clouds, aerosol
layers and surface reflectivity patterns may be irregular. Also, there
is no standard, community-wide treatment of multiple scattering. (If a
standard approach had existed, it would probably have been well
validated by now.) Hence any radiative transfer code requires validation
of its predictions for lines of sight that involve significant multiple
scattering.

The CrIS-based measurement of the ozone total column works by measuring
thermal emission by ozone, not scattered sunlight. A wavelength of 9.6 m
is large compared to stratospheric aerosol particles and most
straospheric cloud particles, but it is not large compared to the
particles in Type II (ice) polar stratospheric clouds, tropospheric
cloud droplets and cirrus crystals and the larger particles in fresh
volcanic emissions. Because CrIS views at nadir, strong scatterers will
sometimes be encountered on its line of sight. Also, strong volcanic
enhancements of the tropospheric and stratospheric aerosol can produce
enough weak scatterers to produce strong scattering at 9.6 m. Hence it
will be necessary to assess the errors in the radiative transfer
predictions for lines of sight that encounter strong scattering.

**5.1.4.2 Calculated Scattering Coefficients**

The forward model for the OMPS Limb Profiler deliberately simplifies the
calculation of scattering by aerosol and cloud particles by assuming
Mie, even when the particles are non-spherical, such as the solid
particles in cirrus clouds, young volcanic plumes, some types of polar
stratospheric clouds (PSCs), and polar mesospheric clouds (PMCs). In
addition, the forward model assumes that the refractive index of the
particles to always correspond to typical liquid sulfate stratospheric
aerosol, which differs from that for PSCs and PMCs. Another deliberate
simplification, forced by the number aerosol channels and their total
spectral span, is the assumption that the particle size distribution is
always described by a single log-normal distribution, although it is
known that in clouds and in volcanically enhanced aerosol layers the
size distribution has multiple peaks. Finally, the algorithm assumes a
one-to-one correspondence between the aerosol extinction coefficient and
its scattering corfficient and angular distribution (phase function).
All these simplifications mean that the radiances calculated by the
forward model must be validated for lines of sight containing aerosols
and clouds with different particle size distributions and compositions,
for cases where single scattering dominates as well as for cases where
multiple scattering is significant.

\[Add Corresponding statements for TOMRAD\]

**5.1.4.3 Atmospheric Refraction**

Refraction is important for limb-viewing lines of sight whose tangent
altitudes are less than about 30 km. In nadir viewing, refraction is not
significant along the path from the scatterer to the spacecraft, but can
be significant on the path from the Sun to the scattering point,
especially when the solar zenith angle at the scatterer is large.
Although calculations of refraction are fairly mature, they are not
completely standardized, and hence the refractive algorithm used in any
particular radiative transfer code may not have been validated via
extensive use. Also, for simplicity and speed refractive calculations
usually use a standard or climatological refractive index profile
instead of a currently measured atmospheric density profile. For all
these reasons the radiative transfer codes used by OMPS must be
validated in clear atmospheres, for limb viewing lines of sight with
tangent altitudes below 30 km and for nadir viewing lines of sight
having a large solar zenith angle at the surface. No such
clear-atmosphere validation of refractive calculations is necessary for
the CrIS-based measurement of the ozone total column, because in that
case thermalization renders irrelevant anything but the nearly vertical
path from the emission point to the spacecraft.

**5.1.4.4 Spectroscopic Approximations**

The radiative transfer algorithms make spectroscopic approximations that
require validation. The temperature dependence of the ozone absorption
spectrum is only partially known. The radiative transfer code used for
the Limb Profiler presently does not include NO~2~ and other interfering
species such as SO~2~. Even if interfering species are added later,
their contributions will have to be calculated using a nominal vertical
profile (standard or climatological) rather than an actual profile,
since those species are not measured by OMPS or by other instruments on
the same platform. The Ring effect -- the partial filling in of
Fraunhofer lines by Raman scattering by molecular nitrogen and oxygen in
the Earth's atmosphere -- is also not included, and is expected to be a
weak function of scattering angle but a moderately strong function of
wavelength. Errors due to neglect of the Ring effect should be
wavelength dependent, but should be much smaller for the
non-monochromatic radiances measured by the instrument than for the
monochromatic radiances incident on the instrument. The instrument model
within the Limb Profiler forward model takes the finite spectral
resolution into account in a simpflified and not yet validated way, and
also neglects jitter. For all these reasons, the forward models'
spectroscopies require validation. The most useful data for all of the
spectroscopic validations will come from clear atmospheres underlain by
dark surfaces, with minimal horizontal gradients in atmospheric
composition and surface reflectivity. For validating the ozone
spectroscopy, data are needed from atmospheres having roughly comparable
ozone profiles but quite different temperatures. For validating the
neglect of interfering species, data are needed from locations having
large and small amounts of NO~2~ and SO~2~. Comparison radiance data for
validating the neglect of the Ring effect would require good spectral
resolution and radiometric accuracy. Calculations indicate that the
maximal Ring effects on limb scattered radiances will be 2 to 40 % for
monochromatic radiances at wavelengths less than 570 nm, 2 to 6% for
monochromatic altitude-normalized radiances near 330, 690 and 760 nm, 2
to 6 % effects on altitude-normalized monochromatic radiances, assuming
adequate wavelength registration. For those tests the forward model
would be run in monochromatic mode rather than with the modeled spectral
slit functions. The effects on the spectrally convolved radiances are
smaller: for the absolute radiances the errors are up to 0.15% at the
shortest wavelegths, and cause a bias of about 0.03% over most
wavelegths, and about 1/3 those numbers while for the
altitude-normalized limb scattered radiances. For spectrally convolved
Nadir Profile radiances, the errors due to neglect of the Ring effect
range from 1 to 6%. Validations of spectroscopic approximations will
necessarily also test the forward model of the instruments' jitter free
spectral resolution. For validating the neglect of jitter on spectral
smoothing, data with and without significant spacecraft jitter will be
needed.

**5.1.4.5 Vertical Smoothing**

Because the vertical profile of the limb scattered radiance is very
smooth, there may be no need to verify that the modeled vertical
resolution of the Limb Profiler gives correct results even for vertical
distributions of ozone and aerosol having large vertical gradients and
fine structure. \[Is that correct?\] If so, the effect of jitter on
vertical resolution can also be ignored, at least in the calculation of
the intrument-reported radiances. Otherwise data with and without
significant spacecraft jitter will be needed for verifying the modeling
of vertical smoothing, and that data will need to be accompanied by
accurate information on the absolute pointing of OMPS.

**5.1.4.6 Types of Earth and Solar Data Needed**

Ideally, the data for validating the calculated radiances would include
the vertical distribution of and total column of ozone, the vertical
distribution and total column of aerosols (ideally including profiles of
compositions and particle size distribution), the vertical profiles and
total columns of interfering species such as of NO~2~ and SO~2,~
vertical profiles of temperature and pressure, cloud parameters (at
least cloud type, cloud fraction, cloud-top altitude or pressure), the
spatial pattern of surface spectral BRDF, and the solar irradiance
spectrum at the top of the atmosphere.

The forward models for OMPS require external information: solar
irradiance spectrum, altitude-registered temperature and pressure
profiles (for calculating atmospheric density profiles and thence
Rayleigh scattering, and for the temperature dependence of the ozone
absorption spectrum), cloud fraction, pressure at cloud top, and rough
information on surface reflectivity. The validation of the radiances
calculated by the OMPS forward models will therefore depend in part on
the validity of this external information.

One source of ancillary data on the atmosphere and surface will be
aircraft. Manned aircraft (such as the NASA DC-8 and NAST) can obtain in
situ measurements only into the lower stratosphere, or up to 20 km for
the ER-2. Aircarft based remote sensing using FTIRs, lidars, microwave
radiometers and other techniques can measure atmospheric parameters
above the aircraft's flight altitude. UAVs can, in principle, perform
in-situ and remote measurements both at manned aircraft altitudes and at
somewhat higher altitudes, although it is not presently known whether
UAVs with scientific payloads will be available in time. Ballon-borne
radiosondes, ozonesondes and dustsondes will provide data up to 30 or 35
km. Airborne measurements of downwelling and upwelling radiances at
flight altitude can, in conjunction with a validated radiative transfer
code, be used to check calculated Earth radiances and solar irradiances
at the top of the atmosphere. Ground-based Dobson and Brewer ozone
spectrometers, lidars, FTIRS and passive microwave instruments will also
be sources of data. But it is expected that because of their wide
geographic coverage of space-based instruments will be the main source
of ancillary data as well as of comparison data.

Currently known sources of ground-based, balloon and space-based
measurements of ozone total columns and profiles are summarized in the
**WMO/CEOS Report on a Strategy for Integrating Satellite and
Ground-Based Observations of Ozone**, World Meteorological
Organization/Global Atmosphere Watch Report No. 140 (WMO TD No. 1046),
January 2001, http//www.wmo.ch/web/arep/reports/gaw140.pdf.

**5.1.4.7 Reflectances versus Radiances**

The OMPS forward model calculates reflectances (atmospheric
radiance/solar irradiance) as well as radiances. Indeed, most parts of
the OMPS inversions work with the reflectances rather than the
radiances, to avoid the need for an accurate absolute measurement of the
solar irradiance spectrum (including its Fraunhofer fine structure) at
the top of the atmosphere. The reflectances calculated by the forward
models should be more accurate than the absolute radiances calculated by
the same models. The calculated reflectances should be validated as well
as the calculated absolute radiances. Comparison data on reflectances
can only be obtained by combining comparison radiances with a measured
solar irradiance spectrum at the top of the atmosphere.

The most important part of the Limb Profiler inversion work in terms of
spectral reflectances normalized by the corresponding spectral
reflectance at a nominal altitude, since these altitude-normalized
reflectances are relatively insensitive to scattering by the surface and
lower atmosphere. The altitude-normalized reflectances should therefore
be more accurate than the unscaled reflectances. Since their quality
could limit the quality of the OMPS products, they should bevalidated.

**5.1.4.8 Pre-launch**

Most aspects of the forward model can be partially validated before
launch by adapting the forward model to existing space-based instruments
and then comparing to validated radiances measured by those instruments.
For OMPS' nadir-viewing forward model (TOMRAD), comparison radiance data
will be sought from TOMS, SBUV/2, OMI, GOME (on ERS-2), and SCIAMACHY
(on ENVISAT) in its nadir-viewing mode. Fot the OMPS Limb Profiler
forward model, comparison radiances will be sought from SOLSE/LORE, from
the small fraction of SAGE III radiances that are obtained by looking at
limb scattered sunlight, and from SCIAMACHY in its limb-viewing mode.
Balloon-borne instruments that measure scattered sunlight will also be
sought. Each comparison will require information on the atmosphere and
surface at the time and place of the measurement. Such ancillary data
will be obtained from aircraft, balloons (for altitides below 30 or 35
km) and satellites, supplemented by assimilated and forecast/hindcast
atmospheric fields obtained from global and mesoscale weather prediction
codes, other methods of deriving global ozone fields (e.g., correlations
between ozone mixing ratios and tracers on isentropic surfaces), and
back-trajectories calculated from assimilated winds. The discriminating
power of the validations will be limited by the accuracy and precision
of the comparison radiance measurements and ancillary data.

When the ancillary data is adequate to do so, the foregoing comparisons
will be performed with the atmospheric and surface conditions
represented at two different levels of fidelity: (1) as accurately as
they can be modeled using the ancillary data, and (2) using the
simplifications and limited knowledge that will be assumed by
operational versions of the forward model.

Additional pre-launch partial validations will be obtained by comparing
the radiances and reflectances calculated using fast versus detailed
versions of the forward model itself, and by comparing to independent
forward models developed for other instruments, such as OMI, GOME,
SCIAMACHY and the limb-scattering forward model for SAGE III. The
model-to-model comparisons will not reveal any errors due to omissions
or simplifications common to all of the models, but have the virtue of
being able to use precisely the same idealized atmospheric and surface
parameters. Access to the results of non-NPOESS models will require
either joint research agreements with the Science Teams that developed
those models or participation in the validation teams for the associated
instruments.

**5.1.4.9 Post-launch**

Post-launch validations of calculated pre-aperture and reported
radiances will compare those calculated radiances and reflectances to
radiances and reflectances measured by OMPS. They will also extend the
types of validations that had been performed before launch, as new
sources of comparison radiance data and ancillary data become available.

When the ancillary data is adequate to do so, the comparisons will be
performed with the atmospheric and surface conditions represented at two
different levels of fidelity: (1) as accurately as they can be modeled
using the ancillary data, and (2) using the simplifications and limited
knowledge that will be assumed by operational versions of the forward
model.

Comparisons of the measured and calculated OMPS radiances and
reflectances will test the full forward model, including instrumental
effects. It is not possible to use these comparisons to independently
validate the pre-aperture calculated radiances, which are produced by
only the radiative transfer part of the fowrad model. Analysis of the
comparisons will look at both the average residuals and their standard
deviations.

## Radiance Validation

Radiance validation consists of independent assessment of the spectral,
spatial, and radiometric accuracy of the calibrated NPP radiances. For
spectral validation, the efforts are focused on top-of-atmosphere
calculations using known spectral features. For radiometric calibration,
the primary validation for the visible and infrared channels of the NPP
instruments is done with coincident observations from NPOESS aircraft
instruments (MAS, NAST-I and S-HIS) and the EOS sensors, with
top-of-atmosphere calculations using validation site atmospheric
profiles and surface characterization. Ground instruments used at
specific calibration sites are also considered in the validation of the
NPP radiances.

### Visible, Infrared and Microwave Radiance Validation

The calibration and validation of NPP radiance measurements can be
divided into two tasks - creation of validation data sets and comparison
of NPP measurements to measurements from aircraft and other satellite
instruments. The accuracy of any validation of the NPP science data
products is dependent on precise characterization of both the
atmospheric and surface states during the satellite observation.
Validation cases, where the uncertainties in knowledge of parameters
such as water vapor profiles have been determined, are crucial for the
validation of the forward model, observed radiances, and science
products.

Validation of NPP radiances falls into two categories: spectral and
radiometric. The first deals with identification of inconsistencies due
to VIIRS channel central frequencies and spectral response function
full-width uncertainties, and confirmation of CrIS wavelength scale
based on comparisons with spectra calculated from radiosondes. The
second involves radiometric calibration from inter-comparisons with
calculations using radiative transfer models of known accuracy. Absolute
radiance validation can be performed by comparing the observed minus
calculated radiance residuals between the NPP instrument data, and
aircraft data (MAS, S-HIS, NAST-I, NAST-M) observations.

Another important activity that will contribute to the long-term
validation of the NPP sensor radiance is the intercomparison with other
coincident data (such as from the EOS Terra and Aqua platforms). Overlap
between VIIRS and MODIS Terra observations is strongly encouraged in
order to provide continuity for VIIRS EDRS. Currently, schedules show no
overlap at all. Lack of overlap places demands on the cal/val effort to
assure continuity that severely tax, and which may be beyond the
capability required for seamless time series at the accuracy needed for
climate research. We strongly urge NASA to consider extending the
operation of MODIS Terra into the VIIRS time frame, assuming that it is
still capable of providing useful data, for at least several months.

This will require participation in the definition of these
intercomparisons in coordination with the various instrument teams. The
Government Team proposes to demonstrate some of these calibration
validation techniques using MODIS,AIRS and AMSU/HSB data as part of
their participation in EOS Science Team activities. Intercomparison with
EOS, GOES, POES, Meteosat, METOP, ENVISAT, EO-3 and GMS will be
undertaken as well. The techniques developed in these intercomparisons
will then establish an important part of the NPP sensors' calibration
validation process.

The EOS calibration sites (e.g. White Sands, Railroad Playa) are highly
relevant to VIIRS calibration and radiance validation activities. These
sites already well characterized, have been used for many years as a
calibration reference for the AVHRR bands and then MODIS. Ground
instruments such as sunphotometer and radiometers, will be deployed
along with aircraft flights to measure surface and atmospheric
properties. These measurements will allow top of the atmosphere radiance
calculations to be compared to VIIRS observations. Discrepancies between
observed and calculated radiance will determine the accuracy of the
radiance and how well the on-board calibration sources are performing.

Comparisons of observed and calculated CrIS, ATMS, and OMPS radiances
for forward model validation should be concentrated about those
radiosonde launch sites that provide additional information about the
state of the atmosphere and the surface. For example, the DOE ARM CART
sites routinely operate microwave radiometers that provide atmospheric
total precipitable water (TPW) measurements that are used to scale
radiosonde water vapor profile to the same TPW amount. In addition,
surface and near surface temperature and water vapor measurements are
made that would impact the observed-calculated radiance bias.

### Approaches for Routine Radiance Validation

NPP radiance products are validated on a routine basis using the
following three approaches:

#### Radiance validation using NWP analysis 

A very useful approach to validate CrIS, ATMS, and OMPS radiances is to
compare them with radiances simulated from Numerical Weather Prediction
(NWP) analysis fields. Analysis fields of temperature, moisture, and
ozone are spatially and temporally interpolated to selected CrIS and
ATMS FOVs. Radiances are computed using a fast radiative transfer model
from the interpolated atmospheric state. The enormous sample provides
the means to study and monitor scan dependent bias and standard
deviation between measured and computed radiances. Time series of
channel bias and standard deviation are updated daily. This capability
will quickly detect apparent outliers and will also detect sensor drift.

Using NWP analyzed fields to simulate OMPS and 9.6 m CrIS radiances will
require ozone fields as well as fields of temperature and cloudiness.
The NWP ozone fields can be obtained from 3D global ozone assimilations
produced by the ECMWF, KNMI, UKMO, NESDIS, and DAO. Access to the
assimilations produced by the first three organizations will require
cooperative agreements.

#### Radiance validation using operational radiosondes.

Ensemble statistics of radiance residuals (bias and standard deviation)
from radiance simulated from operational radiosondes provides a model
independent validation. Approximately 300 matchups (collocated
radiosonde and satellite FOVs) are available each day. Although
operational radiosondes do not generally possess the accuracy neededfor
spectroscopy validation, they are a very good source for long term
monitoring. The collected data can also be used for radiance tuning. The
use of similar radiosonde instrument quality is important. Selection of
radiosonde type can be determined by comparing radiance residuals as a
function of radiosonde instrument.

The use of operational radiosondes to simulate OMPS and 9.6 m CrIS
radiances will require ozone and (whenever possible) aerosol data as
well as temperature and pressure profile data from the balloons.
Balloon-based ozonesondes cover only the troposphere and lower
stratosphere: they do not always reach as high as 30 km, and only
sometimes attain 35 km. The same is true of balloon-borne dustsondes for
validating OMPS aerosol extinction profile product and for checking the
aerosol corrections in the ozone products.

#### Radiance validation using eigenvector decomposition.

Eigenvectors of radiances can be used to validate radiance quality. This
is achieved by reconstructing radiances using a truncated set of
eigenvectors, then comparing it with the observed radiances. If the
differences are very large then the quality of the radiances are
questionable.

### Approaches Planned for Radiance Validation

These NPP radiance products (SDR produced at IDPS and Level 1B produced
at SDS) are to be validated as a combination of three strategies:

1.  **Aircraft under-flight measurements**

2.  **Ground measurements at NPP calibration sites**

3.  **Inter-comparison to other space-based sensors**

#### VIIRS radiance validation

Approach 1: Aircraft Visible, Near Infrared and Thermal Observations

**Product:** VIIRS radiance validation

High altitude airborne measurements from MAS and/or MASTER instruments
will be conducted at EOS calibration sites and/or over ocean sites, and
the estimates will be compared to VIIRS measurements, assuming most of
the atmosphere is below the aircraft.

Viewing geometry and temporal acquisition will be addressed to avoid any
variation linked to the viewing observation, surface heterogeneity or
atmospheric fluctuations. Means of measured radiance from airborne
sensors will be compared. The observed radiance difference is then
attributed to calibration differences or sensor characterization
changes.

Approach 2: NPP Validation Sites Radiance Measurements

**Product:** VIIRS radiance validation

The approach is to compare VIIRS cloud-free radiance spectra to
calculations of the upwelling clear sky radiance for NPP overpasses of
the NPP calibration validation sites. These sites will be chosen for
their surface uniformity and stability. The calculations will be
performed using well-calibrated ground and tower radiometers or/and
spectrometers, and temperature and water vapor best estimate products
from the NPP validation site. The radiative transfer codes will be used
to perform these top-of-the atmosphere radiance calculations. The
differences between the observed and calculated radiances will be
analyzed with respect to the calculation uncertainties (spectroscopic
accuracy, radiative transfer and atmospheric state uncertainty, and
surface emissivity and temperature characterization) to assess the
accuracy of the observed radiances.

Approach 3: Space-Based Sensors Cross-Validation

**Product:** VIIRS radiance validation

Sensors from other programs such as EOS, METOP and ENVISAT programs will
be used in this sensor cross-validation. Resampling methods will be
provided to minimize errors from geolocation processing and spectral
differences. Comparisons of the sensor radiances are then made for
selected scene types of varying homogeneity and signal level (e.g. clear
ocean, desert, vegetation, etc...).

Viewing geometry and temporal acquisition will be addressed to avoid any
variation linked to the viewing observation, surface heterogeneity or
atmospheric fluctuations. Means of measured radiance from space-based
sensors will be compared. The observed radiance difference minus the
forward-calculated clear sky radiance difference is then attributed to
calibration differences or sensor characterization changes.

#### CrIS radiance validation

Approach 1: Aircraft Infrared Radiance Observations

**Product:** CrIS radiance validation

Uniform targets with a range of radiance levels (e.g. uniform ocean for
a range of latitudes, deserts, and uniform cloud decks) will be
selected. Aircraft field campaigns in which NAST under-flights will be
made with aircraft flight tracks arranged parallel to the sub-satellite
track, and where the aircraft view-angle will be adjusted to match the
appropriate CrIS cross-track angle. The approach is to compare both CrIS
and aircraft spectral radiances at a common spectral resolution. This is
possible since the NAST-I and S-HIS are both Fourier Transform
Spectrometers as is the CrIS sensor. The higher resolution aircraft
pixels are summed with appropriate weights to represent the larger CrIS
Spatial Response Function. Unsampled regions are represented by using
imager data to assign spectra from similar sampled regions.

Approach 2: NPP Validation sites TOA Radiance Calculations

**Product:** CrIS radiance validation

The basic approach is to compare CrIS cloud-free and cloud-cleared
radiance spectra to calculations of the upwelling clear sky radiance for
NPP overpasses of the ARM sites, P-AERI sites and ocean sites (ship
cruises). The calculations will be performed using inputs for
temperature and water vapor best estimate products. The CrIS fast model
and line-by-line radiative transfer codes will be used to perform these
clear sky calculations. The differences between the observed and
calculated radiances are then analyzed with respect to the calculation
uncertainties (spectroscopic accuracy, fast model parameterization,
atmospheric state uncertainty, and surface emissivity and temperature
characterization) to assess the accuracy of the observed radiances. The
Atmospheric and Environmental Research Inc. (AER) Optimal Spectral
Sampling (OSS) radiative transfer model will be compared to other fast
transmittance models (including a PFAAST-based model for CrIS). These
comparisons will be done separately for cloud free and cloud cleared
conditions to assess the accuracy of the clear sky CrIS radiances and
the accuracy of the cloud-clearing algorithm and resulting radiances
under cloudy and partly cloudy conditions.

Approach 3: Space-Based Sensors Cross-Validation

**Product:** CrIS radiance validation

The general technique is to reduce the data from different sensors to
the same spectral and spatial resolution using appropriate averaging
methods. Comparisons of the sensor radiances are then made for selected
scene types of varying homogeneity and signal level (e.g. clear ocean,
desert, etc...). Collocation in space and time is required. Spatial and
temporal scaling should be robust enough to avoid any variation that can
be linked to the surface or atmospheric fluctuations. Furthermore, data
will be selected close to nadir for each instrument in order to minimize
viewing angle differences. Means of measured radiance from space-based
sensors will be compared. Clear sky forward calculations (using a global
model for estimation of the atmospheric state) are performed to account
for differences in the spectral response functions (when comparing to
broad band radiometers). The observed radiance difference minus the
forward-calculated clear sky radiance difference is then attributed to
calibration differences.

#### ATMS radiance validation

Approach 1: Aircraft Microwave Radiance Observations

Uniform targets with a range of radiance levels (e.g. uniform ocean
surface for a range of latitudes, deserts, and uniform cloud decks) will
be selected. Aircraft field campaign in which NAST under-flights will be
made with aircraft flight tracks arranged parallel to the sub-satellite
track, and where the aircraft view-angle will be adjusted to match the
appropriate ATMS cross-track angle. The approach is to compare both ATMS
and NAST-M aircraft spectral radiances at a common spectral resolution
from NAST-M. The higher resolution aircraft pixels are summed with
appropriate weights to represent the larger ATMS Spatial Response
Function (SRF). Unsampled regions are represented by using imager data
to assign spectra from similar sampled regions.

Approach 2: NPP Validation Sites TOA Radiance Calculations

The basic approach is to compare ATMS radiance spectra to calculations
of the upwelling clear sky radiance for NPP overpasses of the ARM sites.
The calculations will be performed using input from the ARM site
temperature and water vapor best estimate products from the Southern
Great Plains (central facility), North Slope of Alaska (Barrow site),
and the Tropical Western Pacific (Nauru) sites. The ATMS fast model will
be used to perform these calculations under clear and cloudy conditions.
The differences between the observed and calculated radiances are then
analyzed with respect to the calculation uncertainties (spectroscopic
accuracy, fast model parameterization, atmospheric state uncertainty,
and surface emissivity and temperature characterization) to assess the
accuracy of the observed radiances.

Approach 3: Space-Based Sensors Cross-Validation

Sensors from other programs such as EOS, METOP and EO-3 programs will be
used in this sensor cross-validation. Resampling methods will be
provided to minimize errors from geolocation processing and spectral
differences. Comparisons of the sensor radiances are then made for
selected scene types of varying homogeneity and signal level (e.g. clear
ocean, desert, vegetation). Viewing geometry and temporal acquisition
will be addressed to avoid any variation linked to the viewing
observation, surface heterogeneity or atmospheric fluctuations. Measured
means of radiance from space-based sensors will be compared. The
observed mean of the radiance difference minus the forward-calculated
clear sky radiance difference is then attributed to calibration
differences or sensor characterization changes.

**5.2.3.4 OMPS radiance validation**

This section discusses the validation of the radiances measured by OMPS,
as opposed to section 5.1.4, which discussed the validation of the
radiances calculated by the OMPS forward models.

There are two complementary approaches for validating the radiances
measured by OMPS: (1) comparing measured OMPS radiances to radiances
measured by other instruments, and (2) comparing measured OMPS radiances
to radiances carefully calculated for well-characterized measured
atmospheric and surface conditions.

The sources of comparison data for validating the measured radiances
will include those described in section 5.1.4.5 for validating the
calculated radiances. Those sources provide data on the atmosphere and
surface and/or radiances, and hence pertain to both methods (1) and (2).

Data for radiance validation method (2) must include ozone and aerosol
profiles, clouds, temperature and pressure profiles (for temperature and
bulk number density), and surface reflectivity maps. Since measured OMPS
radiances will be used in part of the validation of the OMPS forward
model (section 5.1.4.5), and radiances calculated via the OMPS forward
model will be used in part of the validation of the measured OMPS
radiances, the comparison between measured and calculated OMPS radiances
will provide a consistency check rather than an independent validation.

The spectral resolution of the comparison radiances will usually differ
from that of OMPS. In each comparison the radiance data having finer
resolution must be degraded to the coarser spectral resolution. When the
OMPS radiances must be degraded to the spectral resolution of the
comparison radiances the comparison will provide a less direct test of
the measured OMPS radiances than in the reverse situation, but in both
cases the comparisons provide some information.

Measured radiances must be validated for the OMPS Nadir Total Column
cells at nadir, for cells at intermediate off-nadir positions and for
cells near the ends of the horizontal swath, to characterize any
systematic error in the measured radiances as a function of off-nadir
angle and degree of keystone deformation. The cell-to-cell variations
should depend somewhat on the spatial distribution and types of
cloudiness, and on the tropospheric and lower stratospheric turbidity.

The Nadir Profile measurement uses a single thin but wide horizontal
cell elongated in the cross-track direction. The radiance for that cell
is obtained \[TBC\] by aggregating signals from multiple pixels of the
Nadir Profile CCD. Signals from those CCD cells must be compared to
characterize the way that different parts of the swath contribute to the
single reported Nadir Profile radiance. The weighting and pixel-to-pixel
variation in the contributions should depend somewhat on the spatial
distribution and types of cloudiness, and on the upper-tropospheric and
lower-stratospheric turbidity.

The radiances measured by the Limb Profiler must be validated for all
three vertical slits: the slit in the orbit plane and the slits on
either side of the orbit plane. Each slit has a low-gain portion for the
troposphere and lower stratosphere and a high gain portion for the upper
stratosphere and mesosphere. Both the low-gain and high-gain radiance
measurements must be validated, as a function of altitude and
wavelength.

The radiances measured by CrIS at 9.6 m (which will be used to provide
an alternative ozone total column product) are being validated as part
of the CrIS Cal/Val activities (section 5.2.3.2). Whenever possible,
tests of the Nadir Total Column radiances should coincide with daytime
tests of the CrIS radiances.

For both the nadir and the limb measurements by OMPS, reflectances must
be validated as well as radiances. Reflectances differ from radiances by
a factor of the reciprocal exoatmospheric solar irradiance spectrum, as
determined by extrapolating a smoothed time series of weekly readings of
the solar calibration diffusers. The measured solar irradiance spectra
must be validated. If the OMPS calibration factor is measured more
precisly and accurately than the radiances, then the reflectances
measured by OMPS should agree better with the comparison reflectances
than should the corresponding radiances, since one source of
variability, namely, the solar irradiance, will have been divided out.
On the other hand, if the calibration factor is significantly noisy or
biased, the reflectances may compare worse than the radiances. So
validating both the reflectances and the radiances will contribute to
validating the solar calibrations of the OMPS data. But direct
comparison measurements of the solar irradiance spectra are needed also.

In addition to validating the reflectances measured by the OMPS Limb
Profiler, it will be necessary to validate reflectance ratios, i.e.,
reflectances normalized by the reflectance at a nominal altitude, at the
same wavelength, since these reflectance ratios are used in the most
important parts of the Limb Profiler inversion. This will require that
the comparison radiance data be registered on an altitude grid. In
favorable cases geometric altitudes will be known directly, but when the
comparison radiances are known only on a pressure grid, altitudes can be
computed at the price of a modest decrease in accuracy.

The OMPS Limb Profiler inversion works with radiances and reflectances
on an altitude grid. Hence the OMPS Limb Profiler altitude registration
must be validated as part of the validation of the Limb Profiler
radiances and reflectances.

Differences in the time and location of sampling frequently complicate
comparisons between instruments on different spacecraft, or between
space-borne and ground-based or air-borne instruments. A further problem
is that the portions of an atmospheric profile at different altitudes
almost always have different origins and histories because of the
altitude variation of the wind field. Trajectories calculated from
analyzed wind fields can be used to both increase the number of close
matches, in an altitude-dependent manner that takes into account the
different histories of different portions of the profiles. Experience
shows that that this approach can considerably improve the comparison.

**Approach 1:** Aircraft

**Product:** Partial validation of radiances measured by OMPS

In the context of validating measured OMPS radiances and products,
"aircraft" will be taken to include balloons and UAVs as well as manned
non-buoyant aircraft, since all are launched from ground sites, provide
only localized geographic coverage, and directly sample only a limited
range of altitudes.

Aircraft can provide in-situ measurements (ozone, aerosols and clouds,
temperature, surface reflectivity) for characterizing the atmosphere and
surface for careful calculations of the radiance at the top of the
atmosphere (method (2), above). Manned aircraft can sample the
troposphere and lower stratosphere (NASA DC-8, ER-2). UAVs can sample a
similar range of altitudes. Balloons can directly sample standard
meteorological parameters and ozone, clouds and aerosols up to 30 or 35
km.

Airborne remote sensing can considerably extend the range of altitudes
that can be characterized. Relevant instruments include lidars, FTIRs
and microwave radiometers.

Airborne measurements of downwelling and upwelling radiances at flight
altitude can, in conjunction with a validated radiative transfer code,
be used to check calculated and measured Earth radiances and solar
irradiances at the top of the atmosphere. They provide a direct check of
the former, and a sanity check of the latter.

**Approach 2:** Ground-based sites

**Product:** Partial validation of radiances measured by OMPS

The NPP Cal/Val gound sites will provide well-characterized surface
reflectivities for validating the radiances measured by the Nadir Total
Column and Nadir Profile components of OMPS, provided that the
well-characterized surface is larger than the footprint of the
appropriate horizontal cell. Even if this condition is satisfied for
Nadir Total Column cells near nadir, it is unlikely to be satisfied for
Nadir Total Column cells near the cross-track limits of the swath, and
it is also unlikely to be satisfied over the entire single horizontal
Nadir Profile cell. \[TBR\]

The NPP Cal/Val sites will \[TBC\] also have well-characterized cloud
fields. If the well-characterized region is large enough, radiance data
from those sites will aid the validation of the calculation of clouds

Finally, the NPP Cal/Val sites will provide well-characterized
downwelling radiance spectra, which will provide checks on both the
measured and calculated upwelling radiances at the top of the
atmosphere. This will be useful primarily for the Nadir Sensor, but does
provide a cross-check for the Limb Sensor.

Besides the NPP Cal/Val ground sites, any ground site hosting Dobson or
Brewer ozone spectrometers, cloud and atmospheric turbidity
measurements, lidars, or microwave radiometers can also provide data for
validating the radiances measured by OMPS. In particular, sites
associated with the Network for the Detection of Stratospheric Change
(NDSC) or with the WMO's Global Atmosphere Watch networks of ground
stations will be used as sources of data. Many of these sites are listed
in the WMO/CEOS **Report on a Strategy for Integrating Satellite and
Ground-Based Observations of Ozone**, World Meteorological
Organization/Global Atmosphere Watch Report No. 140 (WMO TD No. 1046),
January 2001, http//www.wmo.ch/web/arep/reports/gaw140.pdf.

**Approach 3:** Space-based measurements

**Product:** Partial validation of radiances measured by OMPS

Validated satellite-based techniques can characterize the atmosphere and
surface to provide inputs for calculating radiances to compare to those
measured by OMPS, and can also directly measure comparison radiances and
the solar irradiance spectrum.

Space-borne ozone measuring instruments that should be operating while
NPP is in orbit are listed in the WMO/**CEOS Report on a Strategy for
Integrating Satellite and Ground-Based Observations of Ozone**, World
Meteorological Organization/Global Atmosphere Watch Report No. 140 (WMO
TD No. 1046), January 2001,
http//www.wmo.ch/web/arep/reports/gaw140.pdf. Both research and
operational instruments are listed. Many of the listed instruments also
measure other atmospheric parameters relevant to the comparison. Data
will also be obtained from EOS instruments that measure the solar
irradiance spectrum and the surface reflectivity.

## Spatial co-registration validation

The spatial co-registration will be addressed in the Level 1A (RDR)
algorithms for VIIRS, CrIS, OMPS and ATMS by NPP instrument vendors.
Level 1A processing involves unpacking and verifying RDRs, organizing
these data into scan oriented data structures, generating the Earth
location data, adding associated ancillary information (metadata)
required to describe the data set, and producing a data product in a
standard format.

In this context, the Earth location data fields are treated as
additional attributes of the spatial elements that contain the science
data, thus describing explicitly each spatial element\'s ground
location.

A set of parametric equations and a table of sub-pixel corrections for
each detector or band will be included in the data product to capture
the effects of band-to-band and detector to detector offsets.

### Instrument and spacecraft alignment data verification

The geometric characterization and calibration of instrument,
spacecraft, and ancillary data are integral to the verification process.
Geometric calibration activities to be performed by the instrument and
spacecraft contractors will be carried out in accordance with their
contract schedules. Specifically, the preflight instruments' geometric
calibration will be performed by the corresponding vendors for each
instrument following their calibration plan.

The Government Team is responsible for the oversight of these
measurements. Of particular interest to the Earth location model, are
the absolute orientation, mirror positioning, MTF, band-to-band
registration, and antenna tests. Preflight measurements of the
instrument-to-spacecraft alignment will presumably be carried out during
the instrument/spacecraft integration phase.

### Band-to-Band Registration (BBR) verification

**Approach 1:** Earth scenes with high contract features

High contrast scenes, such as coastline, will be used for the
band-to-band co-registration. Because of continuous global coverage by
the sensors, this approach will have plenty of data sets. Scenes with
gradients in surface features will also be used for this purpose.

**Approach 2:** Lunar view

The moon serves as a fairly stable radiometric source and it will be
used for long-term sensor response stability monitoring, especially for
the reflective solar bands. Lunar view responses from different spectral
bands will be used for BBR characterization. This approach has been
successfully used in the MODIS on-orbit BBR monitoring.

### Instrument-to-Instrument Registration In addition to pre-launch spatial characterization that includes the determination of band-to-band registration, it is essential to validate and monitor on-orbit variation of BBR and its long-term stability. For sensors with multi-focal plane assemblies, this also includes the co-registration among different focal planes. For comparison of science products derived from different sensors, sensor-to-sensor co-registration is also needed. Most likely this can only be done from on-orbit observations.

If possible, both along scan and along track directions co-registrations
should be considered. In the following, a number of potential approaches
are listed based on the sensor type.

***CrIS and ATMS spatial co-registration***

The Nyquist-sampled character of ATMS data permits "sharpening" ATMS
images beyond their nominal 15- or 50-km nadir resolution. By comparing
the average response of CrIS and ATMS to unresolved isolated islands,
lakes, and precipitation cells, the average relative offsets of these
two sensors should be determined empirically to within \~2 km. If ATMS
is not Nyquist sampled, this task would be more difficult.

### Sensor Navigation Validation

The goal of navigation validation is to ensure that the radiances and
atmospheric profiles are navigated to Earth located footprints to the
required accuracy. Candidate approaches include the comparison of sensor
images to calculated images for well defined, high contrast coastlines
and similar surface features, and the comparison of coarse and high
resolution sensors such as CrIS and VIIRS.

# Level 2 and Higher Product Post-launch Validation

Product validation is the process of assessing by independent means the
accuracy of the geophysical products derived from each instrument.
Validation establishes the accuracy or confidence levels associated with
each retrieval algorithm geophysical product over the range of scene
conditions associated with the retrieval. Figure 6.1 illustrates some of
the elements associated with the validation process.

Validation of satellite data, both on a global and a regional scale, is
usually conducted in several ways using multiple (independent)
measurement approaches.

![](media/image5.wmf)

[]{#__RefHeading___Toc534619232 .anchor}Figure 6‑4: High Level Schematic
of the EDR/CDRs Validation Process

Experience and strategies learned during the pre-launch period will be
applied to the post-launch EDR validation activities. The EOS validation
period will be complete before the launch of NPP platform. This will be
an invaluable resource for the final validation plans for NPP
instruments.

Ground and aircraft-based as well as satellite over-flights of
well-instrumented land stations, such as those presented in this
section, will also provide a large data set for the EDR/CDR validation
process. Coordinating efforts with NPOESS and other platforms' based
sensors will be a high priority in order to lower costs and provide
cross-suite validation capabilities.

In order to validate NPP data products, it is necessary to validate
land, ocean and atmospheric parameters under a wide variety of
atmospheric conditions, solar illumination and viewing angles, and over
a wide variety of ecosystems worldwide.

In the following sections, we will provide guidelines for validating the
VIIRS, CrIS/ATMS, and OMPS products. A list of validation approaches for
each of the EDR/CDRs is provided in the Appendix H.

## Validation of Groups of NPP Products

The NPP Level 2 products (EDR/CDR) are categorized into six groups: (1)
Atmospheric Sounding, (2) Aerosol, (3) Clouds, (4) Land, (5) Ocean, and
(6) Snow and Ice. These products are to be validated as a combination of
three strategies, shown in Table 6-1.

[]{#__RefHeading___Toc534619240 .anchor}Table 6‑8: NPP Validation
Strategies

> 1\. Surface Validation Sites
>
> 1a. Atmospheric State
>
> 1b. Land Surface Characteristics
>
> 1c. Oceanographic Research Vessels/Buoy Systems
>
> 2\. Airborne Validation Platforms
>
> 2a. Piloted Aircraft with Radiometric Sensors
>
> 2b. Unmanned Airborne Vehicles, including Balloons
>
> 2c. Commercial Aircraft
>
> 3\. Satellites Cross Validation
>
> 3a. Advanced Satellites
>
> 3b. Operational Satellites

Field programs, conducted to generate research data sets, will be
leveraged to bring together a wide variety of surface-based, airborne,
and satellite data products for the purpose of validating NPP SDR/Level
1B and EDR/CDR products. Table 6-2 shows validation strategies that
might be used to validate the NPP EDRs and CDRs, and also provide the
level of priority to be allocated to validation strategies for each NPP
product.

[]{#__RefHeading___Toc534619241 .anchor}Table 6‑9: Validation Strategies
for NPP EDR/CDRs

<table>
<colgroup>
<col style="width: 5%" />
<col style="width: 27%" />
<col style="width: 7%" />
<col style="width: 7%" />
<col style="width: 8%" />
<col style="width: 7%" />
<col style="width: 8%" />
<col style="width: 8%" />
<col style="width: 9%" />
<col style="width: 8%" />
</colgroup>
<tbody>
<tr class="odd">
<td></td>
<td colspan="9">Strategy **</td>
</tr>
<tr class="even">
<td rowspan="26"></td>
<td></td>
<td><strong>1a</strong></td>
<td><strong>1b</strong></td>
<td><strong>1c</strong></td>
<td><strong>2a</strong></td>
<td><strong>2b</strong></td>
<td><strong>2c</strong></td>
<td><strong>3a</strong></td>
<td><strong>3b</strong></td>
</tr>
<tr class="odd">
<td><strong>Imagery</strong> *</td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td>X</td>
<td>Y</td>
</tr>
<tr class="even">
<td><strong>Moisture Profile</strong> *</td>
<td>X</td>
<td></td>
<td></td>
<td>X</td>
<td>Y</td>
<td>Z</td>
<td>Y</td>
<td>Z</td>
</tr>
<tr class="odd">
<td><strong>Temperature Profile</strong> *</td>
<td>X</td>
<td></td>
<td></td>
<td>Y</td>
<td>Y</td>
<td>X</td>
<td>Y</td>
<td>Z</td>
</tr>
<tr class="even">
<td>Pressure Profile</td>
<td>X</td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
</tr>
<tr class="odd">
<td>Precipitable Water</td>
<td>X</td>
<td>X</td>
<td>X</td>
<td>X</td>
<td>Y</td>
<td>Y</td>
<td>X</td>
<td>Z</td>
</tr>
<tr class="even">
<td>Suspended Matter</td>
<td></td>
<td>X</td>
<td></td>
<td></td>
<td></td>
<td></td>
<td>Y</td>
<td></td>
</tr>
<tr class="odd">
<td>Aerosol Optical Thickness</td>
<td>X</td>
<td></td>
<td>X</td>
<td>X</td>
<td>X</td>
<td></td>
<td>X</td>
<td></td>
</tr>
<tr class="even">
<td>Aerosol Particle Size</td>
<td>X</td>
<td></td>
<td>Y</td>
<td>X</td>
<td>X</td>
<td></td>
<td>Z</td>
<td></td>
</tr>
<tr class="odd">
<td>Cloud Base Height</td>
<td>X</td>
<td></td>
<td>X</td>
<td>X</td>
<td>X</td>
<td>Y</td>
<td>X</td>
<td></td>
</tr>
<tr class="even">
<td>Cloud Cover/Layer</td>
<td>Y</td>
<td></td>
<td>Y</td>
<td>X</td>
<td>X</td>
<td></td>
<td>X</td>
<td>Z</td>
</tr>
<tr class="odd">
<td>Cloud Effective Particle Size</td>
<td>Z</td>
<td></td>
<td>Z</td>
<td>X</td>
<td>X</td>
<td></td>
<td>Y</td>
<td></td>
</tr>
<tr class="even">
<td>Cloud Optical Path</td>
<td>Y</td>
<td></td>
<td>Y</td>
<td>X</td>
<td></td>
<td></td>
<td></td>
<td></td>
</tr>
<tr class="odd">
<td><p>Cloud Top Height,</p>
<p>Pressure/Temperature</p></td>
<td>Y</td>
<td></td>
<td>Y</td>
<td>X</td>
<td>X</td>
<td>Z</td>
<td>Y</td>
<td>Z</td>
</tr>
<tr class="even">
<td>Active Fires</td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td>X</td>
<td>Y</td>
</tr>
<tr class="odd">
<td>Albedo (Surface)</td>
<td></td>
<td>X</td>
<td>Y</td>
<td>X</td>
<td></td>
<td></td>
<td></td>
<td>X</td>
</tr>
<tr class="even">
<td>Land Surface Temperature</td>
<td>Y</td>
<td>X</td>
<td></td>
<td>X</td>
<td>X</td>
<td></td>
<td>Y</td>
<td>Z</td>
</tr>
<tr class="odd">
<td>Net Heat Flux</td>
<td>X</td>
<td>X</td>
<td>X</td>
<td>Y</td>
<td></td>
<td></td>
<td></td>
<td></td>
</tr>
<tr class="even">
<td>Soil Moisture</td>
<td>X</td>
<td>X</td>
<td></td>
<td>Y</td>
<td>Y</td>
<td></td>
<td>Z</td>
<td></td>
</tr>
<tr class="odd">
<td>Surface Type</td>
<td>X</td>
<td>X</td>
<td></td>
<td>Y</td>
<td>Y</td>
<td></td>
<td>Z</td>
<td></td>
</tr>
<tr class="even">
<td>Vegetation Index</td>
<td></td>
<td>Y</td>
<td></td>
<td>Y</td>
<td>X</td>
<td></td>
<td>Y</td>
<td>Z</td>
</tr>
<tr class="odd">
<td>Ocean Color and Chlorophyll</td>
<td></td>
<td></td>
<td>X</td>
<td>Y</td>
<td>X</td>
<td></td>
<td>Y</td>
<td>Z</td>
</tr>
<tr class="even">
<td><strong>Sea Surface Temperature</strong> *</td>
<td></td>
<td></td>
<td>X</td>
<td>Y</td>
<td>Y</td>
<td></td>
<td>Z</td>
<td></td>
</tr>
<tr class="odd">
<td>Sea Ice Characterization</td>
<td></td>
<td></td>
<td>X</td>
<td></td>
<td>Y</td>
<td></td>
<td>X</td>
<td>Z</td>
</tr>
<tr class="even">
<td>Ice surface Temperature</td>
<td></td>
<td></td>
<td>X</td>
<td>Y</td>
<td>X</td>
<td></td>
<td>Y</td>
<td>Z</td>
</tr>
<tr class="odd">
<td>Snow Cover and Depth</td>
<td>X</td>
<td>X</td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
</tr>
<tr class="even">
<td rowspan="10"></td>
<td>Clear Column Radiance</td>
<td>X</td>
<td></td>
<td></td>
<td>X</td>
<td>Y</td>
<td></td>
<td>Y</td>
<td>Z</td>
</tr>
<tr class="odd">
<td>Ozone</td>
<td>X</td>
<td></td>
<td></td>
<td>X</td>
<td></td>
<td></td>
<td>Y</td>
<td>Z</td>
</tr>
<tr class="even">
<td>Precipitation Rate</td>
<td>X</td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td>Y</td>
<td></td>
</tr>
<tr class="odd">
<td>Trace Gasses</td>
<td>X</td>
<td></td>
<td></td>
<td>X</td>
<td>Y</td>
<td></td>
<td>Y</td>
<td></td>
</tr>
<tr class="even">
<td>Cloud Ice Water</td>
<td>X</td>
<td></td>
<td></td>
<td>X</td>
<td>Y</td>
<td></td>
<td></td>
<td></td>
</tr>
<tr class="odd">
<td>Cloud Liquid Water</td>
<td>X</td>
<td></td>
<td></td>
<td>X</td>
<td>Y</td>
<td></td>
<td></td>
<td></td>
</tr>
<tr class="even">
<td>Atmospherically Corrected Reflectance</td>
<td>Y</td>
<td></td>
<td></td>
<td>X</td>
<td></td>
<td></td>
<td>Y</td>
<td></td>
</tr>
<tr class="odd">
<td>Active Fire</td>
<td>X</td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td>Y</td>
<td></td>
</tr>
<tr class="even">
<td>LAI/FPAR</td>
<td>X</td>
<td></td>
<td></td>
<td>Y</td>
<td></td>
<td></td>
<td>Y</td>
<td></td>
</tr>
<tr class="odd">
<td>Sea Surface Temperature</td>
<td></td>
<td></td>
<td>X</td>
<td>Y</td>
<td>Y</td>
<td></td>
<td>Z</td>
<td></td>
</tr>
</tbody>
</table>

X: Highest Priority Y: Medium Priority Z:Lowest Priority

\* NPP Primary EDRs \*\* See Table 6.1 for validation strategies

### Surface Validation Sites

The surface validation sites make use of resources already established
around the globe for weather observations, climate monitoring, and
atmospheric and land surface process research. These sites accommodate a
wide variety of remote sensing and in-situ measurement devices. Table
6-3 describes the primary surface validation sites, and available data
products to be acquired from this extensive network of stations.

[]{#__RefHeading___Toc534619242 .anchor}Table 6‑10: NPP Ground
Validation Sites

+-----------+----------------------------+-----------------------------+
| **        | **Location**               | **Primary purpose**         |
| Network** |                            |                             |
+-----------+----------------------------+-----------------------------+
| AERONET   | Multiple locations in      | Aerosol optical thickness,  |
|           | North America, South       | columnar aerosol size       |
|           | America, Europe, Africa,   | distribution, and           |
|           | Asia, and Oceania.         | precipitable water.         |
+-----------+----------------------------+-----------------------------+
| ARM       | Southern Great Plains,     | Cloud base height,          |
|           | North Slope of Alaska,     | temperature and moisture    |
|           | Western Tropical Pacific   | profiles, sky radiance,     |
|           |                            | integrated liquid water     |
|           |                            | path                        |
+-----------+----------------------------+-----------------------------+
| EOS       | North America, Brazil      | Aerosols, radiance,         |
|           |                            | temperature, surface        |
|           | UK, RSA, Zambia,           | biophysical parameters      |
|           |                            |                             |
|           | Russia, Mongolia,          |                             |
|           |                            |                             |
|           | Australia                  |                             |
+-----------+----------------------------+-----------------------------+
| RAOBS     | Global, primarily northern | Temperature and moisture    |
|           | hemisphere over land       | profiles, clear sky         |
|           |                            | radiance (with forward      |
|           |                            | model)                      |
+-----------+----------------------------+-----------------------------+
| FARS      | University of Utah         | Cloud mask, cloud           |
|           |                            | boundaries and              |
|           |                            | microphysical structure,    |
|           |                            | aerosol vertical profile    |
+-----------+----------------------------+-----------------------------+
| CIGSN     | Australia                  | Clear irradiance, clear sky |
|           |                            | radiance for calibration    |
|           |                            | comparison of MODIS         |
|           |                            | radiances, radiosondes, sun |
|           |                            | photometer.                 |
+-----------+----------------------------+-----------------------------+
| P-AERI    | South Pole                 | Clear sky radiance (IR) and |
|           |                            | surface measurements for    |
|           |                            | MODIS validation of cold    |
|           |                            | scenes.                     |
+-----------+----------------------------+-----------------------------+
| Balloon   | North America/Global       | Balloon-born Ice crystal    |
|           |                            | replicators for size        |
|           |                            | distribution and habit of   |
|           |                            | ice crystals in upper       |
|           |                            | atmosphere; /aerosol        |
|           |                            | particle sizes.             |
+-----------+----------------------------+-----------------------------+
| SuomiNet  | Primarily North America    | GPS derived integrated      |
|           |                            | column water vapor          |
+-----------+----------------------------+-----------------------------+
| O         | Global, Primarily northern | Ozone profiles              |
| zonesonde | hemisphere mid-latitudes   |                             |
| Network   |                            |                             |
+-----------+----------------------------+-----------------------------+

The calibration and validation of ocean products will make use of
research vessels with spectral radiometers viewing the sea and the
atmosphere (e.g., M-AERI), active LIDARS and RADARS, and in-situ
measurement devices.

### Airborne Validation Platforms

Piloted research aircraft (e.g., ER-2 and Proteus, P-3, C-130, DC-8,
WB-57, Twin Otter) carrying a variety of active and passive radiometric
sensors will be used in field programs to provide high spatial
resolution validation data. Aircraft such as the ER-2 and Proteus will
be capable of flying over wide range of altitudes, including vertical
profiling which enables precise validation of radiative transfer models
and retrieved atmospheric and surface parameters. Unmanned airborne
vehicles (e.g., the Global Hawk) will enable a global sampling of
surface (land and ocean) and atmospheric products over a wide range of
geographical and atmospheric conditions in a single flight. Commercial
aircraft equipped with meteorological sensors (e.g., ACARS) will provide
time coincident atmospheric sounding validation data obtained during
ascents and descents near airports around the globe. Balloons will
measure standard meteorological parameters, and specialized balloons
will measure ozone and aerosols from the surface to an altitude of 30 or
35 km.

### Satellite Sensor-to-Sensor Cross-Validation

Advanced polar and geostationary satellites to be in orbit during the
NPP mission (e.g., EOS, METOP, ESSPs, EO-3, ENVISAT, etc.) will carry
sensors with comparable, or better, spectral and spatial resolution to
those sensors to be carried on the NPP platform. The products of these
sensors will provide an important cross-validation of the NPP
geophysical products. This is particularly important with regards to
METOP and EO-3 since products from these satellites, and their
operational successors, are intended for use in combination with NPOESS
products to provide a global high spatial and temporal resolution data
set for climate research and operational weather forecast applications.
Well-characterized products of operational satellites (e.g., NOAA, GOES,
and Meteosat) will also provide valuable satellite cross validation
data.

###### 

## Activities Supporting NPP Products Validation

The NPP products validation effort benefits greatly from the
infrastructure of several existing programs, including the EOS, POES and
DMSP programs

For the CrIS/ATMS, the validation will be largely based on the AIRS and
GIFTS experience. Cross-sensors validation with AIRS, GIFTS, and IASI
are planned, as well as aircraft campaigns with the NAST and S-HIS. The
NWP analysis and radiosonde data will play a significant role in the
routine products evaluation. Detailed approaches for CrIS/ATMS EDRs/CDRs
can be found in Appendix H.

For the VIIRS, the validation will benefit greatly from MODIS and AVHRR
experience. Cross-sensor validation are planned, as well as aircraft
measurement inter-comparisons using MAS, MQUALS and AVIRIS data.
Atmospheric data from AERONET network, buoys and ship-based ocean
measurements, and land biophysical parameter measurements from towers at
EOS core sites will provide routine evaluation of VIIRS products.
Detailed approaches for VIIRS EDRs/CDRs can be found in Appendix H.

In the production of most Level 2 and higher products, ATMS radiances
will generally be aggregated with CrIS and other data, and lose their
unique identity. Similarly, in any aircraft validation campaign,
airborne and ground-based microwave sensors will typically also be
aggregated with other sensors to produce the validating product, as is
being demonstrated by NAST-I and NAST-M. A key role of these validating
airborne and ground-based microwave sensors will be to penetrate cloud
and haze to a degree not achievable at shorter wavelengths. This role
will be critical for validation of products such as precipitation rate;
temperature, humidity, and precipitation profiles; cloud particle size,
liquid water content, and cell top altitude; snow cover, depth, and
type; and sea-ice cover and type.

The validation of the OMPS products will be based on experience
validating TOMS, SBUV/2, GOME, OMI, POAM III and SAGE II and III.
Cross-sensor validation will include comparisons with all of the
just-cited instruments that are still operational when NPP is operating,
as well as SCIAMACHY, ILAS-II, HRDLS, and other space-borne
research-grade instruments. Balloon-borne ozonesondes and dustsondes
will also play a major role in the valididation of the OMPS products, by
providing in situ measurements of ozone and aerosols, with good spatial
resolution. Detailed approaches for the OMPS EDRs/CDRs can be found in
Appendix H.

## Routine Validation Approaches

### Product Validation Using NWP Analysis

Differences between EDR and analysis fields provide very useful
information about the quality of the retrieval. Large differences may
indicate problems in the retrieval.

Coherent patterns of discrepancy may also indicate problems in the
forecast. Further inspection using campaign data or operational
radiosondes will determine if the problem is in the EDR or in the
analysis. Further confirmation can be achieved by comparing EDR fields
from other sensors (e.g. AIRS, IASI, ATOVS). Differences as a function
of view angle will allow detection of scan dependent errors.

Because the instantaneous spatial distribution of ozone strongly affects
the temperature field in the stratosphere, systems for the global
assimilation of ozone data are operational at the European Center for
Medium-range Weather Forecasting (ECMWF), the Royal Netherlands
Meteorological Institute (KNMI), the United Kingdom Meteorological
Office (UKMO), the NASA/Goddard Data Assimilation Office (DAO), and
NESDIS. Several NWP programs are being extended to forecast as well as
assimilate stratospheric ozone. Any of these systems that are
operational during the NPP era can be used to aid the validation of the
OMPS ozone products, using the techniques described just above.

For validating EDR/CDR fields giving the concentrations of long-lived
tracers, such as ozone in the extra-tropical lower stratosphere, NWP
analyses can be combined with measurements by non-NPP satellites to
generate 3D global tracer fields that can be compared with the NPP-based
measurements. This technique is described in section 6.5.1.2.

### Product Validation Using Operational Radiosondes

Statistics of EDR error using operational radiosondes network provide a
model of independent validation. Temporal and spatial filtering is used
to ensure that the radiosonde location and time is similar to the
CrIS/ATMS EDR. Monitoring of EDR accuracy provides long-term validation,
using approximately 300 radiosondes and satellite data matchups
collected each day.

Air parcel trajectories computed from analyzed wind fields wll be used
to increase the number and closeness of matches. For valid comparisons
between profiles measured at different places or times, it is essential
to take into account that the air at different altitudes within the same
profile arrives at the location and time of the measurement from quite
different prior locations, because of the strong vertical variation of
the wind field. The use of back-trajectories will allow that variation
to be taken into account.

### Regeneration of Products Using Ancillary Data

One approach for detecting possible inadequacies in EDRs is to
regenerate the EDR with different initial conditions. For example, a
CART site may show that the Level 1 data is of high quality, but the EDR
is not. The problem may be due to a poor estimate in the initial surface
emissivity that can be proven by regenerating the EDR using the
emissivity measured at the CART site. The capability to regenerate
retrievals is important.

### Product Validation Using Sun-photometer Networks (AERONET)

AERONET is an optical ground based aerosol monitoring network and data
archive supported by EOS. AERONET provides hourly transmission of CIMEL
sun-photometer data to the GOES (or METEOSAT) geosynchronous satellites,
which in turn relay the data to GSFC for daily processing and archiving.
By teaming with NPP, science teams should have access to validation data
from a global network of CIMELs in near real-time. This existing network
will help in pre-launch and post-launch validation of the aerosol
optical thickness, aerosol particle size, and spectral reflectance
products.

In addition to routine data collection, several new methods for data
collection were prototyped, most notably a CIMEL sun-photometer modified
to sample surface-reflected

radiances from a tower top position.

### Product Validation Using Ship Cruises and Buoys 

Buoys will be used to validate ocean products. The buoy\'s primary
purpose is to measure visible and near-infrared radiation entering and
emanating from the ocean. It is the variations of the visible
region-reflected radiation that is referred to as ocean color from which
other quantities can be derived, such as the abundance of microscopic
marine plants (phytoplankton). Drifting and moored buoys provide a
sub-surface measurement, conventionally referred to as a bulk
temperature. At wind speeds greater than \~6m/s, the relationship
between skin and bulk temperatures appears to be fairly well
constrained. Given the excellent MOBY performance, NPP ocean color will
rely upon MOBY services cruises to provide data in the clearest waters,
and conduct the initialization cruise in high chlorophyll waters in
order to improve the dynamic range of the initialization data.

### Product Validation Using Tower Data (EOS Validation Core Sites)

A global array of test sites will be available for comprehensive
terrestrial surface measurements from FLUXNET network, including a
global organization of many regional networks of CO2 / H20 flux towers,
including AMERIFLUX for North America, EUROFLUX for Europe, OZFLUX for
Australia, New Zealand, additional stations in South America developed
by LBA, and stations being organized in Japan and China. The Oak Ridge
DAAC will be the point of FLUXNET data archive and distribution. Land
surface characteristics such as albedo, bi-directional reflectance, LAI,
FPAR, and other atmospheric measurements from towers top positions will
be used for product validation.

## Planned Validation Approaches for NPP EDR Operational Products

This section provides a list of validation approaches considered in the
NPP EDR/CDR product validation. An attempt to develop a priority
sequence is provided in the Appendix H for each product from high
priority to optional/low priority. For a specific EDR/CDR, validation
approaches listed as number 1 have high priority, and the last approach
listed is optional or low priority. Additional information on the
technique and estimated uncertainties for each validation approach are
given in the Appendix H.

Some EDRs/CDRs have more than three approach considered for product
validation. Only approaches with required funding will be implemented.

### Validation Approaches for Atmospheric Sounding Products

#### Atmospheric Sounding Validation (Moisture, Temperature and Pressure) (Primary EDRs)

**Approach 1:** Atmospheric profiles using ARM Sites' Observations

The basic technique is to use the routine ARM site observations (at the
Southern Great Plains site in central Oklahoma, at the North Slope of
Alaska site in Barrow, Alaska, and the Tropical Western Pacific site in
Nauru) along with dedicated NPOESS overpass radiosondes to measure the
temperature and water vapor profiles for validation of the CrIS
retrievals. Temporally continuous profiling at the ARM sites will be
used to assess small scale spatial variability. GOES, surface networks,
and the relative variability of the single-FOV CrIS retrievals will be
used to address larger scale spatial gradients. Best estimate profiles
and quantitative error estimates will be provided and compared with the
coincident CrIS retrieved profiles which have been interpolated in space
(using single-FOV CrIS retrievals) to the validation profile locations.

**Approach 2:** International radiosonde sites

High quality radiosondes are at the core of in-situ measurements that
scientists can use for NPP validation of measurements and derived
products. Long term and global coverage of these in-situ measurements
are keys to the statistically meaningful validation for atmospheric
vertical profiles (water vapor, temperature and pressure). Some of the
sites also have the potential to build an NPP direct broadcast (DB)
receiving station and will be able to obtain real time NPP data. The
basic approach is to make measurements of temperature and water vapor
profiles coincident with CrIS retrievals via overpass-coordinated
radiosonde launches. Sonde water vapor calibration errors will be
addressed by scaling the sonde integrated column water vapor to values
measured by a GPS or MWR, or alternatively by scaling to point
measurements made with a high quality meteorological station coincident
with the sonde measurements just prior to launch. VIIRS data will be
used to assess cloud cover and spatial and temporal variability.

**Approach 3:** Retrievals from NAST-I and S-HIS aircraft observations
at ARM

For high altitude NAST-I and/or S-HIS underflights of the CrIS
overpasses, retrievals of atmospheric water vapor and temperature
profiles derived from the NAST-I and/or S-HIS observations will be
compared to the CrIS products. Cross-track scanning will allow the
aircraft observations to be averaged to match the CrIS footprint. The
flight paths and sensor scan angles can be tailored to match the CrIS
viewing angles. These flights should be performed at maximum aircraft
altitude. A complimentary technique is to perform slow ascents with the
aircraft sensors to derive profiles from NAST-I and/or S-HIS data using
opaque spectral channels which represent the local temperature and gas
concentrations. Due to the slow ascents, these comparisons would be
performed on a limited scope for stable, homogeneous meteorological
conditions in order to provide meaningful comparisons to the CrIS
product.

**Approach 4:** Comparison to Other Satellite Retrievals

Water vapor and temperature vertical profiles from EOS (AIRS), EO-3
(GIFTS), METOP (IASI) and other programs will be compared to those
derived from CrIS/ATMS through the annual cycle on a global basis.
Collocation in space and time is required. Spatial and temporal scaling
should be robust enough to avoid any variation that can be linked to the
surface or atmospheric fluctuations. Furthermore, data will be selected
close to nadir for each instrument in order to minimize viewing angle
differences. Means of measured water vapor and temperature from
space-based sensors will be compared for different climate regimes and
surface types.

**Approach 5:** WVSS (ACARS water vapor observations)

Water vapor (and temperature) measurements provided from Water Vapor
Sensor System (WVSS) units mounted on United Parcel Service (UPS)
aircraft offer another source of water vapor information complementing
radiosondes, an Atmospheric Emitted Radiance Interferometer (AERI),
global positioning system, Vaisala ceilometer, and surface
meteorological stations. Preliminary results from prior intercomparisons
indicate WVSS water vapor measurements are of reasonable quality above
the boundary layer, however they exhibit a moist bias that occurs during
ascent and descent through the boundary layer. This problem has been
corrected with the WVSS-II, which shows improved performance in accuracy
due to a single mode diode laser, probe placement on aircraft, and
longer maintenance intervals. Ascending and descending aircraft WVSS-II
data will be intercompared to CrIS moisture profiles.

#### Total Precipitable Water (TPW) (EDR)

**Approach 1:** Comparison to AERONET data

The AERONET network of sun photometers will continue to provide the most
viable total precipitable water validation source. AERONET consists of a
global network of approximately 100 sunphotometers measuring in several
channels in the visible and near-infrared spectrum. The AERONET data can
also be used to derive the spectral total column aerosol optical
thickness and size distribution.

**Approach 2:** Comparison to EOS products

VIIRS and/or CrIS/ATMS TPW products will be compared with those from
MODIS and AIRS/HSB, and probably to other future space-based sensors.
These satellite retrievals will be compared statistically over large
areas (land and ocean) (histograms over large areas, angular and
geographical trends). This exercise will allow the detection of
differences in calibration between these instruments and the assessment
of the correctness of the VIIRS and/or CrIS/ATMS calibration
requirements in each channel intended for TPW retrieval.

**6.4.1.3 Suspended Matter (EDR)**

*To be included.*

### Validation Approaches for Aerosol Products

#### Aerosol Optical Thickness and Aerosol Particle Size (EDRs)

**Approach 1:** AERONET Comparisons

VIIRS retrievals will be matched with the NASA AERONET locations of
surface based multi-spectral sun-photometer observations, as has been
done for AVHRR, MODIS and VIRS. Linear regression analysis will be
performed from daily match-up data sets to predict satellite retrieved
AODs and APS based on AERONET sun-photometer observed values. Retrieval
algorithm performance (systematic and random errors) can be identified
from the statistical parameters of the linear regression, including
intercept, slope, standard error, and correlation coefficient. For
example, a non-zero intercept indicates that the retrieval algorithm is
biased, most likely the result of instrument calibration errors or
improper assumptions about the ocean's surface reflectance. Departure of
the slope from unity suggests that there may be some inconsistency
between the aerosol micro-physical model used in the retrieval algorithm
(such as refractive index and/or size distribution) and the real world
aerosol. Additionally, AERONET validation provides information that can
be used to explain anomalies appearing in the self-consistency checks.

**Approach 2:** POES and EOS comparisons

VIIRS aerosol products will be compared with those from MODIS, AVHRR and
VIRS. These three satellite instrument retrievals will be compared
statistically over large oceanic areas (histograms over large areas in
the southern-hemisphere, angular and geographical trends). Also, AVHRR
and VIRS radiance look-up-tables (radiometrically adjusted, as needed,
to better match the MODIS spectral response functions) will be applied
to the most closely corresponding individual MODIS channels, and AOD and
APS will be derived and evaluated. This exercise will allow the
detection of differences in calibration between these three instruments
and the assessment of the correctness of the VIIRS calibration
requirements in each channel intended for aerosol retrieval.

### Validation Approaches for Cloud Products

#### Cloud Base Height

*To be included*

#### Cloud Cover/Layer Validation (EDR)

**Approach 1:** MAS and MODIS comparisons

The VIIRS CC/L algorithm will be validated against MAS data and MODIS
data along the imagery track centers where Lidar Cloud Profiling data is
available. By inspection, a set of ground truth layered cloud amounts
will be determined. These will then be compared to CC/L layered
assessments and a qualitative indication of CC/L performance can be
attained. At least one case of MODIS data will be used to validate CC/L
algorithm performance. The CC/L product will be validated with
independent cloud measurements either from space or other indirect
means.

#### Cloud Effective Particle Size Validation (EDR)

**Approach 1:** Aircraft replicator profiles from Cloud Particle Imager
(CPI) and MODIS.

VIIRS Cloud Particle Size product will be validated at ARM and EOS
sites. VIIRS assessments will be compared to the CPI measurements, and
to MODIS retrievals.

#### Cloud Optical Thickness/Transmittance

To be included

#### Cloud Top Height/Pressure/Temperature (EDR)

**Approach 1:** Micropulse Lidar (MPL) and Cloud Radar (MMCR)

This approach uses the ARM site ARSCL product to validate the cloud top
heights. ARSCL combines the MPL and MMCR measurements into a single
product of cloud layers (base, top, thickness) versus time at each of
the primary ARM sites (Southern Great Plains (SGP), Tropical Western
Pacific (TWP), and North Slope of Alaska (NSA)). The technique then is
to perform temporal averaging of the ARSCL product that produces an
equivalent spatial averaging to match the extent of the footprint at
that ARM site overpass time and compare the averaged product to the
VIIRS product. Winds data from the Wind Profiler network (for SGP),
model data, and/or sondes can be used to determine the averaging times.

### Validation Approaches for Land Products

#### Active Fires (EDR)

**Approach 1**: POES and EOS comparisons

The fire area and temperature products will be compared to retrievals
from other sensors, such as AVHRR and MODIS.

Statistical relationships will be established between AVHRR, MODIS and
VIIRS derived fire products over specific areas and time periods.
Results of this study will be useful for the construction of a
continuous AVHRR-MODIS-VIIRS data record of fire occurrences for
long-term studies.

#### Surface Albedo Validation (EDR)

**Approach 1**: Pyranometers and albedometers data (ground and aircraft
based)

A tower-based albedometer will be used to generate highly accurate data
at very high temporal resolution at minimal cost. However albedometer\'s
spatial field of view is limited by its relatively short distance above
the vegetation, and its fixed position. These point data can be scaled
to approximate larger area albedo fields. Scaling techniques are being
developed at this time. To sample much larger areas, some scientists
have mounted albedometers on aircraft. This approach appears promising
(at least one instrument vendor recently developed a pyranometer with
appropriate thermal stability for aircraft use), however it is
expensive, and data may need to be corrected for atmospheric effects
(depending on aircraft altitude), geolocation, and aircraft attitude.
Because albedometers essentially measure a quantity equivalent to the
albedo EDR product (assuming appropriate spatial scaling), comparison of
in-situ data to EDR values is straightforward. Advancement of albedo
measurement and scaling approaches will presumably result from the EOS
validation program.

**Approach 2**: POES and EOS comparisons

Albedo products from other sensors, such as MODIS, AVHRR and GOES will
be compared to VIIRS and CrIS LST retrievals. The comparisons will be
conducted under a variety of atmospheric and surface conditions.

#### Land Surface Temperature (LST) (EDR)

**Approach 1:** S-AERI measurements

The DOE SGP ARM site and EOS sites will be used for VIIRS and CrIS LST
validation. Surface temperature and emissivity will be collected from
the S-AERI system co-incident with NPOESS overpasses on a limited
campaign basis. Coincident imager data from the NPOESS platform will
also be acquired.

**Approach 2:** S-AERI measurements

High altitude flights of ER2 and/or the Proteus beneath the NPP will be
conducted with the MAS and NAST-I instruments. Micro-windows will allow
for determination of the LST under minimal atmospheric attenuation
conditions. Comparison of MAS and NAST-I LST retrievals to VIIRS and
CrIS LST products will be conducted under a variety of atmospheric and
surface conditions.

**Approach 3:** POES and EOS comparisons

LST products from other sensors, such as MODIS AVHRR, GOES, AIRS and
GIFTS will be compared to VIIRS and CrIS LST retrievals. The comparisons
will be conducted under a variety of atmospheric and surface conditions.

#### Soil Moisture Validation (EDR)

**Approach 1**: Field measurements at ARM validation site

Validation of soil moisture estimation results is difficult and even
more so if satellite data is involved. The difficulty lies not only in
the estimation process but also in the measurements of soil moisture.
Several issues are involved in soil moisture measurements. Microwave
sensors measure soil moisture in the topmost soil layer (1/10 to 1/4 of
a wavelength). At 19 GHz, this layer can be about 0.1-0.4 cm deep. The
penetration of the microwave signal depends on soil moisture itself. In
view of this, it is difficult to decide the depth of soil samples for
in-situ measurements. Soil moisture changes very rapidly in the top
layer. In addition, there are practical problems in collecting soil
samples at this depth. Also, spatial distribution of soil moisture
depends on soil parameters, which are not distributed homogeneously in
the area. As a result, average soil moisture computed from point
measurements in a footprint area may not be a correct representation of
the soil moisture in the footprint. Close comparison of in-situ
measurements from SGP experiment with the VIIRS soil moisture
predictions will be attempted, as well as the temporal and spatial
comparisons.

#### Surface Type validation (EDR)

**Approach 1**: POES and EOS comparisons

Using climatic and geographic stratification, the accuracy will be
determined for VIIRS surface type. The validation will performed using
high and fine resolution remote sensing data such as MODIS, Landsat-7
data and Ikonos data. Ground field survey and airborne data might also
be used when necessary.

#### Vegetation Index Validation (EDR)

**Approach 1**: Ground and aircraft data

The visible and near-IR channel data from ground sensors, such as
Spectrometers, Parabola, and airborne sensors, such as MAS and MQUALS
will be used over EOS sites to derive vegetation index products. These
products will be compared to VIIRS retrievals over several vegetated
land surface types

**Approach 2**: POES and EOS comparisons

The visible and near-IR channels included on the MODIS instrument permit
evaluation of narrower (and atmospherically clean) wavebands for use in
vegetation indices that might be anticipated to be available from the
VIIRS. This study will examine the influence of the use of narrow band
visible and near-IR channels on vegetation indices anticipated to be
available from the VIIRS. The VIIRS vegetation EDRs will be compared to
those available from the present AVHRR sensor for data continuity.
Differences in the vegetation indices will be assessed for several
vegetated land surface types (IGBP classification). This study should
result in i) assessment of the anticipated improvements in vegetation
index products from the VIIRS and ii) general guidelines for comparisons
of VIIRS-derived vegetation indices, when available, with historical
AVHRR-derived vegetation indices. VIIRS EDR data sets for several
extended periods will be generated through an annual cycle for the whole
globe.

### Validation Approaches for Ocean Products 

#### Sea Surface Temperature (SST) (Primary EDR)

**Approach 1:** M-AERI comparisons

Cloud-free and reasonably uniform and temporally stable targets will be
selected, with a range of radiance levels encompassing the range of
surface temperatures observed by M-AERI and atmospheric water column
amounts measured by CrIS.

VIIRS and CrIS SSTs will be extracted along M-AERI cruise tracks within
predefined time and spatial intervals. Co-location must be within a few
kilometers, and within a few tens of minutes. These conditions may be
relaxed in conditions of high wind speed when diurnal changes are muted.
Some spatial averaging of the near-surface data should be done to avoid
spurious effects of sub-pixel horizontal temperature variations.

The imagery and the M-AERI time series along track of a moving ship the
will be used to characterize the area for which the VIIRS or CrIS SST is
considered valid. Quality flags will be assigned to the comparison and
are dependent on the variability of the scene. As necessary and
appropriate, high-resolution GOES products will be used to characterize
temporal change of the scene during the comparison period.

Approach 2: Satellite Radiometer Comparisons

VIIRS and CrIS SSTs may be validated by comparison with
satellite-derived SSTs from similar imaging radiometers, such as MODIS,
AATSR, GLI and AVHRR that may have a longer and more-established
calibration/validation history. If these radiometers have similar
spectral responses in the corresponding channels, and are on satellites
in orbits close to that of NPP, it may be possible to cross-validate
top-of-atmosphere brightness temperatures. Inter-satellite comparison
can be done over large areas of cloud-free ocean.

Atmospheric radiative transfer modeling may be required to compensate
for the differences in the relative spectral response functions and
different viewing geometries of the pairs of radiomters.

**Approach 3:** Validation using sensors mounted on ships and buoys

This has been the first and primary approach for operational uses. In
this approach in-situ thermometers mounted at a depth of one to several
meters on drifting and moored buoys provide a sub-surface measurement,
conventionally referred to as bulk temperature Similarly, thermometers
mounted on the hulls and in the engine cooling water intake flow of
selected ships can be used if carefully calibrated. At wind speeds
greater than \~6m/s, the relationship between skin and bulk temperatures
appears to be fairly well constrained, so these data should be
restricted to these conditions or during the night. During the day in
conditions of lower wind speed, vertical temperature gradients can
decouple the bulk measurement from the skin temperature. These factors
will be considered in the validation of both the skin and the bulk SSTs.

**Approach 4:** MAS and NAST-I low altitude aircraft measurements

Low altitude flights of the ER2 and/or Proteus beneath the NPP will be
conducted with the NAST-I and MAS. Micro-windows will allow for
determination of the SST under minimal atmospheric attenuation
conditions.

**Approach 5:** GIFTS Observations

The GIFTS in geostationary orbit and with very high spatial and spectral
resolution will enable measurements of SST coincident with underpasses
of the NPP.

#### Ocean Color and Chlorophyll validation (EDR)

**Approach 1:** MOBY measurements

MOBY data and VIIRS ocean color product comparisons will be completed
for a variety of situations ranging from those for which the performance
of the individual algorithm is expected to be excellent to situations
for which the performance is expected to be severely degraded. The
validation will be carried out at various ocean test sites, principally
the MOBY site off Hawaii, the initialization cruise off Southern
California and Baja, and other cruises. The role of Ocean Test Sites
encompasses the somewhat conflicting needs for intensive validation and
initialization data collection, oceanic process studies, time series
stations, as well as providing for stratified global observations.

**Approach 2:** Comparison to POES and EOS products

***Ocean Color:***

Ocean color products from other satellites, such as MODIS, GLI, GOES and
AVHRR will be compared to VIIRS products for a variety of situations
ranging from those for which the performance of the individual algorithm
is expected to be excellent to situations for which the performance is
expected to be severely degraded.

***Chlorophyll Products:***

Bio-optical algorithms to remotely estimate the concentrations of
chlorophyll a and total suspended matter are being developed by the
MODIS Science Team, using measurements collected by highly specialized
instrumentation appropriate for use in turbid environments. Currently,
measurements are primarily collected in the open ocean. Resources will
be allocated to expand the amount of shipboard measurements collected in
turbid coastal waters. Deliverables from MODIS and AVHRR, such as
measurements of chlorophyll concentration and turbidity, will be
inter-compared to the VIIRS retrievals.

#### Net Heat Flux (EDR)

*To be included.*

### Validation Approaches for Snow/Ice Products

#### Sea Ice Characterization Validation (EDR)

**Approach 1:** Airborne and EOS comparisons

The pre-launch plan for the Sea Ice Age and Sea Ice Edge Motion EDR
includes sensitivity studies, analysis of simulated VIIRS data, and
verification using MODIS-type data. Observations from AVIRIS, MAS,
MODIS, GLI, and AVHRR will be used in the pre-launch phase to study the
error characteristics and optimum techniques for the algorithm. It is
expected that MODIS validation data will be of great value. This data is
expected to include *in-situ* field measurements combined with MODIS
observations, MAS underflights, and low level aircraft measurements at
spatial resolutions less than 10 meters. This data is then used in
combination with the VIIRS sensor model to produce simulated VIIRS
scenes, apply to NPOESS/VIIRS Sea Ice Age/Edge Motion algorithms to
retrieve EDR products, and compare these results with "truth" derived
from *in-situ*, aircraft, and MAS data. Participation of the NOHRSC and
ORA, as well as NESDIS/OSDPD and the National Ice Center is planned in
this product evaluation, based on the co-located AVHRR and MODIS images,
and derived products will be compared from local to hemispheric spatial
scales for accuracy and quality. The potential for VIIRS/CMIS data
fusion to produce First Year/Multi-year classification and ice edge
motion will be studied with the use of MODIS data and Advanced Microwave
Scanning Radiometer (AMSR) data.

#### Ice Surface Temperature

**Approach 1:** Comparison to EOS products

The Government Team will evaluate ice surface temperature data from
MODIS with respect to current operational and experimental NWS and
NESDIS products, such as those from AVHRR/3. The purpose is to reduce
the risk associated with the use of NPOESS/VIIRS products in NESDIS and
NWS operations. The benefit of incorporating the additional spectral
information available with MODIS in ice surface temperature retrieval
procedures will be evaluated, as will the use of new field data.
Co-located AVHRR, MODIS and VIIRS images and derived products will be
compared from local to hemispheric spatial scales for accuracy and
quality. For example, IST products over a variety of North American
watersheds, North America and Eurasia, the Northern Hemisphere, arctic
and antarctic will be evaluated. Samples of derived products will be
made available to NCEP, the National Ice Center, and the scientific
community for evaluation. This evaluation process will provide feedback
that may lead to modification of the VIIRS algorithms.

**Approach 2.** In-situ and Airborne Instrument Comparison

*In-situ* and airborne data will be used in the validation. These data
will come primarily from NWS meteorological stations and NPP
calibration/validation sites. Reports detailing the methods and results
of the evaluation, and recommendations for NPOESS VIIRS proposed designs
will be proposed.

#### Snow Cover and Depth Validation (EDR)

**Approach 1:** *In-situ* and Airborne Instrument Comparison

*In-situ* and airborne data will be used in the validation. These data
will come primarily from NWS meteorological stations and DOE ARM CART
sites. Reports detailing the methods and results of the evaluation, and
recommendations for NPOESS VIIRS proposed designs will be produced by
NOHRSC, CMISS, and ORA. This study will also allow NOHRSC to determine
the usefulness of a VIIRS-MODIS ground receiver.

## Planned Validation Approaches for Other NPP EDR and CDR products

### Atmospheric Sounding Profile Validation

#### Clear Column Radiance (CDR)

The validation approaches proposed to validate the Clear Column Radiance
Fire CDR is the same as the ones proposed for CrIS radiance validation
in section 5.2.5.2.

#### Ozone total column and profile, and related aerosol and cloud products (CDR & EDR)

OMPS radiances and 9.6 m radiances measured by CrIS will be used to
derive the ozone total column and profile, as well as aerosol and cloud
information that is needed in deriving the ozone amounts. One of the
aerosol secondary products will be an Aerosol Index associated with the
OMPS Nadir Sensor; it provides altitude-weighted information on the
ozone total column, but the altitude weighting is broad, and the Aerosol
Index by itself provides only ambiguous information on the vertical
distribution of the aerosol. The OMPS Limb Profiler will provide
vertical profiles of aerosol extinction at selected wavelengths, and
information about Polar Stratospheric Clouds (PSCs). Comparison data on
aerosols and high clouds will be sought both for validating the
secondary aerosol and cloud data products and for diagnostic purposes in
analyzing the errors in the OMPS and CrIS ozone products. (The CrIS
radiances are not affected by ordinary amounts of stratospheric
aerosols, but can be affected by Polar Stratospheric Clouds.)

As mentioned in section 6.3.2, air parcel trajectories computed from
analyzed wind fields wll be used to increase the number and closeness of
matches for all of the approaches discussed below. Air parcel
trajectories will also have a second role. The air at different
altitudes within any profile arrives at the location and time of that
profile from quite different prior locations, because of the strong
vertical variation of the wind field. This must be taken into account to
obtain valid comparisons between profiles measured at different places
or times, at least at altitudes where the dynamical lifetime of the
constituent of interest, ozone or aerosol, is shorter than its
photochemical lifetime (for ozone) or its lifetime for condensation and
evaporation (for aerosols). The strong vertical shear of the horizontal
wind must also be taken into account when interpreting and operationally
exploiting localized features in maps of total column amounts. Computed
back-trajectories will be used to take into account the effects of the
vertical variation of the wind.

As briefly indicated in section 6.3.1, for long-lived tracers, such as
ozone in the extra-tropical ozone lower stratosphere and below, methods
of data assimilation allow useful 3D global fields of ozone to be
derived from even sparse data from satellites, balloons, ground-based
instruments and other sources. Such assimilations are available from
several national weather services, and other assimilations are being
produced as a research product, and can be obtained via cooperative
agreements with the relevant research organizations. When independent
measurements are compared to the assimilated fields there are usually
only minor effects of the space-time sampling offsets between the
measurement being validated and the measurements that were used to
produce the assimilation; moreover, the effects of the sampling offsets
can be estimated from the time series of assimilated fields. The
assimilated fields also indicate where the ozone gradient is large, and
the comparison must be made cautiously. Finally, the assimilation
provides data fusion: a way of merging the information from instruments
in space, on balloons, on piloted aircraft and on the ground, to obtain
a coherent 3D global field. Further detail is provided in Appendix H.
Validated data assimilation products, based on measurements by non-NPP
satellites and other data sources, will be used in validating the NPP
ozone products.

\[Include in Appendix H: NWP analyses can be combined with measurements
by non-NPP satellites to generate 3D global tracer fields that can be
compared with the NPP-based measurements. GOME-based (KNMI); NESDIS,
UKMO, ECMWF, Canadian assimilations. OMPS-AE and NOGAPS. Dynamical
tracers (PV) or ideal passive tracers (TREL).At some latitudes and
altitudes where the analyzed wind fields are accurate and where the
distribution of ozone is dominated by transport rather than by
photochemistry, use 3D global fields derived from sparse satellite data
combined with correlations between ozone mixing ratio \]

The WMO/CEOS **Report on a Strategy for Integrating Satellite and
Ground-Based Observations of Ozone**, World Meteorological
Organization/Global Atmosphere Watch Report No. 140 (WMO TD No. 1046),
January 2001, http//www.wmo.ch/web/arep/reports/gaw140.pdf, is cited in
the discussions of each of the approaches discussed below.

External EDRs are used in deriving the ozone and associated aerosol
products, hence the quality of the ozone and aerosol measurements will
depend in part on the quality of the external EDRs. Hence for some of
the comparisons discussed below, the NPP ozone and associated aerosol
products will be computed twice, once using the external EDRs, and again
using higher quality external data from local or temporary data sources.

Approach 1: Comparisons to Other Satellite

Pages 35-45 of the WMO/CEOS Report cited above list satellite
instruments expected to be in operation at the same time as NPP.
Comparison measurements of the ozone total column should be obtainable
from OMI on EOS Aura, from GOME-2 on METOP-1, from SBUV/2 on NOAA-N and
N', from ODUS (possibly renamed OPUS) on GCOM-A1, from AIRS on EOS Aura
(not mentioned in the WMO/CEOS Report, but channel 11 includes the 9.6 m
emission by ozone), and possibly from SCIAMACHY on ENVISAT and TOMS on
QuickTOMS. Comparison nadir profile measurements should be available
from OMI, GOME-2, SBUV/2,and possibly SCIAMACHY. Comaprison limb
profiles should be available from SAGE III (solar and lunar occultation,
and limb scattering), from the EOS Aura instruments HIRDLS, MLS, and TES
(in its limb viewing mode), from SOFIS on GCOM-A1, possibly from GOMOS
and MIPAS (both on Envisat), and possibly from ILAS-2 on ADEOS-2. The
AIRS data will be especially useful for comparisons with the ozone total
columns derived from CrIS measurements of the 9.6 m emission by ozone.

Space-based measurements of upper tropospheric and stratospheric
aerosols and clouds will also be essential for validating the
aerosol/cloud corrections to the OMPS Limb Profiler measurements, and
the associated OMPS aerosol/cloud byproduct EDRs. Data from SAGE III,
HIRDLS, SCIAMACHY (when in its limb viewing mode), and GOMOS will be
used for those validations.

Approach 2: Comparison to In-situ data

Ozonesonde balloons have long been a standard way of measuring ozone
from the ground to the lower stratosphere. A long-term measurement
database exists (as long as 35 years for some sites), mostly within the
Northern Hemisphere mid-latitudes, on a roughly once-a-week basis. A
worldwide list of regularly reporting ozononesonde sites that may be
available for validating OMPS ozone products is given on pages 32-34 of
the WMO/CEOS **Report on a Strategy for Integrating Satellite and
Ground-Based Observations of Ozone** (*op. cit.*) Balloon-borne research
instruments that measure ozone are also sometimes available, as
exemplified by the list on page 35 of the same document. Access to the
data from research instruments and improved chances of good space-time
matches require pre-arrangment with the Principal Investigator of the
research instrument. Ozonesondes measurements offer good precision and
excellent vertical resolution (about 150 m), although results become
more uncertain above 25 km because of inefficiencies in pumping
mechanisms, and corrections may be needed for SO~2~ interference. For
NPP, the major difficulty will be the comparatively low geographical and
temporal density of ozonesonde measurements. Only near-simultaneous
measurements with clear skies should be compared. It is not known
routine and research ozonesonde measurements satisfying these conditions
will be plentiful enough for a statistically significant validation.
However, the trajectory and data-assimilation techniques mentioned above
may significantly reduce this difficulty.

Balloon-borne instruments for measuring aerosols and stratospheric
clouds will be used, when available, as sources of comparison data on
aerosols and high clouds. Dustsondes and other balloon-borne and
airborne aerosol and cloud measuring instruments are used for research;
rather than for regular operational measurements, but fairly regular
observations are made at high latitudes during the winter in each
hemisphere. SHADOZ ozonesonde data provide partial coverage of the
tropics. The problem just discussed for ozonesondes applies with even
more force to the aerosol and cloud measurements. Back-trajectories and
data assimilation are less helpful for aerosols and clouds than for
ozone because the particle size distributions evolve in time. If
available, validated moving-box models of aerosol and cloud microphysics
will be run along back-trajectories to reduce this problem.

***Approach 3: Comparison to Ground-based measurements***

Comparison ozone data will also be sought from ground-based FTIRs,
microwave radiometers, Dobson and Brewer ozone spectrometers, and
lidars. Some lidars will also provide comparison data on aerosols and
high clouds. The number and placement of these instruments varies more
than for the satellite and balloon sources of comparison measurements.
An indication of the types and locations of the instruments available is
given on pages 22 to 32 of the report cited above.

The discussion of Approach 2 described the problem of obtaining a
statistically significant number of good matches with balloon
measurements. The same problem occurs for ground-based measurements.
Again, back-trajectories and data assimilation should mitigate the
problem, especially for ozone.

In comparing OMPS profiles to data from these sources, the relatively
poor vertical resolution of the ground-based measurements must be
considered.

#### Precipitation Rate Validation (CDR & EDR)

**Approach 1:** NEXRAD Precipitation Data

By definition of CPR, the prime validation source must be coincident
NEXRAD data (offsets \< 5 minutes), with emphasis on the eastern United
States where the NEXRAD network has more complete coverage. The NEXRAD
data must be convolved with a response function characterizing the
appropriate 15- or 50-km antenna pattern. Radars operating at other
frequencies and global locations will provide secondary validation. Snow
pillows can provide more accurate ground truth for snowfall retrievals
(water-equivalent mm/h).

Approach 2: BALTRAD Precipitation Data

BALTRAD radar data available for the Baltic region will be used in
conjunction with Snow/Ice cover maps to derive precipitation data. Radar
data will be convolved to the spatial resolution and observation
geometry of the ATMS. Probability of detection as function of the
precipitation intensity will be derived. The precipitation screening
algorithms will be adjusted according to findings.

#### Trace Gasses Validation (CDR & Application)

Approach 1: Comparisons to Other Satellite Observations

AIRS/AMSU/HSB, a sounding instrument suite that will also be in orbit at
the time of the NPP mission, will be making similar IR/MW measurements
with the ability to retrieve trace gas abundances. When these abundances
become validated AIRS products, they will be ideally suited to validate
the CrIS/ATMS trace gas products. Both data sets will be global in
coverage and offer many data points at varied conditions to extensively
validate the trace gas product. Due to the political and social impact
of CO~2~ there are measurement campaigns in the planning stages. They
will focus on CO~2~, CH~4~, and CO *in-situ* measurements and can be
used to validate the AIRS and CrIS products.

### Validation Approaches for Aerosol Products

TBD

### Validation Approaches for Cloud Products

#### Cloud Ice Water Validation (CDR & EDR)

**Approach 1:** EOS and POES comparisons

NOAA AMSU operational cloud ice water /particle size algorithm retrieves
both IWP and particle effective diameter. These products are derived for
thick ice clouds including precipitation conditions. Since ATMS has two
channels similar to AMSU, the algorithm can be modified and tested with
NPP ATMS and CrIS.

**Approach 2:** Aircraft comparisons

Calculations of high-spectral resolution infrared radiances in cirrus
cloud situations indicate that cloud forcing (clear minus cloudy)
spectra are sensitive to ice particle size, ice water path, and cloud
altitude. A numerical procedure based on the DISORT algorithm is used to
retrieve the effective radius and ice water path of cloud layers with
known optical depths and cloud boundaries and with nearby clear sky
atmospheric conditions also known. The reasonable reproduction in a
rather wide window region suggests that the DISORT based algorithm can
distinguish small from large particle clouds as well as provide a fair
estimate of IWP. Ice particle size and ice water path are estimated with
20% variation in the inferred values.

#### Cloud Liquid Water Validation (CDR & EDR)

**Approach 1:** EOS and POES comparisons

NOAA AMSU operational algorithm retrieves both CLW and TPW. It is a
physical retrieval algorithm which uses two AMSU primary channels at
23.8 and 31.4 GHz. These two frequencies are identical to ATMS channel
selection. The algorithm can be directly modified for ATMS applications.
Comparisons between VIIRS and ATMS retrievals are also planned.

**Approach 2:** Aircraft comparisons

Calculations of high-spectral resolution infrared radiances in cirrus
cloud situations indicate that cloud forcing (clear minus cloudy)
spectra are sensitive to ice particle size, ice water path, and cloud
altitude. A numerical procedure based on the DISORT algorithm is used to
retrieve the effective droplet size and water path of cloud layers with
known optical depths and cloud boundaries and with nearby clear sky
atmospheric conditions also known. The reasonable reproduction in a
rather wide window region suggests that the DISORT based algorithm can
distinguish small from large droplet clouds as well as provide a fair
estimate of LWP.

### Validation Approaches for Land Products

#### Atmospherically Corrected Reflectance Validation (CDR)

Land surface reflectance

*To be included.*

#### Active Fires (CDR & Application)

The validation approaches proposed to validate the Fire Area and
Temperature CDR are the same as the ones proposed for Fire Area and
Temperature EDR in section 6.4.4.5.

#### LAI and FPAR validation (CDR)

**Approach 1**: Field measurements at EOS validation site

Generally, LAI or FPAR can be derived from hand-held instruments
(including hemispherical view cameras) which assess light obscuration by
vegetation canopy or crown. The instruments typically employ a modified
form of Beers' Law to derive LAI or FPAR units. To determine LAI or FPAR
at a plot scale, an investigator typically collects many samples over an
area, then attempts to scale these "point" measurements to a larger area
using fine-scale satellite or aircraft imagery. Although there is no
current standard technique for either spatial sampling design or
scaling, an LAI focus group under the auspices of the CEOS WGCV Land
Product Validation Subgroup is developing a "Best Practices" handbook.
Although historically the field instrumentation assumed a homogeneous
distribution of leaf material, newer instrument specifically assess
canopy clumping and reportedly produce superior results. In deciduous
areas, "leaf drop baskets" are sometimes deployed to determine the LAI
via the autumn leaf fall. Comparatively few FPAR validation studies have
been conducted to date, and thus even fewer standards currently exist.
Proper measurement requires measurement of four radiation fluxes
upwelling and downwelling above the canopy, and the same between the
canopy and the soil. Further, some canopy-absorbed PAR radiation is
attributable to non-green leaf, stem or standing dead material; accurate
FPAR measurements require knowledge of these quantities.

### Validation Approaches for Ocean Products

#### Sea Surface Temperature (CDR)

The validation approaches proposed to validate the SST CDR is the same
as the ones proposed for SST EDR in section 6.4.5.1.

#### Ocean Color (Water Leaving Radiance) (CDR)

**Approach 1:** Validation using MOBY data

Automated collection of MOBY data at the time of VIIRS overpasses, and
MOS data during MOBY servicing cruises will be used, and a matchup data
base to sample a useful range of VIIRS swath and sun angles will be
developed. The overall approach for MOBY is discussed by Clark and
Mueller in Chapter 11 of the Revised SeaWiFS Protocols for
Calibration/Validation. Multiple radiometer buoys are maintained, and
are deployed sequentially for three month intervals. The measured
spectral response function of the satellite sensor is convolved with the
high resolution spectrometer data. Upwelled spectral radiances are
collected at 3 depths and propagated to and through the surface to
produce the desired water-leaving radiance values which are compared
with the values retrieved from the satellite sensor.

**Approach 2:** Other In-water radiance measurements

A variety of instrumentation and protocols to make individual and time
series of water-leaving radiance and also above water reflectance
measurements from ship, moorings, drifting buoys, and permanent
platforms have been developed. Details can be found at the SIMBIOS web
site. These measurements, most by independent investigators, are very
important for validation of the global water leaving radiance signals
following initialization at the MOBY site.

**Approach 3:** Validation using aircraft sensors

Use of aircraft sensors for validation of water-leaving radiances is
primarily in the area of providing improved spatial variations and
coverage. Maintaining sufficiently accurate absolute uncertainty of the
instrument and its atmospheric correction for use in direct validation
of water leaving radiance has improved significantly over the past
decade, however. Aircraft sensors show great utility in validation of
bio-optical properties, but cannot provide the high degree of accuracy
of in-water or shipboard observations at this time.

# Data Processing Support for Calibration, Quality Assessment and Validation 

## Calibration, Quality Assessment and Validation 

The performance of the NPP products produced by the IDPS, SDS and direct
broadcast systems will be ensured through calibration, quality
assessment (QA) and validation activities. These three activities are
interrelated and are summarized again below to assist in understanding
the quality assessment implications.

**Calibration**: define the transformation of sensor digital numbers
(DN) to radiance in a traceable manner

-   An operational activity which utilizes on-board measurements
    (internal blackbodies, space views and spectral sources) and
    coefficients to transform digital numbers to radiance units. In
    addition, some corrections might be applied to the radiances, such
    as transformation to a standard frequency, correction to a standard
    instrument response function, polarization corrections, etc..

-   Calibration data are stored as per detector, scan line, focal plane
    etc. These data are attached to the product as metadata.

-   Coefficient tables are generated from pre-launch measurements,
    knowledge of instrument characteristics, post-launch measurements
    (e.g., deep space look), and vicarious calibrations. The associated
    data sets used to determine the calibration coefficients must be
    archived.

**Quality Assessment**: evaluate and documents product quality with
respect to the intended product performance (Roy *et al*., 2001)

-   A near-operational activity that is performed routinely.

-   QA is typically performed by subjective examination of products in
    the absence of inter-comparison with other data.

-   QA results are stored in the product as per-pixel QA flags and QA
    metadata (written in the production code and retrospectively) (Lutz
    *et al.* 2000).

-   QA metadata are used to flag individual product granule quality
    (e.g. granule X = 'Failed QA') and to document issues that need
    rectification.

-   QA results are examined to ensure that poorly performing products
    are not validated or are validated appropriately.

-   Users may query QA metadata as part of the data order & browse
    process.

**Validation**: quantify product accuracy over a range of representative
conditions (Justice *et al*., 2000, Morisette *et al*., 2001)

-   This activity is not operational but typically periodic/episodic.

-   It is usually performed by comparison of products with other data
    that have known uncertainties.

-   Results are published in the literature years after product
    generation.

-   Validation results define error bars for the entire product
    collection and are not intended to capture artifacts and issues that
    may reduce the accuracy of individual product granules.

It is recognized that calibration, validation, and QA activities overlap
and different instruments may have different capacities for these. For
example, ocean products may be compared with buoy data in a near
operational manner providing a routine validation.

There is a strong linkage between the results of near-operational
calibration and QA with algorithm updates. This has implications for
configuration control and oversight (particularly with respect to the
incentive mechanism for updating the IDPS algorithms).

## NPP Data Approach

The NPP data approach attempts to assure consistency of data
requirements across subsystems and releases and to support the data
standardization necessary for total system inter-operability within a
heterogeneous open systems environment. The NPP data approach strives to
ensure that calibration, quality assessment and validation activities
can be performed, and product granules can be retrieved to support these
NPP activities in an efficient and reliable manner:

-   File names allow product granules to be uniquely identified. For
    example, filenames include the sensor acquisition date and time,
    production date and time data; CDR file names include
    grid/geolocation information.

-   File names indicate the place of production: IDPS, SDS or direct
    broadcast.

-   Products include per-pixel quality and processing history
    information (these should be propagated appropriately between
    products).

-   Products carry input pointer metadata recording which upstream
    products were used to make the specific product granule.

-   Products carry sufficient metadata (e.g., temporal, spatial, cloud
    cover) to allow flexible and efficient browse and ordering of the
    product archive \[recognizing that cloud cover definitions may not
    be available or applicable for certain products\].

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-   Product version information is reflected as metadata and preferably
    in the product file name (both IDPS and SDS products).

-   Products carry quality and maturity metadata that may be set after
    production.

## Production System Support for Calibration, Quality Assessment, and Validation 

The IDPS, SDS and direct broadcast NPP product systems have different
algorithm and production specifications. These systems will support the
rapid and efficient processing of products and product subsets /
subsamples, and reprocessing where applicable, for calibration, QA and
validation activities.

### Interface Data Processing Segment (IDPS)

The IDPS

-   produces near-real time, high-quality operational products within
    90-150 minutes

(TBR) (NPP) & 20 minutes (NPOESS)

-   does not reprocess data for operational routine products but has
    significant speed/storage margins.

-   produces RDRs, SDRs & EDRs.

-   requires capacity for SDR & EDR spatial subsetting (e.g., VIIRS) and
    subsampling (e.g., CrIS).

-   requires capacity for automated delivery of data subsets and
    subsamples to a dedicated archive.

-   requires capacity to project EDRs into a variety of Earth based
    coordinate systems

    -   geolocation accuracy of IDPS products may not be as high as SDS
        products and may not be sufficient to meet validation and QA
        co-registration needs (TBD).

    -   access to SDS updated interior & exterior orientation estimates
        is recommended or capacity to improve retrospectively the
        geolocation of IDPS products by image matching.

### Science Data Segment (SDS)

The SDS

-   is not a near-real time production system, reprocessing allowed.

-   produces science high quality Level 1A/B, CDRs and higher products.

-   has internal archive to support the SDS Science Team\'s calibration,
    QA and validation requirements.

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-   requires capacity for spatial subsetting (e.g., VIIRS) and
    subsampling (e.g., CrIS) of Level 2 and Level 3 CDRs.

-   requires an SDS geolocation product with sufficient accuracy to
    enable production of temporally composited CDRs and to meet Level 2
    CDR product validation and QA co-registration needs.

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-   requires automated delivery of data subsets and subsamples to a
    dedicated no-frills archive (e.g. an anonymous ftp site). This
    should be part of the SDS archive.

### Direct Broadcast System 

During normal science operations, NPP will continuously transmit
real-time data from all three instruments to line-of-sight ground
stations via an X-band direct broadcast system. This direct broadcast
capability will offer important benefits, for example:

1.  It will provide rapid access to NPP instrument data for
    time-critical applications.

2.  It will act as temporary backup to the stored-and-down-linked data.

3.  Direct broadcast data processing systems provide a means to quickly
    modify and test improvements to the data processing algorithms and
    stage them for dissemination to the general public.

The NPP In-Situ Ground Segment (NISGS) at the NASA GSFC will be
responsible for developing a prototype, stand-alone test-bed system to
acquire the direct broadcast data and generate instrument -- specific
Level 0, Level 1 and selected higher level data products. As the
interface between the NPP "mainstream" components and the direct
broadcast user community, the NISGS project will provide users with all
the necessary software, documentation and information corresponding to
the establishment, acquisition and processing of NPP direct broadcast
satellite data.

The NPP *In-situ* Data processing System (NISDS) will incorporate the
scientific calibration parameters and algorithms developed by the SDS in
a real-time, stand-alone mode. Selected Level 2 and higher science
algorithms will also be developed. The end-to-end system, from the
X-band broadcast electronics on board the spacecraft through the output
of the Level 1B processing, will be validated by comparison of the
geo-located and calibrated Level 1B radiances from the direct broadcast
data with those obtained by the SDS using the stored-and-down-linked
data. The comparisons will be done using the same data sets used for the
validations in this document. Validation of those higher level products
for which it makes sense (i.e., which are not sensitive to the
differences between the SDS and NISDS algorithms) will also be done by
comparison.

The SDS and NISGS will work together to validate the data products
obtained using the processing system at the NASA GSFC Direct Readout
Laboratory (DRL). It is envisioned that other direct broadcast users
will validate their systems against the DRL system.

## Archive and Distribution System Support for Calibration, Quality Assessment, and Validation 

The NPP archive and distribution systems will support the rapid and
efficient retrieval of products for calibration, QA and validation
activities. In addition the NPP archive and distribution systems will
support the documentation of product performance information as a result
of calibration, quality assessment and validation activities. These
information data are required by production personnel and algorithm
developers to identify products that are performing poorly so that
improvements may be implemented. These information data are also
required by the product users.

### Archive & Distribution Segment (ADS) 

The ADS

-   archives all IDPS & SDS products.

-   enables public and Science Team access.

-   requires capacity to support SDS reprocessing assuming it has
    sufficient resources to enable retrieval of products (RDRs, SDRs and
    EDRs).

-   has sufficient distribution capacity to support Science Team routine
    product QA (scoped at 10% daily production volume).

-   has capability to restrict public access by product, production date
    and product version

### SDS archive 

The SDS Archive

-   archives RDRs sent from the IDPS, SDS products, and certain IDPS
    EDRs retrieved from the ADS for validation purposes.

-   allows Science Team access only.

### ADS and SDS Archive Requirements

The ADS and SDS Archive requirements include

-   Ftp pull/push of products.

-   Media (CD-ROM, DVD, DLT, SDLT etc.) product distribution. (SDS will
    support media generation for only a fraction of the total products
    generated (5 percent).

-   Interactive web-based browse/ordering interface to support.

    -   searching against all metadata using boolean and relational
        operators

    -   searching against multiple metadata using logical operands (AND,
        OR )

    -   browse imagery

    -   on demand spatial (and spectral ?) subsetting

    -   coincident searching of data from different sensors (e.g., VIIRS
        product acquired spatially and temporally coincident with
        Landsat, e.g. spatially and temporally coincident NOAA N', ATMS,
        CriS, VIIRS data)

-   Subscription based access

    -   triggered by metadata ingest

    -   optional email notification

    -   ftp push/pull or media distribution

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-   QA metadata update. QA metadata may be set with a default value and
    defined retrospectively by registered QA personnel at any time after
    product generation. Recommend update mechanism based on an email
    fixed format supporting (i) linked list of product file names and
    corresponding QA metadata values, (ii) summary product filename,
    version and production range information to be set with common QA
    metadata.

-   Archive of field measurements, aircraft and ancillary validation
    data sets is recommended as well as distribution in facilities that
    are independent of the ADS and SDS archives. Recommend use existing
    systems (e.g., SYMBIOS, Oak Ridge DAAC) and note that may need to
    ensure the continuity of some of these systems.

## Validation Data Set Needs and Management 

There are several varieties of validation data associated with a flight
project that must be systematically collected, formatted, documented,
and archived. These activities must be an integral part of the project
calibration and validation program design, including dedication staffing
and computer system resources. The documentation should be developed as
the data are generated, otherwise important details are sure to be lost.
The data sets can be categorized as follows:

1.  prelaunch sensor calibration and characterization data (laboratory
    data including calibration source traceability to NIST standards),

2.  mission simulation data (either derived from existing heritage data
    or modeled) with generation code used to test processing system
    performance and prototype quality assessment procedures,

3.  on-orbit calibration and sensor performance data (e.g., internal
    lamps, blackbody sources, solar diffuser, lunar imaging data),

4.  *in-situ* vicarious calibration data (e.g., surface radiance, Marine
    Optical Buoy),

5.  *in-situ* product validation data (e.g., field data for match up
    comparisons of derived products and for atmospheric correction
    validation),

6.  higher spatial resolution satellite data used as a surrogate for
    \"truth\" with measured uncertainties (e.g., used to \"validate\"
    active fire and snow products),

7.  measurement protocol experiment and calibration round robin data
    (laboratory or field data collected to test the accuracy of a
    particular measurement approach or calibration source).

Items 2 and 7 may not be typically considered as part of a mission, but
experience with missions such as SeaWiFS and MODIS has shown that they
are indeed critical elements of a comprehensive validation program.

Documentation should include a description of the methodology or
measurement protocol followed during data collection as well as
appropriate metadata (time, location, units, etc.). *In-situ* data,
on-orbit calibration data, and the corresponding match-up satellite
subscenes may be best maintained within the project where the
calibration and validation staff resides. These data require specific
expertise in their quality control and analysis. The match-up subscenes
should be extracted at Level 0 so that the data is extracted only once,
as revisions to the Level 1 and 2 processing are certain. The archival
of the *in-situ* data must be handled through a relational database that
allows the data to be queried and extracted in a variety of ways.
Therefore, database management is an additional staffing requirement. In
the case of SeaWiFS, the calibration and validation staff also handles
the validation databases. Finally, it is desirable for the external
community to have access to the *in-situ* data and a data policy that
details the staged release to national archives such as the National
Ocean Data Center and acknowledgment requirements should be outlined in
advance.

Product-specific guidance to validation scientists on what constitutes a
representative and acceptable minimum validation is required. Broadly
the location and timing of validation data define this and should
encompass:

-   the full range of the geophysical parameter

-   a range of representative surface, atmospheric and acquisition
    conditions

-   locations/times where the product generation algorithm is known to
    be sensitive to exogenous factors

## Examples of Validation Data Supporting QA System

A number of examples of how to utilize numerical weather prediction
(NWP) products and systems for QA and validation are now discussed.
These techniques have proven to be useful for QA and validation on
heritage instruments.

***Radiance and EDR QA using NWP analysis products:***

A very useful approach to validate CrIS, ATMS, and OMPS radiances is to
compare them with radiances simulated from NWP analysis fields. Analysis
fields of temperature, moisture, and ozone are spatially and temporally
interpolated to selected CrIS, ATMS, and OMPS FOV's. Radiances are
computed using a fast radiative transfer model from the interpolated
atmospheric state. The enormous sample size provides the means to study
and monitor scan dependent bias and standard deviation between measured
and computed radiances. Time series of channel bias and standard
deviation are updated daily. This capability will quickly detect
apparent outliers and will also detect sensor drift.

Differences between EDR and analysis fields provide very useful
information about the quality of the retrieval. Large differences may
indicate problems in the retrieval. Coherent patterns may also indicate
problems in the forecast. Further inspection using campaign data or
operational radiosondes will determine if the problem is in the EDR or
in the analysis. Further confirmation can be achieved by comparing EDR
fields from other sensors (e.g. AIRS, IASI, ATOVS). Differences as a
function of view angle will allow detection of scan dependent errors.

***Assessment of EDR's using data-assimilation:***

The SDR/Level 1B radiances or EDR/CDR products can be used in an
assimilation system to assess the information content in the EDR's via
data assimilation. In data assimilation the instrument measurements are
used in an analysis with other measurements and a forecast model to
adjust the model. A large quantity of measurements (usually more than 1
month) is required to perform this test. The information content of the
EDR's is evaluated by comparison of the forecast quality with and
without assimilation of the EDR's.

***Radiance and EDR validation using operational radiosondes:***

Ensemble statistics of radiance residuals (bias and standard deviation)
from radiance simulated from operational radiosondes provides a model
independent validation. Approximately 300 matchups (collocated
radiosonde and satellite FOV's) are available each day. Operational
radiosondes should not be used for spectroscopy validation; however,
they are a very good source for long term monitoring. The matchups can
also be used to determine coefficients for radiance tuning. Knowledge of
radiosonde type is important in assessing instrument quality. Radiance
residuals need to be analyzed as a function of radiosonde instrument
type.

Similarly, EDR error using operational radiosonde provides a model
independent validation. Temporal and spatial filtering is used to ensure
that the radiosonde location and time is similar to the CrIS/ATMS EDR.
Monitoring of EDR accuracy provides long term monitoring. Approximately
300 matchups are collected each day.

***Radiance QA using eigenvector decomposition:***

Eigenvectors of radiances can be used to monitor radiance quality. This
is achieved by reconstructing radiances using a truncated set of
eigenvectors. The reconstructed radiances are compared with observed
radiances. If the difference is very large then the radiance quality may
be questionable.

***Regeneration of EDRs using ancillary data:***

One approach for detecting possible inadequacies in EDR algorithms is to
regenerate the EDR with different initial conditions. For example, a
CART site may show that the Level 1B data is of high quality, but the
EDR is not. To test if the problem is due to the algorithms sensitivity
to the initial surface emissivity the EDR could be regenerated using the
emissivity measured at the CART site. Hence, the capability to
regenerate retrievals using a diagnostic algorithm can be very
important.

[]{#__RefHeading___Toc534619217 .anchor}Appendix A: Overall Approach to
Calibration and Validation

A.1 Pre-flight Instrument Testing and Characterization

Pre-launch instrument characterization will be the responsibility of the
vendors for the NPP instruments. As part of the shared responsibility,
the Government Team will work closely with the vendors during the
pre-launch testing and characterization to assure that the post-launch
instrument performance is understood and radiances are suitable for
assimilation (See Section 4 for details on this effort). The Government
Team will provide advice to the IPO and thereby to the vendors on sensor
level requirements realization and characterization/calibration
procedures The Government Team will work with the SSPR contractors to
assure that the sensors are fully characterized during the development
and pre-launch phase and calibrated with NIST-traceable standards and
procedures at the component, sub-system, instrument and spacecraft
levels prior to launch. The Government Team will conduct coordinated
analyses of the data and instrument trending during the NPP mission, and
share these results with the IPO and the sensor vendors in a timely
manner. Details of the work split among the participants will be
delineated in subsequent versions of this plan.

The Government Team will have NASA and IPO components for these
calibration and validation activities :

> The NASA GCST will manage an NPP Calibration Support Team (NCST)
> modeled after the successful MODIS Calibration Support Team (MCST).
> This group will work under the direction of the NASA NPP Project
> Scientist to develop research quality Level 1B data products from
> VIIRS, CrIS, and ATMS data. The NCST will help determine the needs for
> characterization and calibration in concert with the GCST that will be
> developing selected Level 2 and higher data products (CDRs).
>
> The IPO will manage an NPP Calibration/Validation Support Team based
> on the IPO Science Team, the IPO System Engineering and Test &
> Evaluation Teams and the IPO Product Teams. This group will work under
> the direction of the IPO NPP Project Scientist, supported by the IPO
> System Engineer. The purpose of the IPO Calibration/Validation Support
> Team is to help determine the needs for characterization, calibration
> and validation of products as well as to ensure the development of
> high quality RDRs, SDRs and EDRs from the NPP VIIRS, CrIS, and ATMS
> data for possible operational utilization in the NPP era and the
> operational utilization in the NPOESS era.

Finally, the Government Team will develop methods to report their
results. At minimum this should include regular workshops including
SSPR, the Government Team, validation scientists and the user community,
a plan for immediate posting of known issues and results as they become
available, and regular hard-copy reports.

A.2 Calibration Monitoring and System Verification

The NPP sensors have provision for on-board calibration. In most cases,
on-board calibration is better than vicarious calibration. However, the
accuracy and stability of instrument RDRs and SDRs depend on the
specific approach to monitoring and implementing on-board calibration.
Moreover, in rare cases, on-board calibration systems can fail. Thus,
the Government Team should work with the SSPR to develop a suitable
calibration implementation plan (e.g., the frequency of calibration
maneuvers or lunar looks and calibration table updates). Further, the
Government Team should work with the contractor in verifying and
debugging the on-board systems and data sets. The Government Team should
include the expertise to vicariously verify or replace on-board
calibration. Historically, this has been accomplished through high
altitude aircraft under-flights and monitoring of well-instrumented and
stable calibration field sites.

For the sounders, radiative transfer calculations are also used to
calibrate at-sensor radiances.

Geolocation, band-to-band registration, PSF, other instrument system
checks will require pre-launch and post-launch testing as discussed in
sections 4 and 5.

A.3 Quality Assessment and Trend Analysis of SDRs and EDR/CDRs

Early in a typical satellite mission, significant information on
algorithm behavior is developed through trend and basic image analysis
of the SDR/Leve 1B and EDRs/CDRs. The Government Team must plan to
conduct these activities, particularly given the ramp-up schedule for
SSPR EDR delivery and implementation. A successful model for such
assessment is the MODIS LDOPE facility, which monitors and detects
potential algorithm or instrument behavior problems, and investigates
and communicates potential problems reported by product users.

A.4 Uncertainty Determination of EDR/CDRs and Intermediate Products

To validate global atmospheric and surface products derived from NPP
data, an independent sampling of the global variability of the products
is necessary. The expense and effort required to independently measure
each variable over the possible range of environmental conditions is
significant. The following sections, and their associated appendices,
suggest existing infrastructure and facilities available for
cost-effective validation.

A.5 Components of the Cal / Val Effort

***Land Surface Test Site Networks***

As outlined above, NPP validation will include focused field and
aircraft campaigns in specific locations and under specific
environmental conditions, but also include collection of long time
series of selected measurements field or ocean site networks. The
framework for identifying and stratifying field site capabilities
follows the Global Hierarchy of Observation Surface Types developed by
IGBP and adapted for the EOS MODIS Land Team (Table A-1).

Table A-1 describes the tiers and provides examples. This categorization
yields an inverse degree of measurement intensity per site with number
of sites in the tier. NPP land validation will rely on few intensive
field campaigns but a large number of sites for which only satellite
scenes are regularly. One deviation in this scheme is the Tier 5
(instrument calibration sites) which is primarily for radiometric
calibration.

**Table A-1: EOS MODIS Land validation hierarchical test site scheme**

  --------------- --------- --------- -------------------------- ---------------
  Tier            Approx.   Sample    Characteristics            Example
                  Number of Area                                 Sites/Network
                  sites     (km^2^)                              

  1\. Intensive   5         1,000     Intensive sampling of all  FIFE, BOREAS,
  Field Campaign                      relevant land and          HAPEX-Sahel,
  Sites;                              atmospheric parameter;     SAFARI 2000,
  International                       often over land cover      LBA
  Field Campaign                      gradient                   
  Programs                                                       

  2\. Fully       5         100       Full suite of radiation    ARM/CART Sites
  instrumented                        and flux measurements;     
  Sites                               ground, tower and aircraft 
                                      measurements               

  3\. Biome Tower 20-30     100       Long term, select          FLUXNET Sites
  Sites                               instrument packages for    
                                      process studies; ground    
                                      and tower measurements;    
                                      all major ecosystems and   
                                      climatic regions           

  4\. Globally    60        25        Limited surface and        LTER, NOAA
  Distributed                         atmosphere                 CMDL, BSRN and
  Test Sites                          characterization, select   SURFRAD
                                      instrument suit at         networks; MOBY
                                      different sites; widely    Ocean Buoys
                                      distributed variable       
                                      sampling frequencies       
                                      (intermittent to           
                                      continuous) capture        
                                      seasonal or interannual    
                                      variability, climatology;  
                                      permanent site             

  5\. Instrument  \<5       10        Well-instrumented for      White Sand,
  Calibration                         vicarious calibration;     Railroad Playa
  Sites                               unique reflectance and     
                                      emittance properties of    
                                      uniform, typically         
                                      non-vegetated surfaces;    
                                      ground and aircraft        
                                      measurements may include   
                                      geometric calibration      
                                      site(s)                    
  --------------- --------- --------- -------------------------- ---------------

Many global site networks currently exist, as do many field sites, which
are currently not affiliated with any global network. A list of suitable
site networks was provided in Table 6-1 and is described in detail in
Appendix G.

The calibration and validation of NPP reflective and emissive bands will
be based on measurement data sets from the AERI network at the ARM
sites, EOS sites, and MOBY/MOCE sites.

***ARM Sites (Atmospheric Sounding)***

The ARM Southern Great Plains (SGP) site will have five AERI
instruments; ARM North Slope Alaska (NSA) and ARM Tropical Western
Pacific (TWP) will each have two AERIs. These are zenith-viewing
instruments. The calibration accuracy of AERI instruments is NIST
traceable. In addition to the ARM sites, NPP cold scene (\<270 K)
calibration will be validated using P-AERI ground based measurements at
the South Pole. P-AERI may be pointed upward to view zenith or downward
to view surface or any angle in between. Because atmospheric water vapor
concentrations over the South Pole are typically small (\~5% relative
humidity), slant path effects on NPP and P-AERI window band measurements
will be small (\<0.5°C). Importantly, P-AERI is capable of viewing the
snow surface at the South Pole using the same viewing geometry as NPP.
This will minimize surface effects on the calibration validation
exercise.

Performance comparisons with products from other platforms are also
planned. NPP cloud mask algorithms will be compared with those developed
for AVHRR/HIRS on POES and ASTER/CERES/MISR/MODIS on Terra. Atmospheric
profiles will be compared with those from HIRS, GOES,
AIRS/AMSU/HSB/MODIS, GIFTS, and IASI/AVHRR. Cloud properties will be
intercompared with those derived from HIRS, CERES, MISR, MODIS. Aerosol
optical thickness and particle size retrievals from NPP will be compared
to MISR and MODIS analyses as well as to AERONET measurements.
Precipitable water vapor measurements will be compared to (i) radiosonde
measurements over the continents, (ii) AERONET-derived column water
vapor analysis, (iii) ground-based GPS soundings, (iv) ground-based
microwave radiometer measurements at the ARM sites, (v) ground-based
Raman lidar measurements at the SGP CART site, and (vi) periodic
differential absorption lidar measurements from the DC-8 aircraft
(LASE).

EOS Land Validation Core Sites (Land and atmospheric characteristics)

The EOS Land validation Core Sites are being used for MODIS/ASTER Land
validation program, and will provide the science community with timely
ground, aircraft, and satellite data for NPP science and validation
investigations. The sites, currently 24 distributed worldwide, represent
a large range of global biome types, and roughly comprise the area
within 100 km radius of a center point.

In most cases, each EOS site includes a fixed tower on which
above-canopy instrumentation will be mounted to provide near-continuous
sampling of canopy-scale

radiometric and meteorological variables. A conceptual model for a core
site instrument package includes a CIMEL™ ground and sky-scanning
sunphotometer (surface reflectance, vegetation index, BRDF),
albedometers (albedo), and a CO^2^ flux system. These data are augmented
by surface measurements of LAI and FPAR at less frequent time intervals.
Core Sites will receive priority deployment of validation
instrumentation and cover each major biome type delineated in NPP
operational and science algorithms.

***Ocean Test Sites***

The validation will be carried out principally using the Marine Optical
BuoY (MOBY) and the Marine-Atmospheric Emitted Radiance Interferometer
(M-AERI) instruments, using MOCE (Marine Optical Characterization
Experiment) sites off Hawaii
(http://orbit-net.nesdis.noaa.gov/orad/mot/moce/index.html), the
initialization cruise off Southern California and Baja, and other
cruises \[TBR\].

The role of Ocean Test Sites encompasses the somewhat conflicting needs
for intensive validation and initialization data collection, oceanic
process studies, time series stations, as well as providing for
stratified global observations. Data collected at the time series sites
are necessary to validate trends detected in satellite data, and to
monitor the response of marine ecosystems and SST to climate change. By
including physical and

biogeochemical observations, the sites will provide insight on the
mechanisms of coupling between biological and physical systems. Data
collected at the test sites will be of value to NPP instruments as well
as to MODIS, MISR, ASTER, and CERES on the EOS AM-1 Platform, and other
missions (SeaWiFS, ADEOS I, II, and METOP).

The scientific objectives of the ocean validation effort are to provide
data necessary for the initialization of some product algorithms, and
for an ongoing effort to describe the uncertainty fields of the NPP
ocean products. This information will be used to identify and remove
systematic biases in the data products resulting from the instrument,
the algorithms, and data production.

The specific products that require validation are:

-   Fundamental radiance products (water-leaving radiance in the visible
    and surface emittance in the thermal infrared).

-   Products relating to the physical and bio-optical state of the water
    (sea-surface temperature, phytoplankton pigment concentration,
    chlorophyll a concentration, phytoplankton fluorescence,
    photosynthetically active radiation, suspended solids concentration,
    organic matter concentration, coccolith/calcite concentration, ocean
    water attenuation coefficient, total absorption coefficient,
    gelbstoffe absorption coefficient, aerosol optical thickness, and
    phycoerythrin concentration).

-   Higher-level products (ocean primary productivity, chlorophyll
    fluorescence efficiency).

Several fundamentally different, but complementary, data sets are
necessary to provide an adequate sampling of the marine atmospheric
conditions, oceanic bio-optical state, and sea-surface temperature
(SST), needed to validate the NPP Ocean Products. Our validation
strategy is multi-fold: Highly-focused field expeditions using
state-of-the-art calibrated in-water and surface spectral radiometers,
supported by extensive instrument suites to determine the state of the
atmosphere, are utilized to understand the atmospheric and oceanic
processes that limit the accuracy of the derived bio-optical properties
and the SST. A permanent buoy-based oceanic optical station should be
maintained to continuously monitor the performance of the NPP system
(sensor plus algorithms).

Long-time period, global-scale data sets are obtained to provide a
monitoring capability for revealing calibration drift and the
consequences of sudden or extreme atmospheric events, such as volcanic
eruptions, transoceanic transport of terrestrial aerosols, cold-air
outbreaks, etc., on the global products. These data sets will enable NPP
ocean products to define the uncertainties in products under a variety
of conditions as well as provide the information required to fine-tune
corresponding algorithms.

The *in-situ* observations have been developed with the recognition of
their relevance to missions of similar ocean sensors (i.e., SeaWiFS,
OCTS, GLI, MERIS, GOES, AVHRR, MODIS).

The approach to validation of the NPP Ocean Products is to compare
surface- or *in-situ* -measured values with NPP ocean products. The
comparisons will be completed for a variety of situations ranging from
those for which the performance of the individual algorithm is expected
to be excellent to situations for which the performance is expected to
be severely degraded.

For the visible products (bio-optical products and water-leaving
radiance), the validation begins with initialization of the sensor,
i.e., the process of carrying out on-orbit calibration for a
newly-launched sensor, prompted by the fact that it is reasonable to
expect that the stresses associated with launch may alter radiometric
calibration, using a prediction of the radiance expected at the sensor,
based on a rigorous set of *in-situ* atmospheric measurements and
radiative transfer computations. On this basis, the sensor calibration
is revised to provide agreement with the predictions.

***Aircraft Remote Sensing***

Aircraft provide opportunities to sample fairly large areas with
well-characterized and calibrated instrumentation. Historically,
aircraft have been used extensively both independently and in
conjunction with field measurements. Many commercial and research-grade
instruments are available to provide accurate samples of at-sensor
radiances. In most cases, these instruments are significantly different
than satellite sensors. Further, the aircraft may fly at low altitudes
-- well within the troposphere. Thus, sensor-specific algorithms
(including atmospheric correction and scaling) or conversions are
necessary to derive NPP EDR-equivalent values.

A notable exception is NASA's Airborne Science Program
(<http://www.dfrc.nasa.gov/airsci/>), which provides airborne platforms
to carry NASA sensors such as the Airborne Visible Infrared Imaging
Spectrometer (AVIRIS), and Earth Observing System (EOS) sensor
simulators, such as the MODIS Airborne Simulator (MAS), MODIS/ASTER
Airborne Simulator (MASTER), and the Airborne Multi-angle Imaging
SpectroRadiometer (AirMISR). Many of these instruments can be carried on
NASA's ER-2 aircraft.

The IPO developed the NPOESS Airborne SounderTestbed (NAST) suite of
aircraft sensors and similar sensors including NAST-I, NAST-M and S-HIS
which provide NPP-like sounding radiometric observations. These
instruments have flown on the Proteus aircraft since 2000 and with
instruments such as MIR, MAS, CLS, FIRSC, and Intera on the ER-2
aircraft since 1997.

MAS, NAST-I, NAST-M, S-HIS and AVIRIS will play a key role in NPP
products validation. MAS is a fifty channel visible, near-infrared, and
thermal infrared imaging spectrometer with 50 m resolution at nadir
(King et al. 1996), Scanning HIS, a 2 km resolution at nadir
interferometer sounder, NAST-I, a 2.6 km resolution interferometer
covering 3.5 to 16 microns with a spectral resolution greater than 2000,
NAST-M, a 16 channel microwave radiometer sensitive to 50-60 and 113
-119 GHz radiation from 2.5 km resolution footprints, AVIRIS, a 224 band
imaging spectrometer from 0.4-2.5 µm with 20 m resolution at nadir (Vane
et al. 1993). All spatial resolutions cited above are for a NASA ER-2
aircraft altitude of 20 km. The ER-2's cruising altitude of 20 km leads
to stable attitude control and minimal above-aircraft atmospheric
effects. A Table of aircraft instruments planned for use in NPP
calibration/validation is provided below.

Table A-3: Airborne Instruments Planned for NPP Calibration Validation
Efforts and Corresponding URLs.

  ----------------------------- --------------------------------------------------
  **Airborne Instrument**       URL

  MAS                           http://ltpwww.gsfc.nasa.gov/MAS/

  NAST-I                        http://danspc.larc.nasa.gov/NAST/

  NAST-M                        http://www-nastm.mit.edu/

  S-HIS                         http://deluge.ssec.wisc.edu/\~shis/

  PSR                           http://www1.etl.noaa.gov/radiom/psr

  APMIR                         \-\-\-\-\-\-\-\-\-\-\--

  AVIRIS                        http://makalu.jpl.nasa.gov/

  MASTER                        http://masterweb.jpl.nasa.gov/

  MQUALS                        http://gaea.fcr.arizona.edu/validation/index.htm

  AirMISR                       http://www-misr.jpl.nasa.gov/mission/air.html
  ----------------------------- --------------------------------------------------

Other Satellite Sensors

A significant part of the NPP Cal/Val effort will involve
intercomparison of radiances and products derived from NPP with those
from EOS, POES, GOES, METOP, EO-3, ENVISAT programs and other available
satellite sensors. Maintenance of long-term data sets and continuity of
data quality is a mandate for the EOS-NPP-NPOESS series of sensors.
These intercomparisons are necessary both for calibration validation
effort and climate studies. This document outlines many of these
intercomparisons using NPP validation sites \[TBR\].

Table A-4: Space-borne Sensors Planned for NPP Cross-validation

  ------------------------------ -------------------------------------------
  **[Name]{.underline}**         **[URL]{.underline}**

  MODIS                          modarch.gsfc.nasa.gov/MODIS/MODIS.html

  AIRS/AMSU/HSB                  www-airs.jpl.nasa.gov

  GIFTS                          danspc.larc.nasa.gov/GIFTS

  HIRS/AMSU-A                    

  AVHRR                          www.osdpd.noaa.gov/EBB/noaasis.html

  GLI                            adeos2.hq.nasda.go.jp/shosai_gli_e.htm

  CERES                          asd-www.larc.nasa.gov/ceres/ASDceres.html

  VEGETATION                     spot4.cnes.fr/spot4_gb/vegetati.htm

  ASTER                          asterweb.jpl.nasa.gov

  Landsat-7                      landsat.gsfc.nasa.gov

  SAGE-II                        www-sage2.larc.nasa.gov

  IASI                           http://earth.esa.int/METOP.html

  MERIS                          http://envisat.estec.esa.nl/index.html

  AATSR                          http://envisat.estec.esa.nl/index.html
  ------------------------------ -------------------------------------------

Validation Data

Our validation approach relies heavily on the sources of the data that
were used in the algorithm development, which consisted primarily of the
MAS, NAST-I, NAST-M. In addition, we plan to make extensive use of the
AERONET (Aerosol Robotic Network), a network of ground-based sun
photometers established and maintained at Goddard Space Flight Center
(Holben et al. 1998) that measures the directly transmitted solar
radiation and sky radiance, reporting the data via a satellite
communication link from each remotely-located sun photometer to Goddard
Space Flight Center from sunrise to sunset, 7 days a week.

We also plan to utilize ground-based microwave radiometer observations
to derive column water vapor and liquid water path, especially over the
Atmospheric Radiation Measurement (ARM) Clouds And Radiation Testbed
(CART) site in Oklahoma. For validations of the retrievals over oceans,
the measurements of Cloud Liquid Water (CWV) and Liquid Water Path (LWP)
from two tropical islands, Nauru and Manus, can be directly available
because the Department of Energy will continue to support these field
campaigns. The validation of passive microwave estimation of CWV and LWP
is largely dependent on the observations from these oceanic environments
from these stations (Weng et al., 1997; Grody et al., 2001). North
American Radiosondes, in conjunction with GOES retrievals, will be used
to validate atmospheric properties (water vapor, stability). GOES
retrievals provide the bridge to compare the NPP retrievals with
radiosonde measurements. Well-calibrated radiances are essential for the
development of accurate algorithms; the calibration of S-HIS and NAST-I
is of such a high quality that it serves as a reference for line-by-line
radiative transfer models.

The MAS solar channels are calibrated in the field, using a 30"
integrating sphere before and after each ER-2 deployment, as well as a
20" integrating hemisphere shipped to the field deployment site for
periodic calibrations during a mission. The MAS infrared channels are
calibrated through two onboard blackbody sources that are viewed once
every scan, taking into account the spectral emissivity of the
blackbodies. Calibration of short-wave infrared and thermal infrared
channels will be routinely assessed through calibration intercomparisons
with S-HIS and NAST-I flying on the same aircraft. A comprehensive
description of both the short-wave and long-wave calibration procedures,
signal-to-noise characteristics, and thermal vacuum characterization of
the MAS can be found in King et al. (1996).

Aircraft data is important to the NPP calibration and validation both
before and after launch. Before launch, it will provide the means to
demonstrate expected performance and to establish algorithm approaches
that will work in the presence of actual atmospheric cloud conditions.
After launch, it will form the basis for system validation.

Product validation for the NPP can be established on the basis of shared
costs. While expenses associated with maintaining and fielding aircraft
instruments can be significant, the requirements for IPO are compatible
with those of ongoing NASA scientific programs, NOAA Calibration and
Validation of its operational observing capabilities, NASA plans for EOS
validation, and DOE field programs for climate studies. Plans are
already in place from these and other organizations to support a
substantial number of field programs that can be used to leverage IPO
support. More specifically, NASA is conducting missions with these
instruments throughout the current decade, including the SAFARI mission
in South Africa in 2000, a joint water vapor experiment with the DOE
centered around the Atmospheric Radiation Measurement (ARM) site in
Oklahoma in 2000, Aerosol Characterization Experiments (ACE) in 2001,
and a cirrus study with the Cirrus Regional Study of Tropical Anvils and
Layers (CRYSTAL) in 2002 and 2004. NOAA will be conducting calibration
validation of the operational polar orbiting infrared and microwave
sounders periodically in the 2000s; inter-calibration of the ongoing
series of POES and EOS sensors and the associated imaging and sounding
products is a high priority for these efforts.

[]{#__RefHeading___Toc534619218 .anchor}Appendix B: EDR and CDR
Performance Requirements

B.1 EDR Requirements

Figure B-1 shows the complete list of NPOESS EDRs to be derived during
the NPOESS era. Only EDRs from VIIRS, CrIS and ATMS on NPP are addressed
in this document.

![](media/image6.wmf){width="7.2in" height="4.179861111111111in"}

**Figure B-1: EDRs to be Provided by NPOESS Instruments**

The following Environmental Data Record (EDR) requirements define the
environmental data to be derived from the NPP data stream and delivered
to users to meet mission needs. The EDR definitions and requirements are
from the NPOESS Integrated Operational Requirements Document \[IORD\]
and the NPOESS Technical Requirements Document \[TRD\].

Parameter thresholds are cited first and objectives are cited second in
the following paragraphs. Note that thresholds and objectives listed
refer to the minimum requirement at any point where measurements are
sensed, (e.g., a requirement for horizontal resolution of 25 km
indicates a need for data at that resolution or better across the entire
area where data are being measured, unless specifically indicated at
nadir (direct overhead view) or worst case (normally at the edge of
satellite field of view) resolution separately). Any requirement giving
"nadir resolution" as an attribute presumes that the expansion of the
resolution at oblique viewing angles is a natural outcome of observing a
sphere from space, and does not presume a specific scanning methodology.
In these instances, technology will be driven by the nadir, or highest
quality, field of view. Global coverage denotes the observation of all
points on the Earth or its atmosphere at least once per given time
period (consistent with observational requirements).

Data products are required during any weather conditions; however, EDR
requirements apply to clear conditions only unless otherwise specified.
Thresholds given for attributes broken into "cloudy" (greater than or
equal to five-tenths cloud cover), "clear" (less than five-tenths cloud
cover), and "all weather" (all cloud conditions and rainfall rates less
than 2 mm hr^-1^ km^-2^ unless otherwise specified in individual EDRs)
cases indicate the government's recognition that different technologies
shall be employed to provide accurate measurements under these three
different atmospheric conditions. Threshold value differences among
cloudy, clear, and all weather cases demonstrate how the most stringent
of the three is required when obtainable, and will add important
information in the ultimate operational application of the data.

All data are required at the uncertainty/refresh/resolution stated, for
any Earth location/profile. The performance characteristics for the EDR
attributes of Vertical Coverage, Measurement Range, Vertical Reporting
Interval and/or Vertical Cell Size, and Measurement Precision and
Accuracy shall be within the normal/expected sensing range unless
specifically indicated otherwise for each EDR.

**B.2 Tables of NPOESS Environmental Data Records (EDRs) Requirements
for NPP**

1\. Imagery

+---------+--------------------------------+-------------+-------------+
| Para.   |                                | Threshold   | Objectives  |
| No.     |                                |             |             |
+---------+--------------------------------+-------------+-------------+
|         | a\. **\*Horizontal Spatial     |             |             |
|         | Resolution (HSR)**             |             |             |
+---------+--------------------------------+-------------+-------------+
| 40.     | **Deleted**                    |             |             |
| 2.3.1-2 |                                |             |             |
+---------+--------------------------------+-------------+-------------+
| 40.     | Deleted                        |             |             |
| 2.3.1-3 |                                |             |             |
+---------+--------------------------------+-------------+-------------+
| 40.     | **1. Nadir**                   | **0.4 km**  | 0.1 km      |
| 2.3.1-4 |                                |             |             |
+---------+--------------------------------+-------------+-------------+
| 40.     | 2 Worst case                   | 0.8 km      | 0.1 km      |
| 2.3.1-5 |                                |             |             |
+---------+--------------------------------+-------------+-------------+
| 40.     | 3\. Nighttime Visible, worst   | 0.74 km     | 0.65 km     |
| 2.3.1-6 | case                           |             |             |
+---------+--------------------------------+-------------+-------------+
| 40.2    | 4\. All Weather                | 40 km       | 20 km       |
| .3.1-18 |                                |             |             |
+---------+--------------------------------+-------------+-------------+
| 40.     | b\. Horizontal Reporting       | Imagery HSR | Derived     |
| 2.3.1-7 | Interval                       |             | (gapless or |
|         |                                |             | near        |
|         |                                |             | gapless     |
|         |                                |             | coverage)   |
+---------+--------------------------------+-------------+-------------+
| 4       | c\. Horizontal Coverage        | Global      | Global      |
| 0.2.3.1 |                                |             |             |
| -8      |                                |             |             |
+---------+--------------------------------+-------------+-------------+
|         |                                |             |             |
+---------+--------------------------------+-------------+-------------+
| 40.     | Deleted                        |             |             |
| 2.3.1-9 |                                |             |             |
+---------+--------------------------------+-------------+-------------+
|         | d\. Measurement Range          |             |             |
+---------+--------------------------------+-------------+-------------+
| 40.2    | 1\. Nighttime visible          | 4.00E-09 to | Includes    |
| .3.1-10 |                                | 3.00E-02    | threshold   |
|         |                                | W/(cm^2^sr) | range       |
+---------+--------------------------------+-------------+-------------+
| 40.2    | 2\. Other bands                | 0.645 band  | Derived     |
| .3.1-11 |                                | 5.0 to 468  |             |
|         |                                | W/(m^2^ sr  |             |
|         |                                | m)          |             |
|         |                                |             |             |
|         |                                | 3.7 band    |             |
|         |                                | 210 K to    |             |
|         |                                | 353 K       |             |
|         |                                |             |             |
|         |                                | 11.45 band  |             |
|         |                                | 190 K to    |             |
|         |                                | 340 K       |             |
+---------+--------------------------------+-------------+-------------+
| 40.2    | e\. Measurement Uncertainty    | Derived     | Derived     |
| .3.1-12 |                                |             |             |
+---------+--------------------------------+-------------+-------------+
|         | f\. Mapping Uncertainty        |             |             |
+---------+--------------------------------+-------------+-------------+
| 40.2    | 1\. At nadir                   | 0.4 km      | 0.4 km      |
| .3.1-13 |                                |             |             |
+---------+--------------------------------+-------------+-------------+
| 40.2    | 2\. Worst case                 | 1.5 km      | 0.5 km      |
| .3.1-14 |                                |             |             |
+---------+--------------------------------+-------------+-------------+
| 40.2    | 3\. All Weather                | 3 km        |             |
| .3.1-19 |                                |             |             |
+---------+--------------------------------+-------------+-------------+
| 40.2    | **g. \*Maximum Local Average   | **4 hrs**   | (TBD)       |
| .3.1-15 | Revisit Time**                 |             |             |
+---------+--------------------------------+-------------+-------------+
| 40.2    | **h. \*Maximum Local Refresh** | **6 hrs**   | (TBD)       |
| .3.1-16 |                                |             |             |
+---------+--------------------------------+-------------+-------------+
| 40.2    | **i. \*Fraction of Revisit     | **At any    | (TBD)       |
| .3.1-17 | Times Less Than a Specified    | location at |             |
|         | Value**                        | least 75 %  |             |
|         |                                | of the      |             |
|         |                                | revisit     |             |
|         |                                | times will  |             |
|         |                                | be 4 hours  |             |
|         |                                | or less**   |             |
+---------+--------------------------------+-------------+-------------+
| 40.2    | j\. Latency (S)                | [90]{       | 15 minutes  |
| .3.1-20 |                                | .smallcaps} |             |
|         |                                | minutes     |             |
+---------+--------------------------------+-------------+-------------+

***\
***

***2. Atmospheric Vertical Moisture Profile***

[Units]{.underline}: **g**/kg

+---------+------------------------------------+-----------+----------+
| Para.   |                                    | T         | Ob       |
| No.     |                                    | hresholds | jectives |
+---------+------------------------------------+-----------+----------+
| 4       | a\. Horizontal Cell Size           | 14 km @   | 2 km @   |
| 0.2.1-1 |                                    | nadir     | nadir    |
+---------+------------------------------------+-----------+----------+
| 4       | b\. Horizontal Reporting Interval  | 1 to 9    | 2 km     |
| 0.2.1-2 |                                    | per FOR   |          |
+---------+------------------------------------+-----------+----------+
| 4       | c\. Vertical Cell Size             | 2 km      | 2 km     |
| 0.2.1-3 |                                    |           |          |
+---------+------------------------------------+-----------+----------+
|         | d\. Vertical Reporting Interval    |           |          |
+---------+------------------------------------+-----------+----------+
| 4       | 1\. Surface to 850 mb              | 20 mb     | 5 mb     |
| 0.2.1-4 |                                    |           |          |
+---------+------------------------------------+-----------+----------+
| 4       | 2\. 850 mb to 100 mb               | 50 mb     | 15 mb    |
| 0.2.1-5 |                                    |           |          |
+---------+------------------------------------+-----------+----------+
| 4       | e\. Horizontal Coverage            | Global    | Global   |
| 0.2.1-6 |                                    |           |          |
+---------+------------------------------------+-----------+----------+
| 4       | f\. Vertical Coverage              | Surface   | Surface  |
| 0.2.1-7 |                                    | to 100 mb | to 100   |
|         |                                    |           | mb       |
+---------+------------------------------------+-----------+----------+
| 4       | g\. Measurement Range              | 0-30 g/kg | 0 - 30   |
| 0.2.1-8 |                                    |           | g/kg     |
+---------+------------------------------------+-----------+----------+
|         | h\. \*Measurement Uncertainty      |           |          |
|         |                                    |           |          |
|         | (expressed as a percent of average |           |          |
|         | mixing ratio in 2 km layers)       |           |          |
+---------+------------------------------------+-----------+----------+
|         | **Clear**                          |           |          |
+---------+------------------------------------+-----------+----------+
| 4       | **1. \*Surface to 600 mb**         | 15%       | 10%      |
| 0.2.1-9 |                                    |           |          |
+---------+------------------------------------+-----------+----------+
| 40      | 2\. 600 mb to 300 mb               | 14%       | 10%      |
| .2.1-10 |                                    |           |          |
+---------+------------------------------------+-----------+----------+
| 40      | 3\. 300 mb to 100 mb               | 12%       | 10%      |
| .2.1-11 |                                    |           |          |
+---------+------------------------------------+-----------+----------+
|         | **Cloudy**                         |           |          |
+---------+------------------------------------+-----------+----------+
| 40      | **4. \*Surface to 600 mb**         | 16%       | 10%      |
| .2.1-12 |                                    |           |          |
+---------+------------------------------------+-----------+----------+
| 40      | 5\. 600 mb to 300 mb               | 18%       | 10%      |
| .2.1-13 |                                    |           |          |
+---------+------------------------------------+-----------+----------+
| 40      | 6\. 300 mb to 100 mb               | 17%       | 10%      |
| .2.1-14 |                                    |           |          |
+---------+------------------------------------+-----------+----------+
| 40      | i\. Mapping Uncertainty            | 5 km      | 1 km     |
| .2.1-15 |                                    |           |          |
+---------+------------------------------------+-----------+----------+
| 40      | j\. Maximum Local Average Revisit  | 8 hrs     | 3 hrs    |
| .2.1-16 | Time                               |           |          |
+---------+------------------------------------+-----------+----------+
| 40      | k\. Deleted.                       |           |          |
| .2.1-17 |                                    |           |          |
+---------+------------------------------------+-----------+----------+
| 40      | l\. Latency (S)                    | 156 min   | 15       |
| .2.1-18 |                                    |           | minutes  |
+---------+------------------------------------+-----------+----------+
| 40      | m\. Long-term Stability (C)        | 2%        | 1%       |
| .2.1-19 | (CrIS/ATMS)                        |           |          |
+---------+------------------------------------+-----------+----------+

***3. Atmospheric Vertical Temperature Profile***

[Units]{.underline}: K

+---------+--------------------------------+--------------+-----------+
| Para.   |                                | Thresholds   | O         |
| No.     |                                |              | bjectives |
+---------+--------------------------------+--------------+-----------+
|         | a\. Horizontal Cell Size       |              |           |
+---------+--------------------------------+--------------+-----------+
| 4       | 1\. Clear, nadir               | 14 km        | 1 km      |
| 0.2.2-1 |                                | Surface to   |           |
|         |                                | 0.5 mb       |           |
|         |                                |              |           |
|         |                                | 200 km 0.5   |           |
|         |                                | to 0.01mb    |           |
+---------+--------------------------------+--------------+-----------+
| 4       | 2\. Clear, worst case          | 50 km        | (TBD)     |
| 0.2.2-2 |                                |              |           |
+---------+--------------------------------+--------------+-----------+
| 4       | 3\. Cloudy, nadir              | 40 km        | 1 km      |
| 0.2.2-3 |                                |              |           |
+---------+--------------------------------+--------------+-----------+
| 4       | 4\. Cloudy, worst case         | 200km        | (TBD)     |
| 0.2.2-4 |                                |              |           |
+---------+--------------------------------+--------------+-----------+
| 4       | b\. Horizontal Reporting       | One to nine  | (TBD)     |
| 0.2.2-5 | Interval                       | per FOR      |           |
+---------+--------------------------------+--------------+-----------+
|         | c\. Vertical Cell Size         |              |           |
+---------+--------------------------------+--------------+-----------+
|         | Clear                          |              |           |
+---------+--------------------------------+--------------+-----------+
| 4       | 1\. Surface to 300 mb          | 1 km         | (TBD)     |
| 0.2.2-6 |                                |              |           |
+---------+--------------------------------+--------------+-----------+
| 4       | 2\. 300 mb to 30 mb            | 3 km         | (TBD)     |
| 0.2.2-7 |                                |              |           |
+---------+--------------------------------+--------------+-----------+
| 4       | 3\. 30 mb to 1 mb              | 5 km         | (TBD)     |
| 0.2.2-8 |                                |              |           |
+---------+--------------------------------+--------------+-----------+
| 4       | 4\. 1 mb to 0.5 mb             | 5 km         | (TBD)     |
| 0.2.2-9 |                                |              |           |
+---------+--------------------------------+--------------+-----------+
| 40      | 5\. 0.5 to 0.01 mb             |              | (TBD)     |
| .2.2-40 |                                |              |           |
+---------+--------------------------------+--------------+-----------+
|         | Cloudy                         |              |           |
+---------+--------------------------------+--------------+-----------+
| 40      | 6\. Surface to 700 mb          | 1 km         | (TBD)     |
| .2.2-10 |                                |              |           |
+---------+--------------------------------+--------------+-----------+
| 40      | 7\. 700 mb to 300 mb           | 1 km         | (TBD)     |
| .2.2-11 |                                |              |           |
+---------+--------------------------------+--------------+-----------+
| 40      | 8\. 300 mb to 30 mb            | 3 km         | (TBD)     |
| .2.2-12 |                                |              |           |
+---------+--------------------------------+--------------+-----------+
| 40      | 9\. 30 mb to 1 mb              | 5 km         | (TBD)     |
| .2.2-13 |                                |              |           |
+---------+--------------------------------+--------------+-----------+
| 40      | 10\. 1 mb to 0.5 mb            | 5 km         | (TBD)     |
| .2.2-14 |                                |              |           |
+---------+--------------------------------+--------------+-----------+
| 40      | 11\. 0.5 to 0.01 mb            | 5 km         | (TBD)     |
| .2.2-41 |                                |              |           |
+---------+--------------------------------+--------------+-----------+
|         | d\. Vertical Reporting         |              |           |
|         | Interval                       |              |           |
+---------+--------------------------------+--------------+-----------+
| 40      | 1\. Surface to 850 mb          | 20 mb        | 10 mb     |
| .2.2-15 |                                |              |           |
+---------+--------------------------------+--------------+-----------+
| 40      | 2\. 850 mb to 300 mb           | 50 mb        | 10 mb     |
| .2.2-16 |                                |              |           |
+---------+--------------------------------+--------------+-----------+
| 40      | 3\. 300 mb to 100 mb           | 25 mb        | 15 mb     |
| .2.2-17 |                                |              |           |
+---------+--------------------------------+--------------+-----------+
| 40      | 4\. 100 mb to 10 mb            | 20 mb        | 10 mb     |
| .2.2-18 |                                |              |           |
+---------+--------------------------------+--------------+-----------+
| 40      | 5\. 10 mb to 1 mb              | 2 mb         | 1 mb      |
| .2.2-19 |                                |              |           |
+---------+--------------------------------+--------------+-----------+
| 40      | 6\. 1 mb to 0.1 mb             | 0.2 mb \[1   | 0.1 mb    |
| .2.2-20 |                                | mb to .5     |           |
|         |                                | mb\]         |           |
+---------+--------------------------------+--------------+-----------+
| 40      | 7\. 0.1 mb to 0.01 mb          | 0.02 mb      | 0.01 mb   |
| .2.2-21 |                                |              |           |
+---------+--------------------------------+--------------+-----------+
| 40      | e\. Horizontal Coverage        | Global       | Global    |
| .2.2-22 |                                |              |           |
+---------+--------------------------------+--------------+-----------+
| 40      | f\. Vertical Coverage          | Surface to   | Surface   |
| .2.2-23 |                                | 0.01 mb      | to 0.01   |
|         |                                |              | mb        |
+---------+--------------------------------+--------------+-----------+
| 40      | g\. Measurement Range          | [180-335K    | 162-335 K |
| .2.2-24 |                                | \[Earth      | (TBR)     |
|         |                                | Scene\]      |           |
|         |                                | 180-310K     |           |
|         |                                | \[Black      |           |
|         |                                | Body\]]      |           |
|         |                                | {.smallcaps} |           |
+---------+--------------------------------+--------------+-----------+
| 40      | Not Used                       |              |           |
| .2.2-25 |                                |              |           |
+---------+--------------------------------+--------------+-----------+
|         | h\. \*\*\*Measurement          |              |           |
|         | Uncertainty                    |              |           |
+---------+--------------------------------+--------------+-----------+
|         | **Clear**                      |              |           |
+---------+--------------------------------+--------------+-----------+
| 40      | **1. \*Surface to 300 mb**     | 0.9 K/1 km   | 0.5 K/1   |
| .2.2-26 |                                | layer        | km        |
+---------+--------------------------------+--------------+-----------+
| 40      | 2\. 300 mb to 30 mb            | 0.98 K/3 km  | 0.5 K/1   |
| .2.2-27 |                                | layers       | km        |
+---------+--------------------------------+--------------+-----------+
| 40      | 3\. 30 mb to 1 mb              | 1.45 K/5 km  | 0.5 K/1   |
| .2.2-28 |                                | layers       | km        |
+---------+--------------------------------+--------------+-----------+
| 40      | 4\. 1 mb to 0.3 mb\*\*         | 3.5 K /5 km  | 0.5 K/1   |
| .2.2-29 |                                | layers       | km        |
+---------+--------------------------------+--------------+-----------+
| [4      | 5\. 0.3 to 0.001 mb            | [6.5 K/5 km  |           |
| 0.2.2-4 |                                | layer]       |           |
| 1]{.sma |                                | {.smallcaps} |           |
| llcaps} |                                |              |           |
+---------+--------------------------------+--------------+-----------+
|         | **Cloudy**                     |              |           |
+---------+--------------------------------+--------------+-----------+
| 40      | **6. \*Surface to 700 mb**     | 2.0 K/ 1 km  | 0.5 K/1   |
| .2.2-30 |                                | layer        | km        |
+---------+--------------------------------+--------------+-----------+
| 40      | 7\. 700 mb to 300 mb           | 1.4 K/ 1 km  | 0.5 K/1   |
| .2.2-31 |                                | layer        | km        |
+---------+--------------------------------+--------------+-----------+
| 40      | 8\. 300 mb to 30 mb            | 1.3 K/ 1 km  | 0.5 K/1   |
| .2.2-32 |                                | layer        | km        |
+---------+--------------------------------+--------------+-----------+
| 40      | 9\. 30 mb to 1 mb              | 1.45 K/ 1 km | 0.5 K/1   |
| .2.2-33 |                                | layer        | km        |
+---------+--------------------------------+--------------+-----------+
| 40      | 10\. 1 mb to 0.0.5 mb          | 3.5 K /1 km  | 0.5 K/1   |
| .2.2-34 |                                | layer        | km        |
+---------+--------------------------------+--------------+-----------+
| 40      | 11\. 0.5 to 0.01 mb            | 6.5 K/ 5 km  |           |
| .2.2-40 |                                | layer        |           |
+---------+--------------------------------+--------------+-----------+
| 40      | i\. Mapping Uncertainty        | 5 km         | 1 km      |
| .2.2-35 |                                |              |           |
+---------+--------------------------------+--------------+-----------+
| 40      | j\. Maximum Local Average      | 6 hrs (TBR)  | 3 hrs     |
| .2.2-36 | Revisit Time                   |              |           |
+---------+--------------------------------+--------------+-----------+
| 40      | k\. Deleted.                   |              |           |
| .2.2-37 |                                |              |           |
+---------+--------------------------------+--------------+-----------+
| 40      | l\. Latency (S)                | 156 min      | 15        |
| .2.2-38 |                                |              | minutes   |
+---------+--------------------------------+--------------+-----------+
| 40      | m\. Long Term Stability (C)    | Trop Mean    | [Trop     |
| .2.2-39 | (CrIS/ATMS)                    | 0.05 K       | 0.03      |
|         |                                |              | K]{.s     |
|         |                                | Strat Mean   | mallcaps} |
|         |                                | 0.1 K        |           |
|         |                                |              | Strat     |
|         |                                |              | 0.05 K    |
+---------+--------------------------------+--------------+-----------+

*4. Pressure (Surface/Profile)*

[Units: mb]{.underline}

  ----------- -------------------------------- -------------- -------------
  Para. No.                                    Thresholds     Objectives

  40.3.5-1    a\. Horizontal Cell Size         25 km,         5 km

  40.3.5-2    b\. Horizontal Reporting         25 km          5 km
              Interval                                        

  40.3.5-3    c\. Vertical Cell Size           0 km           0 km

              d\. Vertical Reporting Interval                 

  40.3.5-4    1\. \[0 -- 2 km\]                1 km           0.25 km

  40.3.5-5    2\. \[2 -- 5 km\]                1 km           0.5 km

  40.3.5-6    3\. \[\> 5 km\]                  1 km           1 km

  40.3.5-7    e\. Horizontal Coverage          Global         Global

  40.3.5-8    f\. Vertical Coverage            0-30 km        0 -- 30 km

  40.3.5-9    g\. Measurement Range            10-1050 mb     10 -- 1050 mb

              h\. Measurement accuracy                        

  40.3.5-10   1\. \[0 -- 2 km\]                3%             

  40.3.5-11   [2. \[2 -- 10 km\]]{.smallcaps}  3 %            0.5%

  40.3.5-12   3\. \[10 -- 30 km\]              5 % \[10-30    0.5 %
                                               km\]           

  40.3.5-13   i\. Measurement Precision        3 mb           2 mb

                                                              

                                                              

  40.3.5-14   j\. Mapping Uncertainty          3 km           1 km

  40.3.5-15   k\. Maximum Local Average        8 hrs          1 hr
              Revisit Time (S)                                

  40.3.5-16   l\. Deleted.                                    

  40.3.5-17   m\. Latency (S)                  156 minutes    15 minutes
  ----------- -------------------------------- -------------- -------------

*5. Precipitable Water*

[Units]{.underline}: mm of condensed vapor

+--------+------------------------------+------------------+-----------+
| Para.  |                              | Threshold        | O         |
| No.    |                              |                  | bjectives |
+--------+------------------------------+------------------+-----------+
| 40     | a\. Horizontal Cell Size     | 25 km            | 1 km      |
| .3.3-1 |                              |                  |           |
+--------+------------------------------+------------------+-----------+
| 40     | b\. Horizontal Reporting     | 25 km            | HCS       |
| .3.3-2 | Interval                     |                  |           |
+--------+------------------------------+------------------+-----------+
| 40     | c\. Horizontal Coverage      | Global           | Global    |
| .3.3-3 |                              |                  |           |
+--------+------------------------------+------------------+-----------+
| 40     | d\. Measurement Range        | 0 - 75 mm        | 0 - 100   |
| .3.3-4 |                              |                  | mm        |
+--------+------------------------------+------------------+-----------+
| 40     | e\. Measurement Accuracy     | Land or Ice      | 1 mm or   |
| .3.3-5 |                              | Greater of 8% or | 4%        |
|        |                              | 2 mm             |           |
|        |                              |                  |           |
|        |                              | Oceqan, Ice-free |           |
|        |                              | 1mm              |           |
+--------+------------------------------+------------------+-----------+
| 40     | f\. Measurement Precision    | Land or Ice      | 1 mm      |
| .3.3-6 |                              | Greater of 5% or |           |
|        |                              | 1mm              |           |
|        |                              |                  |           |
|        |                              | Ocean,Ice-free 1 |           |
|        |                              | mm               |           |
+--------+------------------------------+------------------+-----------+
| 40     | g\. Mapping Uncertainty      | 3 km             | 0.1 km    |
| .3.3-7 |                              |                  |           |
+--------+------------------------------+------------------+-----------+
| 40     | h\. Maximum Local Average    | 8 hrs            | 3 hrs     |
| .3.3-8 | Revisit Time (S)             |                  |           |
+--------+------------------------------+------------------+-----------+
| 40     | i\. Deleted.                 |                  |           |
| .3.3-9 |                              |                  |           |
+--------+------------------------------+------------------+-----------+
| 40.    | j\. Long Term Stability (C)  | Greater of 1.0   | Greater   |
| 3.3-10 |                              | mm or 10%        | of 0.1 mm |
|        |                              |                  | or 1%     |
+--------+------------------------------+------------------+-----------+
| 40.    | k\. Latency (S)              | 90 minutes       | 15        |
| 3.3-11 |                              |                  | minutes   |
+--------+------------------------------+------------------+-----------+

6\. Suspended Matter

Concentration: g/m^3^

+---------+-----------------------+------------------+----------------+
| Para.   |                       | Threshold        | Objectives     |
| No.     |                       |                  |                |
+---------+-----------------------+------------------+----------------+
| 40.     | a\. Horizontal Cell   | 1.6 km           | 1 km           |
| 3.1.3-1 | Size                  |                  |                |
+---------+-----------------------+------------------+----------------+
| 40.     | b\. Horizontal        | HCS              | (TBD)          |
| 3.1.3-2 | Reporting Interval    |                  |                |
+---------+-----------------------+------------------+----------------+
| 40.     | c\. Vertical Cell     | Total Column     | 0.2 km         |
| 3.1.3-3 | Size                  |                  |                |
+---------+-----------------------+------------------+----------------+
| 40.     | d\. Vertical          | N/A              | Vertical Cell  |
| 3.1.3-4 | Reporting Interval    |                  | Size           |
+---------+-----------------------+------------------+----------------+
| 40.     | e\. Horizontal        | Global           | Global         |
| 3.1.3-5 | Coverage              |                  |                |
+---------+-----------------------+------------------+----------------+
| 40.     | f\. Vertical Coverage | 0-30 km          | (TBD)          |
| 3.1.3-6 |                       |                  |                |
+---------+-----------------------+------------------+----------------+
|         | g\. Measurement Range |                  |                |
+---------+-----------------------+------------------+----------------+
| 40.3    | > 1\. Detection       | Flag cells where | Flag           |
| .1.3-14 |                       | atmosphere       | atmospheric    |
|         |                       | contains         | layers         |
|         |                       | suspended matter | containing     |
|         |                       |                  | suspended      |
|         |                       |                  | matter         |
+---------+-----------------------+------------------+----------------+
| 40.     | 2\. Type              | Dust, sand,      | Dust, sand,    |
| 3.1.3-7 |                       | volcanic ash,    | volcanic ash,  |
|         |                       | sea salt, smoke, | sea salt,      |
|         |                       | SO~2~            | smoke, SO~2~,  |
|         |                       |                  | radioactive    |
|         |                       |                  | material,      |
|         |                       |                  | other          |
+---------+-----------------------+------------------+----------------+
| 40.     | 3\. Concentration     | 0 - 1000 g/m^3^  | 0 - 100 g/m^3^ |
| 3.1.3-8 |                       | for smoke        | for smoke,     |
|         |                       |                  | other types    |
|         |                       |                  | (TBD)          |
+---------+-----------------------+------------------+----------------+
| 40.     | h\. Probability of    | Suspended matter | (TBD) for      |
| 3.1.3-9 | Correct Typing        | 90%              | classes        |
|         |                       |                  |                |
|         |                       | Dust/sand 85%    |                |
|         |                       |                  |                |
|         |                       | Smoke 85%        |                |
|         |                       |                  |                |
|         |                       | Volcanic Ash 85% |                |
|         |                       |                  |                |
|         |                       | Sea Salt 85%     |                |
+---------+-----------------------+------------------+----------------+
| 40.3    | i\. Measurement       | Smoke 50%        | (TBD)          |
| .1.3-10 | Uncertainty           |                  |                |
|         | (concentration)       |                  |                |
+---------+-----------------------+------------------+----------------+
| 40.3    | j\. Mapping           | 1.5 km           | 0.1 km         |
| .1.3-11 | Uncertainty           |                  |                |
+---------+-----------------------+------------------+----------------+
| 40.3    | k\. Maximum Local     | 12 hrs           | 3 hrs          |
| .1.3-12 | Average Revisit Time  |                  |                |
|         | (S)                   |                  |                |
+---------+-----------------------+------------------+----------------+
| 40.3    | l\. Deleted.          |                  |                |
| .1.3-13 |                       |                  |                |
+---------+-----------------------+------------------+----------------+
| 40.3    | m\. Latency (S)       | 90 minutes       | 15 minutes     |
| .1.3-15 |                       |                  |                |
+---------+-----------------------+------------------+----------------+

*7. Aerosol Optical Thickness*

[Units]{.underline}: Dimensionless

+---------+---------------------------+------------------+------------+
| Para.   |                           | Threshold        | Objectives |
| No.     |                           |                  |            |
+---------+---------------------------+------------------+------------+
| 40.     | a\. Horizontal Cell Size  | 1.6 km over      | 1 km       |
| 3.1.1-1 |                           | ocean; 9.6 km    |            |
|         |                           | over land        |            |
+---------+---------------------------+------------------+------------+
| 40.     | b\. Horizontal Reporting  | HCS              | (TBD)      |
| 3.1.1-2 | Interval                  |                  |            |
+---------+---------------------------+------------------+------------+
| 40.     | c\. Vertical Cell Size    | Total Column     | 50 km      |
| 3.1.1-3 |                           |                  |            |
+---------+---------------------------+------------------+------------+
| 40.     | 1\. \[0 -- 2 km\]         | N/A              | 0.25 km    |
| 3.1.1-4 |                           |                  |            |
+---------+---------------------------+------------------+------------+
| 40.     | 2\. \[2 -- 5 km\]         | N/A              | 0.5 km     |
| 3.1.1-5 |                           |                  |            |
+---------+---------------------------+------------------+------------+
| 40.     | 3\. \[\> 5 km\]           | N/A              | 1 km       |
| 3.1.1-6 |                           |                  |            |
+---------+---------------------------+------------------+------------+
| 40.     | d\. Vertical Reporting    | Vertical cell    | Vertical   |
| 3.1.1-7 | Interval                  | size             | cell size  |
+---------+---------------------------+------------------+------------+
| 40.     | e\. Horizontal Coverage   | Global           | Global     |
| 3.1.1-8 |                           |                  |            |
+---------+---------------------------+------------------+------------+
| 40.     | f\. Vertical Coverage     | 0 -- 50 km       | 0 -- 50 km |
| 3.1.1-9 |                           |                  |            |
+---------+---------------------------+------------------+------------+
|         | g\. Measurement Range     |                  |            |
+---------+---------------------------+------------------+------------+
| 40.3    | 1\. Operational           | 0.0 to 2.0 units | 0-10       |
| .1.1-10 |                           | of               |            |
+---------+---------------------------+------------------+------------+
| 40.3    | 2\. Climate               | 0.0 to 5.0 units | 0-10       |
| .1.1-18 |                           | of               |            |
+---------+---------------------------+------------------+------------+
|         | h\. Measurement Accuracy  |                  |            |
+---------+---------------------------+------------------+------------+
| 40.3    | 1\. Operational, Over     | \<0.5 \-- 0.02   | 0.01       |
| .1.1-11 | Ocean                     |                  |            |
|         |                           | [\>              |            |
|         |                           | ]{.underline}0.5 |            |
|         |                           | \-- 0.07- 0.015  |            |
+---------+---------------------------+------------------+------------+
| 40.3    | 2\. Climate, Over Ocean   | Greater of 0.02  | Greater of |
| .1.1-19 |                           | or 7%            | .01 or 5%  |
+---------+---------------------------+------------------+------------+
| 40.3    | 3\. Operational, Over     | [                | 0.1        |
| .1.1-12 | Land                      | \>1]{.underline} |            |
|         |                           | \-- 0.15         |            |
+---------+---------------------------+------------------+------------+
| 40.3    | 4\. Climate, Over Land    | [Greater of 0.04 | Greater of |
| .1.1-20 |                           | or               | 0.03 or 7% |
|         |                           | 10%]{.smallcaps} |            |
+---------+---------------------------+------------------+------------+
|         | i\. Measurement Precision |                  |            |
+---------+---------------------------+------------------+------------+
| 40.3    | 1\. Operational           | Over ocean       | 0.01       |
| .1.1-13 |                           | [\<              |            |
|         |                           | ]{.underline}0.6 |            |
|         |                           | \-- 0.02 0.6     |            |
|         |                           | \--0.03          |            |
|         |                           |                  |            |
|         |                           | Over land -- 0.1 |            |
+---------+---------------------------+------------------+------------+
| 40.3    | 2\. Climate, Over Ocean   | 0.01             | 0.005      |
| .1.1-21 |                           |                  |            |
+---------+---------------------------+------------------+------------+
| 40.3    | 3\. Climate, Over Land    | 0.03             | 0.02       |
| .1.1-22 |                           |                  |            |
+---------+---------------------------+------------------+------------+
| 40.3    | j\. Long Term Stability   | 0.01             | 0.003      |
| .1.1-14 |                           |                  |            |
+---------+---------------------------+------------------+------------+
| 40.3    | k\. Mapping Uncertainty   | 1.5 km           | 1 km       |
| .1.1-15 |                           |                  |            |
+---------+---------------------------+------------------+------------+
|         | l\. Maximum Local Average |                  |            |
|         | Revisit Time              |                  |            |
+---------+---------------------------+------------------+------------+
| 40.3    | 1\. Operational (S)       | 6 hrs            | 4 hrs      |
| .1.1-16 |                           |                  |            |
+---------+---------------------------+------------------+------------+
| 40.3    | 2\. Climate               | N/A              | N/A        |
| .1.1-23 |                           |                  |            |
+---------+---------------------------+------------------+------------+
| 40.3    | m\. Deleted.              |                  |            |
| .1.1-17 |                           |                  |            |
+---------+---------------------------+------------------+------------+
| 40.3    | n\. Measurement           | \<0.45 0.05+0.2  |            |
| .1.1-24 | Uncertainty, Operational, |                  |            |
|         | over land                 | 1 0.14           |            |
|         |                           |                  |            |
|         |                           | \>1 0.18         |            |
+---------+---------------------------+------------------+------------+

***8. Aerosol Particle Size***

[Units]{.underline}: Ångström Wavelength Exponent: Dimensionless.
Effective Radius: m

+---------+---------------------+------------------+------------------+
| Para.   |                     | Threshold        | Objectives       |
| No.     |                     |                  |                  |
|         |                     |                  | (Pertaining to   |
|         |                     |                  | effective        |
|         |                     |                  | radius)          |
+---------+---------------------+------------------+------------------+
| 40.     | a\. Horizontal Cell | 1.6 km over      | 1 km             |
| 3.1.2-1 | Size                | ocean            |                  |
|         |                     |                  |                  |
|         |                     | 9.6 km over land |                  |
+---------+---------------------+------------------+------------------+
| 40.     | b\. Horizontal      | HCS              | (TBD)            |
| 3.1.2-2 | Reporting Interval  |                  |                  |
+---------+---------------------+------------------+------------------+
| 40.     | c\. Vertical Cell   | Total column     | 50 km            |
| 3.1.2-3 | Size                |                  |                  |
+---------+---------------------+------------------+------------------+
| 40.     | 1\. \[0 - 2 km\]    | N/A              | 0.25 km          |
| 3.1.2-4 |                     |                  |                  |
+---------+---------------------+------------------+------------------+
| 40.     | 2\. \[2 - 5 km\]    | N/A              | 0.5 km           |
| 3.1.2-5 |                     |                  |                  |
+---------+---------------------+------------------+------------------+
| 40.     | 3\. \[\> 5 km\]     | N/A              | 1 km             |
| 3.1.2-6 |                     |                  |                  |
+---------+---------------------+------------------+------------------+
| 40.     | d\. Vertical        | N/A              | Vertical cell    |
| 3.1.2-7 | Reporting Interval  |                  | size             |
+---------+---------------------+------------------+------------------+
| 40.     | e\. Horizontal      | Global           | Global           |
| 3.1.2-8 | Coverage            |                  |                  |
+---------+---------------------+------------------+------------------+
| 40.     | f\. Vertical        | 0 -- 30 km       | 0 - 50 km        |
| 3.1.2-9 | Coverage            |                  |                  |
+---------+---------------------+------------------+------------------+
|         | g\. Measurement     |                  |                  |
|         | Range               |                  |                  |
+---------+---------------------+------------------+------------------+
| 40.3    | 1\. Operational     | -1 to +3 units   | 0.05 to 5 m      |
| .1.2-10 |                     | of               |                  |
+---------+---------------------+------------------+------------------+
| 40.3    | 2\. Climate         | 0 to 5 m or 10%  | 0 to 10 m or 10% |
| .1.2-17 |                     | for r~e~         | for r~e~         |
|         |                     |                  |                  |
|         |                     | 0 to 3 for ~e~   | [0]{.smallcaps}  |
|         |                     |                  | to 5 for ~e~     |
+---------+---------------------+------------------+------------------+
|         | h\. Measurement     |                  |                  |
|         | Accuracy            |                  |                  |
+---------+---------------------+------------------+------------------+
| 40.3    | 1\. Operational     | Ocean \<0.04    | 10 %             |
| .1.2-11 |                     | \-- 0.3          |                  |
|         |                     |                  |                  |
|         |                     | Ocean \>0.04 -- |                  |
|         |                     | 0.1              |                  |
|         |                     |                  |                  |
|         |                     | Land 0.6         |                  |
+---------+---------------------+------------------+------------------+
| 40.3    | 2\. Climate         | Greater of 0.1(m | Greater of 0.05  |
| .1.2-19 |                     | or 10% for re    | (m or 5% for re  |
|         |                     |                  |                  |
|         |                     | Greater of 0.3   | Greater of 0.2   |
|         |                     | or 50% for (e (  | or 30% for (e    |
+---------+---------------------+------------------+------------------+
|         | i\. Measurement     |                  |                  |
|         | Precision           |                  |                  |
+---------+---------------------+------------------+------------------+
| 40.3    | 1\. Operational     | Ocean \<0.04    | 10%              |
| .1.2-12 |                     | \-- 0.3          |                  |
|         |                     |                  |                  |
|         |                     | Ocean \>0.04 -- |                  |
|         |                     | 0.1              |                  |
|         |                     |                  |                  |
|         |                     | Land 0.6         |                  |
+---------+---------------------+------------------+------------------+
| 40.3    | 2\. Climate         | Greater of       | Greater of 0.05  |
| .1.2-18 |                     | 0.05(m or 10%    | (m or 5% for re  |
|         |                     | for re           |                  |
|         |                     |                  | Greater of 0.1   |
|         |                     | Greater of 0.1   | or 20% for (e    |
|         |                     | or 40% for (e (  |                  |
+---------+---------------------+------------------+------------------+
| 40.3    | j\. Long Term       | Greater of       | Greater of 0.05  |
| .1.2-13 | Stability (C)       | 0.05m or 10%    | (m or 5% for re  |
|         |                     | for re           |                  |
|         |                     |                  | Greater of 0.1   |
|         |                     | Greater of 0.2   | or 20 % for ~e~  |
|         |                     | or 40 % for (e ( |                  |
+---------+---------------------+------------------+------------------+
| 40.3    | k\. Mapping         | 1.5 km           | 1 km             |
| .1.2-14 | Uncertainty         |                  |                  |
+---------+---------------------+------------------+------------------+
|         | l\. Maximum Local   |                  |                  |
|         | Average Revisit     |                  |                  |
|         | Time (S)            |                  |                  |
+---------+---------------------+------------------+------------------+
| 40.3    | 1\. Operational     | 6 hrs            | 4 hrs            |
| .1.2-15 |                     |                  |                  |
+---------+---------------------+------------------+------------------+
| 40.3    | 2\. Climate         | N/A              | N/A              |
| .1.2-20 |                     |                  |                  |
+---------+---------------------+------------------+------------------+
| 40.3    | m\. Deleted.        |                  |                  |
| .1.2-16 |                     |                  |                  |
+---------+---------------------+------------------+------------------+

*9. Cloud Base Height*

[Units]{.underline}: km

  ----------- -------------------------------- --------------- ---------------
  Para. No.                                    Threshold       Objectives

              a\. Horizontal Cell Size                         

  40.4.1-1    1\. Moderate                     10 km           1.0 km

  40.4.1-10   2\. Fine, nadir                  6 km            1.0 km

  40.4.1-2    b\. Horizontal Reporting         HCS             HCS
              Interval                                         

  40.4.1-3    c\. Horizontal Coverage          Global          Global

              d\. Vertical Cell Size           N/A             N/A

  40.4.1-4    e\. Vertical Reporting Interval  Base of highest Base of all
                                               cloud and       distinct cloud
                                               lowest cloud    layers

  40.4.1-5    f\. Measurement Range            0 -- 20 km      0 -- 30 km

  40.4.1-6    g\. Measurement Uncertainty      2 km            0.25 km

  40.4.1-7    h\. Mapping Uncertainty          1.5 km          1 km

  40.4.1-8    i\. Maximum Local Average        6 hrs           4 hrs
              Revisit Time (S)                                 

  40.4.1-9    j\. Deleted.                                     

  40.4.1-11   k\. Long Term Stability (C)      2.0 km          0.1 km

  40.4.1-12   l\. Latency (S)                  90 minutes      15 minutes
  ----------- -------------------------------- --------------- ---------------

*10. Cloud Cover/Layers*

[Units]{.underline}: Dimensionless

+--------+-------------------------------+--------------------+--------+
| Para.  |                               | Threshold          | Obje   |
| No.    |                               |                    | ctives |
+--------+-------------------------------+--------------------+--------+
|        | a\. Horizontal Cell Size      |                    |        |
+--------+-------------------------------+--------------------+--------+
| 40     | 1\. Moderate                  | 25 km              | 1 km   |
| .4.2-1 |                               |                    |        |
+--------+-------------------------------+--------------------+--------+
| 40.    | 2\. Fine, nadir               | 6 km               | 1 km   |
| 4.2-12 |                               |                    |        |
+--------+-------------------------------+--------------------+--------+
| 40     | b\. Horizontal Reporting      | HCS                | (TBD)  |
| .4.2-2 | Interval                      |                    |        |
+--------+-------------------------------+--------------------+--------+
|        | c\. Vertical Cell Size        | N/A                | N/A    |
+--------+-------------------------------+--------------------+--------+
| 40     | d\. Vertical Reporting        | 4 layers           | 0.1 km |
| .4.2-3 | Interval                      |                    |        |
+--------+-------------------------------+--------------------+--------+
| 40     | e\. Horizontal Coverage       | Global             | Global |
| .4.2-4 |                               |                    |        |
+--------+-------------------------------+--------------------+--------+
| 40     | f\. Vertical Coverage         | 0 - 20 km          | 0 - 30 |
| .4.2-5 |                               |                    | km     |
+--------+-------------------------------+--------------------+--------+
| 40     | g\. Measurement Range         | 0 - 1.0 HCS Area   | 0 -    |
| .4.2-6 |                               |                    | 1.0    |
+--------+-------------------------------+--------------------+--------+
| 40     | h\. Measurement Accuracy      | 0.07 HCS area      | 0.05   |
| .4.2-7 |                               | (nadir)            |        |
|        |                               |                    |        |
|        |                               | 0.1 HCS area (EOS) |        |
+--------+-------------------------------+--------------------+--------+
| 40     | i\. Measurement Precision     | 0.07 HCS area      | 0.025  |
| .4.2-8 |                               | (nadir)            |        |
|        |                               |                    |        |
|        |                               | 0.15 HCS area      |        |
|        |                               | (EOS)              |        |
+--------+-------------------------------+--------------------+--------+
| 40     | j\. Mapping Uncertainty       | 1.5 km             | 1 km   |
| .4.2-9 |                               |                    |        |
+--------+-------------------------------+--------------------+--------+
| 40.    | k\. Max Local Average Revisit | 6 hrs              | 4 hrs  |
| 4.2-10 | Time (S)                      |                    |        |
+--------+-------------------------------+--------------------+--------+
| 40.    | l\. Deleted.                  |                    |        |
| 4.2-11 |                               |                    |        |
+--------+-------------------------------+--------------------+--------+
| 40.    | m\. Latency (S)               | 90 minutes         | 15     |
| 4.2-13 |                               |                    | m      |
|        |                               |                    | inutes |
+--------+-------------------------------+--------------------+--------+
| 40.    | n\. Binary Map HCS            | Pixel Size         |        |
| 4.2-14 |                               |                    |        |
+--------+-------------------------------+--------------------+--------+
| 40.    | o\. Binary Map HRI            | HCS                |        |
| 4.2-15 |                               |                    |        |
+--------+-------------------------------+--------------------+--------+
| 40.    | p\. Binary Map Measurement    | Cloudy/not cloudy  |        |
| 4.2-16 | Range                         |                    |        |
+--------+-------------------------------+--------------------+--------+
| 40.    | q\. Binary Map Probability of | Day, Ocean,        |        |
| 4.2-17 | Correct typing                | OD[                |        |
|        |                               | \<]{.underline}0.5 |        |
|        |                               | 92%                |        |
|        |                               |                    |        |
|        |                               | Day, Ocean,        |        |
|        |                               | OD\>0.5 99%        |        |
|        |                               |                    |        |
|        |                               | Day, Land,         |        |
|        |                               | O                  |        |
|        |                               | D[\<]{.underline}1 |        |
|        |                               | 85%                |        |
|        |                               |                    |        |
|        |                               | Day, Land OD\>1    |        |
|        |                               | 93%                |        |
|        |                               |                    |        |
|        |                               | Night, Ocean       |        |
|        |                               | OD[                |        |
|        |                               | \<]{.underline}0.5 |        |
|        |                               | 90%                |        |
|        |                               |                    |        |
|        |                               | Night, Ocean,      |        |
|        |                               | OD\>0.5 96%        |        |
|        |                               |                    |        |
|        |                               | Night, Land,       |        |
|        |                               | O                  |        |
|        |                               | D[\<]{.underline}1 |        |
|        |                               | 85%                |        |
|        |                               |                    |        |
|        |                               | Night, Land, OD\>1 |        |
|        |                               | 90%                |        |
+--------+-------------------------------+--------------------+--------+

*11. Cloud Effective Particle Size*

[Units]{.underline}: m

+-------+----------------------------+----------------+---------------+
| Para. |                            | Threshold      | Objectives    |
| No.   |                            |                |               |
+-------+----------------------------+----------------+---------------+
| 40.   | a\. Horizontal Cell Size   | 25 km          | 10 km         |
| 4.3-1 |                            | (Moderate,     |               |
|       |                            | EOS)           |               |
|       |                            |                |               |
|       |                            | 5 km (Fine,    |               |
|       |                            | nadir)         |               |
+-------+----------------------------+----------------+---------------+
| 40.   | b\. Horizontal Reporting   | HCS            | (TBD)         |
| 4.3-2 | Interval                   |                |               |
+-------+----------------------------+----------------+---------------+
| 40.   | c\. Vertical Cell Size     | Vertical       | Vertical      |
| 4.3-3 |                            | Reporting      | Reporting     |
|       |                            | Interval       | Interval      |
+-------+----------------------------+----------------+---------------+
| 40.   | d\. Vertical Reporting     | Up to 4 layers | 0.3 km        |
| 4.3-4 | Interval                   |                |               |
+-------+----------------------------+----------------+---------------+
| 40.   | e\. Horizontal Coverage    | Global         | Global        |
| 4.3-5 |                            |                |               |
+-------+----------------------------+----------------+---------------+
| 40.   | f\. Vertical Coverage      | 0 - 20 km      | 0 - 30 km     |
| 4.3-6 |                            |                |               |
+-------+----------------------------+----------------+---------------+
| 40.   | g\. Measurement Range      | 0-50 m         | (TBD)         |
| 4.3-7 |                            |                |               |
+-------+----------------------------+----------------+---------------+
| 40.   | h\. Measurement Accuracy   | 5.5m (Day,     | Greater of 5% |
| 4.3-8 |                            | water,         | or 2 m        |
|       |                            | OD[\<]         |               |
|       |                            | {.underline}1) |               |
|       |                            |                |               |
|       |                            | 8m (Day, ice,  |               |
|       |                            | OD[\<]         |               |
|       |                            | {.underline}1) |               |
|       |                            |                |               |
|       |                            | 2m (Day,       |               |
|       |                            | water, OD\>1)  |               |
|       |                            |                |               |
|       |                            | 3.5m (Day,     |               |
|       |                            | ice, OD\>1)    |               |
|       |                            |                |               |
|       |                            | 4m (Night)     |               |
+-------+----------------------------+----------------+---------------+
| 40.   | i\. Measurement Precision  | 1m (Day,       | 2%            |
| 4.3-9 |                            | water)         |               |
|       |                            |                |               |
|       |                            | 1.5m (Day,     |               |
|       |                            | ice,)          |               |
|       |                            |                |               |
|       |                            | 2m (Night)     |               |
+-------+----------------------------+----------------+---------------+
| 40.4  | j\. Long Term Stability    | 2%             | 1%            |
| .3-10 |                            |                |               |
+-------+----------------------------+----------------+---------------+
| 40.4  | k\. Mapping Uncertainty    | 1.5 km         | 1 km          |
| .3-11 |                            |                |               |
+-------+----------------------------+----------------+---------------+
| 40.4  | l\. Maximum Local Average  | 6hrs           | 3 hrs         |
| .3-12 | Revisit Time (S)           |                |               |
+-------+----------------------------+----------------+---------------+
| 40.4  | m\. Deleted.               |                |               |
| .3-13 |                            |                |               |
+-------+----------------------------+----------------+---------------+
| 40.4  | n\. Latency (S)            | 90 minutes     | 15 minutes    |
| .3-14 |                            |                |               |
+-------+----------------------------+----------------+---------------+
| [40   | o\. Fine Measurement       | 5.5m (Day,     |               |
| .4.3- | Uncertainty                | water,         |               |
| 15]{. |                            | OD[\<]         |               |
| small |                            | {.underline}1) |               |
| caps} |                            |                |               |
|       |                            | 12m (Day, ice, |               |
|       |                            | OD[\<]         |               |
|       |                            | {.underline}1) |               |
|       |                            |                |               |
|       |                            | 2.5 m (Day,    |               |
|       |                            | water, OD\>1)  |               |
|       |                            |                |               |
|       |                            | 4m (Day, ice,  |               |
|       |                            | OD\>1)         |               |
|       |                            |                |               |
|       |                            | 4m (Night)     |               |
+-------+----------------------------+----------------+---------------+

*12. Cloud Optical Thickness*

[Units]{.underline}: Dimensionless

+--------+-----------------------------+----------------+--------------+
| Para.  |                             | Threshold      | Objectives   |
| No.    |                             |                |              |
+--------+-----------------------------+----------------+--------------+
|        | a\. Horizontal Cell Size    |                |              |
+--------+-----------------------------+----------------+--------------+
| 40     | 1\. Moderate                | 25 km          | 10 km        |
| .4.6-1 |                             |                |              |
+--------+-----------------------------+----------------+--------------+
| 40.    | 2\. Fine , nadir            | 5 km           | 1 km         |
| 4.6-11 |                             |                |              |
+--------+-----------------------------+----------------+--------------+
| 40     | b\. Horizontal Reporting    | HCS            | (TBD)        |
| .4.6-2 | Interval                    |                |              |
+--------+-----------------------------+----------------+--------------+
| 40     | c\. Horizontal Coverage     | Global         | Global       |
| .4.6-3 |                             |                |              |
+--------+-----------------------------+----------------+--------------+
| 40     | d\. Measurement Range       | 0.1 to 64      | (TBD)        |
| .4.6-4 |                             | (units) Day,   |              |
|        |                             | water          |              |
|        |                             |                |              |
|        |                             | 0.1 to 10      |              |
|        |                             | (units) Day,   |              |
|        |                             | ice            |              |
|        |                             |                |              |
|        |                             | 0.5 to 10      |              |
|        |                             | (units) Night, |              |
|        |                             | ice            |              |
+--------+-----------------------------+----------------+--------------+
| 40     | e\. Measurement Accuracy    | 0.28 (units)   | 5 %          |
| .4.6-5 |                             | Day, water,    |              |
|        |                             | OD[\<          |              |
|        |                             | ]{.underline}1 |              |
|        |                             |                |              |
|        |                             | 0.08 (units)   |              |
|        |                             | Day, ice,      |              |
|        |                             | OD[\<          |              |
|        |                             | ]{.underline}1 |              |
|        |                             |                |              |
|        |                             | 0.16 (units)   |              |
|        |                             | Night, ice,    |              |
|        |                             | OD[\<          |              |
|        |                             | ]{.underline}1 |              |
|        |                             |                |              |
|        |                             | [10            |              |
|        |                             | %]{.smallcaps} |              |
|        |                             | Day, water[,   |              |
|        |                             | OD\>           |              |
|        |                             | 1]{.smallcaps} |              |
|        |                             |                |              |
|        |                             | 5% Day, ice,   |              |
|        |                             | OD\>1          |              |
|        |                             |                |              |
|        |                             | 10% Night,     |              |
|        |                             | Ice, OD\>1     |              |
+--------+-----------------------------+----------------+--------------+
| 40     | f\. Measurement Precision   | 0.1 (units)    | Greater of 2 |
| .4.6-6 |                             | Day, water,    | % or (TBD)   |
|        |                             | OD[\<          |              |
|        |                             | ]{.underline}1 |              |
|        |                             |                |              |
|        |                             | 0.023 (units)  |              |
|        |                             | Day, ice,      |              |
|        |                             | OD[\<          |              |
|        |                             | ]{.underline}1 |              |
|        |                             |                |              |
|        |                             | 0.025 (units)  |              |
|        |                             | Night, ice,    |              |
|        |                             | OD[\<          |              |
|        |                             | ]{.underline}1 |              |
|        |                             |                |              |
|        |                             | 4 % Day,       |              |
|        |                             | water, OD\>1   |              |
|        |                             |                |              |
|        |                             | 3 % Day, ice   |              |
|        |                             | OD\>1          |              |
|        |                             |                |              |
|        |                             | 5 % Night, ice |              |
|        |                             | OD\>1          |              |
+--------+-----------------------------+----------------+--------------+
| 40     | g\. Long Term Stability     | 2 %            | 1 %          |
| .4.6-7 |                             |                |              |
+--------+-----------------------------+----------------+--------------+
| 40     | h\. Mapping Uncertainty     | 1.5 km         | 1 km         |
| .4.6-8 |                             |                |              |
+--------+-----------------------------+----------------+--------------+
| 40     | i\. Max Local Average       | 8 hrs          | 3 hrs        |
| .4.6-9 | Revisit Time (S)            |                |              |
+--------+-----------------------------+----------------+--------------+
| 40.    | j\. Deleted.                |                |              |
| 4.6-10 |                             |                |              |
+--------+-----------------------------+----------------+--------------+
| 40.    | k\. Latency (S)             | 90 minutes     | 15 minutes   |
| 4.6-12 |                             |                |              |
+--------+-----------------------------+----------------+--------------+
| 40.    | l\. Fine Measurement        | 0.3 (units)    |              |
| 4.6-13 | Uncertainty                 | Day, water,    |              |
|        |                             | OD[\<          |              |
|        |                             | ]{.underline}1 |              |
|        |                             |                |              |
|        |                             | 0.1 (units)    |              |
|        |                             | Day, ice,      |              |
|        |                             | OD[\<          |              |
|        |                             | ]{.underline}1 |              |
|        |                             |                |              |
|        |                             | 0.16 (units)   |              |
|        |                             | Night, ice,    |              |
|        |                             | OD[\<          |              |
|        |                             | ]{.underline}1 |              |
|        |                             |                |              |
|        |                             | [10            |              |
|        |                             | %]{.smallcaps} |              |
|        |                             | Day, water[,   |              |
|        |                             | OD\>           |              |
|        |                             | 1]{.smallcaps} |              |
|        |                             |                |              |
|        |                             | 10% Day, ice,  |              |
|        |                             | OD\>1          |              |
|        |                             |                |              |
|        |                             | 10% Night,     |              |
|        |                             | Ice, OD\>1     |              |
+--------+-----------------------------+----------------+--------------+

*13. Cloud Top Height*

[Units]{.underline}: km

+-------+-----------------------------------+-----------+-------------+
| Para. |                                   | Threshold | Objectives  |
| No.   |                                   |           |             |
+-------+-----------------------------------+-----------+-------------+
|       | a\. Horizontal Cell Size          |           |             |
+-------+-----------------------------------+-----------+-------------+
| 40.   | 1\. Moderate                      | 25 km     | 1 km        |
| 4.7-1 |                                   |           |             |
+-------+-----------------------------------+-----------+-------------+
| 40.4  | 2\. Fine, nadir                   | 5 km      | 1 km        |
| .7-13 |                                   |           |             |
+-------+-----------------------------------+-----------+-------------+
| 40.   | b\. Horizontal Reporting Interval | HCS       | (TBD)       |
| 4.7-2 |                                   |           |             |
+-------+-----------------------------------+-----------+-------------+
| 40.   | c\. Horizontal Coverage           | Global    | Global      |
| 4.7-3 |                                   |           |             |
+-------+-----------------------------------+-----------+-------------+
|       | d\. Vertical Cell Size            | N/A       | N/A         |
+-------+-----------------------------------+-----------+-------------+
| 40.   | e\. Vertical Reporting Interval   | Up to 4   | Top of all  |
| 4.7-4 |                                   | layers    | distinct    |
|       |                                   |           | cloud       |
|       |                                   |           | layers      |
+-------+-----------------------------------+-----------+-------------+
| 40.   | f\. Measurement Range             | 0-20 km   | (TBD)       |
| 4.7-5 |                                   |           |             |
+-------+-----------------------------------+-----------+-------------+
|       | g\. Measurement Accuracy          |           |             |
+-------+-----------------------------------+-----------+-------------+
| 40.   | 1\. Cloud layer optical thickness | 0.5 km    | 0.3 km      |
| 4.7-6 | \> 0.1 (TBR)                      | Day,      |             |
|       |                                   | water,    |             |
|       |                                   | OT\>1     |             |
|       |                                   |           |             |
|       |                                   | 1 km      |             |
|       |                                   | Night,    |             |
|       |                                   | water     |             |
|       |                                   | OT\>1     |             |
|       |                                   |           |             |
|       |                                   | 1 km Ice  |             |
|       |                                   | OT\>1     |             |
+-------+-----------------------------------+-----------+-------------+
| 40.   | 2\. Cloud layer optical thickness | 2 km OT   | 0.3 km      |
| 4.7-7 | 0.1 (TBR)                         | [\<]{.u   |             |
|       |                                   | nderline} |             |
|       |                                   | 1         |             |
+-------+-----------------------------------+-----------+-------------+
| 40.   | h\. Measurement Precision         | 0.3 km    | 0.15 km     |
| 4.7-8 |                                   |           |             |
+-------+-----------------------------------+-----------+-------------+
| 40.   | i\. Long Term Stability           | 0.2 km    | 0.1 km      |
| 4.7-9 |                                   |           |             |
+-------+-----------------------------------+-----------+-------------+
| 40.4  | j\. Mapping Uncertainty           | 1.5 km    | 1 km        |
| .7-10 |                                   |           |             |
+-------+-----------------------------------+-----------+-------------+
| 40.4  | k\. Maximum Local Average Revisit | 6 hrs     | 4 hrs       |
| .7-11 | Time (S)                          |           |             |
+-------+-----------------------------------+-----------+-------------+
| 40.4  | l\. Deleted.                      |           |             |
| .7-12 |                                   |           |             |
+-------+-----------------------------------+-----------+-------------+
| 40.4  | m\. Latency (S)                   | 90        | 15 minutes  |
| .7-14 |                                   | minutes   |             |
+-------+-----------------------------------+-----------+-------------+
| 40.4  | n\. Fine, Measurement Uncertainty | 0.5 km    |             |
| .7-15 |                                   | Day,      |             |
|       |                                   | water,    |             |
|       |                                   | OT\>1     |             |
|       |                                   |           |             |
|       |                                   | 1 km      |             |
|       |                                   | Night,    |             |
|       |                                   | water     |             |
|       |                                   | OT\>1     |             |
|       |                                   |           |             |
|       |                                   | [2 km     |             |
|       |                                   | Day,      |             |
|       |                                   | night,    |             |
|       |                                   | water]{.s |             |
|       |                                   | mallcaps} |             |
|       |                                   |           |             |
|       |                                   | 1 km Ice  |             |
+-------+-----------------------------------+-----------+-------------+

*14. Cloud Top Pressure*

[Units]{.underline}: mb

+--------+--------------------------+-------------------+--------------+
| Para.  |                          | Threshold         | Objectives   |
| No.    |                          |                   |              |
+--------+--------------------------+-------------------+--------------+
|        | a\. Horizontal Cell Size |                   |              |
+--------+--------------------------+-------------------+--------------+
| 40     | 1\. Moderate             | 12.5 km           | 1 km         |
| .4.8-1 |                          |                   |              |
+--------+--------------------------+-------------------+--------------+
| 40.    | 2\. Fine, nadir          | 5 km              | 1 km         |
| 4.8-17 |                          |                   |              |
+--------+--------------------------+-------------------+--------------+
| 40     | b\. Horizontal Reporting | HCS               | (TBD)        |
| .4.8-2 | Interval                 |                   |              |
+--------+--------------------------+-------------------+--------------+
| 40     | c\. Horizontal Coverage  | Global            | Global       |
| .4.8-3 |                          |                   |              |
+--------+--------------------------+-------------------+--------------+
| 40     | d\. Measurement Range    | 50 to 1050 mb     | (TBD)        |
| .4.8-4 |                          |                   |              |
+--------+--------------------------+-------------------+--------------+
|        | e\. Measurement Accuracy |                   |              |
+--------+--------------------------+-------------------+--------------+
| 40     | 1\. \[Surface - 3 km\]   | 100 mb            | 30 mb        |
| .4.8-5 |                          | OT[               |              |
|        |                          | \<]{.underline}1, |              |
|        |                          | day/night, water  |              |
|        |                          |                   |              |
|        |                          | [40]{.smallcaps}  |              |
|        |                          | mb OT\> 1 day,    |              |
|        |                          | water             |              |
|        |                          |                   |              |
|        |                          | [70 mb OT\>1      |              |
|        |                          | night,            |              |
|        |                          | w                 |              |
|        |                          | ater]{.smallcaps} |              |
+--------+--------------------------+-------------------+--------------+
| 40     | 2\. \[3 - 7 km\]         | 65 mb OT          | 22 mb        |
| .4.8-6 |                          | [\<]{.underline}  |              |
|        |                          | 1                 |              |
|        |                          |                   |              |
|        |                          | 40 mb OT\> 1      |              |
+--------+--------------------------+-------------------+--------------+
| 40     | 3\. \[\> 7 km\]          | 30 mb             | 15 mb        |
| .4.8-7 |                          |                   |              |
+--------+--------------------------+-------------------+--------------+
|        | f\. Measurement          |                   |              |
|        | Precision                |                   |              |
+--------+--------------------------+-------------------+--------------+
| 40     | 1\. \[Surface - 3 km\]   | 25 mb             | 10 mb        |
| .4.8-8 |                          |                   |              |
+--------+--------------------------+-------------------+--------------+
| 40     | 2\. \[3 - 7 km\]         | 20 mb             | 7 mb         |
| .4.8-9 |                          |                   |              |
+--------+--------------------------+-------------------+--------------+
| 40.    | 3\. \[\> 7 km\]          | 13 mb             | 5 mb         |
| 4.8-10 |                          |                   |              |
+--------+--------------------------+-------------------+--------------+
|        | g\. Long Term Stability  |                   |              |
|        | (TBR)                    |                   |              |
+--------+--------------------------+-------------------+--------------+
| 40.    | 1\. \[Surface - 3 km\]   | 10 mb             | 3 mb         |
| 4.8-11 |                          |                   |              |
+--------+--------------------------+-------------------+--------------+
| 40.    | 2\. \[3 - 7 km\]         | 7 mb              | 2 mb         |
| 4.8-12 |                          |                   |              |
+--------+--------------------------+-------------------+--------------+
| 40.    | 3\. \[\> 7 km\]          | 5 mb              | 1 mb         |
| 4.8-13 |                          |                   |              |
+--------+--------------------------+-------------------+--------------+
| 40.    | h\. Mapping Uncertainty  | [1.5              | 1 km         |
| 4.8-14 |                          | km]{.smallcaps}   |              |
+--------+--------------------------+-------------------+--------------+
| 40.    | i\. Maximum Local        | 8 hrs             | 3 hrs        |
| 4.8-15 | Average Revisit Time (S) |                   |              |
+--------+--------------------------+-------------------+--------------+
| 40.    | j\. Deleted.             |                   |              |
| 4.8-16 |                          |                   |              |
+--------+--------------------------+-------------------+--------------+
| 40.    | k\. Latency (S)          | 90 minutes        | [15          |
| 4.8-18 |                          |                   | minutes]     |
|        |                          |                   | {.smallcaps} |
+--------+--------------------------+-------------------+--------------+
|        | l\. Fine Measurement     |                   |              |
|        | Uncertainty              |                   |              |
+--------+--------------------------+-------------------+--------------+
| 40.    | 1\. \[Surface to 3 km\]  | 130 mb            |              |
| 4.8-19 |                          | OT[               |              |
|        |                          | \<]{.underline}1, |              |
|        |                          | day, water        |              |
|        |                          |                   |              |
|        |                          | 100 mb OT\<1      |              |
|        |                          | night, water      |              |
|        |                          |                   |              |
|        |                          | [40]{.smallcaps}  |              |
|        |                          | mb OT\> 1 day,    |              |
|        |                          | water             |              |
|        |                          |                   |              |
|        |                          | 80 mb OT\>1       |              |
|        |                          | night, water      |              |
+--------+--------------------------+-------------------+--------------+
| 40.    | 2\. \[3 -- 7 km\]        | 70 mb OT          |              |
| 4.8-20 |                          | [\<]{.underline}  |              |
|        |                          | 1                 |              |
|        |                          |                   |              |
|        |                          | 45 mb OT \> 1     |              |
+--------+--------------------------+-------------------+--------------+
| 40.    | [3. \[\> 7               | 30 mb             |              |
| 4.8-21 | km\]]{.smallcaps}        |                   |              |
+--------+--------------------------+-------------------+--------------+

*15. Cloud Top Temperature*

Units: K

+--------+-------------------------------------+------------+----------+
| Para.  |                                     | Threshold  | Ob       |
| No.    |                                     |            | jectives |
+--------+-------------------------------------+------------+----------+
|        | a\. Horizontal Cell Size            |            |          |
+--------+-------------------------------------+------------+----------+
| 40     | 1\. Moderate                        | 25 km      | 1 km     |
| .4.9-1 |                                     |            |          |
+--------+-------------------------------------+------------+----------+
| 40.    | 2\. Fine, nadir                     | 5 km       | 1 km     |
| 4.9-12 |                                     |            |          |
+--------+-------------------------------------+------------+----------+
| 40     | b\. Horizontal Reporting Interval   | HCS        | (TBD)    |
| .4.9-2 |                                     |            |          |
+--------+-------------------------------------+------------+----------+
| 40     | c\. Horizontal Coverage             | Global     | Global   |
| .4.9-3 |                                     |            |          |
+--------+-------------------------------------+------------+----------+
| 40     | d\. Measurement Range               | 175 to 310 | (TBD)    |
| .4.9-4 |                                     | K          |          |
+--------+-------------------------------------+------------+----------+
|        | e\. Measurement Accuracy            |            |          |
+--------+-------------------------------------+------------+----------+
| 40     | 1\. Cloud layer optical thickness   | 2 K OT\>1, | 1.5 K    |
| .4.9-5 | \> 0.1 (TBR)                        | Water      |          |
|        |                                     | cloud, Day |          |
|        |                                     |            |          |
|        |                                     | 3 K OT\>1, |          |
|        |                                     | Water      |          |
|        |                                     | cloud,     |          |
|        |                                     | Night      |          |
|        |                                     |            |          |
|        |                                     | 3 K OT\>1, |          |
|        |                                     | Ice Cloud  |          |
+--------+-------------------------------------+------------+----------+
| 40     | 2\. Cloud layer optical thickness   | 6 K OT \<  | (TBD)    |
| .4.9-6 | 0.1 (TBR)                           | 1          |          |
+--------+-------------------------------------+------------+----------+
| 40     | f\. Measurement Precision           | 1.5 K      | 0.5 K    |
| .4.9-7 |                                     |            |          |
+--------+-------------------------------------+------------+----------+
| 40     | g\. Long Term Stability             | 1 K        | 0.1 K    |
| .4.9-8 |                                     |            |          |
+--------+-------------------------------------+------------+----------+
| 40     | h\. Mapping Uncertainty             | 1.5 km     | 1 km     |
| .4.9-9 |                                     |            |          |
+--------+-------------------------------------+------------+----------+
| 40.    | i\. Maximum Local Average Revisit   | 6 hrs      | 6 hrs    |
| 4.9-10 | Time (S)                            |            |          |
+--------+-------------------------------------+------------+----------+
| 40.    | j\. Deleted.                        |            |          |
| 4.9-11 |                                     |            |          |
+--------+-------------------------------------+------------+----------+
| 40.    | k\. Latency (S)                     | 90 minutes | 15       |
| 4.9-13 |                                     |            | minutes  |
+--------+-------------------------------------+------------+----------+
| 40.    | l\. Fine Measurement Uncertainty    | 3 K Water  |          |
| 4.9-14 |                                     |            |          |
|        |                                     | 5 K Ice    |          |
+--------+-------------------------------------+------------+----------+

***16. Surface Albedo***

[Units]{.underline}: Dimensionless

+--------+------------------------------+--------------+--------------+
| Para.  |                              | Threshold    | Objectives   |
| No.    |                              |              |              |
+--------+------------------------------+--------------+--------------+
| 40     | a\. Horizontal Cell Size     | 1.6 km (Mod, | 0.5 km       |
| .5.2-1 |                              | EOS)         |              |
|        |                              |              |              |
|        |                              | 0.75 km      |              |
|        |                              | (Fine,       |              |
|        |                              | nadir)       |              |
+--------+------------------------------+--------------+--------------+
| 40     | b\. Horizontal Reporting     | HCS          | (TBD)        |
| .5.2-2 | Interval                     |              |              |
+--------+------------------------------+--------------+--------------+
| 40     | c\. Horizontal Coverage      | Global       | Global       |
| .5.2-3 |                              |              |              |
+--------+------------------------------+--------------+--------------+
| 40     | d\. Measurement Range        | 0 - 1.0      | 0 - 1.0      |
| .5.2-4 |                              | units of     |              |
|        |                              | albedo       |              |
+--------+------------------------------+--------------+--------------+
| 40     | e\. Measurement Accuracy     | 0.025 units  | 0.0125       |
| .5.2-5 |                              | of albedo    |              |
+--------+------------------------------+--------------+--------------+
| 40     | f\. Measurement Precision    | 0.02 units   | 0.01         |
| .5.2-6 |                              | of albedo    |              |
+--------+------------------------------+--------------+--------------+
| 40     | g\. Long Term Stability      | 0.01 units   | 0.01         |
| .5.2-7 |                              | of albedo    |              |
+--------+------------------------------+--------------+--------------+
| 40     | h\. Mapping Uncertainty      | 1.5 km       | 1.0 km       |
| .5.2-8 |                              |              |              |
+--------+------------------------------+--------------+--------------+
| 40     | i\. Max Local Average        | 24 hrs       | 4 hrs        |
| .5.2-9 | Revisit Time (S)             |              |              |
+--------+------------------------------+--------------+--------------+
| 40.    | j\. Deleted.                 |              |              |
| 5.2-10 |                              |              |              |
+--------+------------------------------+--------------+--------------+
| 40.    | k\. Latency (S)              | 150 minutes  | 60 minutes   |
| 5.2-11 |                              |              |              |
+--------+------------------------------+--------------+--------------+
| 40.    | l\. Fine Measurement         | 0.03 units   |              |
| 5.2-12 | Uncertainty                  | of albedo    |              |
+--------+------------------------------+--------------+--------------+

*17. Fire Area and Temperature*

[Units]{.underline}: Degrees latitude and longitude for geolocation, K
for sub-pixel average temperature, m^2^ for active fire area.

  ------------- -------------------------- ------------------ ---------------
  Para. No.                                Thresholds         Objectives

                a\. Horizontal Cell Size                      

  40.6.4.1-1    1\. At nadir               0.75 km            0.5 km

  40.6.4.1-2    2\. Worst case             1.6 km             0.5 km

  40.6.4.1-3    b\. Horizontal Reporting   HCS                (TBD)
                Interval                                      

  40.6.4.1-4    c\. Horizontal Coverage    Land               Land

                d\. Measurement Range:                        

  40.6.4.1-5    1\. Sub-pixel average      [800 K -- 1200     800 K -- 1200 K
                temperature of active fire K]{.smallcaps}     

  40.6.4.1-6    2\. Sub-pixel area of      from 1000 m^2^ to  from (50 m)^2^
                active fire                50 m times ground  to 100 m by
                                           sample distance in greater of
                                           scan direction     pixel in-scan
                                           (TBR)              and in-track
                                                              dimensions
                                                              (TBR).

                [e. Measurement                               
                Uncertainty]{.smallcaps}                      

  40.6.4.1-7    1\. Sub-pixel average      50 K               25 K
                temperature of active fire                    

  40.6.4.1-8    2\. Sub-pixel area of      30%                15%
                active fire                                   

  40.6.4.1-9    f\. Mapping Uncertainty    0.4km              0.1 km

  40.6.4.1-11   g\. Maximum Local Average  6 hrs              1 hour
                Revisit Time (S)                              

  40.6.4.1-12   h\. Deleted.                                  

  40.6.4.1-10   i\. Deleted                                   

  40.6.4.1-13   j\. Latency (S)            90 minutes         15 minutes
  ------------- -------------------------- ------------------ ---------------

*18. Land Surface Temperature*

[Units]{.underline}: K

+--------+-----------------------------+----------------+--------------+
| Para.  |                             | Threshold      | Objectives   |
| No.    |                             |                |              |
+--------+-----------------------------+----------------+--------------+
| 40     | a\. Horizontal Cell Size    | 0.75 km        | 1 km         |
| .6.1-1 |                             | (nadir)        |              |
|        |                             |                |              |
|        |                             | 1.3 km (EOS)   |              |
+--------+-----------------------------+----------------+--------------+
| 40     | b\. Horizontal Reporting    | HCS            | (TBD)        |
| .6.1-2 | Interval                    |                |              |
+--------+-----------------------------+----------------+--------------+
| 40     | c\. Horizontal Coverage     | Land           | Land         |
| .6.1-3 |                             |                |              |
+--------+-----------------------------+----------------+--------------+
| 40     | d\. Measurement Range       | 213 K - 343 K  | 183 K - 343  |
| .6.1-4 |                             |                | K            |
+--------+-----------------------------+----------------+--------------+
| 40     | e\. Measurement Accuracy    | 2.4 K          | 1 K          |
| .6.1-5 |                             |                |              |
+--------+-----------------------------+----------------+--------------+
| 40     | f\. Measurement Precision   | 0.5 K          | 0.025 K      |
| .6.1-6 |                             |                |              |
+--------+-----------------------------+----------------+--------------+
| 40     | g\. Mapping Uncertainty     | 1.5 km         | 1 km         |
| .6.1-7 |                             |                |              |
+--------+-----------------------------+----------------+--------------+
| 40     | h\. Max Local Average       | 6 hrs          | 3 hrs        |
| .6.1-8 | Revisit Time (S)            |                |              |
+--------+-----------------------------+----------------+--------------+
| 40     | i\. Deleted.                |                |              |
| .6.1-9 |                             |                |              |
+--------+-----------------------------+----------------+--------------+
| 40.    | j\. Latency (S)             | 90 minutes     | 15 minutes   |
| 6.1-10 |                             |                |              |
+--------+-----------------------------+----------------+--------------+
| 40.    | k\. Measurement             | 2.50 K         |              |
| 6.1-11 | Uncertainty, Nadir          |                |              |
+--------+-----------------------------+----------------+--------------+

*19. Soil Moisture*

[Units]{.underline}: cm/m (cm of water per meter of soil depth)

+------+--------------------------+--------------+-------------------+
| P    |                          | Threshold    | Objectives        |
| ara. |                          |              |                   |
| No.  |                          |              |                   |
+------+--------------------------+--------------+-------------------+
|      | a\. Horizontal Cell Size |              |                   |
+------+--------------------------+--------------+-------------------+
| 40.2 | 1\. Clear daytime, at    | 0.75 km      | (TBD)             |
| .6-1 | nadir                    |              |                   |
+------+--------------------------+--------------+-------------------+
| 40.2 | 2\. Clear daytime, worst | 1.6 km       | 1.6 km            |
| .6-2 | case                     |              |                   |
+------+--------------------------+--------------+-------------------+
| 40.2 | 3\. All weather, at      | 40 km        | 2 km              |
| .6-3 | nadir                    |              |                   |
+------+--------------------------+--------------+-------------------+
| 40.2 | 4\. All weather, worst   | 50 km        | (TBD)             |
| .6-4 | case                     |              |                   |
+------+--------------------------+--------------+-------------------+
| 40.2 | b\. Horizontal Reporting | HCS          | (TBD)             |
| .6-5 | Interval                 |              |                   |
+------+--------------------------+--------------+-------------------+
| 40.2 | c\. Vertical Cell Size   | 0.1 cm       | 5 cm              |
| .6-6 |                          |              |                   |
+------+--------------------------+--------------+-------------------+
| 40.2 | d\. Vertical Reporting   |              |                   |
| .6-7 | Interval                 |              |                   |
+------+--------------------------+--------------+-------------------+
| 40.2 | e\. Horizontal Coverage  | Land         | Land              |
| .6-8 |                          |              |                   |
+------+--------------------------+--------------+-------------------+
| 40.2 | **f. \*Vertical          | **Surface to | Surface to -80 cm |
| .6-9 | Coverage**               | --0.1 cm**   |                   |
|      |                          |              |                   |
|      |                          | (skin layer) |                   |
+------+--------------------------+--------------+-------------------+
| 4    | g\. Measurement Range    | 0 - 100 cm/m | 0 - 100 cm/m      |
| 0.2. |                          |              |                   |
| 6-10 |                          |              |                   |
+------+--------------------------+--------------+-------------------+
|      | h\. Measurement          |              |                   |
|      | Uncertainty              |              |                   |
+------+--------------------------+--------------+-------------------+
| 4    | 1\. Clear, Bare soil in  | Surface: 5   | Surface: 1% 80 cm |
| 0.2. | regions with known soil  | cm/m up to   | column: 5 %       |
| 6-11 | types (smaller           | field        |                   |
|      | horizontal cell size)    | capacity, 10 |                   |
|      |                          | cm/m beyond  |                   |
|      |                          | capacity     |                   |
+------+--------------------------+--------------+-------------------+
| 4    | 2\. Cloudy , Bare soil   | 20 cm/m      | Surface: 1 cm/m   |
| 0.2. | in regions with known    |              |                   |
| 6-12 | soil types (greater      |              | Total 80 cm       |
|      | horizontal cell size)    |              | column: 5 %       |
+------+--------------------------+--------------+-------------------+
| 4    | i\. Mapping Uncertainty  | 1.5 km       | 1 km              |
| 0.2. |                          |              |                   |
| 6-13 |                          |              |                   |
+------+--------------------------+--------------+-------------------+
| 4    | j\. Maximum Local        | 8 hrs        | 3 hrs             |
| 0.2. | Average Revisit Time     |              |                   |
| 6-14 |                          |              |                   |
+------+--------------------------+--------------+-------------------+
| 4    | k\. Deleted              |              |                   |
| 0.2. |                          |              |                   |
| 6-15 |                          |              |                   |
+------+--------------------------+--------------+-------------------+
| 4    | l\. Latency (S)          | 90 minutes   | 30 minutes        |
| 0.2. |                          |              |                   |
| 6-16 |                          |              |                   |
+------+--------------------------+--------------+-------------------+

*20. Surface Type*

[Units]{.underline}:

Type: N/A

Vegetation Cover: per cent

  ----------- ------------------------------ --------------- -----------------
  Para. No.                                  Threshold       Objectives

  40.6.4-1    a\. Horizontal Cell Size       1 km            0.25 km

                                                             

  40.6.4-2    Deleted                                        

  40.6.4-3    b\. Horizontal Reporting       HCS             (TBD)
              Interval                                       

  40.6.4-4    c\. Horizontal Coverage        Land            Land

                                                             

  40.6.4-5    Deleted                                        

              d\. Measurement Range                          

  40.6.4-6    1\. Vegetation/surface type    17 Types        17 Types
                                             (Specified      (Specified above)
                                             above)          

  40.6.4-7    2\. Vegetation cover           0 - 100 %       0 - 100 %

  40.6.4-8    e\. Measurement Accuracy (veg. 2 0%            2 %
              cover)                                         

  40.6.4-9    f\. Measurement Precision      10 %            0.1 %
              (veg. cover)                                   

  40.6.4-10   g\. Correct Typing Probability 88 %            98 %
              (vegetation /surface type)                     

  40.6.4-11   h\. Mapping Uncertainty        1.5 km          1 km

  40.6.4-12   i\. Max Local Average Revisit  24 hrs          3 hrs
              Time (S)                                       

  40.6.4-13   j\. Deleted.                                   

  40.6.4-14   k\. Latency (S)                90 minutes      15 minutes
  ----------- ------------------------------ --------------- -----------------

*21. Vegetation Index*

[Units]{.underline}: Dimensionless

+-------+----------------------------+---------------+---------------+
| Para. |                            | Threshold     | Objectives    |
| No.   |                            |               |               |
+-------+----------------------------+---------------+---------------+
| 40.   | a\. Horizontal Cell Size   | 0.8 km (Mod,  | 1 km          |
| 6.2-1 |                            | EOS)          |               |
|       |                            |               |               |
|       |                            | 0.375 km      |               |
|       |                            | (Fine, nadir) |               |
+-------+----------------------------+---------------+---------------+
| 40.   | b\. Horizontal Reporting   | HCS           | (TBD)         |
| 6.2-2 | Interval                   |               |               |
+-------+----------------------------+---------------+---------------+
| 40.   | c\. Horizontal Coverage    | Land          | (TBD)         |
| 6.2-3 |                            |               |               |
+-------+----------------------------+---------------+---------------+
| 40.   | d\. Measurement Range      | -1 to +1 NDVI | -1 to +1 NDVI |
| 6.2-4 |                            | units         | units         |
|       |                            |               |               |
|       |                            | -1 to +1 EVI  |               |
|       |                            | units         |               |
+-------+----------------------------+---------------+---------------+
| 40.   | e\. Measurement Accuracy   | 0.016 NDVI    | 0.03 NDVI     |
| 6.2-5 |                            | units (Mod)   | units         |
+-------+----------------------------+---------------+---------------+
| 40.   | f\. Measurement Precision  | 0.02 NDVI     | 0.02 NDVI     |
| 6.2-6 |                            | units (Mod)   | units         |
+-------+----------------------------+---------------+---------------+
| 40.   | g\. Long Term Stability    | 0.01 NDVI     | 0.04 NDVI     |
| 6.2-7 |                            | units         | units         |
+-------+----------------------------+---------------+---------------+
| 40.   | h\. Mapping Uncertainty    | 1.5 km EOS;   | 1 km          |
| 6.2-8 |                            | 0.4 km        |               |
|       |                            | (nadir)       |               |
+-------+----------------------------+---------------+---------------+
| 40.   | i\. Max Local Average      | 24 hrs        | 24 hrs        |
| 6.2-9 | Revisit Time (S)           |               |               |
+-------+----------------------------+---------------+---------------+
| 40.6  | j\. Deleted.               |               |               |
| .2-10 |                            |               |               |
+-------+----------------------------+---------------+---------------+
| 40.6  | k\. Measurement            | 0.11 units of |               |
| .2-11 | Uncertainty for EVI        | EVI           |               |
+-------+----------------------------+---------------+---------------+
| 40.6  | l\. Long Term Stability    | 0.04 NDVI     | 0.04 NDVI     |
| .2-12 | (C)                        | units         | units         |
+-------+----------------------------+---------------+---------------+
| 40.6  | m\. Latency (S)            | 90 minutes    | 15 minutes    |
| .2-13 |                            |               |               |
+-------+----------------------------+---------------+---------------+
| 40.6  | n\. Fine Measurement       | 0.020 NDVI    |               |
| .2-14 | Uncertainty, NDVI          | units         |               |
+-------+----------------------------+---------------+---------------+

***22. Sea Surface Temperature***

[Units]{.underline}: K

  ----------- ------------------------ ------------------ ------------------
  Para. No.                            Threshold          Objectives

              **a. \*Horizontal Cell                      
              Size**                                      

  40.2.4.1    Deleted                                     

  40.2.4.2    Deleted                                     

  40.2.4-3    **1. \*Nadir**           **0.8 km**         0.25 km

  40.2.4-4    2\. Worst case, clear    1.3 km             (TBD)

  40.2.4 --18 3\. All Weather          [40                20 km
                                       km]{.smallcaps}    

  40.2.4-24                                               

  40.2.4-5    b\. Horizontal Reporting HCS                (TBD)
              Interval                                    

  40.2.4-23   c\. Horizontal Coverage  Oceans             Oceans

  40.2.4.6    Deleted                                     

  40.2.4.7    Deleted                                     

  40.2.4-8    d\. Measurement Range    271 K -- 313 K     271 K -- 313 K

              e\. Measurement                             
              Uncertainty (skin)                          

  40.2.4 -- 9 **1. \* Clear**          **0.5 K**          0.1 K

  40.2.4 --   2\. All Weather          0.5 K              0.5 K
  20                                                      

  40.2.4-25   3\. Deleted                                 

  40.2.4-10   f\. Measurement          0.5 K              0.1 K
              Uncertainty (bulk)                          

              g\. Measurement                             
              Precision (skin)                            

  40.2.4 --11 1\. Clear                0.27 K             0.1 K

  40.2.4-19   2\. All Weather          0.5 K              0.1 K

  40.2.4-26   3\. Deleted                                 

              h\. Mapping Uncertainty                     

  40.2.4-12   1\. Nadir                0.4 km             0.1 km

  40.2.4-13   2\. Worst case, clear    0.8 km             (TBD)

  40.2.4-14   3\. All Weather          3 km               3 km

  40.2.4-27   4\. Deleted                                 

  40.2.4-15   Deleted                                     

  40.2.4-16   i\. Maximum Local        6 hrs              3 hrs
              Average Revisit Time                        

  40.2.4-17   Jj. Measurement          0.2 K              0.1 K
              Precision (bulk, clear).                    

  40.2.4-21   k\. Long Term Stability  0.1 K              0.05 K
              (C)                                         

  40.2.4-22   l\. Latency (S)          90 minutes         15 minutes

                                                          
  ----------- ------------------------ ------------------ ------------------

*23. Ocean Color and Chlorophyll*

[Units]{.underline}:

Ocean Color : W m^-2^ m^-1^ sr^-1^, Ocean Optical Properties: m^-1^,
Chlorophyll: mg m^-3^

+--------+-------------------------+------------------+----------------+
| Para.  |                         | Threshold        | Objectives     |
| No.    |                         |                  |                |
+--------+-------------------------+------------------+----------------+
|        | a\. Horizontal Cell     |                  |                |
|        | Size                    |                  |                |
+--------+-------------------------+------------------+----------------+
| 40     | 1\. Worst case          | 1.6 km           | 0.1 km         |
| .7.6-1 |                         |                  |                |
+--------+-------------------------+------------------+----------------+
| 40     | 2\. Nadir               | 0.75 km          | 0.1 km         |
| .7.6-2 |                         |                  |                |
+--------+-------------------------+------------------+----------------+
| 40     | b\. Horizontal          | HCS              | HCS            |
| .7.6-3 | Reporting Interval      |                  |                |
+--------+-------------------------+------------------+----------------+
| 40.7.  | c\. Horizontal Coverage | Oceans           | Oceans         |
| 6.7-29 |                         |                  |                |
+--------+-------------------------+------------------+----------------+
| 40     | Deleted                 |                  |                |
| .7.6-4 |                         |                  |                |
+--------+-------------------------+------------------+----------------+
| 40     | Deleted                 |                  |                |
| .7.6-5 |                         |                  |                |
+--------+-------------------------+------------------+----------------+
|        | d\. Measurement Range   |                  |                |
+--------+-------------------------+------------------+----------------+
| 40.    | 1\. Ocean Color         | 1.0 - 10 W m^-2^ | 0\. 05-10 W    |
| 7.6-13 |                         | m^-1^ sr^-1^     | m^-2^ m^-1^    |
|        |                         |                  | sr^-1^         |
+--------+-------------------------+------------------+----------------+
|        | 2\. Optical Properties  |                  |                |
+--------+-------------------------+------------------+----------------+
| 40.    | a\. Absorption          | 0.01 -- 10 m^-1^ | 0.005 -- 20    |
| 7.6-14 |                         |                  | m^-1^          |
+--------+-------------------------+------------------+----------------+
| 40.    | b\. Scattering          | 0.01 -- 50 m^-1^ | 0.005 -- 75    |
| 7.6-15 |                         |                  | m^-1^          |
+--------+-------------------------+------------------+----------------+
| 40.    | c\. Chlorophyll         | N/A              | Detectable     |
| 7.6-16 | Fluorescence            |                  | signals in     |
|        |                         |                  | waters with    |
|        |                         |                  | chlorophyll    |
|        |                         |                  | from 0.1 to 50 |
|        |                         |                  | mg m^-3^ at 1  |
|        |                         |                  | km resolution. |
+--------+-------------------------+------------------+----------------+
| 40     | 3\. Chlorophyll         | 0.05 -- 50       | 0.001 - 100    |
| .7.6-6 |                         | mg/m^3^          | mg/m^3^        |
+--------+-------------------------+------------------+----------------+
|        | e\. Measurement         |                  |                |
|        | Accuracy                |                  |                |
+--------+-------------------------+------------------+----------------+
|        | 1\. Ocean Color         |                  |                |
+--------+-------------------------+------------------+----------------+
| 40.    | a\. Operational         | 10 %             | 5 %            |
| 7.6-17 |                         |                  |                |
+--------+-------------------------+------------------+----------------+
| 40.    | b\. Deleted.            |                  |                |
| 7.6-18 |                         |                  |                |
+--------+-------------------------+------------------+----------------+
|        | 2\. Optical Properties  |                  |                |
+--------+-------------------------+------------------+----------------+
| 40.    | a\. Operational         | 40 %             | 30 %           |
| 7.6-19 |                         |                  |                |
+--------+-------------------------+------------------+----------------+
| 40.    | b\. Deleted.            |                  |                |
| 7.6-20 |                         |                  |                |
+--------+-------------------------+------------------+----------------+
|        | 3\. Chlorophyll         |                  |                |
+--------+-------------------------+------------------+----------------+
| 40     | a\. Operational         | 15% Chl \<1.0    | 20 %           |
| .7.6-7 |                         | mg/m^3^          |                |
|        |                         |                  |                |
|        |                         | 30% 1.0 \<Chl    |                |
|        |                         | \<10 mg/m^3^     |                |
|        |                         |                  |                |
|        |                         | 50% Chl \>10     |                |
|        |                         | mg/m^3^          |                |
+--------+-------------------------+------------------+----------------+
| 40.    | b\. Deleted.            |                  |                |
| 7.6-21 |                         |                  |                |
+--------+-------------------------+------------------+----------------+
|        | f\. Measurement         |                  |                |
|        | Precision               |                  |                |
+--------+-------------------------+------------------+----------------+
|        | 1\. Ocean Color         |                  |                |
+--------+-------------------------+------------------+----------------+
| 40.    | a\. Operational         | 5 %              | 2 %            |
| 7.6-22 |                         |                  |                |
+--------+-------------------------+------------------+----------------+
| 40.    | b\. Deleted.            |                  |                |
| 7.6-23 |                         |                  |                |
+--------+-------------------------+------------------+----------------+
|        | 2\. Optical Properties  |                  |                |
+--------+-------------------------+------------------+----------------+
| 40.    | a\. Operational         | 20 %             | 20 %           |
| 7.6-24 |                         |                  |                |
+--------+-------------------------+------------------+----------------+
| 40.    | b\. Deleted.            |                  |                |
| 7.6-25 |                         |                  |                |
+--------+-------------------------+------------------+----------------+
|        | 3\. Chlorophyll         |                  |                |
+--------+-------------------------+------------------+----------------+
| 40     | a\. Operational         | 20% Chl \<1.0    | 10 %           |
| .7.6-8 |                         | mg/m^3^          |                |
|        |                         |                  |                |
|        |                         | 30% 1.0 \<Chl    |                |
|        |                         | \<10 mg/m^3^     |                |
|        |                         |                  |                |
|        |                         | 50% Chl \>10     |                |
|        |                         | mg/m^3^          |                |
+--------+-------------------------+------------------+----------------+
|        | g\. Mapping Uncertainty |                  |                |
+--------+-------------------------+------------------+----------------+
| 40     | 1\. Worst Case          | 0.8 km           | 0.1 km         |
| .7.6-9 |                         | (intermediate    |                |
|        |                         | swath)           |                |
+--------+-------------------------+------------------+----------------+
| 40.    | 2\. Nadir               | 0.4 km           | 0.1 km         |
| 7.6-10 |                         |                  |                |
+--------+-------------------------+------------------+----------------+
| 40.    | h\. Max Local Average   | 24 hrs           | 12 hrs         |
| 7.6-11 | Revisit Time (S)        |                  |                |
+--------+-------------------------+------------------+----------------+
| 40.    | i\. Deleted.            |                  |                |
| 7.6-12 |                         |                  |                |
+--------+-------------------------+------------------+----------------+
| 40.    | j.  Long Term Stability | Max Chl          | Max Chl        |
| 7.6-26 |     (W m^-2^ m^-1^      | Absorption 0.5   | Absorption     |
|        |     sr^-1^) (C)         |                  | 0.25           |
|        |                         | Min Chl          |                |
|        | SEE NOTE 1              | Absorption 0.25  | Min Chl        |
|        |                         |                  | Absorption     |
|        |                         | Atmospheric      | 0.125          |
|        |                         | Correction 0.08  |                |
|        |                         |                  | Atmospheric    |
|        |                         |                  | correction     |
|        |                         |                  | 0.04           |
+--------+-------------------------+------------------+----------------+
|        | k\. Latency (S)         |                  |                |
+--------+-------------------------+------------------+----------------+
| 40.    | 1\. Operational         | 180 minutes      | 60 minutes     |
| 7.6-27 |                         |                  |                |
+--------+-------------------------+------------------+----------------+
| 40.    | 2\. Deleted.            |                  |                |
| 7.6-28 |                         |                  |                |
+--------+-------------------------+------------------+----------------+

[Note 1: Stability is for water leaving radiance at the band of Maximum
Chlorophyll absorption (measured at approximately 445 nm), Min
Chlorophyll Absorption (at approximately 555 nm), and Atmospheric
Correction (at approximately 865 nm).]{.smallcaps}

*24. Net Heat Flux*

[Units]{.underline}: W/m^2^

  ----------- ---------------------------------- -------------- ------------
  Para. No.                                      Threshold      Objectives

  40.7.5-1    a\. Horizontal Cell Size           20 km          5 km

  40.7.5-2    b\. Horizontal Reporting Interval  HCS            (TBD)

  40.7.5-3    c\. Horizontal Coverage            Oceans         Global
                                                                Oceans

  40.7.5-4    d\. Measurement Range              0 - 2000       0 - 2000
                                                 W/m^2^         W/m^2^

  40.7.5-5    e\. Measurement Accuracy           10 W/m^2^      1 W/m^2^

  40.7.5-6    f\. Measurement Precision          25 W/m^2^      1 W/m^2^

  40.7.5-7    g\. Mapping Uncertainty            1.5 km         (TBD)

  40.7.5-8    h\. Maximum Local Average Revisit  6 hrs          3 hrs
              Time (S)                                          

  40.7.5-9    i\. Deleted.                                      

  40.7.5-10   j\. Latency (S)                    24 hours       6 hours
  ----------- ---------------------------------- -------------- ------------

*25. Sea Ice Characterization*

Ice age: WMO Nomenclature Class

Ice edge Concentration: Tenths

+-------+------------------------------+------------+-----------------+
| Para. |                              | Threshold  | Objectives      |
| No.   |                              |            |                 |
+-------+------------------------------+------------+-----------------+
| 40.   | a\. Horizontal Cell Size     |            |                 |
| 7.8-1 | (Ice Age)                    |            |                 |
+-------+------------------------------+------------+-----------------+
|       | Clear                        | 2.4 km     | 0.1 km          |
+-------+------------------------------+------------+-----------------+
|       | All Weather                  | 20 km      | 0.05 km         |
+-------+------------------------------+------------+-----------------+
| 40.   | b\. Horizontal Reporting     | HCS        | HCS             |
| 7.8-2 | Interval                     |            |                 |
+-------+------------------------------+------------+-----------------+
| 40.   | c\. Horizontal Coverage      | Oceans     | All ice covered |
| 7.8-3 |                              |            | regions of the  |
|       |                              |            | global ocean    |
+-------+------------------------------+------------+-----------------+
|       | d\. Measurement Range        |            |                 |
+-------+------------------------------+------------+-----------------+
| 40.   | 1\. Ice Age Classes          | New/Young, | Ice free,       |
| 7.8-4 |                              | First      | Nilas,          |
|       |                              | Year,      | GreyWhite,      |
|       |                              | Multi-year | Grey, White,    |
|       |                              |            | First Year      |
|       |                              |            | Medium, First   |
|       |                              |            | Year thick,     |
|       |                              |            | Second Year,    |
|       |                              |            | and Multiyear;  |
|       |                              |            | Smooth and      |
|       |                              |            | Deformed Ice    |
+-------+------------------------------+------------+-----------------+
| 40.   | 2\. Ice Concentration        | 1/10 to    | 0/10 to 10/10   |
| 7.8-5 |                              | 10/10      |                 |
+-------+------------------------------+------------+-----------------+
| 40.   | e\. Probability of Correct   | 80% (First | [90             |
| 7.8-6 | Typing (Ice Age)             | year from  | %]{.smallcaps}  |
|       |                              | M          |                 |
|       |                              | ulti-year) |                 |
|       |                              |            |                 |
|       |                              | [70%       |                 |
|       |                              | (New/Young |                 |
|       |                              | from First |                 |
|       |                              | year)]{.   |                 |
|       |                              | smallcaps} |                 |
|       |                              |            |                 |
|       |                              | 70%        |                 |
|       |                              | (New/Young |                 |
|       |                              | from       |                 |
|       |                              | M          |                 |
|       |                              | ulti-year) |                 |
+-------+------------------------------+------------+-----------------+
| 40.   | f\. Measurement Uncertainty  | 1/10       | 5 %             |
| 7.8-7 | (Ice Concentration)          |            |                 |
+-------+------------------------------+------------+-----------------+
| 40.   | g\. Mapping Uncertainty      | 1.5 km     | 0.05 km         |
| 7.8-8 |                              |            |                 |
+-------+------------------------------+------------+-----------------+
| 40.   | h\. Max Local Average        | 24 hrs     | 6 hrs           |
| 7.8-9 | Revisit Time (S)             |            |                 |
+-------+------------------------------+------------+-----------------+
| 40.7  | i\. Deleted.                 |            |                 |
| .8-10 |                              |            |                 |
+-------+------------------------------+------------+-----------------+
| 40.7  | j\. Long Term Stability (C)  | 1 %        |                 |
| .8-11 |                              | con        |                 |
|       |                              | centration |                 |
+-------+------------------------------+------------+-----------------+
| 40.7  | k\. Latency (S)              | 8 hrs      | 15 minutes      |
| .8-12 |                              |            |                 |
+-------+------------------------------+------------+-----------------+

Fresh Water Ice (Application of Sea Ice Chatracterization)

+---------+----------------------------+------------+-----------------+
| Para    |                            | Threshold  | Objectives      |
| No.     |                            |            |                 |
+---------+----------------------------+------------+-----------------+
|         | a\. Horizontal Cell Size   |            |                 |
+---------+----------------------------+------------+-----------------+
| 40.     | Nadir                      | 0.8 km     | (TBD)           |
| 7.8.1-1 |                            |            |                 |
+---------+----------------------------+------------+-----------------+
| 40.     | Worst Case                 | 3.2 km     | 4 times 0.65 km |
| 7.8.1-2 |                            |            | (TBR)           |
+---------+----------------------------+------------+-----------------+
| 40.     | b\. Horizontal Reporting   | HCS        | HCS             |
| 7.8.1-3 | Interval                   |            |                 |
+---------+----------------------------+------------+-----------------+
| 40.     | c\. Horizontal Coverage    | Fresh      | Fresh water     |
| 7.8.1-4 |                            | water      |                 |
+---------+----------------------------+------------+-----------------+
| 40.     | d\. Measurement Range      | 1/10 to    | 0/10 to 10/10   |
| 7.8.1-5 |                            | 10/10      | concentration   |
|         |                            | con        |                 |
|         |                            | centration |                 |
+---------+----------------------------+------------+-----------------+
|         | f\. Measurement            |            |                 |
|         | Uncertainty                |            |                 |
+---------+----------------------------+------------+-----------------+
| 40.     | 1\. Ice edge Boundary      | 0.4 km     | 5 km            |
| 7.8.1-6 |                            | Nadir      |                 |
|         |                            |            |                 |
|         |                            | 1.0 km EOS |                 |
+---------+----------------------------+------------+-----------------+
| 40.     | 2\. Ice concentration      | 0.10       | 10%             |
| 7.8.1-7 |                            |            |                 |
+---------+----------------------------+------------+-----------------+
| 40.     | g\. Mapping Uncertainty    | 1.5 km     | 1 km            |
| 7.8.1-8 |                            |            |                 |
+---------+----------------------------+------------+-----------------+
| 40.     | h\. Max Local Average      | 24 hrs     | 6               |
| 7.8.1-9 | Revisit Time (S)           |            | [h              |
|         |                            |            | rs]{.smallcaps} |
+---------+----------------------------+------------+-----------------+
| 40.7    | i\. Latency (S)            | 90 minutes | 15 minutes      |
| .8.1-10 |                            |            |                 |
+---------+----------------------------+------------+-----------------+

*26. Ice Surface Temperature*

Above the surface of the ice. This EDR is required under clear
conditions only.

[Units]{.underline}: K

  ----------- -------------------------- ------------------- ---------------
  Para. No.                              Threshold           Objectives

              a\. Horizontal Cell Size                       

  40.7.3-1    1\. Nadir                  1.0 km              0.1 km

  40.7.3-9    2\. Worst case             1.6 km              0.1 km

  40.7.3-2    b\. Horizontal Reporting   1.0 km              0.1 km
              Interval                                       

  40.7.3-3    c\. Horizontal Coverage    Ice-covered         Ice-covered
                                         land/water          land/water

  40.7.3-4    d\. Measurement Range      213 K -- 275 K      213 K - 293 K
                                                             (2 m above ice)

  40.7.3-5    e\. Measurement            0.5 K               (TBD)
              Uncertainty                                    

  40.7.3-6    f\. Mapping Uncertainty,   0.4 km              0.1 km
              nadir                                          

  40.7.3-7    g\. Maximum Local Average  24 hrs              12 hrs
              Revisit Time (S)                               

  40.7.3-8    h\. Deleted.                                   

  40.7.3-10   i\. Latency (S)            90 minutes          15 minutes
  ----------- -------------------------- ------------------- ---------------

*27. Snow Cover and Depth*

+-------+----------------------------+---------------+----------------+
| Para. |                            | Threshold     | Objectives     |
| No.   |                            |               |                |
+-------+----------------------------+---------------+----------------+
|       | a\. Horizontal Cell Size   |               |                |
+-------+----------------------------+---------------+----------------+
| 40.   | 1.Clear                    | 0.8 km        | 1 km           |
| 6.3-1 |                            | (nadir)       |                |
|       |                            |               |                |
|       |                            | 1.6 km (EOS)  |                |
+-------+----------------------------+---------------+----------------+
| 40.   | 2\. All weather and/or     | 12.5 km.      | 1 km           |
| 6.3-2 | nighttime                  |               |                |
+-------+----------------------------+---------------+----------------+
| 40.   | b\. Horizontal Reporting   | HCS           | 1 km           |
| 6.3-3 | Interval                   |               |                |
+-------+----------------------------+---------------+----------------+
| 40.   | c\. Snow Depth Ranges      | Snow/No snow  | \> 8 cm, \> 15 |
| 6.3-4 |                            |               | cm, \> 30 cm,  |
|       |                            |               | \>51 cm, \>76  |
|       |                            |               | cm             |
+-------+----------------------------+---------------+----------------+
| 40.   | d\. Horizontal Coverage    | Land Snow/No  | Land & Ice     |
| 6.3-5 |                            | snow          |                |
+-------+----------------------------+---------------+----------------+
| 40.   | e\. Vertical Coverage      | Land          | 0 - 1 m        |
| 6.3-6 |                            |               |                |
+-------+----------------------------+---------------+----------------+
| 40.   | f\. Measurement Range      | 0 -- 1 HCS    | 0 - 1 per snow |
| 6.3-7 |                            |               | depth category |
+-------+----------------------------+---------------+----------------+
|       | g\. Measurement            |               |                |
|       | Uncertainty                |               |                |
+-------+----------------------------+---------------+----------------+
| 40.   | 1.. Clear - daytime        | 10 %          | 10 % for snow  |
| 6.3-8 |                            |               | depth          |
|       |                            | (snow/no      |                |
|       |                            | snow)         |                |
+-------+----------------------------+---------------+----------------+
| 40.   | 2\. Cloudy and/or          | 20 % (snow/no | 10 %           |
| 6.3-9 | nighttime                  | snow)         |                |
+-------+----------------------------+---------------+----------------+
|       | h\. Mapping Uncertainty    |               |                |
+-------+----------------------------+---------------+----------------+
| 40.6  | 1\. Clear                  | 1.5 km ( EOS) | 1 km           |
| .3-10 |                            |               |                |
+-------+----------------------------+---------------+----------------+
| 40.6  | 2\. Cloudy                 | 3 km ( EOS)   | 1 km           |
| .3-11 |                            |               |                |
+-------+----------------------------+---------------+----------------+
| 40.6  | i\. Max Local Average      | 12 hrs        | 3 hrs          |
| .3-12 | Revisit Time (S)           |               |                |
+-------+----------------------------+---------------+----------------+
| 40.6  | j\. Deleted.               |               |                |
| .3-13 |                            |               |                |
+-------+----------------------------+---------------+----------------+
| 40.6  | k\. Binary HCS             | Clear, day,   |                |
| .3-14 |                            | nadir 0.4 km  |                |
|       |                            |               |                |
|       |                            | Clear, day,   |                |
|       |                            | EOS 0.8 km    |                |
+-------+----------------------------+---------------+----------------+
| 40.6  | l\. Sensing Depth (all     | 0 to 40 cm    | 1 m            |
| .3-15 | weather)                   |               |                |
+-------+----------------------------+---------------+----------------+
| 40.6  | m\. Long Term Stability    | 10 %          | 1% continental |
| .3-16 | (C)                        |               |                |
+-------+----------------------------+---------------+----------------+
| 40.6  | n\. Latency (S)            | 90 minutes    | 15 minutes     |
| .3-17 |                            |               |                |
+-------+----------------------------+---------------+----------------+
| 40.6  | o\. Binary Map-            | Snow/No Snow  |                |
| .3-18 | Measurement Range          |               |                |
+-------+----------------------------+---------------+----------------+
| 40.6  | p\. Binary Map-            | 95%           |                |
| .3-19 | Probability of Correct     |               |                |
|       | Typing                     |               |                |
+-------+----------------------------+---------------+----------------+

**B.3 Climate Data Records (CDRs) Requirements**

*(To be included after NRA selection)*

*These are potential CDRs, but the list of CDRs might include all, part
and/or other CDRs.*

***1. Clear Column Radiance (CDR)***

To be included

2\. Ozone Total Column and Profile (CDR & EDR)

To be included

***3. Precipitation Rate (CDR & EDR)***

To be included

***4. Trace Gases (CDR & EDR)***

To be included

***5. Cloud Ice Water Path (CDR & EDR)***

To be included

***6. Cloud Liquid Water (CDR)***

To be included

***7. Atmospherically Corrected Reflectance (CDR)***

To be included

***8. Fire Area and Temperature (CDR & Application)***

To be included

***9. LAI/FPAR (CDR)***

To be included

***10. Sea Surface Temperature (CDR & EDR)***

To be included

***11.** **Ocean Color (Water Leaving Radiance) (CDR and EDR)***

To be included

[]{#__RefHeading___Toc534619219 .anchor}Appendix C: Verification to NIST
Standards

This appendix provides details of the NIST verification plan summarized
in Section 4.5. It starts by defining the five types of intercomparison
activity in Section C.1. Then Section C.2 gives background information
on the existing NIST measurement infrastructure relevant to the
verification plan. Additional NIST resources that will be developed for
this plan are described in Section C.3."

C.1 Definition of the Types of NPP Intercomparison Activities for NIST
Traceability Verification

***Type A: BRDF Round-Robin of Diffuser Plates.***

This is primarily to verify the bi-directional reflectance distribution
function (BRDF) scale used in the calibration of VIIRS. VIIRS will use
one or more reflective diffuser plates as diffuse reflectance standards
for the solar-reflective bands. To verify the diffuse reflectance scale
used by the Raytheon SBRS team for VIIRS, one or more diffuser plates
will be measured by NIST, SBRS, and any other participants. In such a
round-robin, artifacts are sent around to the different participating
laboratories with measurements performed on the artifacts at each
laboratory. The EOS-heritage example of this is described in Ref. 1
\[see NIST References at the end of this appendix \]. The measurement
performed at each laboratory is the BRDF of the artifact. The artifact
is a sample of the same type of material, such as a Spectralon panel,
that serves as the reflectance reference panel on the flight instrument.
It is not the flight-panel(s) itself. The protocol for the measurements
is developed by NIST after consultation with the participating
measurement laboratories. Table C-1 gives an example protocol. Based on
experience, the expanded uncertainty (k=2) \[see Ref. 18 for uncertainty
definitions, k=2 means a 95% confidence level, commonly called
2-sigma\] determined by combining the standard uncertainties from NIST
and a typical participating laboratory can be expected to be about 1.4%,
and agreement with NIST is generally within 1.5% \[1\].

Such round-robins usually take a few months to complete, since each
laboratory does the measurements at their own facility. The plan for NPP
is to perform this round-robin once during the program, near the time
that Raytheon SBRS will be calibrating the flight reflectance panel(s).
We estimate this to take place in the year 2003.

\[Do this also for the reflective and transmissive diffusers for OMPS\]

**Table C-1: Example Protocol for NPP BRDF Round-Robin Activity**

  ----------------------------------- -----------------------------------
  **Parameter**                       **Value**

  Sample                              Spectralon

  Wavelengths (nm)                    440, 550, 633, 670, 860, 940, 1240

  Bandwidth (nm)                      10

  Incident polar angles (deg)         0, 30, 45, 60

  Viewing polar angles (deg)          -60 to 60 in steps of 10

  Measurement plane                   In-plane and 90 degrees
                                      Out-of-plane

  Polarization state                  Report BRDF for unpolarized light

  Sample size                         50.8 mm diameter

  Sample alignment                    Fiducial mark on holder
  ----------------------------------- -----------------------------------

***Type B. Intercomparison of Lamp-Illuminated Integrating
Spheres/Plaques and Radiometers.***

This is to verify spectral radiance scales used by instrument-providers
in the solar-reflective part of the spectrum. Such scales are normally
established by the instrument-providers using lamp-illuminated spheres
or plaques. It is relevant for the solar-reflective channels of VIIRS,
and for instruments used in validation of solar-reflective products such
as ocean color. One goal is to compare the spectral radiance of the
VIIRS calibration source as used by SBRS to that determined by NIST
using NIST-calibrated radiometers. Another goal is to do the equivalent
for spheres or plaques used by any NPP validation instruments, such as
MAS. Examples of this type of activity from the EOS heritage are
described in Refs. 2-4 \[see NIST References at the end of this
appendix\]. Ref. 2 describes an intercomparison performed at the MODIS
instrument contractor site, SBRS, in 1996. Ref. 4 describes an
intercomparison performed at the MAS aircraft instrument calibration
site. In either case, all participants bring their equipment to a common
site and spend a few days performing radiance measurements. The
participants use one or more standard radiometers or spectroradiometers
to make measurements of the spectral radiance of one or more
lamp-illuminated integrating spheres or lamp-illuminated diffuser
plaques, at a number of wavelengths that overlap flight-instrument
channels. For a filter radiometer looking at a source, the comparison
approach is to compute the spectral radiance using the radiance spectrum
provided by the calibration facility and the relative spectral response
functions of the radiometer. This gives a predicted radiometer response.
The percent difference between the measured radiometer response and the
predicted radiometer response then indicates how well the spectral
radiance spectrum of the calibration facility compares to the NIST
spectral radiance scale. Based on experience, expected agreement is
about 2% in the visible/near-infrared and 4% in the short-wave-infrared
\[4\].

The plan for NPP is to have annual intercomparisons of this type
involving MAS and other solar-reflective-band validation instruments. It
is anticipated that SBRS would also participate in at least one of
these, near the time that its sphere source is used for the VIIRS
pre-flight calibration. The annual intercomparisons, starting in the
year 2004, should be scheduled near the time of actual MAS flight
campaigns to minimize the impact of the drifting of scales.

***Type C. Chamber deployment of NIST thermal-IR spectroradiometer to
view chamber calibration sources in-situ.***

This is to verify the thermal-infrared radiance scales used for
pre-flight calibration of CrIS and VIIRS. A prototypical example of this
type of activity is described in Refs. 5-6 \[see NIST References at the
end of this appendix\], where the NIST Thermal-infrared Transfer
Radiometer (TXR) was used to verify the scale at the calibration chamber
at Los Alamos National Laboratory. This has also been performed at the
GOES calibration chamber at ITT. The goal of this activity is to make
measurements of the thermal-IR radiance that was present at the flight
instrument during its pre-flight radiometric calibration. Thus a
chamber-compatible, portable radiometer from NIST (the TXR for EOS, the
FTXR for NPOESS) is mounted in the vacuum chamber in the location of the
flight instrument and views the sources, usually blackbodies, which the
flight instrument viewed during radiometric calibration. The comparison
approach is similar to that described above for the Type B activity,
except that it is common practice in the thermal-IR to convert
band-integrated radiance to brightness temperature. Based on experience,
expected radiance uncertainties should be around 0.1% to 0.2% at 5
micrometers, which corresponds roughly to a brightness temperature
uncertainty of 0.05 K \[5, 6\]. The strawman protocol is to use the NIST
thermal-IR spectroradiometer to make measurements of the radiance of the
Earth calibration black body target at 5 to 9 temperature settings that
span the temperature range 250 K to 350 K. The space-view target is also
viewed, with the goal of verifying the chamber background contribution.
This activity usually takes about two weeks of chamber time. As the
sources to be viewed are not part of the actual flight instrument
themselves, this activity can be performed either right before or right
after the NPP VIIRS and CrIS pre-flight chamber calibrations. We are
currently planning on this occurring in the year 2004 for both CrIS and
VIIRS.

***Type D. Thermal-infrared intercomparison of blackbodies and
radiometers (at ambient temperature and pressure).***

This is to verify the thermal-infrared radiance scales used for
validation instruments such as those used on the IPO NAST-I and the
thermal-infrared channels of MAS. The participants will use one or more
standard radiometers or spectroradiometers to make measurements of the
radiance (often converted to brightness temperature) of one or more
blackbody sources over the thermal-IR wavelength range. NIST will bring
a standard water-bath blackbody and a well-calibrated radiometer (the
TXR or eventually the FTXR) to enable NIST radiometric traceability.
Other participants (NAST-I, MAS, or others) will bring the blackbody
calibration sources that they use to calibrate their validation
radiometer instruments and, where possible, their validation radiometer
instruments themselves. There will be multiple instruments being
compared at a single site in this activity, similar to the Type B
activity described above. The NIST radiometer will view the blackbodies,
and the validation radiometer instruments will view the NIST blackbody.
In this way the scales can be compared against the NIST radiance scale
and their uncertainties verified.

The plan for NPP is to have annual intercomparisons of this type,
starting in the year 2004, involving NAST-I, MAS and any other
thermal-infrared validation instruments. These intercomparisons will
last a few days and should be scheduled near the time of actual NAST-I
or MAS flight campaigns to minimize the impact of the drifting of
scales.

An example of this type of intercomparison amongst the sea-surface
temperature validation community occurred in May 2001 at the University
of Miami \[7\]. The NIST TXR and water bath blackbody participated, and
data are currently being analyzed. A predecessor to this was the 1998
intercomparison, also at the University of Miami \[8-10\].

In the 1998 intercomparison, an M-AERI, a sea-surface temperature
radiometer developed by the University of Wisconsin and used for MODIS
validation by the University of Miami, viewed a NIST water-bath
blackbody. As an example of the expected results, in the spectral range
of the M-AERI between 3.3 micrometers and 15 micrometers (excluding
regions effected by the air path in the room) the M-AERI agreement with
the NIST water bath blackbody was within 0.02 C at 20 C, within 0.03 C
at 30 C, and within 0.05 C at 60 C \[10\]. This was within the combined
uncertainties of the M-AERI and NIST water bath blackbody, which was
estimated to be about 0.1 C. This type of intercomparison gives
confidence that the standards used in the field for validation campaigns
in the thermal-IR wavelength range are accurate, and it is worth
repeating it occasionally to be sure that these standards have not
drifted.

*Type E: Intercomparison with Portable Laser-Illuminated Integrating
Sphere Source.*

This is a newly developed method at NIST for measuring the spectral
radiance responsivity of spectroradiometers using a portable,
wavelength-tunable laser-illuminated integrating sphere source. The
source has a radiance scale directly traceable through a portable
irradiance trap detector to the NIST radiometric infrastructure as
described below in Section C.3 for the SIRCUS facility. The portable
version of this method, known as the "Travelling-SIRCUS," is currently
being used to unravel stray-light issues in the spectrographs used in
the NOAA MOBY ocean color validation program through a series of three
deployments in 2001-2002 to the MOBY site in Hawaii. The MOBY
measurements of ocean-leaving radiance are being used to validate MODIS
ocean color products, so the correction of MOBY for stray-light is
becoming increasingly significant. Using a set of portable dye lasers
and a portable Ti-Sapphire laser with an appropriate doubling cavity,
the Travelling-SIRCUS currently offers continuous tunability from 360 nm
to 980 nm except for a gap at 450 nm to 550 nm. Soon it will be extended
by adding a laser that is tunable from 1000 nm to 2500 nm, and the 450
nm to 550 nm gap will be closed in the future with the addition of a new
pump laser. For NPP, we propose using the Travelling-SIRCUS to measure
the spectral radiance responsivity of the MOBY spectrograph to update
stray-light corrections in the MOBY spectrographs prior to use of MOBY
for VIIRS ocean-color EDR validation.

C.2 Existing NIST Radiometric Infrastructure

*NIST Reflectance Standards*

The reflectance-measuring facility at NIST that relates to cal/val of
the solar-reflective bands of VIIRS is the Spectral Tri-function
Automated Reference Reflectometer (STARR). This facility is used to
measure absolute BRDF of diffuse reflecting panels from 250 nm to 2500
nm. The incident and reflected fluxes are measured along with the polar
viewing angle and the solid angle of the receiver. The source consists
of xenon-arc or quartz-tungsten-halogen lamps, a monochromator, a
Glan-Taylor polarizer, and associated optical components. The receiver
has a precision aperture, a focussing lens, and Si, Ge, or InAs
photodiodes. The STARR goniometer consists of the sample holder and the
receiver \[20\]. The STARR facility has been used to calibrate
reflectance standards used throughout the remote sensing community, and
it has acted as the central facility in a diffuse reflectance panel
round-robin that was performed for EOS \[1\]. It will act as the central
facility in the round-robin planned for NPP Cal/Val.

***NIST Radiance Standards***

The NIST radiance scale for ultraviolet through infrared wavelengths has
been established and maintained by the Optical Technology Division at
NIST. As it extends over a wide variety of wavelengths, radiance
temperatures, and environmental conditions, there are several facilities
within the division at which different parts of the radiance scale are
implemented. This sub-section describes how the NIST radiance scale is
implemented on the NIST facilities that are relevant for space-based
remote sensing applications. It also describes how NIST portable
transfer radiometers used for verification of radiance scales throughout
the remote sensing community are calibrated at NIST.

The primary standard for radiometric measurements at NIST is the High
Accuracy Cryogenic Radiometer (HACR) \[22\]. This is an electrical
substitution radiometer that works at liquid helium temperatures. During
the last twenty years, electrical substitution radiometers such as the
HACR operated at cryogenic temperatures have become the standard method
in most National Measurement Institutes (NMI) for determination of the
quantity of optical power in SI units. The radiant power scale
maintained by the HACR is disseminated through transfer standards,
usually silicon trap detectors. A silicon trap detector is a set of
three or more silicon photodiodes in a light trapping arrangement. The
responses from the component photodiodes are summed so that the trap
detector as a whole has nearly 100% external quantum efficiency. The
trap detector responsivity is determined by comparison to the HACR in a
series of response measurements of an intensity stabilized, polarized
laser beam of about 1 mW power. The responsivities of trap detectors are
routinely determined at several laser wavelengths spanning the visible
spectrum at an uncertainty of 0.02% uncertainty (k=2) \[see Ref. 18 for
uncertainty definitions, k=2 means a 95% confidence level, commonly
called 2-sigma\]. Trap detectors are small, portable, and repeatable.
After calibration against the HACR they are taken to a variety of other
facilities at NIST and used as reference standards there.

Of particular importance to the method planned for NIST verification of
NPOESS radiance scales is the Spectral Irradiance and Radiance Response
Calibrations with Uniform Sources (SIRCUS) facility at NIST. This
facility is used to measure the spectral responsivity of portable
transfer radiometers, such as the ones currently deployed to EOS
facilities and those planned for deployment to NPP and NPOESS-related
facilities. Laser-illuminated integrating spheres are used as the
sources, and trap detectors calibrated against the HACR are used as
reference detectors. The integrating sphere is illuminated through a
small (few millimeters) input port and provides diffuse radiance to the
radiometer under test through a relatively large (up to several
centimeters) output port. From ultraviolet wavelengths to near
1 micrometer, the integrating spheres are coated with a white diffuse
reflecting material. Beyond 1 micrometer, diffuse-gold-coated
integrating spheres are used. The input laser beam is intensity
stabilized to near 0.01% or better, and it can be shuttered or chopped
as necessary. Laser speckle is removed either by fast scanning of the
input beam against the inside of the sphere wall or by fiber-optically
coupling the laser to the sphere with a length of the fiber passing
through an ultrasonic bath. Using a combination of Ti-Sapphire, dye, and
other lasers, in combination with appropriate doublers and quadruplers,
continuously tunable CW sphere input powers in the Watt range are
available throughout most of the UV, visible, NIR and SWIR. For the
thermal IR, the facility has CO~2~ and CO lasers to provide
discrete-tunable CW powers in the Watt range out to eleven micrometers
with some gaps. With such sphere input power, typical power levels at
the input aperture of radiometers viewing the sphere can be in the
microwatt to milliwatt range if the sphere size is kept to a minimum. A
large computer-controlled translation stage is used to provide
translations. An electronic ruler is installed to provide
source-to-radiometer distance measurements.

The SIRCUS facility is used to provide a known irradiance or radiance so
that the responsivity of a portable transfer radiometer under test can
be determined. The irradiance at any distance from the output of the
sphere can be measured using a reference detector. For the visible
spectrum, the reference detector is simply a calibrated silicon trap
detector fitted with an aperture of known area. Aperture areas are
measured to better than 0.01% using a dedicated facility in the Optical
Technology Division at NIST \[21\]. In the SIRCUS facility, when the
radiometer entrance aperture is far enough from the sphere exit aperture
that the irradiance falls off as 1/s^2^ (where s is the spacing between
the apertures), the sphere exit approximates that of a point source. In
this case, irradiance measurements at one radiometer position (i. e.,
the irradiance trap reference detector) are used to infer the irradiance
at another radiometer position (i. e. the portable transfer radiometer
under test). In this way the radiometer under test is calibrated for
irradiance responsivity against the reference detector, and the aperture
spacing s can usually be determined at negligible uncertainty levels.
From geometric measurements of s and the sphere exit aperture area, the
solid angle subtended by the sphere exit aperture is known. Thus the
irradiance measured by the reference detector at any s can be used to
determine the radiance of the exit port of the sphere. Finally, when the
radiometer under test is positioned close enough to the sphere exit that
its entrance pupil is filled, the sphere appears as an extended source
of known radiance at a particular wavelength. By scanning the wavelength
and comparing reference detector response to the radiometer-under-test
response, the system-level radiance responsivity versus wavelength of
the transfer radiometer is measured. This method has many advantages
over previous methods. In particular, it provides the spectral and
radiometric calibration together in a single step, and has a much
shorter measurement chain to the HACR. The SIRCUS facility has been used
over the past few years to calibrate several radiometers, including NIST
transfer radiometers used in cal/val in the NASA EOS program.

While the SIRCUS facility is a permanent installation at NIST, recently
(FY2001) a portable version has been developed and named the
Travelling-SIRCUS. This consists of set of portable dye lasers, a
portable Ti-Sapphire laser with an appropriate doubling cavity, a
fiber-optically fed portable integrating sphere, and an irradiance trap
detector for determining the radiance output of the sphere during
deployments. The Travelling-SIRCUS currently offers continuous
tunability from 360 nm to 980 nm except for a gap at 450 nm to 550 nm.
Soon it will be extended by adding a laser that is tunable from 1000 nm
to 2500 nm, and the 450 nm to 550 nm gap will be closed in the future
with the addition of a new pump laser.

For the short-wave and thermal-infrared spectrum, where silicon-trap
detectors no longer work, a HACR-traceable, helium-cooled bolometer
fitted with a precision aperture is used as the reference detector at
the SIRCUS facility.. Note that in the thermal-infrared, the
diffuse-gold integrating sphere will itself present a gray body radiance
to radiometer. Thus, in addition to the delta-function from the laser, a
continuum radiance will exist; the exact spectral shape of which will
depend on the spectral emissivity characteristic of the sphere wall
coating and the effective temperature of the sphere wall. This unwanted
continuum is removed by chopping the input laser and using
chopper-synchronous detection on the radiometer response measurements.

The spectral radiance of standard sources such as strip lamps or
lamp-illuminated integrating spheres is routinely measured in the NIST
Facility for Automated Spectroradiometric Calibrations (FASCAL) \[12\].
This is the same facility that supplies calibrated FEL irradiance lamps
to a wide range of customers, including many in the remote sensing
validation community, such as the MAS calibration team, who use the
lamps to set up their own radiance scales. The FASCAL facility uses a
prism/grating double monochromator-based spectroradiometer to transfer
the spectral radiance scale between different sources over the
wavelength range 250 nm to 2500 nm. The starting point is the radiance
from a gold-point black body at 1337.33 K, as defined on the ITS-90. Its
scale is transferred to high-stability vacuum lamp, then to a working
standard lamp. The working standard lamp is used to calibrate a variable
temperature black body, and this is used to calibrate the source under
test. Comparisons of high-temperature sources have indicated that the
radiance scale from the gold-point black body is consistent with the
radiance scale derived from the HACR to within the combined
uncertainties of the measurements, which was roughly +/- 0.5 K \[19\].

For the thermal-infrared, NIST has a vacuum-compatible cryogenic black
body that has large aperture (10 cm diameter) and works over the
temperature range 250 K to 350 K \[16\]. Also, NIST maintains one
on-site water bath blackbody that has a large aperture (10 cm diameter)
and works over the temperature range 15 C to 90 C \[17\]. These
blackbodies have a radiance scale traceable to the NIST temperature
scale through platinum resistance thermometers that measure the
temperature near their radiating surfaces. Though common throughout the
remote sensing community, NIST experience is that several systematic
errors can occur with such black bodies and that they should be checked
radiometrically against electrical-substitution based scales to verify
uncertainty below 1 K. Work at NIST to establish such consistency of the
NIST radiance scale for these NIST black bodies is ongoing, and the goal
for consistency is +/- 0.1 K or less.

C.3 NIST Resources to be Developed for NPOESS

While the HACR, SIRCUS, FASCAL, and other facilities for calibration and
characterization of portable transfer radiometers and sources in the
NIST Optical Technology Division already exist, the actual transfer
radiometers and sources themselves need to be built in the years
FY02-FY03 preceding the intercomparisons. In many cases these
instruments are simply copies of instruments built for other programs.
In some cases improved designs are planned. This section details the
plans for obtaining, characterizing, and calibrating these instruments.

***Portable Lamp-Illuminated Integrating Sphere***

This will be a copy of a portable integrating sphere source developed
for the NASA EOS program \[11\]. It will be calibrated for spectral
radiance from 400 nm to 2400 nm at the NIST FASCAL facility \[12\].

*Portable VIS/NIR Spectro-radiometer*

This instrument will be developed using some existing monochromator
parts. It will be a grating spectroradiometer and cover the wavelength
range 400 nm to 1100 nm. It will be characterized and calibrated for
spectral radiance responsivity over this wavelength range at the NIST
SIRCUS facility \[14\].

***Portable SWIR Spectro-radiometer***

This will be a copy of the grating spectroradiometer that was developed
for the NASA EOS program \[13\]. The wavelength range is 0.8 micrometers
to 2.5 micrometers. It will be characterized and calibrated for
spectral radiance responsivity over this wavelength range at the NIST
SIRCUS facility \[14\].

***Portable Thermal-IR FTIR Spectro-radiometer (FTXR)***

This will be a cryogenic FTIR interferometer with appropriate field of
view-limiting foreoptics and two on-board black bodies to hold the
calibration. It will be analogous to the M-AERI, except that it will be
vacuum/cryogenic compatible in the same sense as the TXR, a two-channel
thermal-infrared transfer radiometer that was developed for the NASA EOS
program \[15\]. The wavelength range will be 2 micrometers to 12
micrometers. It will be characterized and calibrated for spectral
radiance responsivity at the NIST SIRCUS facility \[14\], and it will be
tested prior to intercomparison deployments in cryogenic vacuum chamber
conditions at the NIST MBIR facility \[16\]. Since it will be packaged
in a sealed vacuum cryostat, it will be compatible both with bench-top
deployments for validation field instrument verifications, and with
thermal vacuum chamber deployments for vacuum black body radiance scale
verifications.

***Portable Water Bath Blackbody***

This will be a copy of the Water Bath Blackbody described in Ref. 17.
This design has been successfully replicated several times for different
programs. It was also used once previously in a sea-surface temperature
radiometer intercomparison with the M-AERI and other field instruments
\[8-10\].

**C.4 NIST References**

1.  E. A. Early, P. Y. Barnes, B. C. Johnson, J. J. Butler, C. J.
    Bruegge, S. F. Biggar, P. R. Spyak, and M. M. Pavlov, "Bidirectional
    Reflectance Round-Robin in Support of the Earth Observing System
    Program, J. Atmos. Oceanic Technology **17**, 1077-1091 (2000).

2.  J.J. Butler and B. Carol Johnson, "EOS Radiometric Measurement
    Comparisons at Hughes Santa Barbara Remote Sensing and NASA's Jet
    Propulsion Laboratory," *The Earth Observer, A Bimonthly EOS
    Publication*, NASA Goddard Space Flight Center, Vol. 8 No. 5, 17
    (1996).

3.  J.L. Mueller, B. Carol Johnson, C.L. Cromer, S.B. Hooker, J.T.
    McLean, and S. Biggar, "The Third SeaWiFS Intercalibration
    Round-Robin Experiment, SIRREX-3, 19 - 30 September 1994," *NASA
    Tech. Memo. 104566*, Eds. S.B. Hooker, E.R. Firestone, and J.G.
    Acker, **34**, 78 pages (1996).

4.  J. J. Butler, B. C. Johnson, S. W. Brown, S. F. Biggar, E. F.
    Zalewski, J. W. Cooper, R. A. Barnes, and P. Hajek, "Radiometric
    Measurement Comparison at NASA's Ames Research Center's Sensor
    Calibration Laboratory," CalCon 2000 poster, Space Dynamics
    Laboratory, Utah State University, 2000.

5.  J. P. Rice, S. C. Bender, W. H. Atkins, and F. J. Lovas, "Deployment
    Test of the NIST EOS Thermal-infrared Transfer Radiometer," Int. J.
    Remote Sensing, in press.

6.  J. P. Rice, S. C. Bender, and W. H. Atkins, "Thermal-Infrared Scale
    Verifications at 10 micrometers Using the NIST EOS TXR," Proc. SPIE
    **4135**, 96-107 (2000).

7.  http://www.rsmas.miami.edu/ir2001/

8.  B. Kannenberg, "IR Instrument Comparison Workshop at the Rosenstiel
    School of Marine & Atmospheric Science (RSMAS), *The Earth Observer,
    A Bimonthly EOS Publication*, NASA Goddard Space Flight Center, Vol.
    10, No. 3, 51-54 (1998).

9.  <http://www.rsmas.miami.edu/ir/>

10. <http://arm1.ssec.wisc.edu/~bobk/miami_ir/miami_ir.htm>

11. S. W. Brown, "A Portable Integrating Sphere Source for Radiometric
    Calibrations from the Visible to the Short-Wave Infrared," *The
    Earth Observer, A Bimonthly EOS Publication*, NASA Goddard Space
    Flight Center, Vol. 11, No. 3, 14-19 (1999).

12. J. H. Walker, R. D. Saunders, and A. T. Hattenburg, *Spectral
    Radiance Calibrations*, NBS Special Publication SP250-1, US
    Government Printing Office, Washington, DC (1987).

13. S. W. Brown, "Description of a Portable Spectroradiometer to
    Validate EOS Radiance Scales in the Shortwave Infrared," *The Earth
    Observer, A Bimonthly EOS Publication*, NASA Goddard Space Flight
    Center, Vol. 10, No. 3, 43-48 (1998).

14. K. R. Lykke, P. S. Shaw, L. M. Hanssen, and G. P. Eppeldauer,
    "Development of a Monochromatic, Uniform Source Facility for
    Calibration of Radiance and Irradiance Detectors from 0.2
    micrometers to 12 micrometers," *Metrologia* **35**, 479-483 (1998).

15. J. P. Rice and B. C. Johnson, "The NIST EOS Thermal-infrared
    Transfer Radiometer," *Metrologia* **35**, 505-509 (1998).

16. J. B. Fowler, B. C. Johnson, J. P. Rice, and S. R. Lorentz, "The New
    Cryogenic Vacuum Chamber and Black Body Source for Infrared
    Calibrations at the NIST's FARCAL Facility," *Metrologia* **35**,
    323-327 (1998).

17. J. B. Fowler, "A Third Generation Water Bath Based Blackbody
    Source," J. Res. NIST **100**, 591 (1995).

18. B. N. Taylor and C. E. Kuyatt, "Guidelines for Evaluating and
    Expressing the Uncertainty of NIST Measurement Results," NIST Tech.
    Note 1297 (1994).

19. H. W. Yoon and C. E. Gibson, "Comparison of the Absolute
    Detector-based Spectral Radiance Assignment with the Current
    NIST-Assigned Spectral Radiance of Tungsten Strip Lamps,"
    *Metrologia* **37**, 429-432 (2000).

20. J. E., Proctor and P. Y. Barnes, "NIST High Accuracy Reference
    Reflectometer-spectrophotometer. *J.Res. Natl. Inst. Stand.
    Technol.*, **101**, 619-627 (1996).

21. J. B. Fowler, R. S. Durvasula, and A. C. Parr, "High-accuracy
    Aperture-area Measurement Facilities at the National Institute of
    Standards and Technology, *Metrologia*, **35**, 497-500 (1998).

22. T. R. Gentile, J. M. Houston, J. E. Hardis, C. L. Cromer, and A. C.
    Parr, "National Institute of Standards and Technology High Accuracy
    Cryogenic Radiometer," *Appl. Optics* **35**, 1056-1068 (1996).

**C.5 NIST Acronyms**

ATMS Advanced Technology Microwave Sounder

BRDF Bi-directional Reflectance Distribution Function

CrIS Crosstrack Infrared Sounder

EDR Environmental Data Record

EDU Engineering Design Unit

EOS Earth Observing System

FASCAL Facility for Automated Spectral Irradiance and Radiance
Calibrations

FTIR Fourier Transform Infrared

FTXR FTIR Thermal-infrared Transfer Radiometer

FY Fiscal Year

HACR High Accuracy Cryogenic Radiometer

IR Infrared

ITS-90 International Temperature Scale of 1990

M-AERI Marine-Atmospheric Emitted Radiance Interferometer

MAS MODIS Airborne Simulator

MBIR Medium Background Infrared

MODIS Moderate Resolution Imaging Spectroradiometer

MOBY Marine Optical Buoy

NASA National Aeronautics and Space Administration

NAST-I NPOESS Airborne Sounder Testbed-Interferometer

NAST-M NPOESS Airborne Sounder Testbed-Microwave

NIR Near-infrared

NIST National Institute of Standards and Technology

NMI National Measurements Institute

NPOESS National Polar-orbiting Operational Environmental Satellite
System

NPP NPOESS Preparatory Project

SIRCUS Spectral Irradiance and Radiance Responsivity Calibrations with
Uniform Sources

STARR Spectral Tri-function Automated Reference Reflectometer

SWIR Short-wave infrared

TBD To Be Determined

VIIRS Visible/Infrared Imager/Radiometer Suite

Vis Visible

[]{#__RefHeading___Toc534619220 .anchor}Appendix D: VIIRS Instrument
Characterization and Calibration Tests

D.1 VIIRS Characterization

Instrument characterization enables the determination of the
quantitative effect of the subsystem level performance on the overall
instrument system-level performance. Special care should be taken to
decide which characterization test should be performed in the ambient
environment or in both ambient and vacuum environments. Sensor
Characterization tests include:

D.1.1 Sensor Response Versus Scan Characterization

a\) Variation of mirror reflectance as a function of the
angle-of-incidence, commonly called Response Versus Scan angle.

b\) The variation in reflectance associated with the RVS also coincides
with a variation in emittance as a function of angle. Accordingly, the
sensor self-emission varies as a function of scan angle. This will be
referred to as Emission Versus Scan angle (EVS).

D.1.2 Polarization Characterization

a\) Polarization characteristics with scan angle and spectral bands.

D.1.3 Spectral Response

a\) Relative Spectral Response (RSR) for each detector/spectral channel
combination.

b\) Total system spectral response function (best estimate) for all
bands.

D.1.4 Calibration Tests for Several Instrument Thermal Configurations

a\) Stabilized to isothermal temperatures in foreoptics..

b\) Simulated \"in-flight\" temperature gradients (orbit low in earth
shadow, orbit high in daylight).

c\) Selected foreoptics components at a high temperature (use clip on
heater).

D.1.5 Stray Radiation Characterized as a Function of View Angle

a\) Background when viewing blackbody.

b\) Background when viewing space.

c\) Background when viewing earth target.

D.1.6 Non-linear Detector Response and Repeatability

a\) At least ten external target temperatures.

b\) Repeated measurements at different times.

**D.1.7 Radiance Versus Counts (R=a+b\*C+q(T)\*C^2^)**

a\) For each thermal configuration.

b\) For each detector.

c\) For each spectral channel.

d\) Half of test data will be used to specify algorithm, half to
determine calibration algorithm performance.

D.1.8 Band to Band Cross Talk Characterization

D.1.9 Modulation Transfer Function (MTF) Characterization

a\) Characterization of the MTF using the Integration and Alignment
Collimator (IAC) with the calibration sources (SIS and blackbody).

D.1.10 Alignment and Band to Band Registration

a\) Alignment refers to errors in corresponding pixel alignment between
bands, and it also refers to the alignment between the VIIRS and the
spacecraft platform.

b\) The Integration and Alignment Collimator will be used to determine
pointing knowledge, within band pixel registration, and Spectral Band
Registration (SBR).

c\) Interferometrically measured IAC scan and track motions.

d\) Selection and testing of misalignment correction software.

D.1.11 Ghosting

a)  Characterization of the light backscatter in the instrument's
    optics, such as lenses and mirrors.

D.1.12 Near Field Response and Point spread Function

a\) Near field response characterization in order to verify compliance
with the transient response and electronic cross-talk.

D.1.13 Precision, SNR and NEdT

a\) Precision is a measure of repeatability of the observations.

b\) The SIS will be used to measure the SNR and precision for each
reflective band detector.

c\) The Blackbody Calibration Source (BCS) and Space View Source (SVS)
will be used to measure the NedT's for each detector (i.e., channel) as
part of the calibration process.

D.2 VIIRS Calibration Procedures

Calibration is the process of quantitatively defining the
instrument/system response to known, controlled signal inputs. It is
fundamental for VIIRS instrument, scheduled for the NPP mission, and
NPOESS thereafter, to have a robust pre-launch and post-launch
characterization and calibration plan, describing in details the steps
to effectively understand the sensor components functionality and
eliminate the biases and sensor degradation variability.

The characterization process will follow a well-defined protocol and
guidelines. These are the result from a long-term experience within NASA
and NOAA and from MODIS and AVHRR calibration and characterization
teams.

The VIIRS sensor calibration will incorporate onboard calibration,
including the Solar Diffuser (SD) and thermal blackbody. The calibration
process will also include solar, deep space, and lunar radiometric
calibration capability.

The collected spectral radiance and calibration data are transmitted to
the ground as Raw Data Records (RDRs). The VIIRS algorithms are applied
to the RDRs to produce Sensor Data Records (SDRs) and Environmental Data
Records (EDRs).

The actual test plan that will be implemented will be similar to the
MODIS plan. The algorithms used for data reduction were verified on
MODIS and should be mostly adaptable to VIIRS acceptance testing (Figure
D-1).

![](media/image7.wmf){width="6.85625in"
height="3.3868055555555556in"}The data acquired during VIIRS acceptance
testing are stored, and specialized reduction algorithms are used to
analyze, correct and list final results. The final results are typically
in the form of tabulated averaged values with associated standard
deviations. Most reduction processes have intermediate files that may be
used in a diagnostic mode to determine cause of anomalies.

Figure D-1: Typical Performance Test Plan Flow Diagram

A performance characterization and calibration results database for each
VIIRS instrument evolves as the instrument goes preflight, on-orbit
testing, and operational usage. The database is used to check for
internal inconsistency throughout the instrument lifetime. A
multi-instrument database permits inter-instrument comparisons.

D.2.1 Calibration Sources

The preflight calibration includes the calibration of both VIIRS sensor
and the on-board calibrators using traceable laboratory measurements.

A large Spherical Integrating Source (SIS) is used to perform the
radiometric calibration of the visible (VIS), near infrared (NIR) and
short wavelength infrared (SWIR) bands of the sensor. The
medium-wavelength infrared and long-wavelength infrared (LWIR) will use
a full aperture blackbody calibration source (BCS). These two sources
will be separately calibrated with standards traceable to NIST primary
standards.

On-board sources/stimuli include full-aperture blackbody, Solar Diffuser
(SD) with attenuation screen, and Solar Diffuser Stability Monitor
(SDSM) integrating sphere and filtered detectors will also be calibrated
so that an accurate comparison of calibration accuracy between ground
and on-board calibrators can be established.

Where appropriate, source calibration will be traceable to NIST
standards or other standards while subsystems, such as monochromators,
will be spectrally characterized using standard calibration techniques.
The calibration sources and the associated parameters requiring
calibration are presented in the table below.

**Table D-1: Calibration Sources and Associated Parameter Requiring
Calibration**

  ----------------------------------- -----------------------------------
  **Calibration sources and           **Parameters requiring
  equipment**                         calibration**

  Spherical Integrating Sphere (SIS)  Spectral radiance, degree of
                                      polarization, spatial uniformity,
                                      rate of change of spectral radiance
                                      as a function of current change,
                                      temporal stability.

  Integration and alignment           Effective focal length, image
  collimators (IAC)                   quality, Modulation Transfer
                                      Function (MTF), distortion,
                                      reticles-spatial phase, translation
                                      stage characterization, optical
                                      alignment.

  IAC radiometry                      Spectral radiance, polarization
                                      effects, spatial uniformity, ungula
                                      uniformity, spectral radiance
                                      versus lamp current, temporal
                                      stability.

  Spectral measurement Assembly       Spectral effects: spectral slit
  (SPMA)                              width, solid angle-area product,
                                      wavelength calibration, reference
                                      detector calibration.

  Full aperture Blackbody calibration Emissivity, temperature, thermal
  source (BCS)                        uniformity across the BCS surface,
                                      temperature gradients across paint
                                      layer, spectral radiance.

  Rotary indexing table               Rotational angle calibration

  Scattering Measurement Assembly     Transmission of neutral density
  (SCMA)                              filters, astigmatic image
                                      alignment.

  Polarization Source Assembly (PSA)  Internal alignment, co-alignment of
                                      PSA optical axis with rotation
                                      axis,, degree of polarization,
                                      polarization uniformity across exit
                                      beam

  Space View Source (SVS)             Temperature sensors.
  ----------------------------------- -----------------------------------

Calibration and characterization of the test equipment is necessary to
permit removing test equipment induced effects from the VIIRS
calibration measurements.

D.2.3 Calibration Methodology and Processing

> The requirements for the VIIRS sensor are given in the Sensor
> Specification for the VIIRS, document number PS 154640-101.

The VIIRS calibration will have four components:

-   Absolute radiometric calibration for uniform background

-   Absolute radiometric calibration for structured background

-   Instrument level short-term stability monitor

-   Instrument level long-term stability monitor

D.2.4 Absolute Radiometric Calibration

Absolute radiometric calibration and the associated uncertainties and
stabilities will be verified by analysis, modeling, and/or simulation.
The process of satisfying the radiometric calibration requirements
against both uniform and structured backgrounds will be accomplished
through instrument characterization using National Institute of
Standards and Technology (NIST) standards to the extent possible. The
radiance levels applied to the calibration process will be based on
flow-down requirements for the EDRs based on measuring
top-of-the-atmosphere radiance levels.

Achieving total sensor accuracy is not simply a matter of obtaining good
calibration standards, but one of identifying and controlling the
numerous physical and environmental factors that ultimately limit
measurement accuracy. Experience in sensor calibration has taught much
about identifying sources of uncertainty and learning how to control
them. For most bands, sufficiently accurate standards exist, but
calibration of a total sensor, on the ground and in orbit, includes
multiple transfer standards, auxiliary optics, and test instabilities
that individually can be sources of error many times larger than the
standard itself.

Blackbody sources are used in thermal vacuum chambers for calibration in
the MWIR and LWIR. There are no equivalent reflectance region standards
to use. The driving parameters in blackbody calibration accuracy are
temperature and cavity emissivity. Temperature standards exist through
NIST and have reported accuracies of \<0.01 K -- more than sufficient to
meet VIIRS requirements as long as a uniform temperature field can be
obtained in the cavity. However, knowledge of cavity emissivity is more
difficult since it depends on the emissivity of the cavity coating
material (as measured on a flat witness sample), the cavity shape, and
the validity of assumptions about how well the coating in the cavity can
be simulated by the same type of material coating a flat witness sample.
A model for the SBRS Blackbody Calibration Source (BCS) was developed by
SBRS for MODIS that incorporates many of these effects. To verify this
model at the level of uncertainty relevant for VIIRS, a NIST portable
transfer radiometer as described in Section 4.5 and Appendix C will be
used to measure the in-situ radiance from the BCS in the SBRS
calibration chamber at a number of BCS temperatures.

D.2.5 Calibration Stability

For less than 2 weeks, short-term calibration stability will be
addressed using three approaches:

-   The continuity of calibration data obtained during laboratory
    calibration tests,

-   Monitoring the on-board calibration blackbody source (OBC),

-   On-orbit calibration data from Solar diffuser (SD) together with the
    Solar Diffuser Stability Monitor (SDSM).

Long-term calibration stability for duration in excess of two weeks will
be addressed by monitoring the SD and comparing it to the SDSM
measurements. The On-Board Calibrator (OBC) blackbody will also be used
to monitor the long-term calibration stability, in addition to the lunar
calibration.

D.2.6 Multiple independent measurement approaches

The VIIRS calibration will be accomplished through three interrelated
techniques:

-   Laboratory characterization/calibration of the sensor

-   Extensive on-board calibration using the sun, internal sources and
    the moon

-   Comparison with the ground truth.

Confidence in the precision and accuracy is accrued by iterative use of
these interrelated calibration methods.

Preflight calibration for the sensor and on-orbit calibrators is itself
a series of repeated tests. Initial calibration is done in a clean-room
ambient environment, and is then repeated after environmental
qualification (vibration and shock) tests, during thermal vacuum tests,
and finally after thermal vacuum. The stability of the trends in the
data continuity is used in assessing the VIIRS preflight calibration
validity.

The advantage of the in-flight calibration over pre-flight calibration
is that it will account of the actual, not simulated, operational
environment of the sensor. The goal of the in-flight calibration is to
measure the changes between pre-flight and on-orbit calibration, to
update the associated calibration coefficients when necessary, and to
provide the continuity in these data for the lifetime of the mission.

D.2.7 Reflectance Band Calibration

A preliminary setting of the gain and offset is obtained using the
Spherical Integration Source (SIS). The on-orbit calibration update is
accomplished by viewing the Solar Diffuser. The error allocation is
independent of the radiance level and hence of DNEV. The predominant
error source arises from uncertainties in the reflectance of the solar
diffuser, BRFSD, which is taken as 1.6% for bands below one micron. The
RMS allocation for all the terms is 2.0%.

Clearly, the solar diffuser BRF is the principal source of error. The
errors introduced by variation in the focal plane temperature are
insignificant if the temperature is measured with a 0.1 K accuracy.

The angle of incidence error is the next largest error contributor. Flat
fielding is a potential problem because small systematic errors occur if
an incorrect normalization is applied. The remaining errors are
generally insignificant.

D.2.8 Emissive Band Calibration

A Blackbody Calibration Source (BCS) and the Space View Source (SVS) are
used to calibrate the radiometric response of the VIIRS sensor. The BCS
temperature calibration accuracy is traceable to NIST. The on-board
calibration (OBC) blackbody radiance is made traceable to NIST standards
by comparing it to the BCS. Maintaining the VIIRS calibration relies on
the long-term stability of OBC.

The BCS provides the principal reference standard of spectral radiance
energy for the MWIR and LWIR (3.75 to 14.3 µm) bands. During ambient
testing, the MWIR/LWIR relative instrument response vs. scan angle will
be measured with the VIIRS instrument on the rotary table. The VIIRS
will view the BCS and the SVS. The SVS will rotate with VIIRS, while the
BCS will remain stationary.

The vacuum calibration is the primary radiometric calibration. The VIIRS
system allows for viewing the full dynamic range with background
illumination present in the vacuum environment.

[]{#__RefHeading___Toc534619221 .anchor}Appendix E: CrIS Instrument
Characterization and Calibration Tests

**E.1 Spectral Calibration Using a Gas Cell**

One technique for measuring gas transmittances for spectral calibration
is to use a simple gas cell coupled to a variable temperature blackbody.
The following procedures illustrate the simple radiometric measurements
that could be made with CrIS in the laboratory to form accurate gas
transmittances. Similar tests were used successfully with EOS-AIRS and
verified that both the instrument line shape function width and
centroids were within specification. Four measurements per transmittance
spectrum are needed:

-   Empty cell, warm black-body source, ≡ R^BB^warm

-   Empty cell, hot black-body source, ≡ R^BB^~hot~

-   Gas in cell, warm black-body source ≡ R^gas^~warm~

-   Gas in cell, hot black-body source ≡ R^gas^~hot~

where *R* could either be a calibrated radiance or just the Fourier
Transform of a raw interferogram, as long as stable instrument
conditions are maintained. It is easily shown that the gas cell
transmittance is then given by

~gas~ = (*R^gas^~hot~ - R^gas^~warm~*)/( *R^BB^~hot~ -- R^BB^~warm~*)

If *R* is not calibrated, then ~gas~ is actually the real part of the
right-hand-side of the equation. Notice that ~gas~ is independent of the
transmission of the gas cell optics. A gas cell that is nominally 100/50
cm long and pressure of 10/20 torr of gas will suffice.

This test does not require an accurate blackbody (windows can be used in
the optical path), just a stable one. The hot blackbody temperature
should be set to the maximum allowed by the CrIS detector chain. The
warm blackbody temperature should be set to approximately halfway
between the hot blackbody temperature and room temperature.

These tests permit both the wavenumber scale factors and geometrical
dependencies of the ILS to be determined as described below:

[Wavenumber Scale]{.underline}. A single wavenumber scale-factor will be
determined for each detector channel using reasonably long dwell-time
observations to minimize noise. The process involves:

> \* Calibrate and co-add CrIS radiances for the gas-filled cell,
> assuming a nominal wavenumber scale for each detector (based on
> nominal laser and optical alignment parameters)
>
> \* Interpolate to a dense spectral point spacing by performing double
> FFTs with zero-filling in the interferogram domain, and
>
> \* Determine the ratio of the line center of a calculated spectrum to
> the observed line center.

This ratio is the desired scale-factor adjustment factor that
establishes the wavenumber scale calibration for the entire spectral
band of each detector. Any scale-factor adjustments that exceed
expectations based on laser and optical alignment tolerances should be
investigated.

The final radiance data processing for CrIS will use these scale-factors
to perform a normalization of the spectral scale for each individual
detector to a chosen standard spectral scale.

[Instrument Line Shape]{.underline}: The difference of gas cell
observations, with and without gas in the cell, can be used to refine
knowledge of the ILS for each detector. Here the process involves:

-   Calibrate, co-add and difference CrIS radiances for the gas cell
    observations, with and without gas in the cell,

-   Select a localized spectral feature, zero-fill in the spectral
    domain, and FFT to the interferogram domain to obtain a densely
    sampled interferogram,

-   Normalize the interferogram amplitude with the amplitude observed in
    the Zero Path Difference (ZPD) region, and

-   Compare the dependence of the local maxima of the interferogram on
    Optical Path Difference (OPD) to that of a calculated spectrum to
    determine the Self Apodization for the observed spectral feature.

These test results should establish the geometrical parameters needed to
correct for self-apodization. Any substantial deviations from
expectations should be investigated.

[]{#__RefHeading___Toc534619222 .anchor}Appendix F: ATMS Instrument
Characterization and Calibration Tests

**F.1 ATMS Pre-Launch Characterization and Calibration**

The pre-launch characterization and calibration of ATMS will rely
heavily on the thermal vacuum calibration program, supplemented by more
thorough measurements at ambient pressure and multiple temperatures, as
discussed below. Some measurements should be performed on all flight
instruments, and some only on an engineering unit or a single flight
unit, as discussed below.

**F.1.1 ATMS Temperature Sensitivity (NEDT)**

Temperature sensitivities, referred to the antenna aperture, will be
determined for all flight instruments for the following conditions:

> 1\. The antenna should view a nominal 293 K isothermal blackbody
> target that fills the field of view (including reflectors),
>
> 2\. The bandpass characteristics, nominal instrument temperature, and
> integration times should be the same as expected during inflight
> operation,
>
> 3\. The calibration procedure should be the same as for nominal
> inflight operation,
>
> 4\. The procedure should be sufficiently lengthy that further
> measurements would not alter the results by more than 0.01K rms.

**F.1.2 ATMS Bandpass Characteristics**

The bandpass characteristics for each channel should be measured and
documented over the extreme operational temperature range to be
encountered in space; two or three temperatures would normally suffice,
and thermal vacuum would normally not be required. The accuracy should
be sufficient to ensure that no indicated brightness temperatures would
depart by more than 0.1 K (goal) from that expected for any reasonable
atmospheric profile solely as a result of incorrect or incomplete
pre-launch bandpass characterization. This is most difficult for those
spectral bands where the radiance depends strongly on frequency. One
standard procedure is to measure the instrument response to a calibrated
swept-frequency generator radiating into the instrument antenna in order
to reveal standing-wave resonances in the antenna/receiver structure. In
the absence of resonances sufficient to threaten the 0.1K objective, a
calibrated swept-frequency generator can feed the receiver at the
waveguide antenna port. Ten equally spaced frequency samples within each
passband and ten adjacent, five above and five below, should normally
suffice, as should rms relative accuracies of 0.3 dB. Each flight model
should be measured at its antenna port for all frequency-sensitive
channels unless two ATMS instruments demonstrate sufficient inter-unit
consistency.

**F.1.3 ATMS System Linearity**

The amplifiers and detectors in sensitive radiometers often exhibit
non-linearities that threaten calibration accuracy at antenna
temperatures removed from those of the two calibration loads. Tests
should be performed to ensure that such compensated non-linearities will
introduce less than 0.1K calibration error under the most challenging
plausible combinations of antenna and instrument temperature. One
standard procedure is to measure in the laboratory the relation between
the radio frequency (RF) intensity and the output counts by using a
broadband thermal RF source in series with an extremely well calibrated
variable attenuator. Small non-linearities are most easily detected at
the highest signal levels. To the extent that the system gain may vary
in orbit, these measurements assume increased importance.

A second type of non-linearity is "gain stealing" or "channel
cross-talk" that can occur when a single non-linear broadband amplifier
amplifies two or more spectral passbands (channels). Bench measurements
using broadband thermal signals should ensure that the worst case
cross-talk will not introduce more than 0.1K uncompensated errors for
any plausible changes in on-orbit channel characteristics. If cross-talk
levels are found to be more than 10 dB below the 0.1K threshold for all
shared-amplifier channels for the first instrument, then the need for
testing additional instruments diminishes. Related tests of the
instrument should verify that changes in any one channel\'s intensity
does not alter any other output, as can happen if the post-detection
circuits are insufficiently isolated.

**F.1.4 ATMS Calibration**

ATMS is calibrated every scan cycle in space using cold space and an
unheated blackbody load. Calibration errors in ATMS-like prior
instruments have generally been dominated by: bandpass variations (see
4.4.2), non-linearities (see 4.4.3), unknown blackbody emissivities
below unity, temperature gradients within the calibration blackbody,
errors in blackbody temperature sensors, variations of instrument
response with calibration switch position (in ATMS this is the position
of the scanning mirror), and angle- and situation-dependent
contributions to antenna temperature due to the Earth/space boundary,
spacecraft, sun, and moon. Most of these potential error sources can be
measured and compensated pre-launch using thermal vacuum calibration
tests, laboratory measurements, and antenna range measurements. Each of
these sources of calibration error should be measured so that
uncompensated contributions to calibration error from each are less than
\~0.05 K rms, a level that helps ensure cumulative errors less than
\~0.2 K rms.

Blackbody emissivities can be evaluated in the laboratory by comparison
with a known "perfect" blackbody standard or by reflectivity
measurements using strong signals in a reflection-free environment.
Errors due to temperature gradients are more easily minimized by
blackbody design than by measurement, and measurements in thermal vacuum
are generally required for final evaluation. The external calibration
blackbody used in thermal vacuum must therefore be of extremely high
quality and well coupled to the antenna at its multiple view angles. Any
flight model not undergoing such thermal vacuum tests should be
calibrated in a similar manner at standard pressure. The PRT and similar
temperature sensors should be traceable to NIST standards and accurate
to 0.05 K. Antenna-angle-dependent errors can be measured in part with
external "perfect" blackbodies that fill the view to space and capture
all antenna sidelobes. Errors can originate if the antenna reflects
signals transmitted by the RF pre-amplifier in an initial
mixer-preamplifier in a scanning way. Error contributions from the
Earth/space boundary, spacecraft, sun, and moon can be evaluated using
antenna pattern measurements (see 4.4.5) and appropriate environmental
models.

**F.1.5 ATMS Antenna Pattern Measurements**

Accurate antenna patterns are needed to (1) facilitate the image
sharpening made possible by Nyquist sampling, and (2) assess and correct
the scan-angle dependent sidelobe contributions to brightness
temperature error. The uncompensated brightness temperature error, to
the extent possible, should always be less than 0.1K. These errors are
most critical for channels 52.8-58 GHz and are most serious when the
sidelobes have significant amplitude and large-scale structure near the
planetary limb. The patterns for at least one flight unit should be
measured at least at the center frequency of each channel. The
sensitivity of these antenna pattern measurements should permit
accuracies of 2 dB rms at absolute antenna gains 20 dB below isotropic,
which implies a dynamic range of at least 65 dB (TBR) for the narrow
beams, essentially free of antenna-range-wall and surface-reflection
effects. The rms accuracy of the absolute pattern measurement should
otherwise generally be no worse than the less restrictive of 3 percent
in absolute gain or 0.5 dB (TBR), and the rms precision should be
one-fifth of that.

For each pattern the two principal axes of the nadir beam should be
scanned at least 90^o^ at increments no greater than one tenth of the 3
dB beamwidth. For one of the channels above 140 GHz and one channel
below, nadir patterns should also be measured for both polarizations
along lines 45^o^ removed from the principal axes. For the antenna
pointed approximately 40^o^ to one side, antenna patterns for both
principal polarizations (referenced to Earth) of the 31.4 GHz and 53.6
GHz channels should be measured over 180^o^ of scan (over the nominal
Earth view), and two such patterns should also be obtained for scans at
45^o^ to the principal axes. Several ATMS channels are affected by
surface emissivity, which varies with view angle and polarization.
Because ATMS mixes both polarizations, a 1^o^ misalignment of
polarization angle could introduce an angular asymmetry across the full
scan of approximately 1 K, comparable to that observed near 23 and 31
GHz for AMSU on NOAA 15 and NOAA 16 (alternate explanations for
asymmetry include unexpected antenna sidelobe asymmetries or pointing
errors). This angle should be measured +/-0.2 degrees (goal) on-axis for
the antenna pointed +/-40 degrees from nadir. Greater pattern
measurement accuracy and more complete mapping than suggested here would
be helpful

**F.1.6 ATMS Polarization Angle Alignment**

ATMS has several channels with temperature weighting functions peaking
in the lower troposphere or below the surface. These measurements are
affected by surface emissivity. Over oceans, the emissivity varies with
view angle and polarization. Therefore, the observed ATMS radiance for
these channels displays an angular dependence although the ATMS measures
a mixing signal from both vertical and horizontal polarization. A small
misalignment of the polarization angle would result in an asymmetric
radiance across the scan lines. The asymmetric radiance along the scan
line was first identified from NOAA-15 AMSU and continued to be present
for NOAA-16 AMSU. The adverse impacts of the asymmetry on AMSU derived
atmospheric and surface products manifest some false features such as
asymmetric cloud liquid water across the scan line. The empirical scheme
was developed over the oceans where the surface emissivity can be
accurately simulated.

The AMSU asymmetry could be a combination of several causes: (1)
polarization angle alignment; (2) antenna pointing angle; and (3) an
intrusion of the solar array. The initial analyses for NOAA-15 and -16
AMSU show that the adjustment of -1.5 degree to the instrument
polarization angle is needed in order to eliminate the asymmetry. Thus,
the Government Team needs to monitor the ATMS pre-launch calibration
procedure including the polarization angle alignment. It is recommended
that the angle be accurate to a few tenths of a degree.

[]{#__RefHeading___Toc534619223 .anchor}Appendix G: Surface Based
Networks / Field Campaigns Relevant to NPP Cal/Val

G.1 Introduction

This appendix gives information on the various field observations
relevant to the NPP calibration/validation efforts, including routine
observations from surface based networks and observations from
intermittent field campaigns. Note that there are other resources
required for the NPP efforts (such as NIST calibration facilities,
numerical model data, data from other satellites,...) which are not
covered here; this appendix addresses field data only.

The National Polar-orbiting Operational Environmental Satellite System
(NPOESS) is a joint NASA, NOAA, and DOD program merging the current POES
& DMSP systems into a common system of polar satellites with the goal of
providing meteorological and other environmental data products
operationally. The Earth Observing System (EOS) is an international
multi-satellite program for global remote sensing of the Earth, with a
mission goal to advance the scientific understanding of the Earth system
(i.e., including land, oceans, and atmosphere) as well as the influences
of natural and anthropogenic processes on this system. In order to
achieve these goals, these programs must produce accurate and precise
long-time series of radiometric measurement data from multiple
instruments on multiple platforms. Understanding and correctly
interpreting these data require the ability to separate geophysical
variability from instrument response changes in the observed signal
during the missions. This requires a detailed instrument system-level
characterization pre-launch, as well as extensive in-flight calibration
and validation activities.

Validation is the process of assessing by independent means the
uncertainties of derived geophysical data products from instrument
system outputs. This is generally approached by direct comparison with
independent correlative measurements from ground-based networks,
comprehensive test sites, and field campaigns; along with comparisons
with independent satellite retrieval products from instruments on the
same and different platforms. Pre-launch activities usually focus on
algorithm development and characterization of instrument uncertainties,
while post-launch emphasis is on algorithm refinement and
measured/retrieved data product assessments. It is essential to have an
integrated strategy for validation, including contributions from
airborne field campaigns, surface networks, as well as satellites. LEO
satellites provide measurements within a spatial context, while surface
networks bring in the temporal context; airborne field measurements,
however, provide both spatially & temporally registered observations
from a configuration geometry (i.e. nadir viewing) similar to the
space-based sensor being validated. Multi-platform observation campaigns
can provide simultaneous radiometric and geophysical parameter
measurements over spatially and spectrally homogeneous Earth scenes
enabling validation of the on-orbit satellite radiometric calibration as
well as geophysical parameter retrieval validations. The overall goal is
to enable a timely assessment of data product uncertainty for the new
space-based sensor being validated.

In order to validate global atmosphere and surface properties derived
from NPP satellite data, a reasonable sampling of the global variability
of these products is necessary. Given that each NPP product may vary
widely in space and time, most of the difficulty in validating the
global products arises from sparse sampling of the range of values
encountered by each variable. Hence, the NPP validation strategy
includes not only focussed field campaigns in specific locations and
under specific environmental conditions, but also a long time-series of
selected measurements from a select distribution of **surface validation
sites**. The primary surface validation sites promoted for use by NPP
are those currently also being used by EOS. This allows for continuity
in the validation data used to assess both the EOS and NPP products.
Required site/network instrumentation and measurements are specific to
each discipline (atmosphere, land, ocean, cryosphere, clouds, and
aerosols). Such networks include, for example, AERONET, the ARM CART
sites, the EOS Land Validation Core sites, the international radiosonde
network, and MOBY sites.

To supplement the routine observations taken at the various surface
sites and to extend the range of observation variability, the NPP
validation will benefit from additional key observations collected
during intermittent **field campaigns**. These campaigns can take many
forms and include both pre- and post-launch experiments, aimed at both
algorithm and product validation. As with many recent campaigns, most of
the experiments should be conducted in the context of larger science
objectives, while also leveraging the campaign for NPP satellite
validation. The experiments should be oriented toward providing a larger
spatial context for the routine, on-going observations made at the
surface networks, while also covering a larger range of conditions
(surface types, temperature, air mass, clouds, ...) not observed at the
routine sites. Other experiments with specific, targeted validation
goals are also envisioned. These campaigns will often involve high
altitude aircraft based sensors, as well as profiling aircraft, ship
based cruises, and additional surface based sensors. The IPO developed
NAST suite of aircraft sensors and similar sensors including S-HIS and
MAS (for example) which provide NPP-like radiometric observations, will
be used. These and other in-situ, remote sensing, and active sensing
observations can provide the proper spatial and temporal context needed
for satellite validation. The higher spectral and spatial resolution
data can be spectrally and spatially convolved, respectively, to
simulate what should be measured by the concurrent satellite
observations during overpass events.

The successful validation of NPP measurements will require the
utilization of many resources, some of which are supported by agencies
or countries outside of the NPP. Some of the resources are extant now,
but may not be at the time they will be needed for the NPP activities,
and it is incumbent on the NPP to endeavor to ensure the continued
existence of these vital assets in to the NPP era. In addition to the
sensors and instrument networks that will be required, there is also the
need to nurture the expertise in the scientific community so that this
will be available to make the appropriate contributions to the
validation exercise.

The remainder of this appendix lists specific networks and aspects of
field campaigns desired for NPP validation. Where appropriate, the
resources are divided into the six disciplines: atmospheric sounding,
land, ocean, cryosphere, clouds, and aerosols.

G.2 Surface Based Networks

Surface based networks provide long term, continuous sources of
validation data, often providing for a wide range of observed parameter
space, and large sample sizes and statistics required for validation.
The NPP validation sites are chosen to have significant overlap with
those of EOS and similar programs, to provide for continuity in the
scientific assessment of satellite products. Intrinsic to the NPP
validation activities are sites around the globe where there are
specific concentrations of appropriate sensors, such as the ARM sites,
as well as networks of individual sensors. Many of these sites can be
used to validate several EDRs and CDRs and over several of the
discipline groups, such as oceanic and atmospheric variables. Table G-1
lists many of the larger existing networks, primarily suited to the
validation of atmospheric variables. Other sites include, for example,
BigFoot, LTER, MPLnet, the BSRN/SurfRad network, CMDL sites, and MOBY.

Table G-1: Networks and Web Sites Associated with Global Climate Change

  ---------------- ----------------------- ------------------------------------------------------------------------ ----------- ---------
  **Acronym**      **Source**              **Status / Location**                                                    **Data?**   **\#
                                                                                                                                sites**

  **Field                                                                                                                       0
  Campaigns and                                                                                                                 
  Focused                                                                                                                       
  Studies**                                                                                                                     

  ARM              Atmospheric Radiation   http://www.arm.gov/docs/index.html                                       Yes         5
                   Monitoring Program                                                                                           
                   (NSA, AAO, SGP, TWP)                                                                                         

  BigFoot          BigFoot                 http://www.fsl.orst.edu/larse/bigfoot/index.html                         Yes         5

  Intensive Field  Field Campaigns: FIFE,  Mostly in ORNL DAAC                                                      Yes         13
  Campaign         BOREAS (NSA,SSA), LBA,                                                                                       
                   S2K, SNF, HAPEX-Sahel,                                                                                       
                   OTTER, Miombo                                                                                                

  **Long-Term                                                                                                                   
  Research Sites**                                                                                                              

  LTER             US LTER                 http://www.lternet.edu/                                                  Yes         22

  **Monitoring                                                                                                                  
  Networks**                                                                                                                    

  FLUXNET          FLUXNET                 *http://daacl.esd.ornl.gov/FLUXNET*                                      Yes         140

  FLASK-NET        Cooperative Air         http://www.cmdl.noaa.gov/ccgg/flask/ccgnetwork.dat                       Yes         80
                   Sampling Network                                                                                             

  AERONET          Aerosol Robotic Network http://aeronet.gsfc.nasa.gov:8080/                                       Yes         230

  BSRN             Baseline Surface        http://bsrn.ethz.ch/wrmc/bsrn_mainframeset.html                          Yes         33
                   Radiation Network                                                                                            

  ISIS             Integrated Surface      In process. http://zepher.atdd.noaa.gov/isis/isis.htm                    ?           0
                   Irradiance Study                                                                                             

  NADP             National Atmospheric    http://nadp.sws.uiuc.edu/                                                Yes         266
                   Deposition                                                                                                   
                   Program/National Trends                                                                                      
                   Network                                                                                                      

  TRAGNET          Trace Gas Network       http://www.nrel.colostate.edu/PROGRAMS/ATMOSPHERE/TRAGNET/TRAGNET.html   Yes         25

  **Global-Scale                                                                                                                
  Demonstration                                                                                                                 
  Networks**                                                                                                                    

  VALCORE          EOS Land Validation     Mercury                                                                  Soon        24
                   Core Test Sites                                                                                              

  VCC/VCF          MODLAND Land Cover      In process.                                                              ?           0
                   Change: Vegetation      http://modarch.gsfc.nasa.gov/MODIS/LAND/VAL/products/vcc_vcf.html#ref                
                   Cover Conversion (VCC)                                                                                       
                   and Vegetation                                                                                               
                   Continuous Fields (VCF)                                                                                      
  ---------------- ----------------------- ------------------------------------------------------------------------ ----------- ---------

G.2.1 Surface Networks Supporting Atmospheric Sounding

***ARM Sites***

ARM, the Atmospheric Radiation Measurement Program, was initiated by the
U.S. Department of Energy (DOE) with the ultimate goal of improving the
parameterizations of clouds and radiation used in climate models.

Programmatic Objectives are: (1) to relate observed radiative fluxes and
radiances in the atmosphere, spectrally resolved and as a function of
position and time, to the temperature and composition of the atmosphere,
specifically including water vapor and clouds, and to surface
properties, and sample sufficient variety of situations so as to span a
wide range of climatologically relevant possibilities; (2) to develop
and test parameterizations that can be used to accurately predict the
radiative properties and to model the radiative interactions involving
water vapor and clouds within the atmosphere, with the objective of
incorporating these parameterizations into general circulation models.

Figure G-1 show the location of the three ARM sites considered in the
NPP calibration and validation plan

![](media/image8.png){width="6.652777777777778in"
height="3.6395833333333334in"}

Figure G-1. Locations of the NSA, SGP, and TWP ARM sites.

The ARM sites ([www.arm.gov](http://www.arm.gov/)) are well established
resources that are expected to make important contributions to the
validation of primarily atmospheric variables. They include the
following sensors: up-looking infrared spectrometers (AERIs), mm cloud
radars, uplooking microwave radiometers, GPS, Raman LIDARs,
balloon-bourne sensors (radiosondes) launched at the satellite overpass
times, sun-photometers, MFRSRs and a full suite of surface
meteorological measurements including up- and down-looking broadband
radiometers. In addition to the three standard sites which each have the
potential to contribute validation data in three distinct climate
regimes, there is a need for comparable data taken in other conditions,
and this can be achieved by using a mobile ARM site, which was planned
at the outset of ARM program and which may become a reality in the NPP
timeframe. ARM data are available through the ARM Archive at ORNL
(www.arm.gov/archive).

***Oceanic atmospheric measurements***

For validation over the oceans, comparable sets of instruments are
available on ships, such as the *Explorer of the Seas* which undertakes
a weekly cruise circuit between Miami and the US Virgin Islands (see
[www.rsmas.miami.edu/rccl](http://www.rsmas.miami.edu/rccl)). An
extensive suite of instruments has been installed on research ships for
specific campaigns, such as on NOAA's *Ronald H. Brown* for the Nauru99
expedition, which also involved coordinated measurements from the
Japanese RV *Mirai* and the ARM site on Nauru (see
http://www.arm.gov/docs/news/nauru99/).

***International Radiosonde Network***

Making good forecasts first requires that we have measurements of wind
speed and direction, temperature, pressure, and humidity everywhere in
the atmosphere simultaneously.

In practice, this is impossible. However, getting the best set of
observations will help forecast the weather with more accuracy as far
into the future as possible.

The main device used to measure the state of the atmosphere above the
Earth\'s surface is the radiosonde, a balloon-borne package of
instruments that measure temperature, pressure and humidity as the
balloon ascends from the Earth\'s surface through the troposphere and
well into the stratosphere. In addition, as the radiosonde rises, it can
be tracked visually or by radar to provide information about the winds
aloft. (In that case, it is sometimes called a rawindsonde.)

The radiosonde balloon expands continuously under ever-lower pressure as
it floats upward, until ultimately it pops, releasing the package of
instruments with a parachute

back to the surface.

To get measurements throughout the atmosphere, an international network
of radiosonde stations is maintained by countries around the world. The
network for North America comprises stations typically 200-400 miles
apart on land.

There are virtually no radiosonde stations over the oceans except on
occasional islands and a few specially equipped ships, which makes
forecasting for the West Coast of North America difficult because our
winter storms (midlatitude cyclones) generally come from the west, from
over the Pacific Ocean, where there are few detailed observations of the
state of the weather. China maintains a denser radiosonde network than
North America does, but many poorer parts of the world, like the oceans,
lack good coverage, making good forecasts there very difficult.

To get approximately simultaneous observations throughout the
atmosphere, radiosondes are launched twice a day from each station in
the international network, at 00Z and 12Z (that is, at 5:00 P.M. and
5:00 A.M. PDT, or 4:00 P.M. and 4:00 A.M. PST). Each radiosonde
transmits its measurements to the launch station on the ground, whence
they are forwarded to a central location for analysis. (In the U.S.,
that location is the National Centers for Environmental Prediction, or
NCEP, outside Washington D.C.).

***SuomiNet***

SuomiNet is a proposed network of GPS receivers to be located at
universities and other locations to provide realtime atmospheric
precipitable water vapor measurements and other geodetic and
meteorological information. Detail on this network facility and
equipment can be found in this URL:
http://www.unidata.ucar.edu/suominet/

***ECC Ozone Sondes***

The ozonesonde is a lightweight, balloon-borne instrument that is mated
to a conventional meteorological radiosonde. As the balloon carrying the
instrument package ascends through the atmosphere, the ozonesonde
telemeters to a ground receiving station information on ozone and
standard meteorological quantities such as pressure, temperature and
humidity. The balloon will ascend to altitudes of about 115,000 feet (35
km) or about 4 hPa before it bursts. The heart of the ozonesonde is an
electrochemical concentration cell (ECC) that senses ozone as is reacts
with a dilute solution of potassium iodide to produce a weak electrical
current proportional to the ozone concentration of the sampled air.

The CMDL network of eight ozonesonde sites makes weekly ozone vertical
profile observations from the surface to about 35 km using
electrochemical concentration cell (ECC) ozonesondes. Three of these
sites, Boulder, Colorado, Hilo, Hawaii, and South Pole, Antarctica have
records of at least 15 years in length covering a significant portion of
the period that stratospheric ozone has been declining. There are about
50 locations around the world that make regular (approximately weekly)
ozone vertical profile measurements using ozonesondes. More details are
located at this URL: http://www.cmdl.noaa.gov/

G.2.2 Surface Networks Supporting Land and Atmosphere Properties

Aeronet

The AERONET (AErosol RObotic NETwork) program is an inclusive federation
of over 100 ground-based remote sensing aerosol networks. AERONET
provides hourly transmission of CIMEL sunphotometer data (spectral
aerosol properties and total water vapor) to the GOES (or METEOSAT)
geosynchronous satellites, which in turn relay the data to GSFC for
daily processing and archiving. By teaming with AERONET, MODLAND
scientists have access to validation data from a global network of
CIMELs in near real-time. The Aeronet network will be the main source of
atmospheric characterization for MODLAND Validation activities. AERONET
data are on-line at *http://aeronet.gsfc.nasa.gov:8080.*

FLUXNET

The FLUXNET network is dedicated to long-term measurements of carbon
dioxide, water vapor, and energy exchange from a variety of worldwide
ecosystems, integrated into consistent, quality assured, documented data
sets. FLUXNET is a network of networks, which integrates worldwide
CO~2~/H~2~0 flux measurements through the ASIAFLUX, AmeriFlux,
CARBOEROFLUX, and Oznet networks. There are currently over 140 towers
registered with FLUXNET and over 60 of these have submitted data or
defined a start date for doing so. The Oak Ridge DAAC will be the point
of contact for FLUXNET data archive and distribution. Details on FLUXNET
can be found at *http://daacl.esd.ornl.gov/FLUXNET/*.

***Land -- EOS Core Sites***

To provide the *in-situ* and other reference data, NPP land validation
program will utilize the EOS five-tiered categorization of field site
measurement capabilities and intensity (TableA-1).

The EOS Land validation Core Sites are being used for MODIS Land
validation program, and will provide the science community with timely
ground, aircraft, and satellite data for NPP science and validation
investigations. The sites, currently 24 distributed worldwide, represent
a large range of global biome types, and roughly comprise the area
within 100 km radius of a center point
(<http://modis-land.gsfc.nasa.gov/val/coresite_gen.asp>). (Figure G-2).

![](media/image9.jpeg){width="5.991666666666666in" height="2.69375in"}

**Figure G-2: EOS core sites (24 sites) located in North America, South
America, Europe, South Africa and Australia. Detailed information for
each site can be found at this URL
http://modis-land.gsfc.nasa.gov/val/coresite_gen.asp.**

In most cases, each EOS site includes a fixed tower on which
above-canopy instrumentation will be mounted to provide near-continuous
sampling of canopy-scale radiometric and meteorological variables. A
conceptual model for a core site instrument package includes a CIMEL™
ground and sky-scanning sun-photometer (surface reflectance, vegetation
index, BRDF), albedometers (albedo), and a CO~2~ flux system. These data
are augmented by surface measurements of LAI and FPAR at less frequent
time intervals. Core Sites will receive priority deployment of
validation instrumentation and cover each major biome type delineated in
NPP operational and science algorithms.

Table G-2 provides a description of the EOS sites and data types
collected. EOS core validation test sites. Detailed information on the
surface measurements and airborne data at each site can be found in
[http://modisland.gsfc.nasa.gov/val/coresite_gen.asp](http://modis-land.gsfc.nasa.gov/val/coresite_gen.asp)

The EOS validation program is planning to continue its collaboration
with the NASA's Airborne Science Program
(<http://www.dfrc.nasa.gov/airsci>), which provides airborne platforms
to carry NASA sensors such as AVIRIS, MAS, MASTER and AirMISR.

A new airborne system developed for MODIS vegetation reflectance and
vegetation index validation called MODIS Quick Airborne Looks (MQUALS)
([http://gaea.fcr.arizona.edu/validation/index.htm)](http://gaea.fcr.arizona.edu/projrcts/validation/index.htm)),
carrying digital cameras, a radiometer and albedometer is being used
over EOS sites.

A selection of product specific validation data performed at the EOS
core sites is described in the Table G-3.

Data collected at these validation sites, over a large number of field
campaigns are available through Mercury system at the ORNL DAAC (Cook et
al., 1999). The Mercury system performs a key role in centralizing the
distribution and archiving of field data, and provides both the team
collecting the data and the users significant advantages relative to
traditional data management systems
([http://mercury.ornl.gov](http://mercury.ornl.gov/)).

Table G-2: EOS core validation test sites

+-----------+-----------+------------+---------+---------+-----------+
| **Site    | **Biome** | **Coo      | **Lat   | **Long  | **Tower   |
| Name**    |           | rdinator** | itude** | itude** | (Network) |
|           |           |            |         |         | \***      |
+-----------+-----------+------------+---------+---------+-----------+
| ARM/CART  | Ag        | Meyer      | 36.64   | -97.5   | YES(B, F, |
|           | riculture |            |         |         | G)        |
+-----------+-----------+------------+---------+---------+-----------+
| Bon       | Ag        | Meyers     | 40.01   | -88.29  | YES(A, B) |
| dville,IL | riculture |            |         |         |           |
+-----------+-----------+------------+---------+---------+-----------+
| BOREAS,   | Boreal    | Baldocchi  | 55.88   | -98.48  | YES(A, B, |
| NSA       | Forest    |            |         |         | F)        |
+-----------+-----------+------------+---------+---------+-----------+
| BOREAS,   | Boreal    | Baldocchi  | 53.98   | -105.12 | YES(A, F) |
| SSA       | Forest    |            |         |         |           |
+-----------+-----------+------------+---------+---------+-----------+
| Cascades  | LTER      | Forest     | 44.5    | -121.62 | YES(A, B, |
|           | Evergreen | Running    |         |         | F, G)     |
+-----------+-----------+------------+---------+---------+-----------+
| Harvard   | Decid.    | Baldocchi  | 42.37   | -72.25  | YES(B, F, |
| Forest    | Forest    |            |         |         | G)        |
+-----------+-----------+------------+---------+---------+-----------+
| Howland   | Decid.    | Baldocchi  | 45.3    | -68.8   | YES (A,   |
|           | Forest    |            |         |         | F, G)     |
+-----------+-----------+------------+---------+---------+-----------+
| Ji-Parana | Trop.     | Huete      | -10.22  | -61.89  | Planned   |
|           | Forest    |            |         |         |           |
+-----------+-----------+------------+---------+---------+-----------+
| Jornada   | LTER      | Shrubland  | 32.5    | -106.75 | YES (G)   |
| Huete     |           |            |         |         |           |
+-----------+-----------+------------+---------+---------+-----------+
| Konza     | Prairie   | Baldocchi  | 39.08   | -96.62  | YES (F,   |
|           | Grassland |            |         |         | G)        |
+-----------+-----------+------------+---------+---------+-----------+
| Kr        | Forest    | Murphy     | 56.5    | 92.5    | YES       |
| asnoyarsk |           |            |         |         |           |
+-----------+-----------+------------+---------+---------+-----------+
| Maricopa  | Ag        | Huete      | 33.04   | -111.58 | None      |
| Ag.       | riculture |            |         |         |           |
+-----------+-----------+------------+---------+---------+-----------+
| Mongu     | Woodland  | Privette   | -15.45  | 23.25   | Planned   |
|           |           |            |         |         | (A, G)    |
+-----------+-----------+------------+---------+---------+-----------+
| SALSA     | Desert    | Huete      | 31.74   | -109.85 | None      |
|           | shrub     |            |         |         |           |
|           | /         |            |         |         |           |
|           | grassland |            |         |         |           |
|           |           |            |         |         |           |
|           | Montagne  |            |         |         |           |
|           | forest    |            |         |         |           |
+-----------+-----------+------------+---------+---------+-----------+
| Sevilleta | Desert/   | Holben     | 34.32   | -106.8  | None (A)  |
| LTER      | grassland |            |         |         |           |
+-----------+-----------+------------+---------+---------+-----------+
| Skukuza   | Savanna   | Privette   | -25     | 31.67   | YES (A,   |
|           |           |            |         |         | G)        |
+-----------+-----------+------------+---------+---------+-----------+
| Tapajos   | Trop.     | Huete      | -3.23   | -54.75  | Planned   |
|           | Forest    |            |         |         |           |
+-----------+-----------+------------+---------+---------+-----------+
| Uardry    | Grassland | Hook       | -34.39  | -145.3  | None      |
+-----------+-----------+------------+---------+---------+-----------+
| Ulan      | Grassland | H          | 45.75   | 106.26  | None      |
| Bator     |           | uete/Honda |         |         |           |
+-----------+-----------+------------+---------+---------+-----------+
| USDA ARS  | Ag        | Liang      | 39.03   | -76.85  | YES(A, F) |
|           | riculture |            |         |         |           |
+-----------+-----------+------------+---------+---------+-----------+
| Virginia  | Coastal   | Justi      | 37.5    | -75.67  | None (A)  |
| Coast     | Area      | ce/Vermote |         |         |           |
+-----------+-----------+------------+---------+---------+-----------+
| Walker    | Decid.    | Baldocchi  | 35.9    | -84.3   | YES(F, G) |
| Branch    | Forest    |            |         |         |           |
+-----------+-----------+------------+---------+---------+-----------+
| Wisc.     | Forest    | Norman     | 46      | 89.6    | YES(F)    |
| LTER      |           |            |         |         |           |
+-----------+-----------+------------+---------+---------+-----------+

\*A= AERONET, B = Bigfoot, F=FLUXNET, G = Global Land Cover Test Sites

**Table G-3: MODIS land products and instrumentation data used for
validation**

+---------------+------------------------+------------+---------------+
| **Product**   | **Field Instrument**   | **         | **Satellite   |
|               |                        | Airborne** | data**        |
+---------------+------------------------+------------+---------------+
| Surface       | Hand held Radiometer,  | MQUALS,    | MODIS, MISR   |
| Reflectance   |                        | MAS,       |               |
|               |                        | AirMISR,   |               |
|               |                        | AVIRIS     |               |
+---------------+------------------------+------------+---------------+
| Land Surface  | Thermal Infrared       | MAS,       | ETM+, ASTER,  |
| Temperature   | Spectrometer,          | MASTER     | MODIS         |
|               |                        |            |               |
|               | Spectral Infrared      |            |               |
|               | Bidirectional          |            |               |
|               | Reflectance and        |            |               |
|               | Emissivity,            |            |               |
|               |                        |            |               |
|               | Heinman Thermometer    |            |               |
+---------------+------------------------+------------+---------------+
| Snow and Sea  | Field Survey, NOHRSC   | MAS        | ETM+, ASTER,  |
| Ice           |                        |            | MODIS         |
+---------------+------------------------+------------+---------------+
| BRDF\*/Albedo | Albedometer            | AVIRIS     | MISR, MODIS,  |
|               | (Kipp+Zonen CM21)      |            | VEGETATION,   |
|               |                        |            | MODIS         |
|               | BSRN                   |            |               |
+---------------+------------------------+------------+---------------+
| Vegetation    | Spectrometer           | MQUALS,    | ETM+, MODIS,  |
| Index         |                        |            |               |
|               |                        | MAS        |               |
+---------------+------------------------+------------+---------------+
| LAI/FPAR\*    | LAI-2000, TRAC, Field  | MAS        | ASTER, MODIS  |
|               | ceptometer,            |            |               |
|               | Spectrometer           |            |               |
+---------------+------------------------+------------+---------------+
| PSN/NPP\*     | CO2 flux towers        |            | MODIS         |
+---------------+------------------------+------------+---------------+
| Fire and Burn | Field survey           | MAS        | TM, ETM+,     |
| Scan          |                        |            | MODIS         |
+---------------+------------------------+------------+---------------+
| Land Cover    | Field survey           | AVIRIS,    | IKONOS, MODIS |
|               |                        | MAS        |               |
+---------------+------------------------+------------+---------------+
| VCC/VCF\*     | Field survey           | AVIRIS,    | IKONOS, MODIS |
|               |                        | MAS        |               |
+---------------+------------------------+------------+---------------+

\* BRDF: Bidirectional Reflection Distribution Function

LAI: Leaf Area Index PSN: Daily Photosynthesis

NPP: Net Primary Production VCC: Vegetation Cover Conversion

VCF: Vegetation Continuous Field

**G.2.3 Ocean Surface Networks**

***Marine Optical BuoY (MOBY)***

The validation of oceanic retrievals in the visible part of the spectrum
will be carried out principally using the Marine Optical BuoY (MOBY)
sites off Hawaii, and using MOCE cruises (Marine Optical
Characterization Experiment) initialization cruise. MOBY\'s primary
purpose is to measure visible and near-infrared radiation entering and
emanating from the ocean. It is the variations of the visible
region-reflected radiation that is referred to as ocean color from which
other quantities can be derived, such as the abundance of microscopic
marine plants (phytoplankton).

Approximately 50 feet long, MOBY is the world\'s largest marine optical
device. In the ocean, only its antennae, solar panels, strobe light, and
surface buoy (which houses the computer and cellular phone for data
transmission) are visible, standing about 7 feet above the waterline. A
fiberglass mast extends more than 40 feet directly down beneath the buoy
to the instrument bay. At depths of about 6, 16, and 28 feet
respectively, 9-foot long booms or \"arms\" extend outward perpendicular
to the mast.

Optical collectors (irradiance and radiance) have been placed at the
ends of the arms, as well as on top of the buoy above the surface to
collect light coming into the ocean, and then the light reflected back
out of the ocean. The reflected portion of the light has been modified
by particles such as phytoplankton suspended at various depths, that
modifies the signal available to the satellite sensors. Lenses within
these collectors focus the light onto fiber optic cables which then
transmit the light to a fiber optic multiplexer housed in the instrument
bay. The multiplexer relays the light into a dual spectrograph with
detectors that measure the spectral radiant energy. These signals are
then digitized and relayed by microprocessors and transmitted up to a
main computer housed in the surface buoy. This information is then
stored on a disk drive, which may be accessed via cellular phone and
downloaded for processing back at the MOBY Team facility.

The optical system uses two spectrographs with a dichroic (\"water\")
mirror to measure radiometric properties with high spectral resolution
and stray light rejection. This \"water\" mirror is designed to transmit
the red (630 to 900 nm) and reflect the blue (380 to 600 nm) portions of
the spectrum, making the transition from reflectance to transmittance
between 590 and 650 nm. Thus potential for stray light is greatly
reduced by splitting the visible spectrum at the beginning of the water
absorption region since most of the short wavelength energy is diverted
from the entrance slit of the long wavelength spectrograph. The
splitting also allows the spectrographs (free spectral range and
integration times) to be optimized for the two distinctive spectral
domains. Internal calibration and ancillary sensors (temperature,
inclination, pressure, etc.) are included.

***Ocean - SST***

The validation of SST derived from VIIRS and CrIS/ATMS requires the use
of infrared radiometers or interferometers accurate to better than 0.1K.
These are mounted on ships so that they measure the skin temperature of
the ocean ahead of any influence of the vessel. The primary instrument
for this is the M-AERI, which operates in the range of infrared
wavelengths from \~3 to \~18µm and measures spectra with a resolution of
\~0.5 cm^-1^. It uses two infrared detectors to achieve this wide
spectral range, and these are cooled to \~78^o^K (i.e. close to the
boiling point of liquid nitrogen) by a Stirling cycle mechanical cooler
to reduce the noise equivalent temperature difference to levels well
below 0.1K. The M-AERI includes two internal black-body cavities for
accurate real-time calibration. A scan mirror directs the field of view
from the interferometer to either of the black-body calibration targets
or to the environment from nadir to zenith. The mirror is programmed to
step through a pre-selected range of angles. When the mirror is angled
below the horizon the instrument measures the spectra of radiation
emitted by the sea-surface, and when it is directed above the horizon it
measures the radiation emitted by the atmosphere. The sea-surface
measurement also includes a small component of reflected sky radiance.
The interferometer integrates measurements over a pre-selected time
interval, usually a few tens of seconds, to obtain a satisfactory signal
to noise ratio, and a typical cycle of measurements including two view
angles to the atmosphere, one to the ocean, and calibration
measurements, takes about five minutes. The M-AERI is equipped with
pitch and roll sensors so that the influence of the ship's motion on the
measurements can be determined. The radiometric calibration of the
M-AERI is done continuously throughout its use. As with simpler
self-calibrating radiometers, an FTIR spectroradiometer can be
calibrated by using two black-body targets at known temperatures. These
provide two reference spectra to determine the gains and offsets of the
detectors and associated electronics. A fuller description is given by
Minnett et al, 2001.

Other simpler, filter radiometers are capable of achieving the required
accuracy and can be used to extend the data set achieved by the M-AERIs
(of which three currently exist). These are the CIRIMS, ISAR, SISTeR,
DAR011 and the JLP Nulling Radiometers which all participated in the
Infrared Radiometer Intercomparison held in Miami in the summer of 2001
(see <http://rsmas.miami.edu/ir2001>). Several of these sensors belong
to foreign investigators or groups, and their participation in NPP
validation campaigns may require special arrangements.

Comparison with *in-situ* measurements of the ocean temperature taken
below the surface from ship or buoys, moored and drifting, can
contribute to the validation of NPP SSTs provided they are limited to
moderate wind speed conditions (\<\~6ms^-1^) where the relationship
between the subsurface bulk temperature and the surface skin temperature
is moderately well constrained (Donlon et al, 2001). At lower wind
speeds, the vertical temperature gradients are sufficiently variable
that they decouple the skin surface temperature from the bulk
measurement at a depth of centimeters and more, at least at the accuracy
of 0.1K that is required for SST validation (Minnett and Ward, 2000;
Ward and Minnett, 2001).

Ocean Color

We recommend that NOAA sponsor a dedicated NPP initialization cruise,
dedicated to characterization of the NPP EDR\'s and CDRs (especially
ocean color and SST), in the first several months of the mission, in
which members of the cal/val team can participate and collect a complete
initialization and validation data set. The objectives would be to
enable complete characterization of the atmosphere and ocean relevant to
NPP. For ocean color and ocean bio-optics, the SeaWiFS Protocols
document gives a good listing of variables which should be measured and
techniques that should be employed. For SST, results of the round-robin
calibration workshops conducted at U. Miami serve as an excellent
framework for skin radiance measurements.

It is impossible to obtain a robust suite of measurements by adding a
few scientists to a scientific process-oriented cruise effort due to
berth limitations, and the low priority given to validation activities
within a science process investigation framework for ship location, ship
resources (winch schedules), and the like.

Within NASA, planning is underway for a Global Carbon/Climate Program
which includes carbon process studies in the NPP timeframe that address
the oceanic realm of the global carbon system. The first of these is
being planned for the N. Atlantic, with a later focus on the South
Pacific. These studies will involve ships, buoys, and aircraft
platforms, and satellite observations will play a major role in defining
the spatial and temporal variability and global context of these
studies. These studies will provide a basis for collection of essential
validation data for the NPP sensors.

References for Ocean Surface Observations

Donlon, C. J., P. J. Minnett, C. Gentemann, T. J Nightingale, I. J.
Barton, B. Ward and J. Murray, 2001. Towards improved validation of
satellite sea surface skin temperature measurements for climate
research. *J. Climate*. Accepted.

Minnett, P. J., R. O. Knuteson, F.A. Best, B.J. Osborne, J. A. Hanafin
and O. B. Brown, 2001. The Marine-Atmosphere Emitted Radiance
Interferometer (M-AERI), a high-accuracy, sea-going infrared
spectroradiometer. *Journal of Atmospheric and Oceanic
Technology*,**18**, 994-1013.

Minnett, P.J. and B. Ward. Measurements of near-surface ocean
temperature variability -- consequences on the validation of AATSR on
Envisat. ERS -- ENVISAT Symposium "Looking down to Earth in the New
Millennium" Gothenburg, Sweden. 16-20 October 2000.

Ward, B. and P. J. Minnett, 2001. An autonomous profiler for near
surface temperature measurements. Gas Transfer at Water Surfaces. edited
by M. A. Donelan, W.M. Drennan, E.S. Saltzmann and R. Wanninkhof.
American Geophysical Union Monograph. In the press.)

G.2.4 Cryosphere

*Need more here on routine cryosphere surface sites*

In-situ measurements of cryosphere surface parameters are available from
a limited number of sites, including Dome C and the North Slope of
Alaska ARM site.

Dome Concordia (Dome C) is a broad topographic dome roughly centred at
75° 06'06"S, 123° 23'42"E on the polar plateau of East Antarctica (at
3233 m elevation a.s.l.),

and is situated more than 700 km from the coast. This location is about
65 km south of the old U.S. Dome C camp, and has been selected as the
optimal site for a new

collaborative European (EPICA) deep ice core \[Tabacco and others, J.
Glac, 2000\]. The chosen core site will allow recovery of a core to a
depth of 3250m with a climate

history of some 400,000 years. In conjunction, the French and Italians
have agreed to cooperate in the establishment of a research programme,
including construction and

operation of a scientific base "Concordia". The use of this station for
scientific research is open to the world-wide scientific community and
is considered here as a candidate

calibration site for SMOS.

This site is well suited for cal/val activity for the following reasons:

1)  homogeneity of snow surface at the 100 km scale

2)  topography is known from satellite altimetry to high precision at
    100km scale

3)  surface roughness is minimal relative to other ice sheet locations

4)  the sky is clear, and the atmosphere is extremely dry and stable

5)  ionospheric activity is minimal (and daily and long term variation
    in Total Electron Content is minimized)

6)  snow accumulation is low (around 3.7cm/yr).

The Dome C Site is well characterized, with:

-   10 years of consistent automatic weather station data (air
    temperature, pressure, wind speed and direction)

-   Topography is known at the kilometric scale (from ERS geodetic phase
    altimetry and GPS measurements), and ICESAT will provide meter
    accuracy laser profiles over Dome C in 2001-2002.

-   Bedrock is mapped at the same kilometer scale using P-band
    ground-penetrating radar, by subtracting ice thickness (along
    profiles) from the detailed altimeter topographic profiles.

-   Ice surface velocity field is characterized by ERS SAR
    interferometry, GPS, and DORIS tracking.

-   Ice core data provides the mean snow accumulation rate, and density
    profile information

-   Embedded thermistor strings provide firn temperature profile
    temporal variability.

-   Ancillary data sets exist from microwave radar scatterometers (5 and
    13.6 GHz), radiometers (5, 19, 22, 37, 85 GHz), and radar altimeters
    (13.6 GHz).

Further in-situ measurement activities are planned in the frame of the
Concordia project.

Priority Objectives (for Science Definition Studies):

-   Establish temporal and spatial variability in SMMR C-band Tb over
    Dome C, and establish time-space correlation statistics/length
    scales in this region and

-   rms variability information (\*this should be done in view of the
    SMOS temporal revisit which is possible at high latitudes).

-   Establish seasonal variation in Tb due to seasonal temperature cycle

-   Correct for temperature and investigate variation in emissivity
    (independent of physical temperature).

-   Establish whether radiative transfer models for ice sheet can be
    generalized to L-band (is dense-medium theory still appropriate, or
    are simple approximations

-   Establish whether the Dome C ice sheet target is stable enough to
    check for long term stability and drift in total power (i.e.
    calibration).

-   Is Dome C appropriate as a distributed target for checking
    consistency of image reconstruction, by using known trajectories of
    target through the FOV (in incidence and azimuth -- through
    simulated swaths).

-   Can the stability of the ice sheet emission be used to test the
    consistency in reconstructed brightness temperatures (for a
    reasonably uniform radiator)

-   Does radiometric anisotropy in the surface matter at L-band for this
    pixel scale? If so, extrapolate existing definitions of anisotropy
    from NSCAT/SSMI/QuikScat data to define modulation pattern (as a
    function of local incidence and azimuth angle), and define Tb
    trajectories and range of variability according to relative orbit
    swath direction relative to surface anisotropy.

-   Can Dome C indicate what the averaging requirement is to achieve a
    known level of accuracy such as a 0.1 K criterion (i.e. defined
    reproducibility in Tb over time).

G.2.5 Clouds

***ARM sites***

In addition to facilities for atmospheric sounding, the ARM sites also
possess state-of-the-art measurements of cloud properties. These are
provided by sensors including a millimeter cloud radar, micropulse
lidar, Vaisala ceiliometer, whole sky imager, and Raman Lidar.

See section G.2.1 for description of the ARM sites instruments available
for cloud characterization.

***FARS***

The Facility for Atmospheric Remote Sensing is a permanent cloud
research station located on the eastern edge of the University of Utah
campus (40.77degrees North by 111.83 degrees East) on the bench of the
Wasatch Mountains (1.52 km above mean sea level). It was established in
1987 with joint funding from the National Science Foundation and the
University of Utah to house the Cloud Polarization (ruby) Lidar, and has
steadily grown through the addition of state-of-the-art remote sensors,
including a suite of radiometers, a 3.2-mm polarimetric Doppler radar
(from NSF and U. Utah), and the dual-wavelength scanning Polarization
Diversity Lidar (PDL, from the Department of Energy Atmospheric
Radiation Measurement -ARM- program). Both the radar and the PDL are
mobile units that have participated in major cloud research programs,
such as the Project FIRE II and ARM program CART Intensive Observation
Period field campaigns. The current specifications of this unique
university-based facility can be found at the facility equipment link
below.

Since its inception, FARS has been applied to the regular study of
high-level cirrus clouds in support of basic research and the satellite
validation effort of Project FIRE through its Extended Time Observations
(ETO) component. As of this time, more than 2,000 hours of ruby lidar
ETO data from high level clouds have been collected from FARS. Current
support for FARS observations comes from NSF and NASA for basic cirrus
cloud and FIRE ETO satellite studies, the NASA Atmospheric Effects of
Aviation Project for aircraft contrail/cirrus research, and the DOE ARM
program for multiple remote sensor cloud retrieval algorithm
development. Data from FARS and the field campaigns also support cloud
microphysical and radiative transfer modeling research components at the
Department of Meteorology. More details on the instruments avaialble at
this site can be found in this URL:
http://www.met.utah.edu/ksassen/fars.html.

***MPLnet***

MPL-Net is a worldwide network of micro-pulse lidar (MPL) systems.
MPL-Net is run by members of the Cloud and Aerosol Lidar Group in Code
912 at GSFC and is funded by NASA/EOS . Additional funding for research
cruises at sea is provided by the NASA SIMBIOS project. The MPL is a
single channel (523nm), autonomous, eye-safe lidar system originally
developed at GSFC and is now commercially available. The MPL is used to
determine the vertical structure of clouds and aerosols. The MPL data is
analyzed to produce optical properties such as extinction and optical
depth profiles of the clouds and aerosols.

The primary goal of MPL-Net is to provide long-term data sets of cloud
and aerosol vertical distributions at key sites around the world. The
long-term data sets will be used to validate and help improve global and
regional climate models, and also serve as ground-truth sites for
NASA/EOS satellite programs such as the Geoscience Laser Altimeter
System (GLAS) on the ICESat spacecraft. (launch date Spring 2002).

MPL-Net is composed of NASA operated sites, incorporated sites from the
ARM MPL network, and sites privately operated by researchers from around
the world. Also, all MPL-Net sites are co-located with AERONET
sunphotometers. Instrument calibrations and data processing for all
sites are accomplished using techniques developed by our group over 7
years of MPL development and deployment.

In addition to the long-term sites, MPL-Net provides support for field
experiments each year using MPL systems reserved for field use (land and
ship based deployments possible).

More details on the instruments avaialble at this site can be found in
this URL: http://virl.gsfc.nasa.gov/mpl-net/.

**G.2.6 Aerosols**

***ARM sites***

See section G.2.1 for description of the ARM sites instruments available
for aerosol characterization.

***Aeronet***

See section G.2.2 for description of instruments available at Aeronet to
measure spectral aerosol properties.

***FARS***

See section G.2.5 for description of FARS sites and instruments
available to measure spectral aerosol properties.

***EOS core sites***

See section G.3.2 for description of EOS sites and instruments available
to measure spectral aerosol properties.

G.3 Field Campaigns

To supplement the routine observations taken at the various surface
sites and to extend the range of observation variability, the NPP
validation will benefit from additional key observations collected
during intermittent field campaigns. These campaigns can take many forms
and include both pre- and post-launch experiments, aimed at both
algorithm and product validation. As with many recent campaigns, most of
the experiments should be conducted in the context of larger science
objectives, while also leveraging the campaign for NPP satellite
validation. The experiments should be oriented toward providing a larger
spatial context for the routine, on-going observations made at the
surface networks, while also covering a larger range of conditions
(surface types, temperature, air mass, clouds, ...) not observed at the
routine sites. Other experiments with specific, targeted validation
goals are also envisioned. These campaigns will often involve high
altitude aircraft based sensors, as well as profiling aircraft, ship
based cruises, and additional surface based sensors. The IPO developed
NAST suite of aircraft sensors and similar sensors including S-HIS and
MAS (for example) which provide NPP-like radiometric observations, will
be used. These and other in-situ, remote sensing, and active sensing
observations can provide the proper spatial and temporal context needed
for satellite validation. The higher spectral and spatial resolution
data can be spectrally and spatially convolved, respectively, to
simulate what should be measured by the concurrent satellite
observations during overpass events.

Field experiment measurements are critical for inter-comparisons with
satellite-based instrument data to help validate such sensors, and their
corresponding geophysical retrieval algorithms and data products, and
achieve confidence in their subsequently-obtained data products. The
NPOESS sensors are tasked with providing an operational monitoring
capability for EDRs to be measured globally. This requires the
calibration/validation process to be applicable over the range of
observations to be encountered, and thus requires field experiment
programs to be implemented such that this extent in observation
variability is addressed. Correspondingly, field experiment geographic
location and temporal insertion must be selected to cover the range in
surface characteristics (topography, emissivity, temperature), and
seasonal atmospheric content (water vapor & trace constituents) and
weather (clouds & precipitation) variability. To the greatest extent
possible, field experiment locations should be collocated near existing
surface instrumentation networks (such as the SGP & NSA CART sites) to
bring in additional, previously-validated "ground-truth" data.
Additionally, these ground site measurements (i.e. radiosonde launches
and ground-based instrument recording times) need to be temporally
coincident with times of NPP satellite overpasses (or in an Intensive
Operating Period, IOP, mode) during these field experiment validation
periods.

Field validation measurements from high-altitude airborne sensors are
critical for successful space-based instrument validation, since only
observations from such platforms can provide the proper spatial &
temporal context needed as well as be used to simulate expected
satellite measurements for the instrument being validated. The higher
spectral and spatial resolution aircraft sensor data can be spectrally
and spatially convolved, respectively, to simulate what should be
measured by the concurrent satellite observations during overpass
events. The much higher spatial resolution of the aircraft sensor data
can play an important role in validating satellite-derived data products
under the conditions of variable surface and atmospheric radiance (e.g.,
due to clouds) within the satellite sensor footprint.

NPP field campaigns will draw heavily upon the aircraft based sensors
developed by NPOESS including NAST and S-HIS. Aircraft data is important
to the NPP calibration and validation both before and after launch.
Before launch, it will provide the means to demonstrate expected
performance and to establish algorithm approaches that will work in the
presence of actual atmospheric cloud conditions. After launch, it will
form the basis for system validation. The NPOESS Airborne Sounder
Testbed (NAST) is a suite of airborne infrared and microwave
spectrometers, developed for the Integrated Program Office (IPO), that
has been flying on the NASA high altitude ER-2 aircraft as part of the
risk reduction effort for NPOESS. In addition to their stand alone
scientific value, data from these airborne instruments have been used to
simulate possible satellite-based radiance measurements, therefore
enabling experimental validation of instrument system specifications and
data processing techniques for future advanced atmospheric remote
sensors. The NAST-I is a high resolution Michelson interferometer that
derives its heritage from the non-scanning High resolution
Interferometer Sounder (HIS) developed by researchers at the University
of Wisconsin and serves as one important component of the NAST
instrument suite. It scans the Earth beneath the ER-2 or Proteus with a
nominal spatial resolution of approximately 0.13 km per km of aircraft
altitude (i.e. 2.6 km from a 20 km ER-2 altitude) and within a
cross-track swath width of about 2 times the aircraft altitude (i.e.,
\~46 km for the ER-2); its unapodized spectral resolution of 0.25 cm-1
within the 3.6 - 16.1 micron spectral range will enable experimental
simulation of future infrared sounding instruments. NAST-M is the
microwave component of NAST, currently with channels covering the 54 &
118 GHz oxygen bands; this microwave component enables atmospheric
sounding in the presence of clouds. The Scanning High resolution
Interferometer Sounder (S-HIS) is an angular scanning Michelson
interferometer also deriving its heritage from the non-scanning HIS
instrument. While initially developed for operation on an unpiloted
aircraft, S-HIS has flown on both the NASA DC-8 & ER-2 platforms. NAST-I
& -M have both participated in the Wallops98, CAMEX-3, and WINTEX field
measurement campaigns, and S-HIS served as an integral part of both
CAMEX-3 & WINTEX. The NAST & S-HIS are validated airborne sensors that
are available to: support NPOESS sounding instrument (i.e., CrIS & ATMS)
development & validation activities; serve as an EOS Validation Tool
(e.g., AIRS, CERES, MODIS, MOPITT, & TES); provide mesoscale Earth
science observations (from field experiment campaigns, e.g. CAMEX-3,
WINTEX, and other flights of opportunity, e.g. Wallops98/99); as well as
to serve as an engineering testbed for infusion of new technology (i.e.,
to explore enhancing airborne sounding; optimizing space-based sounding
performance; and applicability toward other measurements, e.g.
chemistry).

The primary focus of the combined NAST & S-HIS payload will be to
provide upwelling infrared and microwave radiance measurements and
retrieved geophysical parameters to assist with or enable the following:

-   accurate, spatially & temporally registered infrared & microwave
    calibrated radiance spectra for observed Earth scenes

-   detailed characterization of atmospheric thermal and moisture
    structure, under clear to cloudy conditions

-   radiative trace gas detection & transport (e.g. O3, CO, CH4, N2O,
    CO2)

-   biomass burning studies: atmospheric radiative impact; radiative
    temperatures of fires; and Earth scene type classification

-   NPOESS IPO instrument and forward model pre-launch specification
    optimization and post-launch calibration/validation (e.g. CrIS &
    ATMS)

-   EOS instrument and forward model calibration/validation (e.g. CERES,
    MODIS, MOPITT, AIRS, TES)

-   NAST-I, NAST-M, & S-HIS instrument performance
    verification/calibration/validation

-   synergistic retrieval studies (e.g., NAST-I + NAST-M, MAS + S-HIS),
    including other platform measurements, etc.)

-   EOS & NPOESS follow-on sensors for T, H2O, & chemistry: instrument
    concept definition and optimization studies

-   advanced Geostationary Earth Orbit (GEO) sounding & chemistry
    applications: instrument concept definition and optimization studies

The following radiance and geophysical data products may be obtained
from field implementation of the NAST-I, NAST-M, and S-HIS instrument
suite:

-   calibrated radiances (IR & U-wave)

-   atmospheric temperature profiles

-   atmospheric water vapor profiles

-   surface temperature & emissivity

-   cloud properties (altitude, temp. & emiss., LWP, effective particle
    size)

-   tropospheric species column concentrations & some profiling (e.g.
    ozone, carbon monoxide, methane, & water vapor)

-   atmospheric transport via H2O winds

-   aerosol IR optical depth

NAST-I & NAST-M have flown on NASA's high-altitude ER-2 aircraft and on
the high-altitude profiling Proteus aircraft. S-HIS has flown the bulk
of its time on the NASA DC-8 (i.e. during the CAMEX-3 field mission),
while also having several flights on the ER-2 (i.e., during WINTEX). In
addition to enabling flight opportunities when the ER-2 is booked, the
Proteus has several beneficial flight attributes making it very
attractive stand-alone or for flying combined sorties with the ER-2
during field deployments. The Proteus-unique platform attributes
include:

-   Ultra­fine and variable spatial resolution by not being constrained
    with a minimum flight altitude

-   Improved geophysical data product quality with increased sample
    averaging afforded by slower ground speed

-   Extended time observation capability of pollution episode evolution
    and transport processes with long duration flight capability

-   Measurement altitude profiling capability using platform cruise
    altitude variations

-   Further complementary benefits may be achieved by combining Proteus
    flights with ER-2 and DC-8 field deployments, including:

-   Inter­platform validation capability

-   Radiation Divergence (cooling rate) measurements via formation
    flying at different levels

-   Enhanced total measurement set through combined instrument diversity

-   Extend effective swath width of airborne remote sensing observations
    through offset formation flying

-   Broader spatial scale coverage through varying simultaneous flight
    patterns

Many of these attributes are also provided by the S-HIS when flying
onboard the profiling DC-8. Plans are also currently being made for NAST
deployment on the GlobalHawk, an unmanned high altitude aircraft that
would allow flight durations of up to 24 hours and global transits,
allowing for spatial sampling similar to that of a single satellite
orbit. Also possible in the NPP post-launch time frame is NAST-2, a NAST
follow-on with additional observation capabilities.

G.4 Discipline Specific Field Campaigns

G.4.1 Atmospheric Sounding Field Campaigns

Piloted research aircraft (e.g., ER-2 and Proteus) carrying a variety of
active and passive radiometric sensors will be used in field programs to
provide high spatial resolution validation data. These aircraft will be
capable of flying over wide range of altitudes, including vertical
profiling which enables precise validation of radiative transfer models
and retrieved atmospheric parameters. Unmanned airborne vehicles (e.g.,
the Global Hawk) will enable a global sampling of surface and
atmospheric products over a wide range of geographical and atmospheric
conditions in a single flight. Commercial aircraft equipped with
meteorological sensors (e.g., ACARS) will provide time coincident
atmospheric sounding validation data obtained during ascents and
descents near airports around the globe.

Required scientific measurements and potential instrumentation include
the following:

***[Measurement Sensor/Instrumentation Platform]{.underline}***

IR spectral radiance FTS/NAST-I, S-HIS, INTESA high-altitude a/c

IR spectral radiance FTS/AERI, MAERI surface-based

H2O LIDAR/LASE high-altitude a/c

T, H2O, P (in-situ) Radiosondes balloon

H2O LIDAR/SRL ground-based

H2O LIDAR/DIAL ground-based

Microwave radiance radiometer/NAST-M high-altitude a/c

Vis/ir narrowband radiance multispectral scanner/MAS high-altitude a/c

Vis/ir narrowband radiance multispectral scanner/MASTER high-altitude
a/c

Microwave radiance imaging radiometer/PSR high-altitude a/c

Vis/ir narrowband radiance multispectral imaging/AVIRIS high-altitude
a/c

Vis/ir narrowband radiance multispectral radiometers/MQUALS
high-altitude a/c

Vis imagery multiangle imaging/AirMISR high-altitude a/c

Vis/ir narrowband radiance multispectral scanner/MODIS satellite-based

Vis/ir narrowband radiance multispectral scanner /ASTER satellite-based

Vis/ir imagery multispectral imaging/Landsat-7 satellite-based

Infrared spectral radiance IR grating/AIRS satellite-based

Infrared spectral radiance IR FTS/IASI satellite-based

Microwave radiance radiometer/AMSU satellite-based

Microwave radiance radiometer/HSB satellite-based

IR spectral radiance FTS/GIFTS satellite-based

***Atmosphere -- aircraft measurements***

A series of sensors developed for deployment on aircraft is required for
NPP validation of atmospheric variables, and some surface parameters
too. These are the MAS, a fifty channel visible, near-infrared, and
thermal infrared imaging spectrometer with 50 m resolution at nadir
(King et al. 1996), the Scanning HIS, a 2 km resolution at nadir
interferometer sounder, NAST-I, a 2.6 km resolution interferometer
covering 3.5 to 16 microns with a spectral resolution greater than 2000,
NAST-M, a 16 channel microwave radiometer sensitive to 50-60 and 113
-119 GHz radiation from 2.5 km resolution footprints, AVIRIS, a 224 band
imaging spectrometer from 0.4-2.5 µm with 20 m resolution at nadir (Vane
et al. 1993). All spatial resolutions cited above are for a NASA ER-2
aircraft altitude of 20 km. More information on the airborne instruments
described above and others can be found at the URLs presented in Table
A-3.

Additional Airborne Instruments include:

-   Aircraft *in-situ* spectrometer for IR active trace gases at
    platform altitude

-   Airborne LIDAR (i.e. LASE) for upper tropospheric H 2 O and aerosol
    profiles; co-incident observations with NAST-I/S-HIS would be
    invaluable to addressing H 2 O spectroscopic issues, particularly in
    the hard to measure upper troposphere.

-   MAS for much higher spatial resolution to address small-scale scene
    variability

-   FIRSC for far-IR measurements and cirrus cloud characterization

-   MicroMAPS for measurements of layer integrated CO amounts

-   MIR for microwave measurements of water vapor profiles

***Atmosphere / oceanic measurements***

For validation of over the oceans, comparable sets of instruments are
available on ships, such as the *Explorer of the Seas* which undertakes
a weekly cruise circuit between Miami and the US Virgin Islands (see
[www.rsmas.miami.edu/rccl](http://www.rsmas.miami.edu/rccl)). An
extensive suite of instruments has been installed on research ships for
specific campaigns, such as on NOAA's *Ronald H. Brown* for the Nauru99
expedition, which also involved coordinated measurements from the
Japanese RV *Mirai* and the ARM site on Nauru (see
http://www.arm.gov/docs/news/nauru99/).

G.4.2 Land Field Campaigns

The EOS land validation program is planning to continue its
collaboration with the NASA's Airborne Science Program
(<http://www.dfrc.nasa.gov/airsci>), which provides airborne platforms
to carry sensors such as AVIRIS, MAS, MASTER and AirMISR.

A new airborne system developed for MODIS vegetation reflectance and
vegetation index validation called MODIS Quick Airborne Looks (MQUALS)
([http://gaea.fcr.arizona.edu/validation/index.htm)](http://gaea.fcr.arizona.edu/projrcts/validation/index.htm)),
carrying digital cameras, a radiometer and albedometer is being used
over EOS sites.

Data collected at these validation sites, over a large number of field
campaigns are available through Mercury system at the ORNL DAAC (Cook et
al., 1999). The Mercury system performs a key role in centralizing the
distribution and archiving of field data, and provides both the team
collecting the data and the users significant advantages relative to
traditional data management systems (http://mercury.ornl.gov).

G.4.3 Ocean Field Campaigns

There are several approaches to validating the NPP oceanic EDRs and CDRs
that vary depending on whether the ocean color, and derived variables,
or surface temperature are concerned. The validating measurements for
ocean color are taken primarily using sensors in the upper layer of the
ocean, in the top tens of meters, whereas those for SST are taken
primarily using radiometers from ships or aircraft. Ocean color
validation requires the use of a specialized buoy (MOBY) or a ship which
must stop to lower sensors into the water. SST validation data can be
taken form a ship underway (e.g. Kearns et al, 2000) or from a low
flying aircraft (e.g. Smith et al. 1994).

***Dedicated cruises***

For both color and temperature validation from ships it makes best use
of resources if the cruise is in an area where clear skies can be
expected, and where a large range of environmental variability, both
oceanic and atmospheric, is to be experienced. An example of such a
dedicated research cruise is the MOCE-5 (Marine Optical Characterization
Experiment) that took place off Baja California in October 1999 (see
<http://orbit-net.nesdis.noaa.gov/orad/mot/moce/synopses/moce_5.html>).
The cruise track and surface temperature variability are shown in
Figures G-3 and G-4.

***Long transects - SST***

Another approach to sampling a large variety of environmental
variability is to mount radiometers on ships on transit across the
oceans. Examples of such cruises in which M-AERIs have been used to
provide measurements to validate AVHRR and MODIS SSTs is shown in Figure
G-5. Several of the trans-oceanic sections are US Coast Guard
icebreakers on their annual round trip between Seattle and Australia.
Other such opportunities include the German icebreaking research vessel
*Polarstern* on its route from Germany to the RSA, and the British ship
RRS James Clark Ross that goes from the UK to Antarctica, via the
Falklands each year (not shown). Other opportunities are research
vessels en route between two areas of operations, such as the R/V *Roger
Revelle* between Hawaii and New Zealand (purple track in Figure below).

![](media/image10.png){width="3.125in"
height="3.125in"}![](media/image11.png){width="2.879166666666667in"
height="3.125in"}![](media/image10.png){width="3.1166666666666667in"
height="3.1166666666666667in"}

![](media/image12.png){width="4.25in" height="3.2263888888888888in"}

Alternative vessels that may be suited to hosting SST validation
radiometers are commercial vessels linking ports on different
continents. In such cases, those that have a large latitudinal range
rather than a wide longitudinal span are to be preferred because of the
larger range of conditions to be expected. Also, to provide a more
complete set of validation data it is necessary to include sensors for
measuring the surface meteorological conditions and the state of the
atmosphere at the times of the satellite overpasses.

Other cruises of opportunity include the summer research cruises into
the Arctic on the US Coast Guard icebreakers each year, allowing
validation data to be taken in extreme environmental conditions.

***Process studies cruises***

Further validation cruise opportunities are to be found in research
ships of opportunity that will host the validation activities that
compliment their primary objectives, (or at least not compromise them).
These should be selected so that the full range of environmental
conditions (atmospheric as well as oceanic) are sampled, and where other
activities on the ship provide auxiliary data. Good examples of this are
the Aerosol Characterization Experiment --ACE- cruises especially the
ACE-ASIA cruise of 2001 (<http://saga.pmel.noaa.gov/aceasia>).

***Repeated cruise tracks***

A new approach to gathering oceanic validation data is to equip a cruise
liner with state-of-the-art instruments that operate quasi-autonomously.
Such ships are at sea for most of the time and provide a valuable
platform for data gathering. The prime example of this is the *Explorer
of the Seas* which follows a weekly track from Miami to St Thomas
(Figure G-6).

![](media/image13.jpeg){width="4.8493055555555555in"
height="2.673611111111111in"}

Figure G-6. Weekly cruise track of the Explorer of Seas.

The instruments mounted on the *Explorer of the Seas* are shown in
Figure G-7, and include an M-AERI and a full suite of meteorological
sensors. Further details are at
<http://www.rsmas.miami.edu/rccl/facilities.html>.

Another example of an instrumented ship doing repeat tracks is the *M/V
Val de Loire*, a ferry that plies between the UK and Spain and which
will carry a filter radiometer capable of measuring the skin SST to an
accuracy of \~0.1K.

![](media/image14.png){width="4.707638888888889in"
height="3.7243055555555555in"}

**Figure G-7. Instruments on the *Explorer of the Seas.***

***Multi-national campaigns***

Multi-national oceanographic campaigns offer good opportunities for
satellite validation as they often provide a concentration of
instruments and expertise. Sometimes they include several ships and
aircraft and this enables the determination of the effects of spatial
variability, and example being the Nauru99 which involved the NOAA's
*Ronald H. Brown*, the Japanese RV *Mirai*, the Australian Cessna 404
instrumented aircraft, and the ARM site on Nauru
(http://www.arm.gov/docs/news/nauru99/). They also sometimes provide
access to inhospitable parts of the ocean which otherwise remain out of
reach, or difficult to access. An example of this is the North Water
Polynya Project which took place in the Arctic, in the north of Baffin
Bay in 1997-99; the cruise tracks for 1998 and 1999 are shown in Figure
G-5. (See also http://www.fsg.ulaval.ca/giroq/now/).

Ocean Field Campaign References

Kearns, E. J., J. A. Hanafin, R.H. Evans, P.J. Minnett and O.B. Brown
(2000). An independent assessment of Pathfinder AVHRR sea surface
temperature accuracy using the Marine-Atmosphere Emitted Radiance
Interferometer (M-AERI). Bulletin of the American Meteorological
Society. 81(7): 1525-1536.

Smith, A.H., Saunders, R.W. and Zavody, A.M. (1994): The validation of

ATSR using aircraft radiometer data over the tropical Atlantic. J.
Atmos.

Oceanic Technology, 11, 789-800

G.4.4 Cryosphere Field Campaigns

Because of the inhospitable environment and the logistical difficulties
of operating in polar regions, the validation of the cryosphericR EDRs
and CDRs will probably be limited to campaigns involving the aircraft
sensors (for albedo and temperature) and opportunities to mount sensors
on ice-breakers. *In-situ* measurements of snow depth at the ARM North
Slope of Alaska site, at research bases in Antarctica and at Arctic
meteorological stations can also be used.

NPP cold scene (\<270 K) radiance calibration can be validated using
P-AERI ground based measurements at the South Pole. P-AERI may be
pointed upward to view zenith or downward to view surface or any angle
in between. Because atmospheric water vapor concentrations over the
South Pole are typically small (\~5% relative humidity), slant path
effects on NPP and P-AERI window band measurements will be small
(\<0.5°C). Importantly, P-AERI is capable of viewing the snow surface at
the South Pole using the same viewing geometry as NPP. This will
minimize surface effects on the calibration validation exercise. The
combination of accurate skin temperature measurements, spatial
homogeneity, very small atmospheric effects, and a large number of
satellite overpasses make the South Pole PAERI deployment an essential
validation tool for NPP CrIS and VIIRS.

G.4.5 Cloud Field Campaigns

Field campaigns which use aircraft and other ground based sensors to
provide context to the routine surface site observations are required.
Additional experiments which target observational conditions not
normally encountered at the surface sites are also required. Aircraft
based sensors include active cloud sensors (lidar, radar) and in-situ
sampling devices.

G.4.6 Aerosol Field Campaigns

Field campaigns which use aircraft and other ground based sensors to
provide context to the routine surface site observations are required.
Additional experiments which target observational conditions not
normally encountered at the surface sites are also required. Aircraft
based sensors include active cloud sensors (lidar, radar) and in-situ
sampling devices.

G.5 Future Experiments

Product validation for the NPP can be established on the basis of shared
costs. While expenses associated with maintaining and fielding aircraft
instruments can be significant, the requirements for IPO are compatible
with those of ongoing NASA scientific programs, NOAA Calibration and
Validation of its operational observing capabilities, NASA plans for EOS
validation, and DOE field programs for climate studies. Plans are
already in place from these and other organizations to support a
substantial number of field programs that can be used to leverage IPO
support. More specifically, NASA is conducting missions with these
instruments throughout the current decade, including the SAFARI mission
in South Africa in 2000, a joint water vapor experiment with the DOE
centered around the Atmospheric Radiation Measurement (ARM) site in
Oklahoma in 2000, Aerosol Characterization Experiments (ACE) in 2001,
and a cirrus study with the Cirrus Regional Study of Tropical Anvils and
Layers (CRYSTAL) in 2002 and 2004. NOAA will be conducting calibration
validation of the operational polar orbiting infrared and microwave
sounders periodically in the 2000s; intercalibration of the ongoing
series of POES and EOS sensors and the associated imaging and sounding
products is a high priority for these efforts.

There are several relevant field experiments and new instrument launches
that will provide data for many of the activities in preparation for NPP
calibration validation. They are presented in summary here and discussed
in the various following sections in more detail (Table G-4, Table-5).

Table G-7 shows the NASA ER-2 flight schedules for FY02-FY06. current 5
yr schedule for both the ER-2 & DC-8 are available from
<http://www.dfrc.nasa.gov/airsci/er25yr.html>. Specific field
deployments for which the NAST/S-HIS package could significantly
contribute toward both field mission science goals and EOS instrument
validation include those listed in the following Tables.

**Table G-4: NPP relevant field experiments and new instruments
launches**

+----------------+-----------------------------------------------------+
| Schedule \*    | **Field Experiment**                                |
+----------------+-----------------------------------------------------+
| 1Q01           | TRACE P                                             |
+----------------+-----------------------------------------------------+
| 2Q01           | MCV                                                 |
+----------------+-----------------------------------------------------+
| 3Q01           | CLAMS                                               |
+----------------+-----------------------------------------------------+
| 4Q01           | CHAMEX-4                                            |
|                |                                                     |
|                | AQUA LAUNCH                                         |
|                |                                                     |
|                | ADEOS-II LAUNCH                                     |
+----------------+-----------------------------------------------------+
| 1Q02           | TERRA/AQUA CAL/VAL                                  |
+----------------+-----------------------------------------------------+
| 2Q02           | CLAP                                                |
|                |                                                     |
|                | AIRS CAL/VAL                                        |
+----------------+-----------------------------------------------------+
| 2Q02           | IHOP                                                |
+----------------+-----------------------------------------------------+
| 3Q02           | CRYSTAL FACE                                        |
|                |                                                     |
|                | MODIS AND AIRS CAL/VAL                              |
+----------------+-----------------------------------------------------+
| 4Q02           | CHEM LAUNCH                                         |
|                |                                                     |
|                | CHAMEX-4                                            |
+----------------+-----------------------------------------------------+
| 1Q03           | CHEM CAL/VAL                                        |
+----------------+-----------------------------------------------------+
| 2Q03           | MODIS AIRS CAL/VAL                                  |
|                |                                                     |
|                | USWRP Gulf Experiment-Moisture Return Flow          |
+----------------+-----------------------------------------------------+
| 3Q03           | MODIS AIRS CAL/VAL                                  |
|                |                                                     |
|                | AURA LAUNCH                                         |
|                |                                                     |
|                | CRYSTAL                                             |
+----------------+-----------------------------------------------------+
| 4Q03           | EOS Cloud Radiation Forcing and Aerosol Feedback    |
|                | CAL/VAL                                             |
+----------------+-----------------------------------------------------+
| 1Q04           | CHEM CAL/VAL                                        |
+----------------+-----------------------------------------------------+
| 2Q04           | CRYSTAL                                             |
|                |                                                     |
|                | USWRP Gulf Experiment-Moisture Return Flow          |
+----------------+-----------------------------------------------------+
| 3Q04           | CRYSTAL TWP                                         |
+----------------+-----------------------------------------------------+
| 4Q04           | METOP LAUNCH                                        |
|                |                                                     |
|                | EOS Cloud Radiation Forcing and Aerosol Feedback    |
|                | CAL/VAL                                             |
+----------------+-----------------------------------------------------+
| 1Q05           | THORPEX                                             |
+----------------+-----------------------------------------------------+
| 2Q05           | SVWXEX                                              |
+----------------+-----------------------------------------------------+
| 3Q05           | TCEX                                                |
|                |                                                     |
|                | GIFTS LAUNCH                                        |
|                |                                                     |
|                | EOS Cloud Radiation Forcing and Aerosol Feedback    |
|                | CAL/VAL                                             |
+----------------+-----------------------------------------------------+
| 4Q05           | GIFTS CAL/VAL                                       |
|                |                                                     |
|                | NMP EO-3 GIFTS Wind Profiling Validation Mission    |
|                |                                                     |
|                | EO-3 GIFTS Chemistry Validation Mission             |
+----------------+-----------------------------------------------------+
| 1Q06           | NPP LAUNCH                                          |
|                |                                                     |
|                | GIFTS CAL/VAL                                       |
+----------------+-----------------------------------------------------+
| 2Q06           | THORPEX                                             |
+----------------+-----------------------------------------------------+
| 2Q06           | GIFTS CAL/VAL                                       |
+----------------+-----------------------------------------------------+
| 3Q06           | CRIS/VIIRS/ATMS CAL/VAL                             |
|                |                                                     |
|                | GIFTS CAL/VAL                                       |
|                |                                                     |
|                | EOS Cloud Radiation Forcing and Aerosol Feedback    |
+----------------+-----------------------------------------------------+
| 4Q06           | CRIS/VIIRS/ATMS CAL/VAL                             |
+----------------+-----------------------------------------------------+
| 1Q07           | CRIS/VIIRS/ATMS CAL/VAL                             |
|                |                                                     |
|                | GOES, ABI and GIFTS CAL/VAL                         |
|                |                                                     |
|                | EOS Chemistry Mission                               |
+----------------+-----------------------------------------------------+

***\* N*Q*YY*: N is the year quarter number, and YY is the year.**

**Table G-5: List of references for field campaigns and programs
relevant to NPP calibration validation effort.**

+----------+------------------------+-----------------+---------------+
| Cal      |                        |                 |               |
| ibration |                        |                 |               |
| and      |                        |                 |               |
| Va       |                        |                 |               |
| lidation |                        |                 |               |
| Field    |                        |                 |               |
| C        |                        |                 |               |
| ampaigns |                        |                 |               |
+----------+------------------------+-----------------+---------------+
| **[Nam   | **[Reference           | **[Principal    | **[Primary    |
| e]{.unde | URL]{.underline}**     | Airborne        | Purpose]{     |
| rline}** |                        | Sensors         | .underline}** |
|          |                        | ]{.underline}** |               |
+----------+------------------------+-----------------+---------------+
| SAF      | sa                     | MAS, MQUALS,    | Biophysical   |
| ARI-2000 | fari.gecp.virginia.edu |                 | validation,   |
|          |                        |                 | LST, VI,      |
|          |                        |                 | Albedo,       |
|          |                        |                 | Aerosol, Fire |
+----------+------------------------+-----------------+---------------+
| BOREAS   | bore                   |                 | Biophysical   |
|          | as.gsfc.nasa.gov/html_ |                 | validation,   |
|          | pages/boreas_home.html |                 | LST, VI,      |
|          |                        |                 | Albedo,       |
|          |                        |                 | Aerosol, Fire |
+----------+------------------------+-----------------+---------------+
| LBA      | www-eosdis.ornl.gov    | MAS             | Biophysical   |
|          | /lba_cptec/indexi.html |                 | validation,   |
|          |                        |                 | LST, VI,      |
|          |                        |                 | Albedo,       |
|          |                        |                 | Aerosol, Fire |
+----------+------------------------+-----------------+---------------+
| CLAMS    | snowdog.lar            | NAST-I, NAST-M, |               |
|          | c.nasa.gov/cave/cave2. | MAS             |               |
|          | 0/CLAMS.dir/index.html |                 |               |
+----------+------------------------+-----------------+---------------+
| TRACE    |                        |                 |               |
+----------+------------------------+-----------------+---------------+
| CRYSTAL  |                        |                 |               |
+----------+------------------------+-----------------+---------------+
| THORPEX  | [www.n                 |                 |               |
|          | rlmry.navy.mil/\~langl |                 |               |
|          | and/THORPEX_document/T |                 |               |
|          | horpex_plan.htm](http: |                 |               |
|          | //www.nrlmry.navy.mil/ |                 |               |
|          | ~langland/THORPEX_docu |                 |               |
|          | ment/Thorpex_plan.htm) |                 |               |
+----------+------------------------+-----------------+---------------+
| WINTEX   | cimss.ssec.wisc.       |                 |               |
|          | edu/wintex/wintex.html |                 |               |
+----------+------------------------+-----------------+---------------+
| CAMEX    | ghr                    |                 |               |
|          | c.msfc.nasa.gov/camex3 |                 |               |
|          | /instruments/lase.html |                 |               |
+----------+------------------------+-----------------+---------------+
| ARMCAS   | lt                     |                 | Detect and    |
|          | pwww.gsfc.nasa.gov/MOD |                 | differentiate |
|          | IS/MAS/armcashome.html |                 | between       |
|          |                        |                 | clouds, ice,  |
|          |                        |                 | and snow.     |
|          |                        |                 | Determine the |
|          |                        |                 | scattering    |
|          |                        |                 | albedo of     |
|          |                        |                 | clouds        |
+----------+------------------------+-----------------+---------------+
| ACE-1    | saga.p                 |                 |               |
|          | mel.noaa.gov/ace1.html |                 |               |
| ACE-2    |                        |                 |               |
|          | www.ei.jrc.it/ace2     |                 |               |
+----------+------------------------+-----------------+---------------+
|          |                        |                 |               |
+----------+------------------------+-----------------+---------------+

**Table G-6: NASA ER-2 schedule for FY00-FY03**

+-----------------+-----------------+-----------------+-----------------+
| **Experiment**  | **Location**    | **Instrument**  | **Date**        |
+-----------------+-----------------+-----------------+-----------------+
| CALVEX-M        | CART/GMEX       | MAS, CLS, S-HIS | Mar-Apr/00      |
|                 |                 |                 |                 |
|                 |                 | MOPITT-A (ER-2) |                 |
|                 |                 |                 |                 |
|                 |                 | NAST, FIRSC     |                 |
|                 |                 | (Proteus)       |                 |
+-----------------+-----------------+-----------------+-----------------+
| SAFARI-2000     | South Africa    | MAS, CLS, S-HIS | Aug-Sep/00      |
|                 |                 |                 |                 |
|                 |                 | MOPITT-A (ER-2) |                 |
+-----------------+-----------------+-----------------+-----------------+
| CRYSTAL         | Guam            | NAST, MAS, CLS, | Jul-Aug 02      |
|                 |                 |                 |                 |
|                 |                 | LASE, S-HIS     |                 |
+-----------------+-----------------+-----------------+-----------------+
| CAMEX-4         | PAFB, FL        | NAST, MAS, CLS, | Aug-Sep 01      |
|                 |                 |                 |                 |
|                 |                 | S-HIS           |                 |
+-----------------+-----------------+-----------------+-----------------+

![](media/image15.png){width="6.227083333333334in"
height="8.60138888888889in"}

**Figure G-7: Deployment schedule for NASA\'s high-altitude ER-2
aircraft.**

[]{#__RefHeading___Toc534619224 .anchor}Appendix H: EDR/CDRs Validation
Specifics

This section provides a list of validation approaches considered in the
NPP EDR/CDR product validation. It's an attempt to develop a priority
sequence for each product validation from High priority to optional/low
priority. For a specific EDR/CDR, approach listed as number 1 is high
priority, and the last approach listed is optional or low priority.

Some EDR/CDR has as many as eight approaches considered for product
validation. Only approaches with required funding will be implemented.

NPP validation approaches of the EDR (Section H.1) and the CDR (Section
H.2) group of products (Atmosphere and sounding, Aerosol, Clouds, Ocean,
Land, and Snow and Ice group) are presented in this Appendix.

**H.1 Validation of the EDR Operational Products**

**H.1.1 Atmospheric Sounding Profiles**

H.1.1.1 Atmospheric Sounding Profiles (Moisture, Temperature and
Pressure) (Primary EDRs)

Approach 1: ARM Site Observations

***Product:***

Moisture, Temperature and Pressure Profiles (and integrated column water
vapor)

***Primary Validation Source:***

Routine ARM site observations and dedicated NPP overpass radiosondes.
(ARM site T/q best estimate).

***Ancillary Data Sources:***

GOES and Oklahoma Mesonet data for assessing spatial variability.

***Technique:***

The basic technique is to use the routine ARM site observations (at the
Southern Great Plains site in central Oklahoma, at the North Slope of
Alaska site in Barrow, Alaska, and the Tropical Western Pacific site in
Nauru) along with dedicated NPOESS overpass radiosondes to measure the
temperature and water vapor profiles for validation of the CrIS
retrievals. Temporally continuous profiling at the ARM sites will be
used to assess small scale spatial variability. GOES, surface networks,
and the relative variability of the single-FOV CrIS retrievals will be
used to address larger scale spatial gradients. Best estimate profiles
and quantitative error estimates will be provided and compared with the
coincident CrIS retrieved profiles which have been interpolated in space
(using single-FOV CrIS retrievals) to the validation profile locations.
AIRS/HSB data will also be used when available.

***Scope and Schedule:***

The best estimate products produced from the routine ARM observations
will be available for validation purposes from the launch of NPOESS
onward. During yearly three month long periods, dedicated NPOESS
overpass sondes will be launched and incorporated into the best estimate
products. During these periods, sondes will be launched every \~90
minutes and \~5 minutes prior to overpass time to provide improved
collocation with the satellite overpasses, for the lowest view angle
(closest to nadir) overpass of the ARM site each day (e.g. 1 overpass
per day, 2 sondes per overpass). Estimates of the number of clear and
cloudy overpasses of each site are given in the supporting document.

***Comparison and Accuracy:***

Rough estimates of the validation profiles show that their accuracies
surpass the validation needs of CrIS.

***Supporting Documents*:**

"Position Paper on ARM T/q Best Estimate Profiles for AIRS Validation",
D. Tobin et al., March 1, 2000.

***Funding:*** TBD

Approach 2: International Radiosonde Sites

***Product:***

Moisture, Temperature and Pressure Profiles (integrated column water
vapor)

***Primary Validation Source:***

International Radiosonde sites

***Techniques:***

The basic approach is to make measurements of temperature and water
vapor profiles coincident with CrIS retrievals via overpass coordinated
radiosonde launches. Sonde water vapor calibration errors will be
addressed by scaling the sonde integrated column water vapor to values
measured by a GPS or MWR, or alternatively by scaling to point
measurements made with a high quality met station coincident with the
sonde measurements just prior to launch. Imager data will be used to
assess cloud cover and spatial and temporal variability.

**Approach 3:** Retrievals from NAST-I and S-HIS aircraft observations
at ARM and EOS sites

***Product:***

Moisture, Temperature and Pressure Profiles (and integrated column water
vapor)

***Primary Validation Data Source:***

NAST-I and S-HIS retrievals

***Ancillary Data Sources:***

NAST-M, MAS, CLS

***Technique:***

For high altitude NAST-I and/or S-HIS underflights of the CrIS
overpasses, retrievals of atmospheric profiles derived from the NAST-I
and/or S-HIS observations will be compared to the CrIS products.
Cross-track scanning will allow the aircraft observations to be averaged
to match the CrIS footprint. As with radiance validation approaches with
S-HIS and NAST-I, the flight paths and sensor scan angles can be
tailored to match the CrIS viewing angles. These flights should be
performed at maximum aircraft altitude.

A complimentary technique is to perform slow ascents with the aircraft
sensors to derive profiles from NAST-I and/or S-HIS data using opaque
spectral channels which represent the local temperature and gas
concentrations. Such experiments have recently been performed with
NAST-I on the Proteus aircraft during the ARM WVIOP 2000 experiment. Due
to the slow ascents, these comparisons would be performed on a limited
scope for stable, homogeneous meteorological conditions in order to
provide meaningful comparisons to the CrIS product.

***Funding:*** TBD

**H.1.1.2 Total Precipitable Water (TPW) (EDR)**

**Approach 1:** Comparison to AERONET data

The AERONET network of sun photometers will continue to provide the most
viable total precipitable water validation source. AERONET consists of a
global network of approximately 100 sunphotometers measuring in several
channels in the visible and near-infrared spectrum. The AERONET data can
also be used to derive the spectral total column aerosol optical
thickness and size distribution.

Funding: TBD

**Approach 2:** Comparison to EOS products

The comparison plan for the Total Precipitable Water EDR includes
sensitivity studies, analysis of VIIRS data, and verification using
MODIS products. Observations from MODIS and other space-based sensors
will be used in the pre-launch phase to study the error characteristics
and optimum techniques for the algorithm. It is expected that MODIS
validation data will be of great value. These data are expected to
include *in-situ* field measurements combined with MODIS observations.
MODIS TPW product will be used in the post-launch era for evaluation
purposes of VIIRS TPW EDR at regional and global scales.

Funding: TBD

**Approach 3:** Comparison to International Radiosondes

The basic approach is to integrate measurements of water vapor profiles
coincident with CrIS retrievals via overpass coordinated radiosonde
launches. Sonde water vapor calibration errors will be addressed. Imager
data will be used to assess cloud cover and spatial and temporal
variability.

Funding: TBD

**H.1.1.3 Suspended Matter (EDR)**

*To be included.*

**H.1.2 Validation Approaches for Aerosol Products**

**H.1.2.1 Aerosols Optical Depth and Particle Size (EDRs)**

Aerosol optical thickness and particle size are primary VIIRS products.
Aerosol cal/val, in preparation of using the VIIRS data, is composed of
three tasks: 1) to evaluate the self-consistency of VIIRS retrievals of
multi-spectral (0.55, 0.66, 0.86, 1.64 µm) aerosol optical depths (AOD),
and aerosol particle size parameters (Angstrom Exponent (AE)); 2) to
quantify improvement in VIIRS derived AOD and AE relative to those
parameters derived from multi-channels of MODIS and two channels of
AVHRR and TRMM/VIRS; and 3) to validate VIIRS retrievals of AOD and AE
against AERONET sun-photometer measurements.

The overall results from the aerosol cal/val activity (self-consistency
checks, inter-satellite comparisons and ground-truth validation) will be
used to anticipate and verify the performance of the NPOESS/VIIRS
aerosol parameters, and to identify improvements to the VIIRS instrument
design and/or retrieval algorithm science.

**Approach 1**: Comparison to AERONET data

The AERONET network of sun photometers will continue to provide the most
viable aerosol validation source. AERONET consists of a global network
of sun photometers measuring in several channels in the visible and
near-infrared spectrum. The AERONET data can be used to derive the
spectral total column aerosol optical thickness and size distribution.

Funding: TBD

**Approach 2**: Comparison to Lidar data

Surface based lidars at the ARM sites and others provide routine
measurements of the profile aerosol backscattering. When other
observations can be used to reduce the uncertainty in the backscatter to
extinction ratio, lidars can provide accurate estimates of aerosol
optical thickness. Dual wavelength lidars also provide profile
information on aerosol size. Information on aerosol shape can be derived
from the lidar depolarization signal.

***Scope and Schedule**.*

NPP overpasses with AERONET and lidar sites need to be collected.

***Comparison and Accuracy***

AERONET optical depth measurements can achieve 5% accuracy. Lidar
profiles of extinction have a nominal accuracy of 30 % can be improved
to 10% with some knowledge of the aerosol type.

Funding: TBD

H.1.3 Validation Approaches for Cloud Products

**H.1.3.1 Cloud Base Height / Pressure / Temperature (EDRs)**

Cloud base height determinations from VIIRS rely on microwave and
ancillary data sources. MODIS offers additional information on cloud
base height through its near-infrared water vapor channels (0.94, 1.38
m). The approach is to develop cloud base height algorithms using MODIS
data. Once MODIS-AQUA data is available with the AMSU and HSB microwave
instruments, all required inputs to the VIIRS cloud base height
algorithm will be available. Then comparison of the methods will be
pursued to verify the consistency of the VIIRS algorithm with the MODIS
algorithm. In addition, VIIRS does possess one strongly absorbing water
vapor channel (1.38 m). This channel allows estimation of cirrus cloud
base height which is one area where the VIIRS algorithm is known to have
problems due to the difficulty in detecting cirrus in microwave data.
The NPP Cal Val Team will explore ways to include the 1.38 m channel
information to improve the VIIRS cloud base height estimation.

**Approach 1**: Surface based LIDAR and Ceiliometer Measurements

Validation will be conducted using surface based LIDARS, such as those
at the ARM and FARS sites and large networks of ceilometers, providing
direct estimate of the cloud base altitude.

Funding: TBD

**Approach 2**: Cloud Radars measurements

Undermost conditions, cloud radars will be able to provide direct
measurement of cloud base. Only for clouds with heavy precipitation, do
cloud radars become unable to sense the cloud boundaries. Cloud radars
available for NPP validation are those at the ARM and FARS sites and
possibly the CloudSat mission. CloudSat will fly with EOS/AQUA and
provide validation of the NPP approach using EOS instruments.

Funding: TBD

**Approach 3:** Comparison with Rawinsondes

Profiles of atmospheric humidity from co-located radiosondes are able to
provide information on moist layers which are indicative of cloud
positions.

***Ancillary Data Sources:***

The additional data needed for validation include the products from
co-located NWP model outputs, and radiosondes.

***Scope and Schedule:***

These comparisons can be preformed regularly for NPP overpasses of lidar
and ceilometer sites for the duration of the mission.

***Comparison and Accuracy:***

The lidar, radar and ceiliometer cloud base estimates should be accurate
to 100m for most scenarios

Funding: TBD

**H.1.3.2 Cloud Cover/Layers Validation (CC/L) (EDR)**

Cloud cover and layers are primary VIIRS EDRs, and it can be produced as
CDR from CrIS. VIIRS has difficulty sensing overlapping cloud layers.
Validation technique described below use active sensors or instruments
with high spatial resolution to validate the NPP CC/L estimates.

**Approach 1: Comparison with MAS/MODIS and LIDAR data**

The VIIRS CC/L algorithm will be validated against MAS data and MODIS
data along the imagery track centers where Lidar Cloud Profiling data is
available. By inspection, a set of ground truth layered cloud amounts
will be determined. These will then be compared to CC/L layered
assessments and a qualitative indication of CC/L performance can be
attained. At least one case of MODIS data will be used to validate CC/L
algorithm performance. The CC/L product will be validated with
independent cloud measurements either from space or other indirect
means.

***Scope and Schedule:***

The Lidar Cloud Profiling measurements of land the SGP and EOS site will
be performed once during each season (Spring, Summer, Fall, and Winter).
The measurements will be timed to coordinate with any supporting
measurements.

***Comparison and Accuracy:*** TBD

Funding: TBD

**H.1.3.3 Cloud Effective Particle Size Validation (EDR)**

Cloud effective particle is a primary EDR of VIIRS. Information on cloud
particle size is also possible from CrIS, ATMS, and CMIS. Cloud
effective particle size is defined as the ratio of the third to second
moment of the cloud particle radius distribution. The cloud effective
particle size varies throughout a cloud layer. Knowledge of the particle
size is critical in describing the radiative characteristics of cloud
layers. Estimation of liquid water path also critically depends on
knowledge of the particle size.

**Approach 1:** Cloud particle sensing probes flown on aircraft can
profile the size distribution of cloud particles through the atmosphere.

Funding: TBD

**Approach 2**: Cloud particle size profiles can be determined from
surface based LIDAR, RADAR and microwave radiometers from the ARM and
FARS sites. Similar cloud particle size profiles will be available from
the CloudSat mission.

Funding: TBD

**Approach 3**: Highly calibrated radiometers such as the MAS or AVIRIS
that measure radiance in the visible and near-infrared region can be
used to derive information on the cloud effective particle size and
provide limited information on its profile.

Funding: TBD

**Approach 4**: Imagers with three or four broad band (spectral
resolution around 10 - 20 cm^-1^) measurements in the infrared window
region between 800 to 1000 cm^-1^ are likely to be able to distinguish
large from small particle size cirrus and to provide IWP estimates.
Cirrus clouds with small ice particles (*r~eff~* \< 10 m) exhibit a
non-linear *S*-shaped cloud forcing in 800 - 1000 cm^-1^ that gradually
disappears as the particle size is increased. . A numerical procedure
based on the DISORT algorithm is used to retrieve the effective radius
and ice water path of cloud layers with known optical depths and cloud
boundaries and with nearby clear sky atmospheric conditions also known.
The reasonable reproduction in a rather wide window region suggests that
the DISORT based algorithm can distinguish small from large particle
clouds as well as provide a fair estimate of IWP.

***Scope and Schedule:*** TBD

***Comparison and Accuracy:***

Ice particle size and ice water paths are estimated with 20% variation
in the inferred values. The best sets of effective radius and ice water
path can reproduce the observed HIS cloud forcing within 2 K in 800-1000
cm^-1^ and within 4.5 K in 1150-1250 cm^-1^ for both small (*r~eff~* \<
10 m) and large (*r~eff~* \> 10 m) particle clouds.

Funding: TBD

**H.1.3.4 Cloud Optical Thickness (EDR)**

Algorithms for estimating cloud optical thickness and effective particle
size will be evaluated in terms of their sensitivity to variations in
the vertical profile of particle size and to ice particle shape
(\"habit\"). The vertical distribution of particle size in water and ice
clouds is rarely uniform, so inhomogeneous cloud structure will affect
retrievals of particle size. MODIS/VIIRS bands at 1.6, 2.1, and 3.75 m
can be used to provide additional information about the vertical
structure of particle size because they are sensitive to different
portions of the cloud, depending on the particle density. Ice particle
shape will also effect retrievals of the bulk micro-physical properties,
as different shapes exhibit different optical properties. A
parameterization of ice cloud optical properties for various particle
habits has been recently developed, so the tools are in place for this
task. The NPP Cal Val Team is interested in the performance of the
algorithms under extreme conditions, specifically for very small ice
particles, mixed-phase clouds, and bright, anisotropically reflecting
surfaces found in the Polar Regions. Lastly, Numerical Weather
Prediction (NWP) impact studies regarding the assimilation of
satellite-derived cloud properties will be initiated.

**Approach 1:** Cloud Optical Thickness can be derived from the profile
of cloud liquid and ice water contents and particle size distributions
measured from probes mounted on aircraft. Optical depth can also be
derived from highly calibrated airborne sensors such as AVIRIS.

Funding: TBD

**Approach 2**: Surface based RADAR, LIDAR and microwave radiometers
such as those at the ARM and FARS sites also provide estimate of cloud
optical and its vertical profile.

Funding: TBD

**Approach 3**: Satellite based RADAR and LIDAR missions such as
CloudSat and ESSP-3 will provide independent measurements of cloud
optical thickness.

***Ancillary Data Sources:***

Data for NPP overpasses will need to be collected as well as estimates
of atmospheric temperature and moisture profiles.

***Scope and Schedule:* TBD**

***Comparison and Accuracy:*** Expected accuracy of aircraft derived
optical depth is 5%. The RADAR and LIDAR estimates of optical depth are
typically 10 % for water clouds and 30 % for ice clouds.

Supporting Documents:\
Funding: TBD

**H.1.3.5 Cloud Top Height / Pressure / Temperature (EDRs)**

The proposed VIIRS algorithm for cloud top pressure relies on radiances
at 0.67, 3.7 and 10.8 microns to retrieve cloud top properties including
cloud top pressure. The CrIS algorithm employs the CO2 slicing
technique. The VIIRS channels do not allow for CO2 slicing but the NPP
Cal Val Team will study supplementing the VIIRS with CrIS measurements
observations. Algorithms for estimating cloud top pressure will be
evaluated in terms of their sensitivity to variations in the cloud
vertical profile, particularly for optically thin clouds. The
performance in Polar regions and regions with non-black surfaces must
also be evaluated.

**Approach 1: CO2 slicing comparisons**

Cloud top pressure will be estimated using the CO~2~-slicing approach
with high spectral resolution sounder (e.g., CrIS , AIRS , IASI or HIS)
radiances and/or co-located high spatial resolution multi-spectral
imaging radiances (e.g., MODIS). These algorithms retrieve the single
layer atmospheric CTP and ECA from a single field-of-view (FOV) with
higher accuracy than the current operational sounders (HIRS).

Funding: TBD

**Approach 2:** Satellite, aircraft and surface based LIDAR and RADAR
product

LIDARs and RADARS provide direct estimates of the cloud top altitude.
For example, the ARM site ARSCL product can validate both the CrIS and
VIIRS cloud top heights. ARSCL combines the MPL and MMCR measurements
into a single product of cloud layers (base, top, thickness) versus time
at each of the primary ARM sites. Similar approaches will be used to
compare the CloudSat and other aircraft based RADAR and LIDAR estimates
of cloud top height to those from VIIRS/CrIS.

Funding: TBD

**Approach 3:** Geometric stereo determinations

Estimation of cloud top height are possible from LEO/GEO high spatial
resolution imagers viewing the same cloud at the same time from
different view angles

***Ancillary Data Sources:*** The additional data needed for validation
include the products from co-located Vaisala ceiliometer, winds from
Wind Profilers, NWP model outputs, and radiosondes.

***Scope and Schedule:*** These comparisons can be preformed regularly
for NPP overpasses of each ARM site and the FARS sites for the duration
of the mission.

***Comparison and Accuracy:*** For single layer clouds with optical
depths \<=1, the ARSCL product is expected to be accurate to better than
\~100m. For CO2 slicing validation estimates, cloud pressure will be
determined within 30 hPa.

Funding: TBD

**H.1.4 Validation Approaches for Land Products**

**H.1.4.1 Land Surface Temperature (LST) (EDR)**

An accurate measure of the LST is essential to initialize, validate and
verify climate models designed to assess the role of the land surface in
governing seasonal-to-interannual variability at regional-to-global
scales. The ability to monitor the land-surface energy flux will improve
the understanding of the land-atmosphere climate interactions.

The surface emissivity is a physical property that relates the emitted
radiance to the surface temperature -- analogous to a radiative
efficiency. Knowledge of the emissivity of land surface components is
necessary for accurate determination of land surface temperatures. The
emissivity of healthy vegetation is predictably high in the TIR (and may
be assumed with relatively small error to be approximately 0.98), the
emissivity of bare ground is another matter.

The variation of emissivity of soils is dependent on constituents,
surface texture and moisture content. The TIR emissivity has also been
observed to be directional dependent for some soil surfaces.

Validation approaches should include a large range of surface
emissivities, and determine the performance of the LST algorithm over a
various environmental conditions.

**Approach 1:** Comparison to ground and aircraft observations

The validation data set is expected to include *in-situ* field
measurements using S-AERI instrument at ARM sites, and using over top
radiometers at EOS sites, combined with MAS and NAST-I underflights, and
low level aircraft measurements at spatial resolutions less than 10
meters. MODIS and ASTER are planning multiple field validations using
AERI instruments, and VIIRS will find beneficial cost saving in
collaborating in these future activities. VIIRS LST EDR products will be
compared with "truth" derived from *in-situ* and aircraft data, and
performance characteristics will be provided.

Funding: TBD

**Approach 2:** Comparison to EOS products

The comparison plan for the Land Surface Temperature (LST) EDR includes
sensitivity studies, analysis of VIIRS data, and verification using
MODIS products. Observations from MODIS, ASTER and GLI will be used in
the pre-launch phase to study the error characteristics and optimum
techniques for the algorithm. It is expected that MODIS validation data
will be of great value. This data is expected to include in-situ field
measurements combined with MODIS observations. MODIS LST product will be
used in the post-launch era for evaluation purposes of VIIRS LST data at
regional and global scales.

Funding: TBD

H.1.4.2 Albedo (Surface) (EDR)

**Approach 1:** Pyranometers and albedometers data

**Product:**

Surface Albedo

*Primary Validation Data Source:*

Albedo is defined as the ratio of surface exitance to surface
irradiance, both measured over the full shortwave spectrum. These
quantities are measured with pyranometers (two required: one facing up,
the other down). Some vendors package the pyranometers in a single unit,
called an albedometer. The pyranometers are typically mounted on towers
at heights well above the vegetation height, or on aircraft.

*Ancillary Data Sources:*

Several observation networks, composed of multiple ground sites hosting
standard instrumentation packages, measure albedo routinely. These
include SurfRad, BSRN and FLUXNET. The latter includes forested sites,
while the SurfRad and BSRN commonly include primarily desert and
grassland locations. These data can typically be downloaded from the
internet, though the networks and archives tend to be voluntarily
maintained and inconsistently funded. Global albedo data sets have been
derived from other satellite systems, including AVHRR (Los et al., xxx)
and EOS MODIS (ref?). Both are widely available, however their
accuracies are not well known. Coarser scale albedo data are available
from EOS CERES, ADEOS POLDER (\< 1 year) and METEOSAT (regional only).
The EOS MISR team produces accurate but temporally and spatially
inconsistent albedo products. A second POLDER instrument is awaiting
launch on ADEOS II. Some of these products are top-of-canopy, while
others are top-of-atmosphere.

*Technique:*

The key problem in validating albedo concerns temporal vs. spatial
resolution. A tower-based albedometer generates highly accurate data at
very high temporal resolution at minimal cost, however that
albedometer\'s spatial field of view is limited by its relatively short
distance above the vegetation, and its fixed position. These point data
can be scaled to approximate larger area albedo fields, however scaling
techniques are not well developed at this time. To sample much larger
areas, some scientists have mounted albedometers on aircraft. This
approach appears promising (at least one instrument vendor recently
developed a pyranometer with appropriate thermal stability for aircraft
use), however it is expensive, and data may need to be corrected for
atmospheric effects (depending on aircraft altitude), geolocation, and
aircraft attitude. Standard processing packages do not exist to our
knowledge. Because albedometers essentially measure a quantity
equivalent to the albedo EDR product (assuming appropriate spatial
scaling), comparison of in-situ data to EDR values is straightforward.
Advancement of albedo measurement and scaling approaches will presumably
result from the EOS validation program.

*Scope and Schedule:*

Because albedo is a key parameter in any energy budget calculation, the
EDR and its validation are critical. Validation of this parameter should
be a priority after launch, and correlative data from all major
ecosystems should be acquired for the duration of the mission. At least
some global analysis should be possible within several months of initial
product generation. Albedo varies at high frequency, both temporally and
spatially. A large wind gust or rainfall event can immediately change a
surface\'s albedo, and the \"recovery\" time to near stable conditions
varies greatly. Thus, data averaging is essential, although there are no
established standards.

*Comparison and Accuracy:*

Field measurements of albedo are accurate to the uncertainty of the
instrument\'s calibration, typically about 1 %. Scaling point albedo
measurements to larger areas (commensurate with the EDR pixel size)
introduces additional errors that vary with the aggregation method.

*Supporting Documents:*

MODIS and VIIRS ATBDs.

Funding: TBD

H.1.4.3 Surface Type Validation (EDR)

**Approach 1:** Comparison to EOS-derived surface type maps

***Primary Validation Data Source:***

MODIS, Landsat-7, Ikonos, field survey

***Ancillary Data Sources:***

Training data set over a year at well distributes sites.

***Technique:***

Using climatic and geographic stratification, the accuracy will be
determined for VIIRS surface type. The validation will performed using
high and fine resolution remote sensing data such as Landsat-7 data and
Ikonos data. Ground field survey and airborne data might also be used
when necessary.

***Scope and Schedule:***

Surface type maps at elected ARM and EOS \[TBD\] data will be derived
for 4 seasons using fine-, high- and moderate resolution.

***Comparison and Accuracy:***

The analysis to be performed at these selected site is based on the
comparison between VIIRS product, MODIS and other commonly used land
cover maps (USGS and UMD land cover maps) over one year and for 4
seasons.

The analysis will include percent error for each class and percent of
estimating and underestimating each class globally and regionnally.

***Supporting Documents:***

VIIRS ATBD, MODIS ATBD

Funding: TBD

H.1.4.4 Vegetation Index Validation (EDR)

Approach 1: MODIS and POES comparisons

The visible and near-IR channels included on the MODIS instrument permit
evaluation of narrower (and atmospherically clean) wavebands for use in
vegetation indices that might be anticipated to be available from the
VIIRS. This study will examine the influence of the use of narrow band
visible and near-IR channels on vegetation indices anticipated to be
available from the VIIRS. The VIIRS vegetation EDRs will be compared to
those available from the present AVHRR. Differences in the vegetation
indices will be assessed for several vegetated land surface types (IGBP
classification). This study should result in i) assessment of the
anticipated improvements in vegetation index products from the VIIRS and
ii) general guidelines for comparisons of VIIRS-derived vegetation
indices, when available, with historical AVHRR-derived vegetation
indices. VIIRS EDR data sets for several extended periods will be
generated through an annual cycle for the whole globe, and
inter-comparison will be performed with MODIS products.

***Comparison and Accuracy:***

***Supporting Documents:***

VIIRS Vegetation Index ATBD\
Funding: TBD

Approach 2: Ground and aircraft data

***Primary Validation Data Source:***

Spectrometer, Parabola, MQUALS, MAS

***Ancillary Data Sources:***

Atmospheric data

***Technique:***

The airborne data from MQUALS instrument over representative biome types
(i.e. desert, grasses/cereal crops, broadleaf crops, shrub land/savanna,
needleleaf forests, broadleaf forests) will be acquired during the
validation period. The flights will be conducted on days in which VIIRS
will scan the targets at a near-nadir position. Except for an AERONET
sunphotometer (to assess atmospheric aerosols) and a stable reference
plate (irradiance). These rapid but low cost assessments will satisfy
three goals:

1.  They will provide feedback on different Level 1B processing chains
    and their potential effects on land products,

2.  they will provide early quantitative checks on two critical
    "upstream" land products (atmospheric correction, surface
    reflectance, vegetation index, and albedo), and

3.  they will provide high spatial resolution surface heterogeneity data
    for more accurate scaling at validation test sites.

The spatial variability of reflectance and albedo observed by MQUALS at
the selected validation sites will allow the construction of a spatial
model of reflectance and albedo based on TM-derived spatial patterns.
This will link the data stream generated at the core site with the
coarser spatial resolution of VIIRS land products. Longer transects to
be flown by MQUALS across landscape gradients will allow investigation
of transitions in land cover type and the properties of NDVI and
reflectance products across a surface area to be determined. Initial
results are anticipated within 1-2 weeks of VIIRS data delivery.

***Scope and Schedule:***

***Comparison and Accuracy:***

Supporting Documents:\
Funding: TBD

**H.1.4.5 Fire Area and Temperature Validation (EDR)**

Approach 1: Comparison to EOS fire product

Statistical relationships will be established between AVHRR, MODIS and
VIIRS-derived fire numbers over specific areas and time periods. Results
of this study will be useful for the construction of a continuous
AVHRR-MODIS-VIIRS data record of fire occurrences for long-term studies.
VIIRS products will be evaluated for their physical limits, precision,
and accuracy, using theoretical calculations and ground observations
wherever available. These results will be compared with the VIIRS EDR
requirements and recommendations will be made regarding VIIRS fire
algorithm design. Potential for MODIS-VIIRS product stitching will be
evaluated. Finally, it is also proposed to perform feasibility studies
towards a time-integrated burned area product from VIIRS. This product
eliminates most of the active fire misses due to clouds and low temporal
resolution and thus is much more suitable for emission estimates and
hazard assessment. Changes in the signal of near-IR channels from
healthy vegetation to post-burning conditions will be studied and
recommendations will be made for an operational algorithm.

***Scope and Schedule:***

***Comparison and Accuracy:***

***Supporting Documents:***

MODIS fire ATBD

Funding: TBD

H.1.4.6 Soil Moisture (EDR)

Approach 1: Validation using SGP ground measurements

***Primary Validation Data Source:***

Southern Great Plain (SGP) experiment data

***Ancillary Data Sources*:**

Soil type data, Digital Elevation Model data

***Technique:***

Validation of soil moisture estimation results is difficult and even
more so if satellite data is involved. The difficulty lies not only in
the estimation process but also in the measurements of soil moisture.
Several issues are involved in soil moisture measurements. Microwave
sensors measure soil moisture in the topmost soil layer (1/10 to 1/4 of
a wavelength). At 19 GHz, this layer can be about 0.1-0.4 cm deep. The
penetration of the microwave signal depends on soil moisture itself. In
view of this, it is difficult to decide the depth of soil samples for
in-situ measurements. Soil moisture changes very rapidly in the top
layer. In addition, there are practical problems in collecting soil
samples at this depth. Also, spatial distribution of soil moisture
depends on soil parameters, which are not distributed homogeneously in
the area. As a result, average soil moisture computed from point
measurements in a footprint area may not be a correct representation of
the soil moisture in the footprint.

Close comparison of *in-situ* measurements from SGP experiments with the
VIIRS soil moisture predictions will be attempted, as well as the
temporal and spatial comparisons.

***Scope and Schedule:***

Data acquisition of a large range of soil moisture will be conducted
coincident with cloud-free VIIRS data. Coordination with other field
campaigns is required.

***Comparison and Accuracy:***

VIIRS Temporal and spatial pattern/trend in soil moisture at the
selected sites will be compared from all averaged samples for a
particular location on a given day.

***Supporting Documents:***

Soil moisture ATBD**\
*Funding:*** TBD

H.1.5 Validation Approaches for Ocean Products

**H.1.5.1 Sea Surface Temperature (SST) (Primary EDR)**

The measured SST is the temperature of the surface skin of the water
surface, that temperature that gives rise to the infrared emission that
is detected by the VIIRS and CrIS. At the level of accuracy of a few
tenths of a degree, anticipated for NPP sensors, the remotely sensed
skin temperature is distinct from the *in situ* measured subsurface
(upper 1 meter) bulk temperature. The EDR requirements are given for
skin and bulk SST in Appendix B, Table 22 derived from the NPOESS
Integrated Operational Requirements Document \[IORD\] and the NPOESS
Technical Requirements Document \[TRD\]. Both the skin and bulk SST EDR
products will be generated from the NPP data. Both the bulk and the skin
SSTs will be validated.

The bulk SST has been used operationally and has gotten wide use in
numerical weather modeling. Both NPP SST EDRs are expected to be widely
utilized for ocean, climate, and NWP purposes. National and
International collaboration with other projects will have high priority.

**Approach 1:** Marine-Atmospheric Emitted Radiance Interferometer
(M-AERI) Comparisons

***Ancillary Data Sources:***

Atmospheric characterization at the time of the comparisons, using
instruments on the ships and NWP data assimilation model output.

Aerosol characterization from NPP/NPOESS and from sensors on other
spacecraft.

GOES SST product for temporal stability from NOAA.

Techniques:

VIIRS and CrIS SSTs are extracted along M-AERI cruise tracks within a
predefined time and spatial intervals. Co-location must be within a few
km, and within a few tens of minutes. Cloud-free and reasonably uniform
and temporally stable targets will be selected with a range of radiance
levels encompassing the range of surface temperatures observed by M-AERI
and atmospheric water column amounts measured by CrIS

***Scope and Schedule:***

All ocean basins must be covered, and atmospheric variability as well as
the full range of SST should be sampled. The validation should begin
soon after launch and continue throughout the mission.

***Comparison and Accuracy:***

The comparison of VIIRS and CrIS SST and M-AERI SST products is
simplified by the high absolute accuracy of the M-AERI (\< 0.1 K) but
complicated by the large mismatch between the CrIS SST domain (order 45
km) and the M-AERI measurements along the ships' tracks. A major source
of uncertainty in the SST product comparison is expected to be the
spatial variability within the CrIS and VIIRS scene. Uncertainty
estimates will be developed to allow error bars to be attributed to
each, VIIRS, CrIS, and M-AERI comparison. The goal of this activity will
be to validate the CrIS and VIIRS SST product to within about 0.1 Kelvin
over as wide range of atmospheric conditions as possible.

***Funding:***

The M-AERIs cost about \$250,000 each and over the period of the NPP and
NPOESS validation they will need significant refurbishment, possibly
replacement. Funding is required for the sea-going technicians, and for
the shore based facilities that are used to maintain and calibrate the
equipment. IPO, NOAA, NASA and DOD contributions will likely be
required.

**Approach 2**: Shipboard radiometer comparisons.

Well-calibrated infrared radiometers, such as CIRIMS, ISAR, SISTeR,
DAR011 and the JPL Nullling Radiometers can provide measurements of skin
SST for VIIRS and CrIS validation. These, and others, are mounted on
ships. They require internal calibration, traceable to national standard
thermometers, and must include a correction for sky radiance reflected
at the sea surface. Each radiometer should be accompanied by a suite of
auxiliary sensors to characterize the environment in which the
measurements are taken, such as cloud influences, aerosol effects,
atmospheric water vapor loading, surface wind speed, near-surface wind
speed and air sea temperature difference.

**Approach 3:** Satellite Radiometer Comparisons

VIIRS and CrIS SSTs may be validated by comparison with
satellite-derived SSTs from similar imaging radiometers, such as MODIS,
AATSR, GLI and AVHRR that may have a longer and more-established
calibration/validation history. If these radiometers have similar
spectral responses in the corresponding channels, and are on satellites
in orbits close to that of NPP, it may be possible to cross-validate
top-of-atmosphere brightness temperatures. Inter-satellite comparison
can be done over large areas of cloud-free ocean.

Funding: TBD

**Approach 4:** Validation using sensors mounted on ships and buoys

This has been the first and primary approach for operational uses. In
this approach in-situ thermometers mounted at a depth of one to several
meters on drifting and moored buoys provide a sub-surface measurement,
conventionally referred to as bulk temperature Similarly, thermometers
mounted on the hulls and in the engine cooling water intake flow of
selected ships can be used if carefully calibrated. At wind speeds
greater than \~6m/s, the relationship between skin and bulk temperatures
appears to be fairly well constrained, so these data should be
restricted to these conditions or during the night. During the day in
conditions of lower wind speed, vertical temperature gradients can
decouple the bulk measurement from the skin temperature. These factors
will be considered in the validation of both the skin and the bulk SSTs.

Funding: TBD

**Approach 5:** Retrievals from MAS and NAST-I low altitude aircraft
measurements

Low altitude flights of the Proteus and/or ER-2beneath the NPP will be
conducted with the NAST-I and MAS. Micro-windows will allow for
determination of the SST under minimal atmospheric attenuation
conditions.

Funding: TBD

**Approach 6:** GIFTS Observations

The GIFTS in geostationary orbit and with very high spatial and spectral
resolution will enable measurements of SST coincident with underpasses
of the NPP.

Funding: TBD

H.1.5.2 Ocean Color and Chlorophyll Validation (EDR)

Retrieval of normalized water leaving radiances for the ocean visible
and near-IR bands from TOA radiances involves correction for numerous
atmospheric effects and reflection of sky and sun light from the air-sea
interface, as well a removal of small, but crucial instrument biases and
effects. The initialization phase discussed under Level 1 validation
using primarily data from the MOBY buoy and ship based measurements of
in-water radiances and above water reflectances produces a consistent
retrieval system comprised of the instrument data and atmospheric
correction algorithm. This is discussed in detail in the MODIS ATBD for
water-leaving radiances by Gordon. The resulting water-leaving radiances
for the visible bands provide the basis for all other ocean color (ocean
bio-optical) properties, including chlorophyll a and suspended sediment
load. Therefore the careful validation of this SDR is crucial to ocean
color products.

**Approach 1:** MOBY Comparisons

***Primary Validation Source:***

Marine Optical Spectrometer (MOS) instruments on MOBY or ship.

***Ancillary Data Sources:***

VIIRS data, nLw from heritage sensors on orbit, or historical

MOBY servicing cruise data, MOBY calibration/characterization data

Techniques:

Automated collection of MOBY data at the time of VIIRS overpasses, and
MOS data during MOBY servicing cruises within the swath, and development
of a matchup data base to sample the useful range of VIIRS swath and sun
angles. The overall approach for MOBY is discussed by Clark and Mueller
in Chapter 11 of the Revised SeaWiFS Protocols for
Calibration/Validation. Multiple radiometer buoys are maintained, and
are deployed sequentially for three month intervals. Refurbishment and
recalibration is done on shore. Optical collectors are cleaned monthly,
and in-water calibration stability sources are employed before and after
by divers. The measured spectral response function of the satellite
sensor is convolved with the high resolution spectrometer data. Upwelled
spectral radiances are collected at 3 depths and propagated to and
through the surface to produce the desired water-leaving radiance values
which are compared with the values retrieved from the satellite sensor.

***Scope and Schedule:***

The MOBY facility, located on Oahu and off Lanai, must be maintained
continuously throughout the mission, and will provide daily
observations. Useful matchups will be obtained for only a fraction of
the days due to cloud obscuration and solar/scan geometry.

Comparison and Accuracy:

Through concentrated effort, including active participation of NIST
personnel and facilities, the uncertainty of the MOBY water-leaving
radiance time series is on order of 3%.

***Funding:***

Currently MOBY is funded via EOS (80%), SeaWiFS (10%), and NESDIS (10%)
with annual total \~2M\$/yr. Continued funding by EOS will likely
decrease over the next few years, even with a planned shift from MODIS
science team to EOS Project Validation Facility (analogous to AERONET).
It is crucial that resources be identified to continue this national
facility. IPO, NOAA, DOD contributions will likely be required.

***Accuracy Requirements:***

The goals for these measurements are an overall accuracy in an absolute
sense of 2%, with a relative spectral (band-band) of 0.5%, over the time
series.

**Approach 2:** Other In-water radiance measurements

A variety of instrumentation and protocols to make individual and time
series of water-leaving radiance and also above water reflectance
measurements from ship, moorings, drifting buoys, and permanent
platforms have been developed. Details can be found at the SIMBIOS web
site. These measurements, most by independent investigators, are very
important for validation of the global water leaving radiance signals
following initialization at the MOBY site. An extensive round robin
comparison and inter-calibration network has been established, led by
the SIMBIOS Project at GSFC and with participation by NIST, including US
and many international programs. Included in this network are permanent
locations on oceanic research towers and moorings. Absolute and relative
uncertainty goals are as for MOBY. These are maintained through
intercomparison activities, and use of portable stability sources during
cruises and field programs.

The SIMBIOS paradigm is that each ocean color flight project will mount
a focused calibration validation program, with SIMBIOS serving as an
inter-mission comparison facility, maintaining protocols, data bases for
global intercomparisons, and center of expertise.

Funding: TBD

**Approach 3:** Validation using aircraft sensors

Use of aircraft sensors for validation of water-leaving radiances is
primarily in the area of providing improved spatial variations and
coverage. Maintaining sufficiently accurate absolute uncertainty of the
instrument and its atmospheric correction for use in direct validation
of water leaving radiance has improved significantly over the past
decade, however. Aircraft sensors show great utility in validation of
bio-optical properties, but cannot provide the high degree of accuracy
of in-water or shipboard observations at this time.

Funding: TBD

**H.1.5.3 Ocean Chlorophyll Validation (EDR)**

Calculation of chlorophyll concentration is from normalized water
leaving spectral radiances at the pixel level. Major sources of
uncertainty involve the uncertainty of the water leaving spectral
radiance, and the complexity of the optical absorption and scattering
properties of the marine hydrosol. Current in-water bio-optical models
work best when phytoplankton dominate the optical properties (Case I)
which comprise about 90% of the global oceans, and further assume a
relatively constant and uniform species composition. Departure from
these conditions can result in significant errors. These errors can be
due to increased influence of bottom reflectance, presence of suspended
sediments, absorption by non-phytoplanktonic particulate matter,
departures from \'nominal\' absorption and scattering of phytoplankton
due to biological variability, and absorption by dissolved. The goals of
the measurement are to determine chlorophyll accuracy to within 30% in
Case I waters, and 50% in other regions. (check this for consistency)

**Approach 1:** In-water measurements

Techniques:

The primary approach for validation of chlorophyll concentration is
filtration of a water sample followed by High Performance Liquid
Chromatography (HPLC) using recognized standards. Determination by
fluorescence, both in-vivo and in vitro is also routine, the former
especially for underway measurements from ships and from buoy
instruments. It is also important that other bio-optical properties of
the water be determined at the same time, in order to assess the nature
and quality of the overall measurement. Procedures are covered in the
SeaWiFS Protocols documents, which cover the need to spatial variability
and determination of the accuracy and precision of the measurements. One
needs to distinguish between an algorithm validation activity, and a
product validation activity. Algorithm validation tends to be more
robust, and relies upon relating estimates of water leaving radiance (or
reflectance) obtained on shipboard simultaneously with in water
measurements of chlorophyll (or other bio-optical property as
appropriate). The number of values for such comparisons for doing this
is large and covers many conditions and is contained in various data
bases. Those data are used to parameterize the algorithm used to derive
chlorophyll from satellite derived water-leaving radiance values. This
work will continue. Validation of the NPP data product encompasses the
total error budget for the values given in the product. The product
validation reveals regional and temporal differences, includes
uncertainties within the satellite calibration and data processing, and
spatial and temporal sampling issues. Generally comparisons are done
using data collected within hours and at the pixel or averaged over a
few pixels. Consideration also needs to be given to direct comparisons
of weekly averaged in-situ and satellite data.

***Scope and Schedule:***

These measurements should be global in extent, and should address the
geographic and temporal/spatial variability in that is found in the
marine environments. Development of a data base of matched VIIRS and
in-situ data is essential, and the SeaBASS system developed by SeaWiFS
and also used for EOS would serve as a good model for the operational
system. These efforts are primarily at a local level, and should begin
shortly after the VIIRS begins visible data collection.

***Accuracy Requirements:***

The goals for these measurements are an overall accuracy in an absolute
sense of 2%, with a relative spectral (band-band) of 0.5%, over the time
series.

***Funding:***

There should be a dedicated VIIRS component for leading this effort,
performing the matchups and the comparisons. Collection of the data is
best approached through a combination of operational data collection by
NOAA and USN, and collection of data by scientific researcher funded
separately by the three agencies and others (NSF, DOE, EPA, and state
and local management agencies for coastal regions). Great potential
exists for international participation through joint and reciprocal
activities with ESA and NASDA. Some of these are coordinated through the
International Ocean Color Coordinating Group (IOCCG), which is organized
under CEOS and IGBP.

**Approach 2:** Comparisons with other satellite sensors

Comparison with concurrent and heritage satellite derive chlorophyll
concentrations is extremely valuable due to the very sparse global
distribution of in-water measurements. The goal of this comparison is to
first define inconsistencies and differences between sensor products,
both of which are subject to uncertainty. Such comparisons can identify
sources of uncertainty relating to spectral band characteristics,
observation time of day, in-water BRDF, and particular atmospheric
correction implementations. In this regard, data from sensors using a
similar approach as VIIRS are given higher priority, such as SeaWiFS and
MODIS and GLI, in contrast to POLDER.

***Funding:*** TBD

**Approach 3:** Validation using aircraft sensors

Several aircraft sensors are very useful for validation of derived
chlorophyll concentration over regional areas. These include the
Airborne Oceanographic Lidar and the Satlantic Airborne Simulator.

Funding: TBD

**H.1.5.4 Net Heat Flux (EDR)**

*To be included.*

**H.1.6 Validation Approaches for Snow and Ice**

H.1.6.1 Fresh water Ice edge Motion (EDR)

**Approach 1:** Comparison using EOS data

***Primary Validation Data Source:***

AVIRIS, MAS and MODIS

***Ancillary Data Sources:***

***Technique:***

The comparison plan for the Sea Ice Age and Sea Ice Edge Motion EDR
includes sensitivity studies, analysis of VIIRS data, and verification
using MODIS products. Observations from AVIRIS, MAS, MODIS, GLI will be
used in the pre-launch phase to study the error characteristics and
optimum techniques for the algorithm. It is expected that MODIS
validation data will be of great value. This data is expected to include
in-situ field measurements combined with MODIS observations, MAS
underflights, and low level aircraft measurements at spatial resolutions
less than 10 meters. VIIRS data are used to produce Sea Ice Age/Edge
Motion EDR products, and compare these results with "truth" derived from
in-situ, aircraft, and MAS data. The potential for VIIRS/CMIS data
fusion to produce First Year/Multi-year classification and ice edge
motion will be studied with the use of MODIS data and Advanced Microwave
Scanning Radiometer (AMSR) data.

***Scope and Schedule:***

***Comparison and Accuracy:***

***Supporting Documents:***

Sea Ice Age/ Edge Motion ATBD

***Funding:*** TBD

**H.1.6.2 Ice Surface Temperature (EDR)**

**Approach 1:** Comparison to EOS products

The Government Team will evaluate ice surface temperature data from
MODIS with respect to current operational and experimental NWS and
NESDIS products, such as those from AVHRR/3. The purpose is to reduce
the risk associated with the use of NPOESS/VIIRS products in NESDIS and
NWS operations. The benefit of incorporating the additional spectral
information available with MODIS in ice surface temperature retrieval
procedures will be evaluated, as will the use of new field data.
Co-located AVHRR, MODIS and VIIRS images and derived products will be
compared from local to hemispheric spatial scales for accuracy and
quality. For example, IST products over a variety of North American
watersheds, North America and Eurasia, the Northern Hemisphere, arctic
and antarctic will be evaluated. Samples of derived products will be
made available to NCEP, the National Ice Center, and the scientific
community for evaluation. This evaluation process will provide feedback
that may lead to modification of the VIIRS algorithms.

***Scope and Schedule:***

***Comparison and Accuracy:***

***Supporting Documents:***

Funding: TBD

**Approach 2.** In-situ and Airborne Instrument Comparison

*In-situ* and airborne data will be used in the validation. These data
will come primarily from NWS meteorological stations and NPP cal/val
sites. Reports detailing the methods and results of the evaluation, and
recommendations for NPOESS VIIRS proposed designs will be proposed.

Funding: TBD

**H.1.6.3 Snow Cover and Depth (EDR)**

**Approach 1. MODIS and POES Comparison**

The Government Team will evaluate a combination of Levels 1, 2, and 3
snow and ice data, imagery, and derived products from MODIS with respect
to current operational and experimental NWS and NESDIS products, such as
those from AVHRR/3. The purpose is to reduce the risk associated with
the use of NPOESS/VIIRS products in NESDIS and NWS operations. The NWS
National Operational Hydrologic Remote Sensing Center (NOHRSC) will
obtain the MODIS products via the Internet from the Cooperative
Institute for Meteorological Satellite Studies (CIMSS) at the University
of Wisconsin. The NESDIS/ORA will obtain the MODIS products through the
NESDIS provided server at NASA/GSFC. The benefit of incorporating the
additional spectral information available with MODIS in ice surface
temperature retrieval procedures will be evaluated, as will the use of
new field data and improved model in the retrieval of snow albedo from
satellite. MODIS and VIIRS snow and ice mapping products are very
similar. MODIS imagery and derived products for snow cover, snow albedo,
snow/cloud discrimination, sea ice cover, ice surface temperature,
ice/cloud discrimination, and lake ice will be evaluated relative to
existing NWS and NESDIS products. Personnel from NOHRSC, CMISS, and ORA,
as well as operational meteorologists from NESDIS/OSDPD, and the
National Ice Center will perform the evaluation. Co-located AVHRR and
MODIS images and derived products will be compared from local to
hemispheric spatial scales for accuracy and quality. For example, NOHRSC
will evaluate imagery and products over a variety of North American
watersheds, ORA will focus its investigation on the regions of North
America and Eurasia, and the Northern Hemisphere, and CIMSS will
investigate ice surface temperature and albedo in the arctic and
antarctic regions. Samples of derived products for snow cover and depth,
surface albedo, sea ice age/edge motion, ice surface temperature, and
fresh water ice, and cloud cover/layers will be made available to NCEP,
the National Ice Center, and the scientific community for evaluation.
This evaluation process will provide feedback that may lead to
modification of the VIIRS algorithms VIIRS design.

**Approach 2. In-situ and Airborne Instrument Comparison**

*In-situ* and airborne data will be used in the validation. These data
will come primarily from NWS meteorological stations and DOE ARM CART
sites. Reports detailing the methods and results of the evaluation, and
recommendations for NPOESS VIIRS proposed designs will be produced by
NOHRSC, CMISS, and ORA. This study will also allow NOHRSC to determine
the usefulness of a VIIRS-MODIS ground receiver.

Funding: TBD

**H.2 Validation Approaches of the CDR Climate Research Products**

**H.2.1 Atmospheric Sounding Profiles Validation**

**H.2.1.1 Clear Column Radiance (CDR)**

Validation approaches proposed for the Clear Column Radiance CDR is the
same as the ones proposed for the CrIS radiance validation described in
section H.1.1.1

H.2.1.2 CrIMSS Precipitation Rate (CDR)

Instantaneous precipitation rate (mm/h) is desired for operational,
research, and climatological purposes. Cloud Precipitation Rate is that
instantaneous precipitation-rate estimate achievable using CrIMSS that
produces on a global scale the best achievable equivalent to
NEXRAD-based estimates. To the extent that CrIMSS may prove to be more
sensitive to snowfall or light rain, CPR will deviate from NEXRAD
results to take advantage of these characteristics. That CrIMSS can
provide useful CNTPR estimates has been demonstrated (Staelin et al.,
2000; Chen and Staelin, 2001).

**Approach 1**: Comparison to NEXRAD data

By definition of CPR, the prime validation source must be coincident
NEXRAD data (offsets \< 5 minutes), with emphasis on the eastern United
States because of NEXRAD's more complete coverage there. The NEXRAD data
must be convolved with a response function characterizing the
appropriate 15- or 50-km antenna pattern. Radars operating at other
frequencies and global locations will provide secondary validation. Snow
pillows can provide more accurate ground truth for snowfall retrievals
(water-equivalent mm/h).

Supporting Document:

D. H. Staelin and F. W. Chen, "Precipitation observations near 54 and
183 GHz using the NOAA-15 satellite," *IEEE Trans. Geosci. and Remote
Sensing, 38*, 5, 2000, pp2322-2332; and F. W. Chen and D. H. Staelin,
*IEEE Trans. Geosci. and Remote Sensing*, 2002, in press.)

Funding: TBD

**Approach 2**: Comparison to BALTRAD data

BALTRAD radar data available for the Baltic region will be used in
conjunction with Snow/Ice cover maps to derive precipitation data. Radar
data will be convolved to the spatial resolution and observation
geometry of the ATMS. Probability of detection as a function of the
precipitation intensity will be derived. The precipitation screening
algorithms will be adjusted according to findings.

Funding: TBD

**H.2.1.3 Ozone validation (CDR)**

A likely candidate for a CrIS/ATMS CDR is the total column ozone and the
vertical ozone profile. Although ozone will be measured on NPOESS/OMPS
it is desirable to produce an ozone product in the NPP timeframe in
order to extend the ozone record that will be available from AIRS. The
ozone vertical structure in the troposphere will be indicative of the
oxidizing potential of the lower atmosphere, and the stratospheric
profile is needed for data assimilation and studies of stratospheric
ozone recovery.

The total column ozone should be retrieved at a resolution of .03
atm-cm, with an accuracy better than 10%.

Approach 1: Comparison to EOS and other program products

Currently, the available satellite instruments producing ozone products
include TOMS, SAGE-II, SBUV-2, HALOE, and MLS. After NPP launch, in
addition to CrIS/ATMS, AIRS and SAGE-III will also be producing ozone
products. One of the advantages of satellite based calibration is the
availability of a large number of observations at periodic time
intervals. The lifetime of the AIRS instrument on the Aqua platform is
expected to overlap with that of NPP. AIRS will be producing similar
ozone products on a global scale that will continue after NPP launch.
This makes it a perfect candidate for cross-instrument calibration.
Except for differences in the temporal and geographic position of the
retrieved profiles, that need to be accounted for, the ozone products
from AIRS and CrIS/ATMS are readily comparable and can be used for
calibration of CrIS/ATMS. Longer-term trends and instrument degradations
can also be determined using AIRS data. Total column measurement of
ozone is currently measured from space through the TOMS series of
instruments (EP-TOMS currently on the Earth Probe satellite and on the
QuikTOMS platform). An indirect measure of the tropospheric column is
possible by subtracting an integrated stratospheric profile from
SAGE-III, from a total column. These calibration methods will be of less
importance with the successful operation of AIRS.

***Funding:*** TBD

Approach 2: Comparison to In-situ data

Ozonesondes have been a standard instrument for measuring ozone from the
ground to the lower stratosphere. A long-term measurement database
exists (as long as 35 years for some sites), mostly within the Northern
Hemisphere mid-latitudes, on a roughly once-a-week basis. Ozonesondes
measurements offer good precision and excellent vertical resolution
(about 150 m), although results become more uncertain above 25 km
because of inefficiencies in pumping mechanisms, and corrections may be
needed to SO~2~ interference. For NPP, the major difficulties are the
comparatively low geographical and temporal density of ozonesonde
measurements. In addition, comparison to ozonesondes should be only done
for near-simultaneous measurement and clear skies. It is not known if
there will be enough routine ozonesonde measurements satisfying these
conditions for a statistically significant validation.

Supporting Document:

World Meteorological Organization, Global Ozone Research and Monitoring
Project Report No. 44, Chapter 4, Ozone Variability and Trends, 1999.

AIRS home page:
[http://www-airs.jpl.nasa.gov](http://www-airs.jpl.nasa.gov/)

SAGE-III home page:
[http://www-sage3.larc.nasa.gov](http://www-sage3.larc.nasa.gov/)

TOMS home page: [http://toms.gsfc.nasa.gov](http://toms.gsfc.nasa.gov/)

Funding: TBD

H.2.1.4 Trace Gases Validation (CDR)

Because of their effect on the global climate, trace gases have become
an important field of study. The CrIS/ATMS suite will be able to
retrieve abundances of trace gases (CH~4~, CO, N~2~O, and CO~2~) in the
atmosphere and therefore, the SDS may be producing a trace gas CDR. The
algorithm for trace gas retrieval is similar to ozone and is readily
implemented with minimal resource impact. In addition, the CO~2~
measurements will improve the temperature retrieval whereas the CH~4~
will improve the water retrieval. Most of the signal for trace gases
comes from the middle troposphere. The signal from the boundary layer,
though is typically buried in the overall noise and is more difficult to
retrieve.

+----------+-----------+----------+----------+----------+-----------+
| Gas      | W         | Int      | Geo      | Time     | Me        |
|          | avenumber | erfering | physical | Scale    | asurement |
|          |           |          |          |          |           |
|          |           |          | Range    |          |           |
+----------+-----------+----------+----------+----------+-----------+
| CH~4~    | 1250-1370 | H~2~O,   | 1.7 ppmv | Months   | Column    |
|          |           | N~2~O,   | 1%       |          |           |
|          |           | HNO~3~,  | yearly   |          |           |
+----------+-----------+----------+----------+----------+-----------+
| CO       | 2160-2195 | H~2~O,   | 5-10,000 | Months   | Column    |
|          |           | O~3~,    | ppbv     |          |           |
|          |           | N~2~O,   |          |          |           |
|          |           | CO~2~    |          |          |           |
+----------+-----------+----------+----------+----------+-----------+
| N~2~O    | 2220-2260 | H~2~O,   | 300 ppbv | Decades  | Zonal     |
|          |           | CO~2~,   |          |          | averages  |
|          |           | CO, O~3~ |          |          |           |
+----------+-----------+----------+----------+----------+-----------+
| CO~2~    | 650-750   | H~2~O,   | 360 ppmv | Years    | Ca        |
|          |           | O~3~,    |          |          | libration |
|          | 2250-2350 | N~2~O    | 0.4%     |          |           |
|          |           |          | yearly   |          |           |
+----------+-----------+----------+----------+----------+-----------+

Summary of the studies of Haskins and Kaplan (1993) on the ability of
AIRS to retrieve trace gases.

CO column abundances accuracies of 10% may be possible with a vertical
resolution of the upper and lower troposphere. CH4, with a strong 7.7 m
band and higher abundance than CO, may be able to be measured with an
accuracy of 5-10%. Measurement uncertainties of 1% in CO~2~ may be
obtainable. N~2~O will be measured with an accuracy of TBD

Approach 1: Comparison to EOS products

AIRS/AMSU/HSB, a sounding instrument suite that will also be in orbit at
the time of the NPP mission, will be making similar ir/mw measurements
with the ability to retrieve trace gas abundances. When these abundances
become validated AIRS products, they will be ideally suited to validate
the CrIS/ATMS trace gas products. Both data sets will be global in
coverage and offer many data points at varied conditions to extensively
validate the trace gas product. Other programs such as IASI, TES and
In-situ data will be included in this effort.

Funding: TBD

**H.2.2 Validation Approaches for Aerosol Products**

TBD

**H.2.3 Validation Approaches for Cloud Products**

**H.2.3.1 Cloud Ice Water Path (ATMS/VIIRS) (CDR)**

**Approach 1:** Comparison to EOS and POES products

NOAA AMSU operational cloud ice water /particle size algorithm retrieves
both IWP and particle effective diameter. These products are derived for
thick ice clouds including precipitation conditions. Since ATMS has two
channels similar to AMSU, the algorithm can be modified and tested with
NPP ATMS and CrIS.

Funding: TBD

**Approach 2:** Comparison to Validation Site Data

Calculations of high-spectral resolution infrared radiances in cirrus
cloud situations indicate that cloud forcing (clear minus cloudy)
spectra are sensitive to ice particle size, ice water path, and cloud
altitude. A numerical procedure based on the DISORT algorithm is used to
retrieve the effective radius and ice water path of cloud layers with
known optical depths and cloud boundaries and with nearby clear sky
atmospheric conditions also known. The reasonable reproduction in a
rather wide window region suggests that the DISORT based algorithm can
distinguish small from large particle clouds as well as provide a fair
estimate of IWP. Ice particle size and ice water path are estimated with
20% variation in the inferred values.

Funding: TBD

H.2.3.2 Cloud Liquid Water (ATMS/VIIRS) (CDR)

**Approach 1:** Comparison to EOS and POES products

NOAA AMSU operational algorithm retrieves both CLW and TPW. It is a
physical retrieval algorithm which uses two AMSU primary channels at
23.8 and 31.4 GHz. These two frequencies are identical to ATMS channel
selection. The algorithm can be directly modified for ATMS applications.
Comparison between VIIRS and ATMS retrievals are also planned.

Funding: TBD

**Approach 2:** Comparison to Validation Site Data

Calculations of high-spectral resolution infrared radiances in cirrus
cloud situations indicate that cloud forcing (clear minus cloudy)
spectra are sensitive to ice particle size, ice water path, and cloud
altitude. A numerical procedure based on the DISORT algorithm is used to
retrieve the effective droplet size and water path of cloud layers with
known optical depths and cloud boundaries and with nearby clear sky
atmospheric conditions also known. The reasonable reproduction in a
rather wide window region suggests that the DISORT based algorithm can
distinguish small from large droplet clouds as well as provide a fair
estimate of LWP.

Funding: TBD

**H.2.4 Validation Approaches for Land Products**

**H.2.4.1 Atmospheric Corrected Reflectance Validation (CDR)**

*To be included.*

**H.2.4.2 Fire Area and Temperature Validation (CDR)**

Approach 1: Comparison to EOS fire product

Statistical relationships will be established between AVHRR, MODIS and
VIIRS-derived fire numbers over specific areas and time periods. Results
of this study will be useful for the construction of a continuous
AVHRR-MODIS-VIIRS data record of fire occurrences for long-term studies.
VIIRS products will be evaluated for their physical limits, precision,
and accuracy, using theoretical calculations and ground observations
wherever available. These results will be compared with the VIIRS EDR
requirements and recommendations will be made regarding VIIRS fire
algorithm design. Potential for MODIS-VIIRS product stitching will be
evaluated. Finally, it is also proposed to perform feasibility studies
towards a time-integrated burned area product from VIIRS. This product
eliminates most of the active fire misses due to clouds and low temporal
resolution and thus is much more suitable for emission estimates and
hazard assessment. Changes in the signal of near-IR channels from
healthy vegetation to post-burning conditions will be studied and
recommendations will be made for an operational algorithm.

***Scope and Schedule:***

***Comparison and Accuracy:***

***Supporting Documents:***

MODIS fire ATBD

Funding: TBD

**H.2.4.3 LAI/FPAR Validation (CDR)**

Approach 1 : Using EOS validation sites measurements

*Primary Validation Data Source:*

These products are often considered together since their computation and
field measurement generally employ similar techniques. Plot-level
validation data are available from field measurements acquired with
commercial off-the-shelf instrumentation.

*Ancillary Data Sources:*

Additional validation data may include the 1 km MODIS LAI/FPAR product,
provided its accuracy is sufficiently well characterized and the
vegetation is known to be stable since the MODIS data acquisition. LAI
is being measured fairly regularly at FLUXNET sites, consisting of
eddy-covariance tower locations around the world. The accuracy of these
data probably varies substantially. All major NASA land measurement
campaigns (e.g., BOREAS, FIFE, etc.) include LAI assessment. Many of
these historical data sets reside in the Oak Ridge National Laboratory
DAAC.

*Technique:*

Generally, LAI or FPAR can be derived from hand-held instruments
(including hemispherical view cameras) which assess light obscuration by
vegetation canopy or crown. The instruments typically employ a modified
form of Beers' Law to derive LAI or FPAR units. To determine LAI or FPAR
at a plot scale, an investigator typically collects many samples over an
area, then attempts to scale these "point" measurements to a larger area
using fine-scale satellite or aircraft imagery. Although there is no
current standard technique for either spatial sampling design or
scaling, an LAI focus group under the auspices of the CEOS WGCV Land
Product Validation Subgroup is developing a "Best Practices" handbook.
Although historically the field instrumentation assumed a homogeneous
distribution of leaf material, newer instrument specifically assess
canopy clumping and reportedly produce superior results. In deciduous
areas, "leaf drop baskets" are sometimes deployed to determine the LAI
via the autumn leaf fall. Comparatively few FPAR validation studies have
been conducted to date, and thus even fewer standards currently exist.
Proper measurement requires measurement of four radiation fluxes
upwelling and downwelling above the canopy, and the same between the
canopy and the soil. Further, some canopy-absorbed PAR radiation is
attributable to non-green leaf, stem or standing dead material; accurate
FPAR measurements require knowledge of these quantities.

*Scope and Schedule:*

LAI and FPAR are key biophysical parameters, and are used in many modern
regional and global climate and ecosystem models. However, field
measurement of these parameters can require significant effort.
Therefore, plot-level assessments are generally conducted episodically
through the growing season. It is critically important that such sites
be globally and ecologically stratified. Assessments along ecological
gradients (e.g., precipitation) may allow more efficient data collection
over a large range of values.

Leaf Area Index tends to be conserved over 1- to 2 week periods for most
vegetation types, except during "green-up" or scenescent periods, or
during harvesting for cropped areas. Therefore, in many cases,
relatively few (\~3) episodic measurement periods are sufficient for
validation. These measurements can be made at any time of day. Because
FPAR is a radiative parameter, it varies with solar angle, atmospheric
condition, soil moisture and canopy conditions. Its field measurement
should therefore correspond to satellite overpass time. If canopy
conditions are well-known, canopy radiative transfer models may provide
accurate estimates for simple canopies (e.g., homogeneous grasslands)
given measured LAI and leaf optical values.

*Comparison and Accuracy:*

Several significant error sources (e.g., ratio of green leaf area to
plant area, canopy clumping, scaling) can make field assessment of these
parameters fairly inaccurate. A reasonable estimate of LAI uncertainty
is 0.2 for fairly uniform areas, and perhaps 0.5 elsewhere. Equivalent
values for FPAR are about 0.075 and 0.15 (absolute).

***Supporting Documents**:*

International Workshop on LAI Validation (2001), Best Practices for
Field LAI Measurements, forthcoming.

Funding: TBD

H.2.5 Validation Approaches for Ocean Products

H.2.5.1 Ocean Color (Water Leaving Radiance) Validation (CDR)

Retrieval of normalized water leaving radiances for the ocean visible
and near-IR bands from TOA radiances involves correction for numerous
atmospheric effects and reflection of sky and sun light from the air-sea
interface, as well a removal of small, but crucial instrument biases and
effects. The initialization phase discussed under Level 1 validation
using primarily data from the MOBY buoy and ship based measurements of
in-water radiances and above water reflectances produces a consistent
retrieval system comprised of the instrument data and atmospheric
correction algorithm. This is discussed in detail in the MODIS ATBD for
water-leaving radiances by Gordon. The resulting water-leaving radiances
for the visible bands provide the basis for all other ocean color (ocean
bio-optical) properties, including chlorophyll a and suspended sediment
load. Therefore the careful validation of this SDR is crucial to ocean
color products.

**Approach 1:** MOBY Comparisons

***Primary Validation Source:***

Marine Optical Spectrometer (MOS) instruments on MOBY or ship.

***Ancillary Data Sources:***

VIIRS data, nLw from heritage sensors on orbit, or historical MOBY
servicing cruise data, MOBY calibration/characterization data

***Techniques:***

Automated collection of MOBY data at the time of VIIRS overpasses, and
MOS data during MOBY servicing cruises within the swath, and development
of a matchup data base to sample the useful range of VIIRS swath and sun
angles. The overall approach for MOBY is discussed by Clark and Mueller
in Chapter 11 of the Revised SeaWiFS Protocols for
Calibration/Validation. Multiple radiometer buoys are maintained, and
are deployed sequentially for three month intervals. Refurbishment and
recalibration is done on shore. Optical collectors are cleaned monthly,
and in-water calibration stability sources are employed before and after
by divers. The measured spectral response function of the satellite
sensor is convolved with the high resolution spectrometer data. Upwelled
spectral radiances are collected at 3 depths and propagated to and
through the surface to produce the desired water-leaving radiance values
which are compared with the values retrieved from the satellite sensor.

***Scope and Schedule:***

The MOBY facility, located on Oahu and off Lanai, must be maintained
continuously throughout the mission, and will provide daily
observations. Useful matchups will be obtained for only a fraction of
the days due to cloud obscuration and solar/scan geometry.

***Comparison and Accuracy:***

Through concentrated effort, including active participation of NIST
personnel and facilities, the uncertainty of the MOBY water-leaving
radiance time series is on order of 3%.

***Funding:***

Currently MOBY is funded via EOS (80%), SeaWiFS (10%), and NESDIS (10%)
with annual total \~2M\$/yr. Continued funding by EOS will likely
decrease over the next few years, even with a planned shift from MODIS
science team to EOS Project Validation Facility (analogous to AERONET).
It is crucial that resources be identified to continue this national
facility. IPO, NOAA, DOD contributions will likely be required.

***Accuracy Requirements:***

The goals for these measurements are an overall accuracy in an absolute
sense of 2%, with a relative spectral (band-band) of 0.5%, over the time
series.

**Approach 2:** Other In-water radiance measurements

A variety of instrumentation and protocols to make individual and time
series of water-leaving radiance and also above water reflectance
measurements from ship, moorings, drifting buoys, and permanent
platforms have been developed. Details can be found at the SIMBIOS web
site. These measurements, most by independent investigators, are very
important for validation of the global water leaving radiance signals
following initialization at the MOBY site. An extensive round robin
comparison and inter-calibration network has been established, led by
the SIMBIOS Project at GSFC and with participation by NIST, including US
and many international programs. Included in this network are permanent
locations on oceanic research towers and moorings. Absolute and relative
uncertainty goals are as for MOBY. These are maintained through
intercomparison activities, and use of portable stability sources during
cruises and field programs. The SIMBIOS paradigm is that each ocean
color flight project will mount a focused calibration validation
program, with SIMBIOS serving as an inter-mission comparison facility,
maintaining protocols, data bases for global intercomparisons, and
center of expertise.

Funding: TBD

**Approach 3:** Validation using aircraft sensors

Use of aircraft sensors for validation of water-leaving radiances is
primarily in the area of providing improved spatial variations and
coverage. Maintaining sufficiently accurate absolute uncertainty of the
instrument and its atmospheric correction for use in direct validation
of water leaving radiance has improved significantly over the past
decade.. Aircraft sensors show great utility in validation of
bio-optical properties, but cannot provide the high degree of accuracy
of in-water or shipboard observations at this time.

Funding: TBD

H.2.5.2 Sea Surface Temperature (CDR)

Validation approaches proposed for the Sea Surface Temperature CDR is
the same as the ones proposed for the Sea Surface Temperature EDR
described in section H.1.5.1.

**H.2.6 Validation Approaches for Snow and Ice CDR Products**

TBD

[]{#__RefHeading___Toc534619225 .anchor}Appendix I: Matrix of Who, What,
When and How Much (from Co-Chairs)

[]{#__RefHeading___Toc534619226 .anchor}Appendix J: Definitions

This Appendix provides a brief set of definitions to establish common
language for the discussion of calibration and validation. The terms
*accuracy,* *precision*, and *uncertainty* are used as defined in the
Sensor Requirements Documents.

[Calibration]{.underline}

Calibration is the process by which the output (usually a digital word)
of an instrument is related to an input radiance, irradiance, or
electromagnetic radiation signal. The input stimulus is generated such
that it is related to a traceable standard. Depending on spectral
region, sources/targets may have reflective and/or emissive properties.

[Calibration Equation]{.underline}

Each photoactive element or feedhorn (with associated analog electronics
and analog-to-digital converters) provides an output measured in digital
numbers (DN) when it is stimulated by incident radiation. The plot of
incident radiance versus DN is termed the radiometric transfer curve.
The objective of radiometric calibration is to develop a calibration
equation which best represents the observed radiometric transfer curve,
and to provide both a quantitative determination of the gain
coefficients and zero-input bias (offset) of the equation, as well as
the uncertainties in measuring radiances using these coefficients.
Radiometric calibration is best conducted using a full-aperture source
that is spectrally and spatially homogeneous. Multiple radiometric
levels are used, spanning that portion of the sensor dynamic range that
is representative of the scene dynamic range in given spectral regions.
Uncertainties are inherent in the design and measurement of the source
itself. Instrument characteristics add other uncertainties. Calibration
assigns precision and accuracy values to all contributors to errors
associated in determining a \"true\" scene radiance value for a given
digital number. A number of optical (antenna pattern) and electronic
crosstalk effects are usually observed during pre-launch test and
orbital operations. The calibration equation is modified to ameliorate
the impact of these effects. Aging and environmental conditions cause
changes in the gain coefficients and signal offset. These are
characterized over the life cycle of the instrument.

The set of calibration equations for each instrument is the basis for
the radiometric part of the Level 1B product. Further radiometric
adjustments may be necessary after geometric re-sampling of the product
to register spectral bands to one another and to the surface of the
Earth.

[Characterization]{.underline}

This defines the output response in terms of variations within the
instrument field-of-view and field-of-regard, of changes in instrument
temperature, of power supply variations, of instrument modes of
operation, or due to any other parameter that causes a measurable change
in response for a calibrated/fixed/constant external input. The part of
characterization that deals with changes over time is segregated and
identified as *calibration trending*.

[Error Analysis]{.underline}

During pre-flight calibration, an Error Analysis is used to estimate the
uncertainty in the inverse regression from the radiometric equation
(i.e., the uncertainty in measured radiance for a given DN). Error
Interval Analysis defines the number of radiometric levels to be used
during calibration, as well as the number of independent data
repetitions. This tool continues to be used through the post-launch
period to estimate uncertainties in the radiance computed by the SDS.

[Field-of-View (FOV)]{.underline}

Every electro-optic or microwave instrument has a means of focussing
energy onto a transducer that converts the input to a current, voltage,
or change in impedance. The limit in object space of the angular extent
of the radiation field that can place energy on the transducer is the
field-of-view. In an ideally baffled instrument the FOV is identical to
the extent of the angular subtence of the focal plane.

[Field-of Regard (FOR)]{.underline}

The FOR is the angular space/volume through which the FOV of an
instrument can be pointed. In the case of a cross-track or conical
scanning instrument, the field-of-regard can have several active
segments. There is the imaging FOR (the Earth\'s surface and
atmosphere), the calibration target FOR (cold space, solar diffuser,
calibration lamps, blackbody), and the scan cavity (noise and
serendipity).

[Trending]{.underline}

Trending is the process initiated at the time of first measurement and
extending through the post-launch periods. Drifts in performance are
tracked and used to refine databases that permit instrument measurements
to be related to actual/estimated signals at the instrument aperture.

In this document, trending is primarily a post-launch activity.

[Validation]{.underline}

Validation has two contexts. For the hardware community \"validation\"
of instrument performance should be thought of as instrument performance
verification. For the science community, validation emphasizes algorithm
products and establishes the accuracy of these products over a range of
operating conditions and for a variety of science environmental
conditions.

In this document, validation is the process of assessing by independent
means the uncertainties of the data products derived from the system
outputs.

[General Validation Approaches]{.underline}

Direct Comparison with Independent Correlative Measurements

-   Ground- based Networks

-   Comprehensive Test Sites

-   Field Campaigns

-   Comparisons with Independent Satellite Retrievals

Products from Instruments on the same satellite platform

Products from Instruments on different satellite platforms: IPO, NASA,
international

[Basic Stages of Science Validation]{.underline}

Pre-launch - emphasis on algorithm development and characterization of
uncertainties from parameterizations and algorithmic implementation

Post-launch - emphasis on algorithm refinement and data product
assessments

**Selected Project Definitions**

[Ancillary Data]{.underline}: Data outside of the NPP mission system
used to generate NPP products, ex: NWP, DEMs.

[Auxiliary Data]{.underline}: Data generated/provided by the spacecraft
to be either included with the instrument data and/or transmitted as
part of the S-band telemetry data

[Granule]{.underline}: The smallest aggregation of data which is
independently managed (i,e., described, inventoried, retrievable).

[Instrument Developers]{.underline}: This provides a shorthand
description of the joint agency development of instruments for NPP. The
IPO has responsibility to manage the contracts for the CrIS and VIIRS
instruments. NASA has contract responsibility for the ATMS. The
\"developers\" are the government agencies. The instrument
\"contractors\" are the instrument/algorithm designers and builders
using the Government RDRs and EDRs as the end-to-end requirements.

[Investigators\' Team:]{.underline} A group of science investigators
selected through a NASA Research Announcement to participate in NPP
instrument development advisories, research science Climate Data Record
development and validation, and selected EDR validation.

[IPO Science Team:]{.underline} A group of science and engineering
investigators on any one of the IPO research and risk-reduction thrust
areas for the NPP and NPOESS programs. These teams often support
on-going validation tasks for the POES, DMSP and EOS programs.

[Level 0]{.underline}: Raw instrument data at original resolution, time
ordered, with duplicate packets removed.

[Level 1A]{.underline}: Reconstructed unprocessed instrument/payload
data at full resolution; any and all communications artifacts (e.g.
synchronization frames, communications headers) removed.

[Level 1B and SDR]{.underline}: Instrument data that has been
radiometrically corrected and geolocated. They are fully time-ordered
and overlap removed. The Level 1B algorithm developed at SDS will be
different from the one used by IDPS.

[Metadata]{.underline}: Information about data. Provides a description
of the data including instrument, type of data, location of data, and
quality of data.

[OATS:]{.underline} The Operational Algorithm Teams that support the
development of NPP instruments. The OATS are IPO peer science panels.
There is a VIIRS OAT (VOAT), a Sounder Suite OAT (SOAT), a microwave OAT
(MOAT) and within the sounder suites a Ozone OAT (OOAT) and a GPS
Occultation Sensor OAT and a Space Environment Sensor Suite (SESS) OAT.

[Raw Data Record]{.underline}: Data in their original packets, as
received from the observer.

CAL LUTs: Calibration Look-Up-Tables (LUT) derived for NPP instruments
at the IDPS or at the SDS.

[Verification]{.underline}: The process of ensuring mission/segment
requirements is satisfied. Verification occurs using one or more methods
(analysis, test, demonstration, or inspection).

[]{#__RefHeading___Toc534619227 .anchor}Appendix K: References

**Bennartz,** R., 1999: On the use of SSM/I measurements in coastal
regions. J. Atmos. Oceanic Technology, 16, 417-431.

**Bennartz,** R., 2000: Optimal convolution of AMSU-B to AMSU-A. J.
Atmos. Oceanic Technology, 17, 1215-1225.

Bennartz, R., A. Thoss, A. Dybbroe, and D. B. Michelson, 2001:
Precipitation Analysis Using the Advanced Microwave Sounding Unit in
Suppport of Nowcasting Applications. In press Meteorological
Applications.

**Bennartz**, R. and G. W. Petty, 2001: The sensitivity of microwave
remote sensing observation of precipitation to ice particle size
distributions. J. Applied Meteorology, 40, 345-364.

**Bennartz,** R., and D. B. Michelson, 2001: Correlation of
precipitation estimates from spaceborne passive microwave sensors and
weather radar imagery during BALTEX-PIDCAP. In press, Int. J. Remote
Sensing.

**Chen,** F. W. and D. H. Staelin, *IEEE Trans. Geosci. and Remote
Sensing*, 2002, in press.

**Cook,** R., Voorhees L., Woodcock C., 1999: May 1999 User Working
Group Meeting \-- ORNL DAAC for Biogeochemical Dynamics. The Earth
Observer 11(5):8-10

**Grody**, N., J. Zhao, R. Ferraro, F. Weng, and R. Boers, 2001:
Determination of precipitable water and cloud liquid water over ocean
from the NOAA 15 Advanced microwave sounding unit, *J. Geophys. Res.*,
106, 2943-2953.)

**Justice,** C.O., Belward, A., Morisette, J., Lewis, P., Privette, J.,
Baret, F., (2000), Developments in the \'validation\' of satellite
sensor products for the study of the land surface, *International
Journal of Remote Sensing*, 21:3383-3390.

**King,** M. D., W. P. Menzel, P. S. Grant, J. S. Myers, G. T. Arnold,
S. E. Platnick, L.

E. Gumley, S. C. Tsay, C. C. Moeller, M. Fitzgerald, K. S. Brown and F.
G. Os-terwisch,

1996: Airborne scanning spectrometer for remote sensing of cloud,
aerosol, water vapor and surface properties. J. Atmos. Oceanic
Tech-nol., 13, 777--794.

**Liu,** Q. and F. Weng: 2001:A polarimetric two-stream radiative
transfer model. *Submitted to J. of Atmos. Sci.*

**Lutz, B.**, Roy, D., Leff, C., Lewicki, S., Geir, E., Ziskin, D.,
Kilpatrick, K., Chu, A., (2000), A review of EOS Terra quality
assessment (QA)*, Proceedings of IEEE Geoscience and Remote Sensing
Symposium* 2000 (*IGARSS)*, Honolulu, Hawaii, 24-28 July 2000, CD-ROM
INT_27_06.pdf, 0-7803-6362-0/00 (c) 2000 IEEE.

**Morisette,** J., Privette, J., Justice, C.O., The Validation of EOS
MODIS Land Products, MODIS Land special issue of *Remote Sensing of
Environment*, in press.

**Rodgers**, C.D., Inverse Methods for Atmospheric Sounding, World
Scientific, Singapore, 2000.

**Roy,** D.P., Borak, J.S., Devadiga, S., Wolfe, R.E., Descloitres, J.,
The MODIS Land Product Quality Assessment Approach*,* MODIS Land special
issue of *Remote Sensing of Environment*, in press.

**Staelin,** D. H. and F. W. Chen, "Precipitation observations near 54
and 183 GHz using the NOAA-15 satellite," *IEEE Trans. Geosci. and
Remote Sensing, 38*, 5, 2000, pp2322-2332

**Weng**, F., N. C. Grody and R. R. Ferraro and A. Basist and D.
Forsyth, 1997: Cloud liquid water climatology derived from the Special
Sensor Microwave Imager. *J. Climate*, 10, 1086-1098.

**Weng,** F., R. R. Ferraro, and N. C. Grody, 2000: Effects of AMSU
cross-scan asymmetry of brightness temperatures on retrieval of
atmospheric and surface parameters. *Microwave Radiometry & Remote
Sensing of theEarth's Surface and Atmosphere,* Ed. P. Pampaloni and S.
Paloscia, VSP, Netherlands, 255

**Weng**, F., B. Yan, and N. C. Grody, 2001: A microwave land emissivity
model, *J. Geophys. Res.* (in press).

**Wisler**, M. M., and J. P. Hollinger, 1977: Estimation of marine
environmental parameters using microwave radiometric remote sensing
systems. N.R.L. Memo. Rept. 3661, pp 27.

**Yueh**, S., 1997: Modeling of Wind Direction Signals in Polarimetric
Sea Surface Brightness Temperatures. *IEEE Trans. Geosci. Remote
Sensing,* **35**, 1400-1418.

**Zhao,** L. and F. Weng, 2001: Retrieval of Ice Cloud Parameters Using
the Advanced Microwave Sounding Unit (AMSU). *J. Appl. Meteorol.*
(Submitted).

[]{#__RefHeading___Toc534619228 .anchor}Appendix L: Acronyms

ABI - Advanced Baseline Imager

ACARS - Aeronautical Radio Incorporated Communications Addressing and
Reporting

System

ACE - Aerosol Characterization Experiment

ADEOS - Advanced Earth Observing Satellite

ADS - Archive and Distribution Segment

AER - Atmospheric and Environmental Research Inc

AERI - Atmospheric Emitted Radiance Interferometer

AERONET - Aerosol Robotic Network

AFWA - Air Force Weather Agency

AIRS - Atmospheric Infrared Sounder

AMSR - Advanced Microwave Scanning Radiometer

AMSU - Advanced Microwave Sounding Unit

AOD - Aerosol Optical Depth

AOS - Acquisition of Signal or Advanced Orbiting Systems

AOT -- Aerosol Optical Thickness

APL - Applied Physics Laboratory

APMIR - Airborne Polarmetric Microwave Imaging Radiometer

AQUA - Afternoon EOS spaceborne platform (PM1)

ARAD - Atmospheric Research and Applications Division

ARM - Atmospheric Radiation Measurement (DOE)

ARSCL - Active Remotely Sensed Cloud Layers

ASTER - Advanced Spaceborne Thermal Emission Radiometer

ATMS - Advanced Technology Microwave Sounder

ATOVS - Advanced TOVS

AVHRR - Advanced Very High Resolution Radiometer

AVIRIS - Airborne Visible Infrared Imaging Spectrometer

BALTEX - Baltic Sea Experiment

BALTRAD - BALTEX Radar Data

BBR - Band to Band Registration

BCS - Blackbody Calibration Srource

BOREAS - Boreal Ecosystem-Atmosphere Study

BRDF - Bi-directional Reflectance Distribution Function

BSRN - Baseline Surface Radiation Network

BTDF -- Bi-directional Transmittance Distribution Function

C3 - Command, Control and Communications

CALVEX - Calibration Validation Experiment

CAMEX - Convection and Moisture Experiment

CART - Clouds and Radiation Testbed

CC/L Cloud Cover/Layer

CCS - Climate Calibration Service

CCSDS - Consultative Committee for Space Data Systems

CDR - Climate Data Record (not to be confused with Critical Design
Review)

CEOS -- Committee on Earth Observation Satellites

CERES - Clouds and the Earth's Radiant Energy System

CHAMEX - Cloud Height and Motion Experiment

CHEM - Renamed EOS AURA, part of NASA EOS spaceborne platforms, and
follows Terra and Aqua

CIGSN - Continental Integrated Ground Site Network (Australia)

CIMSS - Cooperative Institute for Meteorological Satellite Studies

CLAMS - Chesapeake Lighthouse and Aircraft Measurements

CLAP - Central Laboratory of Air Pollution

CLI - Canopy Lidar Initiative

CLS - Cloud Lidars

CLW - Cloud Liquid Water

CMDL - Climate Monitoring and Diagnostics Laboratory

CMIS - Conical scanning Microwave Imager/Sounder

CPI - Cloud Particle Imager

CRYSTAL - Cirrus Regional Study of Tropical Anvils and Cirrus Layers

CrIS - Cross track Infrared Sounder

CrIMSS - Cross-Track Infrared/Microwave Sounder Suite

CTP - Cloud-Top Pressure

CWV - Cloud Water Vapor

DAAC - Data Active Archive Center

DAO -- NASA/Goddard Data Assimilation Office

DB - Direct Broadcast

DEM - Digital Elevation Model

DISORT - DIScrete Ordinate method Radiative Transfer

DMSP - Defense Meteorolgical Satellite Program

DOC - Department Of Commerce

DOE - Department of Energy

DOD - Department of Defense

DPI - Derived Product Image

ECMWF -- European Center for Medium-Range Weather Forecasting

EDR - Environmental Data Record

EMD -- Engineering and Manufactoring Development

EO-3 - Earth Observing 3

EOS - Earth Observing System

ESA - European Space Agency

ETM+ - Enhanced Thematic Mapper Plus

EUMETSAT - EUropean organization for the exploitation for METeorological

SATellites

EVS - Emission Versus Scan

FARS - Facility for Atmospheric Remote Sensing

FASCAL - Facility for Automated Spectral Irradiance and Radiance
Calibrations

FFT - Fourier function transform

FIFE - First ISLSCP (International Satellite Land Surface Climatology
Project) Field Experiment (FIFE)

FIRSC - Far-InfraRed Sensor for Cirrus

FOV - field of view

FPA - Focal Plane Arrays

FPAR - Fraction Photosynthetically Active Radiation

FTIR Fourier Transform Infrared

FTS - Fourier Transform Spectrometer

FY - Fiscal Year

GAW -- Global Atmosphere Watch (a project within the WMO)

GCST - Global Climate Science Team

GEO - Geo-stationary Earth Orbit

GHz - Giga-Hertz

GIFTS - Geostationary Imaging Fourier Transform Spectrometer

GLI - Global Imager

GMS - geostationary Meteorological Satellite (Japan)

GOES - Geostationary Operational Environmental Satellite

GOME -- Global Ozone Monitoring Experiment

GPS - Global Positioning System

GSE - Ground Support Equipment

GSFC - Goddard Space Flight Center

HAPEX-Sahel -- Hydrology-Atmosphere Pilot Experiment in the Sahel

HIRS - High resolution Infrared Radiation Sounder

HIS - High-resolution Interferometer Sounder

HSB - Humidity Sounder for Brasil

IAC - Integrated Alignment Collimator

IASI - Infrared Atmospheric Sounding Interferometer

IDPS - Interface Data Processing Segment

IGBP - International Geosphere-Biosphere Programme

IGS - Internal Governmental Studies NPOESS IPO Program

ILS - Instrument Line Shape

IORD - Integrated Operational Requirements Document

IPO - Integrated Program Office

IPT - Integrated Product Team

IR - InfraRed

IWP - Integrated Water Profile

Kbps - Kilobits per second

KNMI -- Koninklijk Nederlands Meteorologisch Instituut (Royal
Netherlands Meteorological Institute)

LEO - Low Earth Orbit

LAI - Leaf Area Index

LaRC - Langley Research Center

LASE - Lidar Atmospheric Sensing Experiment

LBA - Large Scale Biosphere-Atmosphere Experiment in Amazonia

LBL - Line By Line

LBLRTM -- Line-By-Line Radiative Transfer Model

LSS - Launch Support Segment

LST - Land Surface Temperature

LSU - Louisiana State University

LTER - Long Term Ecological Research

LUTs - Look-Up-Tables

LV - Launch Vehicle

LWIR - Long Wave InfraRed

LWP - Liquid Water Path

M-AERI - Marine AERI

MAS - MODIS Airborne Simulator

MASTER -- MODIS/ASTER Airborne Sensors

MBIR - Medium Background Infrared

Mbps - Megabits per second

MCST - MODIS characterization Support Team

MCV - ?

MERIS - MEdium Resolution Imaging Specrometer

METEOSAT - METEOrological SATellite

METOP - Meteorological Operational Platform

MHS - Microwave Humidity Sounder

MHz - Mega-Hertz

MISR - Multi-angle Imaging Spectro-Radiometer

MIT - Massachusetts Institute of Technology

MMCR - Millimeter Wave Cloud Radar

MOBY - Marine Optical Buoy

MOCE - Marine Optical Characterization Experiment

MODIS - Moderate resolution Imaging Spectroradiometer

MOPITT - Measurements of Pollution in the Troposphere

MPL - Micropulse Lidar

MQUALS - MODIS Quick Airborne Looks

MTF - modulation transfer function

MWIR -- Middle Wave InfraRed

NASA - National Aeronautics and Space Administration

NAST - NPOESS Airborne Sounder Testbed

NAST-I - NPOESS Airborne Sounder Testbed-Interferometer

NAST-M - NPOESS Airborne Sounder Testbed-Microwave

NAVOCEANO - Naval Oceanographic Office

NCEP - National Center for Environmental Prediction

NCST - NPP Characterization Support Team

NDVI - Normalized Difference Vegetation Index

NEDT - noise equivalent temperature or noise equivalent delta-T

NESDIS - National Environmental Satellite, Data, and Information Service

NESR - Noise Equivalent Spectral Radiance

NIR - Near-infrared

NIST - National Institute of Standards and Technology

NMP - New Millennium Program

NOAA - National Oceanographic and Atmospheric Administration

NOHRSC - National Operational Hydrologic Remote Sensing CenterNORPEX --
Northern Pacific Experiment

NPOESS - National Polar-orbiting Operational Environmental Satellite
System

NPP - NPOESS Preparatory Project

NRA - NASA Research Announcement

NSA- North Slope Alaska

NSF - National Science Foundation

NWP - Numerical Weather Prediction

NWS - National Weather Service

OATs - Operational Algorithm Teams

OBC - On-Board Blackbody Calibration

OCTS - Ocean Color and temperature Scanner

OLS - Operational Linescan System

OMI - ?

OPD - Optical Path Difference

ORA - Office of Research and Applications

ORNL - Oak Ridge National Laboratory

OSDPD - Office of Satellite Data Processing and Distribution

OSS - Optimal Spectral Sampling

P-AERI -- Polar- AERI

PFAAST

PIDCAP - Pilot Study for Intensive Data Collection and Analysis of
Precipitation

POAM -- Polar Ozone and Aerosol Measurement

POES - Polar Operational Environmental Satellite

PRT - Platinum Resistance Thermometer

PSA Polarization Source Assembly

PSO - Project Science Office

PSR - Polarimetric Scanning Radiometer

RDR - Raw Data Record

RF - Radio Frequency

RMS - root mean square

RSR - Relative Spectral Response

RVS - Response Versus Scan

6S - Second Simulation of Satellite Signal in the Solar Spectrum

S-AERI -

SAFARI - South African Regional Science Initiative

SAGE - Stratospheric Aerosol and Gas Experiment

SALSA -- Semi-Arid Land Surface Atmosphere

SBRS - Santa Barbara Remote Sensing

SCIAMACHY -- SCanning Imaging Absorption spectroMeter for Atmospheric
CHartographY

SCP - Satellite Cloud Product

SD - Solar Diffuser

SDR - Sensor Data Record

SDS - Science Data Segment

SDSM -- Solar Diffuser Stability Monitor

SeaWIFS - Sea-Viewing Wild Field of View

SGP - South Great Plain

S-HIS - Scanner-HIS

SHDOM - Spherical Harmonic Discrete Ordinate Method

SIS - Spherical Integrating Source

SIRCUS - Spectral Irradiance and Radiance Responsivity Calibrations with
Uniform Sources

SPMA - Spectral Measurement Assembly

SRF - Spatial Response Function

SSA - S-band Single Access

SSEC - Space Science and Engineering Center

SSM/I - Special Sensor Microwave/Imager

SSPR -- Shared System Performance Responsibility

SST - Sea Surface Temperature

SURFRAD - Monitoring Surface Radiation in the Continental United States

SVS - Space View Source

SVWXEX- To be supplied

SWIR Short-wave infrared

TBD - To Be Determined

TBR - To Be Resolved

TBS - To Be Supplied

TCEX - To be supplied

Terra - Morning EOS spaceborne platform (EO1)

TES - Tropospheric Emission Spectrometer

THORPEX - The Hemispheric Observing System Research and Predictability
Experiment

TIROS - Television InfraRed Operational Satellite

TOA - Top of the Atmosphere

TOMS - Total Ozone Mapping Spectrometer

TOVS - TIROS Operational Vertical Sounder

TPW - Total Precipitable Water

TRACE- Transport and Chemistry near the Equator

TRMM - Tropical Rainfall Measuring Mission

SSPR - Sharedl System Performance Responsibilities

TXR - Thermal-infrared Transfer Radiometer

TV - Thermal Vacuum

TWP - Tropical Western Pacific

UAV -- Unmanned Air Vehicle

UMBC - University of Maryland at Baltimore County

UMD - University of Maryland

UPS - U.S. Postal Office

USGS - U.S. Geological Survey

USWRP - United States Weather research Program

VIS - Visible

VIRS - Visible Infrared Scanner

VIIRS - Visible Infrared Imaging Radiometer Suite

WINTEX - Winter Experiment

WMO -- World Meterological Organization (United Nations)

WVIOP - Water Vapor Intensive Operational Periods

WVSS - Water Vapor Sensing System

ZPD - Zero Path Difference