MODIS Atmosphere Validation Plan

MODIS Atmosphere Discipline Team

Michael D. King

Earth Sciences Directorate

Goddard Space Flight Center, Greenbelt, MD

Yoram J. Kaufman

Laboratory for Atmospheres

Goddard Space Flight Center, Greenbelt, MD

W. Paul Menzel

NOAA/NESDIS

University of Wisconsin, Madison, WI

Didier Tanré

Université des Sciences et Technique de Lille

Villeneuve d'Ascq, France

Bo-Cai Gao

Naval Research Laboratory, Washington, DC

MODIS Validation Collaborators

Steve Ackerman

Bryan Baum

Rich Ferrare

Andrew Heymsfield

Brent Holben

Merv Lynch

Gerald Mace

Vanderlei Martins

Alexander Marshak

Chris Moeller

Steven Platnick

Lorraine Remer

Si-Chee Tsay

Taneil Uttal

Von Walden

May 16, 2001

Table of Contents

Executive Summary [iv](#__RefHeading___Toc388510078)

1.0 Introduction [1](#__RefHeading___Toc388510079)

*1.1* *Scientific Objectives* [1](#__RefHeading___Toc388510080)

*1.2* *Missions* [2](#__RefHeading___Toc388510081)

*1.3* *Science Data Products* [2](#__RefHeading___Toc388510082)

2.0 Validation Overview [2](#__RefHeading___Toc388510083)

*2.1* *Overall Approach* [2](#__RefHeading___Toc388510084)

*2.2* *Terra Validation Investigations*
[5](#__RefHeading___Toc388510085)

*2.3* *Sampling Requirements* [6](#__RefHeading___Toc388510086)

*2.4* *Measures of Success* [7](#__RefHeading___Toc388510087)

3.0 Validation Sites [7](#__RefHeading___Toc388510088)

*3.1* *University of Utah Facility for Atmospheric Remote Sensing
(FARS)* [8](#__RefHeading___Toc388510089)

*3.2* *Continental Integrated Ground Site Network (CIGSN)*
[11](#__RefHeading___Toc388510090)

*3.3* *Surface Measurements for Atmospheric Radiative Transfer (SMART)*
[12](#__RefHeading___Toc388510091)

*3.4* *Raman Lidar* [15](#__RefHeading___Toc388510092)

*3.5* *Radar and Lidar Measurements* [17](#__RefHeading___Toc388510093)

4.0 Pre-launch Activities [18](#__RefHeading___Toc388510094)

*4.1* *Field Experiments* [18](#__RefHeading___Toc388510095)

*4.1.1* *ARMCAS* [20](#__RefHeading___Toc388510096)

*4.1.2* *SCAR-B* [21](#__RefHeading___Toc388510097)

*4.1.3* *SUCCESS* [22](#__RefHeading___Toc388510098)

*4.1.4* *TARFOX* [22](#__RefHeading___Toc388510099)

*4.1.5* *WINCE and WINTEX* [23](#__RefHeading___Toc388510100)

*4.1.6* *FIRE Arctic Cloud Experiment*
[24](#__RefHeading___Toc388510101)

*4.1.7* *CALVEX-N* [24](#__RefHeading___Toc388510102)

*4.2* *Operational Surface Networks* [25](#__RefHeading___Toc388510103)

*4.3* *Existing Satellite Data* [25](#__RefHeading___Toc388510104)

*4.4* *Instrument Development* [25](#__RefHeading___Toc388510105)

5.0 Post-launch Activities [26](#__RefHeading___Toc388510106)

*5.1* *Field Campaigns* [26](#__RefHeading___Toc388510107)

*5.1.1* *WISC-T2000* [28](#__RefHeading___Toc388510108)

*5.1.2* *PRIDE* [29](#__RefHeading___Toc388510109)

*5.1.3* *SAFARI 2000* [30](#__RefHeading___Toc388510110)

*5.1.4* *Antarctica* [31](#__RefHeading___Toc388510111)

*5.1.5* *TX-2001* [31](#__RefHeading___Toc388510112)

*5.1.6* *ACE-Asia* [32](#__RefHeading___Toc388510113)

*5.1.7* *CLAMS* [33](#__RefHeading___Toc388510114)

*5.1.8* *CAMEX 4* [33](#__RefHeading___Toc388510115)

*5.1.9* *CLAP-2002* [34](#__RefHeading___Toc388510116)

*5.1.10* *CRYSTAL-FACE and CRYSTAL-TWP*
[34](#__RefHeading___Toc388510117)

*5.1.11* *California Stratus and Valley Fog*
[35](#__RefHeading___Toc388510118)

*5.2* *Other Satellite Data* [35](#__RefHeading___Toc388510119)

*5.3* *In Situ Measurement Needs at Calibration/Validation Sites*
[35](#__RefHeading___Toc388510120)

*5.4* *Needs for Instrument Development*
[36](#__RefHeading___Toc388510121)

*5.5* *Geometric Registration Site* [36](#__RefHeading___Toc388510122)

*5.6* *Intercomparisons* [37](#__RefHeading___Toc388510123)

*5.7* *Modeling Studies and Quality Assessment*
[38](#__RefHeading___Toc388510124)

6.0 Implementation of Validation Results
[39](#__RefHeading___Toc388510125)

*6.1* *Approach* [39](#__RefHeading___Toc388510126)

*6.2* *Role of EOSDIS* [39](#__RefHeading___Toc388510127)

*6.3* *Archival, Processing, and Distribution of Validation Data*
[39](#__RefHeading___Toc388510128)

7.0 Summary [39](#__RefHeading___Toc388510129)

8.0 References [41](#__RefHeading___Toc388510130)

9.0 World Wide Web Links for Validation
[44](#__RefHeading___Toc388510131)

10.0 Acronyms [45](#__RefHeading___Toc388510132)

Executive Summary[]{#__RefHeading___Toc388510078 .anchor}

The MODIS Atmosphere Team will use several validation techniques to
develop uncertainty estimates for its various data products. The methods
include (i) comparisons with in situ data collected over a distributed
network of ground validation sites, (ii) comparisons with data and
products from other airborne and spaceborne sensors, and (iii) analysis
of trends in atmosphere data products.

The primary validation techniques include collection of, and comparison
with, field experiment data collected from collocated airborne field
experiments, and intercomparison with long time series of ground-based
observations at a selected set of ground validation sites worldwide. The
imagery, data analysis, and field experiment data will be archived at
either the Goddard or Langley DAAC, and made available to the outside
scientific community.

MODIS Atmosphere validation work will involve:

-   Comparisons with land validation sites in Africa, North America, and
    Australia

-   Close cooperation with EOS validation investigators to meet specific
    product validation needs

-   Interaction with established data networks (e.g., ARM, AERONET,
    radiosondes)

-   Participation in community field campaigns (e.g., FIRE ACE,
    SAFARI 2000) and EOS-targeted field campaigns (e.g., WISC-T2000)

-   Collaboration with other Terra, ADEOS II, and Aqua instrument teams

-   Collaboration with a worldwide effort to derive column precipitable
    water from a network of surface GPS receivers

Taken together, these activities provide the foundation for operational
product validation, and outline the planned validation activities of the
MODIS Atmosphere Discipline Team and collaborators from 1995 through
2002.

1.0 Introduction[]{#__RefHeading___Toc388510079 .anchor}

This document describes the activities of the MODIS Atmosphere team and
principal validation investigators aimed at assessing the accuracy,
reliability, and representativeness of the atmosphere data products
routinely derived from MODIS satellite data. It reviews recent
activities as well as activities planned through 2002.

*1.1 Scientific Objectives*[]{#__RefHeading___Toc388510080 .anchor}

We intend to collect a well-calibrated data set of high spectral and
spatial resolution measurements to support radiometric calibration of
the MODIS shortwave and longwave channels and the development of the
following MODIS atmosphere algorithms:

-   Cloud mask for distinguishing clear sky from clouds

-   Cloud radiative and microphysical properties

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-   cloud top pressure, temperature, and effective emissivity

-   cloud optical thickness, thermodynamic phase, and effective radius

-   thin cirrus reflectance in the visible

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-   Aerosol optical properties

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-   optical thickness over the land and ocean

-   aerosol size distribution (parameters) over the ocean

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-   Atmospheric moisture and temperature gradients

-   Column water vapor amount

-   Gridded time-averaged (level-3) atmosphere product

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-   daily (1° × 1°)

-   8-day (1° × 1°)

-   monthly (1° × 1°)

-   mean, standard deviation, marginal probability density function,
    joint probability density functions

A summary of these algorithms can be found in King et al. (1992), as
well as in detailed descriptions of each algorithm, to be found in
Algorithm Theoretical Basis Documents (ATBDs) available at
http://eospso.gsfc.nasa.gov/atbd/pg1. html.

*1.2 Missions*[]{#__RefHeading___Toc388510081 .anchor}

MODIS is currently onboard the first EOS spacecraft, designated Terra,
launched on December 18, 1999. In addition to Terra, MODIS will fly
aboard Aqua in December 2001. These two spacecraft will likely be
repeated in the morning and afternoon orbits with advanced operational
versions of MODIS, providing a consistent data set equivalent to MODIS
from 2000-2018. In order to validate the MODIS atmospheric products to
be derived from these data, it is necessary to validate specific
geophysical parameters under a wide variety of atmospheric conditions
from arctic stratus clouds in the summertime arctic, multi-layer cloud
systems in the polar night, convective cloud systems in the
intertropical convergence zone, aerosol properties over the ocean and
several land surface covers for different aerosol types, precipitable
water over a wide range of atmospheric conditions from the dry arctic to
the humid tropics, total ozone content, atmospheric temperature and
moisture profiles, and atmospheric stability.

*1.3 Science Data Products*[]{#__RefHeading___Toc388510082 .anchor}

This MODIS atmosphere validation plan addresses the MODIS cloud mask as
well as investigator science products characterizing cloud top
properties, cloud radiative and microphysical properties, aerosol
optical thickness and (over the ocean) aerosol size distribution,
precipitable water vapor over the land and ocean sun glint regions, and
atmospheric profiles of moisture and temperature. Validation of the
shortwave and longwave radiances will also be assessed and discussed in
this validation plan.

2.0 Validation Overview[]{#__RefHeading___Toc388510083 .anchor}

*2.1 Overall Approach*[]{#__RefHeading___Toc388510084 .anchor}

In order to validate MODIS atmosphere data products, it is necessary to
validate specific atmosphere parameters under a wide variety of
atmospheric conditions and solar illumination angles, and over a wide
variety of ecosystems worldwide. The MODIS atmosphere team will use a
wide variety of validation techniques to develop uncertainty information
on its products. These include (i) coordination and collocation with
higher resolution aircraft data, (ii) intercomparison with ground-based
and aircraft in situ observations, (iii) intercomparisons with other
Terra, ADEOS II, and Aqua instruments (e.g., MISR, AIRS, AMSR, GLI), and
(iv) analysis of trends over time and consistency across boundaries
(e.g., land *vs.* ocean, day *vs.* night).

Our validation approach relies heavily on the sources of the data that
were used in the algorithm development, which consisted primarily of the
MAS, a fifty channel visible, near-infrared, and thermal infrared
imaging spectrometer with 50 m resolution at nadir (King et al. 1996),
HIS, a 2 km resolution nadir-viewing (now modified for scanning, S-HIS)
Michelson interferometer, AVIRIS, a 224 band imaging spectrometer from
0.4-2.5 µm with 20 m resolution at nadir (Vane et al. 1993). In
addition, we plan to make extensive use of the AERONET (Aerosol Robotic
Network), a network of more than 160 ground-based sunphotometers
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 Cimel sunphotometer to Goddard Space Flight Center
from sunrise to sunset, 7 days a week (cf. Figure 1).

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) CART (Clouds And Radiation
Testbed) site in Oklahoma. North American Radiosondes (Figure 2), in
conjunction with GOES retrievals, will be used to validate atmospheric
properties (water vapor, stability). GOES retrievals provide the bridge
to compare the MODIS retrievals with radiosonde
measurements.![](media/image1.png){width="5.936111111111111in"
height="2.9680555555555554in"}

Well-calibrated radiances are essential for the development of accurate
algorithms. The calibration of the S-HIS is such that it serves as a
reference for line-by-line radiative transfer models. 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 the shortwave infrared and thermal
infrared channels will be routinely assessed through vicarious
calibration and intercomparisons with the S-HIS flying on the same
aircraft. 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. A comprehensive description of
both the shortwave and longwave calibration procedures, signal-to-noise
characteristics, and thermal vacuum characterization of the MAS can be
found in King et al. (1996). MODIS IR calibration will be evaluated
using the AERI network at the ARM sites (Figure 3). The ARM SGP site
will have five AERI instruments; ARM NSA and ARM TWP will each have two
AERIs. These are zenith-viewing instruments. The calibration accuracy of
AERI surpasses that of S-HIS (calibration of AERI instruments is NIST
traceable). In addition to the ARM sites, MODIS 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 MODIS 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 MODIS. This will minimize surface effects on the calibration
validation exercise.

Comparisons with products from other platforms are also planned. Cloud
masks will be compared with those from AVHRR and HIRS/2 data, and ASTER
and MISR (also on the Terra platform). The CERES cloud mask makes use of
MODIS data (on Terra) and is essentially the same algorithm as MODIS, so
no independent verification is thereby possible. Atmospheric profiles
will be compared with those from HIRS, GOES, and AIRS/AMSU/HSB (also on
the Aqua platform). Cloud properties will be intercompared with those
derived from HIRS, CERES, and MISR (cloud top altitude), as well as from
in situ aircraft (see below). Aerosol optical thickness and particle
size retrievals from MODIS will be compared to MISR 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 (\~2000 worldwide, including 800 in Japan), (iv) ground-based
microwave radiometer measurements at the ARM sites as well as surface
measurements at the MODIS land validation site in Skukuza, South Africa,
(v) ground-based Raman lidar measurements at the SGP CART site, and (vi)
periodic differential absorption lidar measurements from the ER-2
aircraft (LASE; Dr. Edward
Browell).![](media/image2.png){width="6.108333333333333in"
height="4.372222222222222in"}

*2.2 Terra Validation Investigations*[]{#__RefHeading___Toc388510085
.anchor}![](media/image3.png){width="5.93125in" height="3.15in"}

The EOS Validation program funded 21 Type I (Research & Analysis) and 44
Type II (EOS Validation) investigations
(ftp://eospso.gsfc.nasa.gov/sterling/ Validation/NRA_selectees.pdf) as a
result of a NASA Research Announcement issued in 1997. Six of these
investigations are especially relevant to validation of MODIS atmosphere
data products. In addition, international collaboration from Australia
will play a valuable role in assessing the accuracy and reliability of
MODIS atmosphere and land products at three surface sites on the
Australian continent. Table 1 lists the validation investigations that
are especially relevant to the MODIS atmosphere products.

Table 1. EOS Validation Investigations of relevance to the MODIS
atmosphere data products.

+----------------+---------------+-------------------------------------+
| ***I           | ### *         | ### *Investigation*                 |
| nvestigator*** | Organization* |                                     |
+----------------+---------------+-------------------------------------+
| Richard        | NASA Langley  | Validation of aerosol and water     |
| Ferrare        |               | vapor profiles by Raman lidar       |
+----------------+---------------+-------------------------------------+
| Andrew         | NCAR          | In-situ and remote sensing          |
| Heymsfield     |               | measurements in support of the      |
|                |               | EOS/MODIS retrieval algorithm       |
|                |               | validation program                  |
+----------------+---------------+-------------------------------------+
| Merv Lynch     | Curtin        | Temperature and moisture profiles,  |
|                | University    | surface radiation budget, and       |
|                |               | spectral radiance at the sensor     |
|                |               | (vicarious calibration sites)       |
+----------------+---------------+-------------------------------------+
| Gerald Mace    | University of | Cloud property and surface          |
|                | Utah          | radiation observations and          |
|                |               | diagnostics in support of EOS       |
|                |               | CERES, MODIS, and MISR validation   |
|                |               | efforts                             |
+----------------+---------------+-------------------------------------+
| Alexander      | Univ. of      | Validation of cloud optical depths  |
| Marshak        | Maryland      | retrieved from EOS/MODIS data       |
+----------------+---------------+-------------------------------------+
| Steven         | Univ. of      | A study of uncertainties for MODIS  |
| Platnick       | Maryland      | cloud retrievals of optical         |
|                |               | thickness and effective radius      |
+----------------+---------------+-------------------------------------+
| Taneil Uttal   | NOAA/ETL      | Validation of CERES cloud           |
|                |               | retrievals over the Arctic with     |
|                |               | surface-based millimeter-wave radar |
+----------------+---------------+-------------------------------------+

*2.3 Sampling Requirements*[]{#__RefHeading___Toc388510086 .anchor}

Comparison of MODIS radiances and products with those from other
instruments should be made periodically (perhaps annually) in different
seasons in daytime and nighttime conditions. We anticipate numerous
opportunities, unspecified, in which scientists worldwide will
intercompare MODIS-derived atmospheric, land, and ocean data products
with local measurements of the geophysical property of interest. This
wide-scale synthesis of data sets from scientists from Australia, Japan,
China, Europe, South America, and Africa will greatly enhance the
confidence that we place in the MODIS-derived products, and will, with
time, aid our ability to assess the quality of the data products from a
wide variety of climatic conditions and seasons. This would not be
possible, nor appropriate, for the small MODIS Science Team to
accomplish entirely on its own.

*2.4 Measures of Success*[]{#__RefHeading___Toc388510087 .anchor}

Adjustments will be made to MODIS algorithms so that three-sigma
confidence will be achieved. This will take at least two years to
assemble and implement the lessons learned from these validation
assessments. As these assessments become known to the MODIS algorithm
developers, adjustments will be made to the algorithms as required. We
anticipate periodic reprocessing of the data set to periodically
incorporate these refinements.

3.0 Validation Sites[]{#__RefHeading___Toc388510088 .anchor}

In order to validate global atmosphere properties that can be derived
from MODIS satellite data, a reasonable sampling of the global
variability of these products is necessary. Each MODIS atmosphere
product varies widely in space and time, so much of the difficulty in
validating these global data products arises from sparse sampling of the
range of values encountered by each variable. Hence, as outlined above,
our strategy includes not only focused 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. Table 2 describes the primary surface
validation sites, responsible investigators, and available data products
to be acquired from this extensive network of stations.

Table 2. MODIS atmosphere ground validation sites.

+---------+---------------------+-------------+-----------------------+
| ***Net  | ***Location***      | ***         | ***Primary Purpose*** |
| work*** |                     | Responsible |                       |
|         |                     | Inves       |                       |
|         |                     | tigators*** |                       |
+---------+---------------------+-------------+-----------------------+
| AERONET | Multiple locations  | Brent       | Aerosol optical       |
|         | in North America,   | Holben      | thickness, columnar   |
|         | South America,      |             | aerosol size          |
|         | Europe, Africa,     | Yoram       | distribution, and     |
|         | Asia, and Oceania   | Kaufman     | precipitable water    |
|         | (cf. Fig. 1)        |             |                       |
|         |                     | Didier      |                       |
|         |                     | Tanré       |                       |
|         |                     |             |                       |
|         |                     | Lorraine    |                       |
|         |                     | Remer       |                       |
+---------+---------------------+-------------+-----------------------+
| ARM     | Southern Great      | Paul Menzel | Cloud base height,    |
|         | Plains, North Slope |             | temperature and       |
|         | of Alaska, Western  | Gerald Mace | moisture profiles,    |
|         | Tropical Pacific    |             | sky radiance,         |
|         | (cf. Fig. 2)        | Rich        | integrated liquid     |
|         |                     | Ferrare     | water path            |
|         |                     |             |                       |
|         |                     | Taneil      |                       |
|         |                     | Uttal       |                       |
+---------+---------------------+-------------+-----------------------+
| Rad     | North America,      | Paul Menzel | Temperature and       |
| iosonde | Latin America       |             | moisture profiles,    |
|         |                     | Steve       | clear sky radiance    |
|         |                     | Ackerman    | (with forward model)  |
+---------+---------------------+-------------+-----------------------+
| FARS    | University of Utah  | Gerald Mace | Cloud mask, cloud     |
|         |                     |             | boundaries &          |
|         |                     |             | microphysical         |
|         |                     |             | structure, aerosol    |
|         |                     |             | vertical profile      |
+---------+---------------------+-------------+-----------------------+
| CIGSN   | Australia           | Merv Lynch  | Surface irradiance,   |
|         |                     |             | clear sky radiance    |
|         |                     | Fred Prata  | for vicarious         |
|         |                     |             | calibration of MODIS  |
|         |                     |             | radiances,            |
|         |                     |             | radiosondes,          |
|         |                     |             | sunphotometer         |
+---------+---------------------+-------------+-----------------------+

Table 2. MODIS atmosphere ground validation sites (continued).

+---------+---------------------+-------------+-----------------------+
| ***Net  | ***Location***      | ***         | ***Primary Purpose*** |
| work*** |                     | Responsible |                       |
|         |                     | Inves       |                       |
|         |                     | tigators*** |                       |
+---------+---------------------+-------------+-----------------------+
| P-AERI  | South Pole and      | Von Walden  | Clear sky radiance    |
|         | Dome-C, Antarctica  |             | (IR) and surface      |
|         |                     | Chris       | measurements for      |
|         |                     | Moeller     | MODIS validation of   |
|         |                     | Paul Menzel | cold scenes           |
+---------+---------------------+-------------+-----------------------+
| SMART   | Skukuza, South      | Si-Chee     | Surface radiation     |
|         | Africa (cf. Fig. 5) | Tsay Jeff   | budget, precipitable  |
|         |                     | Privette    | water and integrated  |
|         |                     |             | liquid water path,    |
|         |                     |             | cloud base altitude   |
+---------+---------------------+-------------+-----------------------+
| Balloon | North America       | A.          | Balloon-borne ice     |
|         |                     | Heymsfield  | crystal replicators   |
|         |                     |             | for size distribution |
|         |                     |             | and habit of ice      |
|         |                     |             | crystals in upper     |
|         |                     |             | atmosphere            |
+---------+---------------------+-------------+-----------------------+

The distribution and location of these land validation sites being used
by the MODIS atmosphere team is shown in Figure
4.![](media/image4.pct){width="5.9944444444444445in"
height="2.998611111111111in"}

*3.1 University of Utah Facility for Atmospheric Remote Sensing
(FARS)*[]{#__RefHeading___Toc388510089 .anchor}

FARS is a permanent cloud research station located on the eastern edge
of the University of Utah campus (40° 49' 00" N, 111° 49' 38" E). Its
location overlooks the Salt Lake Valley from a branch of the Wasatch
Mountains (1.52 km MSL) (cf. Figure 5). The facility was established in
1986 with joint funding from the National Science Foundation and the
University of Utah to house a Cloud Polarization (ruby) Lidar. The
diverse combination of portable and stationary equipment (Table 3) makes
FARS the only research facility of its kind west of the Rocky Mountains.
A unique feature of the FARS location is its proximity to a diversity of
surface types. Within 50-100 km, the albedo can vary from one of the
highest (salt flats and winter snow fields) to the lowest (Great Salt
Lake) on the planet. Furthermore, the close proximity of a diverse range
of land types (vegetated level ground to desert mountains) will allow
for validation exercises to be conducted over many surfaces of interest
to EOS investigators.

Table 3. Facility for Atmospheric Remote Sensing (FARS) instrumentation.

+---+--------------------------+---------------------------------------+
| # |                          | ***Specifications***                  |
| # |                          |                                       |
| # |                          |                                       |
| # |                          |                                       |
|   |                          |                                       |
| * |                          |                                       |
| I |                          |                                       |
| n |                          |                                       |
| s |                          |                                       |
| t |                          |                                       |
| r |                          |                                       |
| u |                          |                                       |
| m |                          |                                       |
| e |                          |                                       |
| n |                          |                                       |
| t |                          |                                       |
| * |                          |                                       |
+---+--------------------------+---------------------------------------+
| # |                          |                                       |
| # |                          |                                       |
| # |                          |                                       |
| # |                          |                                       |
| # |                          |                                       |
|   |                          |                                       |
| P |                          |                                       |
| a |                          |                                       |
| s |                          |                                       |
| s |                          |                                       |
| i |                          |                                       |
| v |                          |                                       |
| e |                          |                                       |
|   |                          |                                       |
| r |                          |                                       |
| e |                          |                                       |
| m |                          |                                       |
| o |                          |                                       |
| t |                          |                                       |
| e |                          |                                       |
|   |                          |                                       |
| s |                          |                                       |
| e |                          |                                       |
| n |                          |                                       |
| s |                          |                                       |
| o |                          |                                       |
| r |                          |                                       |
| s |                          |                                       |
+---+--------------------------+---------------------------------------+
|   | Net flux radiometers     |                                       |
+---+--------------------------+---------------------------------------+
|   | Narrow-beam (0.14°)      | 9.5-11.5 μm                           |
|   | radiometer (co-aligned   |                                       |
|   | with lidar)              |                                       |
+---+--------------------------+---------------------------------------+
|   | Infrared flux radiometer | broadband                             |
+---+--------------------------+---------------------------------------+
|   | Pyradiometer             | 0.3-2.8 µm                            |
+---+--------------------------+---------------------------------------+
|   | Rotating shadow band     | a\. 0.63-2.5 µm                       |
|   | radiometer               |                                       |
|   |                          | b\. 6 channels, 0.14-0.94 µm (10 nm   |
|   |                          | bandpass)                             |
+---+--------------------------+---------------------------------------+
|   | Pyrheliometer with solar | 0.63-2.8 μm                           |
|   | tracker                  |                                       |
+---+--------------------------+---------------------------------------+
|   | Sunphotometer            | 10 channels, 0.38-1.03 μm (10 nm      |
|   |                          | bandpass)                             |
+---+--------------------------+---------------------------------------+
|   | All-sky 35 mm            |                                       |
|   | photography              |                                       |
+---+--------------------------+---------------------------------------+
|   | All-sky video time lapse |                                       |
|   | imagery                  |                                       |
+---+--------------------------+---------------------------------------+
| # |                          |                                       |
| # |                          |                                       |
| # |                          |                                       |
| # |                          |                                       |
| # |                          |                                       |
|   |                          |                                       |
| A |                          |                                       |
| c |                          |                                       |
| t |                          |                                       |
| i |                          |                                       |
| v |                          |                                       |
| e |                          |                                       |
|   |                          |                                       |
| r |                          |                                       |
| e |                          |                                       |
| m |                          |                                       |
| o |                          |                                       |
| t |                          |                                       |
| e |                          |                                       |
|   |                          |                                       |
| s |                          |                                       |
| e |                          |                                       |
| n |                          |                                       |
| s |                          |                                       |
| o |                          |                                       |
| r |                          |                                       |
| s |                          |                                       |
+---+--------------------------+---------------------------------------+
|   | Polarization Cloud Lidar | a\. 2 channels (0.694 and 0.347 µm) , |
|   | (ruby)                   | vertically pointed                    |
|   |                          |                                       |
|   |                          | 3.  Manually "tiltable" ±5° from      |
|   |                          |     zenith                            |
|   |                          |                                       |
|   |                          | 4.  0.1 Hz PRF (7.5 m maximum range   |
|   |                          |     resolution)                       |
|   |                          |                                       |
|   |                          | 5.  1-3 mrad receiver beamwidths      |
|   |                          |                                       |
|   |                          | 6.  25 cm diameter telescope          |
|   |                          |                                       |
|   |                          | 7.  1.5 J maximum output              |
+---+--------------------------+---------------------------------------+
|   | Two-color Polarization   | a.  4 channels (0.532 and 1.06 μm,    |
|   | Diversity Lidar (PDL)    |     dual polarization)                |
|   |                          |                                       |
|   |                          | b.  Vertical (0.532 µm) + horizontal  |
|   |                          |     (1.06 µm) polarizations           |
|   |                          |                                       |
|   |                          | c.  Fully scannable (5° s-1)          |
|   |                          |                                       |
|   |                          | d.  10 Hz PRF                         |
|   |                          |                                       |
|   |                          | e.  1.5 m maximum range resolution    |
|   |                          |                                       |
|   |                          | f.  0.2-3.8 mrad variable receiver    |
|   |                          |     beamwidths                        |
|   |                          |                                       |
|   |                          | g.  35 cm diameter telescopes (two)   |
|   |                          |                                       |
|   |                          | h.  0.45 J output                     |
+---+--------------------------+---------------------------------------+
|   | 95 GHz Polarimetric      | a.  6 channels (two Doppler)          |
|   | Doppler Radar            |                                       |
|   |                          | b.  Vertical + horizontal             |
|   |                          |     polarization transmitted          |
|   |                          |                                       |
|   |                          | c.  Fully scannable (5° s-1)          |
|   |                          |                                       |
|   |                          | d.  10 Hz-80 kHz PRF                  |
|   |                          |                                       |
|   |                          | e.  7.5 m maximum range resolution    |
|   |                          |                                       |
|   |                          | f.  600 range gates                   |
|   |                          |                                       |
|   |                          | g.  0.25° beamwidth                   |
|   |                          |                                       |
|   |                          | h.  90 cm diameter dish (57 dB gain)  |
|   |                          |                                       |
|   |                          | i.  1.2 kW peak power                 |
+---+--------------------------+---------------------------------------+

Table 3. Facility for Atmospheric Remote Sensing (FARS) instrumentation
(continued).

+---+--------------------------+---------------------------------------+
| # |                          | ***Specifications***                  |
| # |                          |                                       |
| # |                          |                                       |
| # |                          |                                       |
|   |                          |                                       |
| * |                          |                                       |
| I |                          |                                       |
| n |                          |                                       |
| s |                          |                                       |
| t |                          |                                       |
| r |                          |                                       |
| u |                          |                                       |
| m |                          |                                       |
| e |                          |                                       |
| n |                          |                                       |
| t |                          |                                       |
| * |                          |                                       |
+---+--------------------------+---------------------------------------+
| # |                          |                                       |
| # |                          |                                       |
| # |                          |                                       |
| # |                          |                                       |
| # |                          |                                       |
|   |                          |                                       |
| P |                          |                                       |
| a |                          |                                       |
| s |                          |                                       |
| s |                          |                                       |
| i |                          |                                       |
| v |                          |                                       |
| e |                          |                                       |
|   |                          |                                       |
| r |                          |                                       |
| e |                          |                                       |
| m |                          |                                       |
| o |                          |                                       |
| t |                          |                                       |
| e |                          |                                       |
|   |                          |                                       |
| s |                          |                                       |
| e |                          |                                       |
| n |                          |                                       |
| s |                          |                                       |
| o |                          |                                       |
| r |                          |                                       |
| s |                          |                                       |
+---+--------------------------+---------------------------------------+
|   | Temperature              |                                       |
+---+--------------------------+---------------------------------------+
|   | Relative humidity        |                                       |
+---+--------------------------+---------------------------------------+

The FARS site possesses extensive active and passive remote sensing
instrumentation. Active systems include the vertically pointing ruby
lidar (0.694 µm wavelength, with 7.5 m maximum range resolution; Sassen
1997), a portable high resolution two-color (0.532 and 1.06 µm, with 1.5
m maximum range resolution) Polarization Diversity Lidar (Sassen 1994),
and a 6 channel 3.2 mm polarimetric Doppler radar (with 7.5 m maximum
range resolution; Sassen and Chen 1995). The passive systems include a
narrow-beam infrared (IR) radiometer (PRT-5, 9.5-11.5 µm), a broadband
infrared radiometer (PIR), global visible pyranometer (PSP, 0.3-2.8 µm),
normal incidence pyrheliometer (NIP, 0.63-2.8 µm), and a rotating
shadowband radiometer (RSR, 0.63-2.8 µm). Recent additions to the site
include a 9-channel sunphotometer, a multifilter rotating shadowband
radiometer (MFRSR; Harrison et al. 1994) and a two-channel microwave
radiometer.

The scientific objective of FARS has been the study of middle and high
level clouds for basic research, as well as supporting the satellite
validation effort of the FIRE project's extended time observation (ETO)
component. To this end, over 2200 hours of ruby-lidar and supporting
data have been collected through the end of 1996.

The capability of FARS, like the ARM sites, is nearly ideal for use in
validation of the EOS-derived cloud and radiation parameters. The
particular combination of active and passive sensors will allow for a
direct intercomparison of derived parameters, and, when combined with in
situ data, will enable a thorough validation of many algorithms. The EOS
validation program has funded approximately 20 hours of in situ data
collection per year over the FARS instruments. Presently, we are
subcontracting with Dr. Paul Lawson of SPEC Inc. to perform the in situ
data collection. Our goal is to use the surface-based remote sensing
observations validated by in situ data to derive a suite of products
tailored for efficient use by the EOS science teams and to deliver these
products along with any in situ data to the appropriate science team
members or archival locations. Our scientific objective is to
sequentially examine the assumptions underlying the surface and
satellite retrievals with the ultimate goal being the convergence of the
end results.

To this end, we will operate the FARS site in clear and cloudy
conditions for periods bracketing satellite overpasses. The length of
operation in each situation will be determined on a case-by-case basis
but will in general consist of 1-3 hour periods. We are prepared to
support roughly 15 overpasses each month. The overpasses we will support
(high and low angle, day or night) will be determined in consultation
with the EOS team scientists. In cloudy conditions, the full suite of
FARS instrumentation will be operated. In clear conditions, the
radiometric instrumentation and ruby lidar will be operated to map the
vertical properties of aerosol layers. Additionally, we will perform one
extended observing period each quarter where we will collect data for at
least 12 hours centered on an overpass so as to characterize a
representative cloud system.

*3.2 Continental Integrated Ground Site Network
(CIGSN)*[]{#__RefHeading___Toc388510090 .anchor}

Australia's Continental Integrated Ground Site Network (CIGSN) currently
has two operational sites - one is 50 km E of Hay (New South Wales) on
the Uardry sheep station, referred to hereafter as the Uardry site, and
the other is 100 km WNW of Alice Springs (Northern Territory) on the
Amburla brahman cattle stud, referred to hereafter as the Amburla site.
The CIGSN is producing accurate and long time series data of surface
radiative fluxes and meteorological parameters such as surface
temperature, air temperature, relative humidity, wind speed and surface
pressure. A third site is situated on Thangoo station, 40 km SSE of
Broome in the Kimberley region of Western Australia. This site should be
operational by mid 1999.

The Uardry site has been providing near-continuous surface measurements
since July 1992. This site is near the center of the largest and most
uniform plain on the Australian continent. The layout consists of a
central data logging and processing site coupled with 8 "satellite"
sites that telemeter data via a radio communications link to the central
site up to 6 times per minute. The solar powered, low maintenance,
autonomous data collection system can handle up to 48 channels of 16-bit
resolution data stored onto a computer hard disk. Upward and downward
shortwave and longwave irradiances are monitored. The upwelling
irradiances are obtained from instruments mounted on a boom extending
from a 15 m tower. Air and ground temperatures are monitored at points
across a 1 km2 area.

The Amburla site has been providing near-continuous surface measurements
since March 1995. This instrumentation is on a 12 x 12 km2 plain, which
is sparsely covered with grass tufts. The layout consists of a central
data-logging site coupled with 6 "satellite" sites. Electronics,
communications and geophysical variables monitored are similar to those
at the Uardry site.

The Thangoo site lies about 3 km from the coastal plain surrounding
Roebuck Bay, SSE of Broome. The land cover for the region varies from
broad mud flats near the coast on which little grows, often under water
during the wet season and because of the large tidal range (typically
8 - 10 m), to mangrove swamps, grassy plains and extensive coverage of
acacia bush (wattle) that grows up to 5 m in height. The soil is
typically sandy and red-colored. The layout consists of a central
data-logging site coupled with "satellite" sites in a similar fashion to
the other two sites. Electronics, communications and geophysical
variables monitored are also similar to those at the other sites.

For all three sites, measurements of the surface irradiances and surface
temperature are made at a spatial scale that matches the resolution of
satellite imagers such as MODIS, the Along-Track Scanning Radiometer
(ATSR), and the Advanced Very High Resolution Radiometer (AVHRR).
Therefore the CIGSN measurements constitute a highly valuable data set
with which to calibrate and validate satellite remote sensing data. The
CIGSN data set has greatly assisted efforts in developing algorithms for
land surface temperature (LST) and also surface-leaving shortwave (SW)
irradiance from ATSR 1.6 µm top-of-the-atmosphere (TOA) radiances.

The CIGSN was designed to provide ground-truth temperature data for
comparison with satellite-derived LSTs. Results from measurements made
at Uardry have been published (Prata 1994).

Further information can be found at the CSIRO Division of Atmospheric
Research web site: [http://www.dar.csiro.au](http://www.dar.csiro.au/);
follow links to the Atmospheric Processes-Surface Processes page.

*3.3 Surface Measurements for Atmospheric Radiative Transfer
(SMART)*[]{#__RefHeading___Toc388510091 .anchor}

Si-Chee Tsay has developed a suite of surface radiation and remote
sensing instruments for field deployment, known as the SMART (Surface
Measurements for Atmospheric Radiative Transfer). Figure 6 depicts the
instrumentation setup, to be mobilized and operated readily in most
remote locations in conjunction with satellite overpasses that serve to
enhance the AERONET database and provide data for intercomparisons. The
SMART consists of three Eppley-PSP (0.3-2.8 µm, 0.7-2.8 µm, 0.2-4.5 µm)
pyranometers, one Eppley-NIP (0.3-2.8 µm) pyrheliometer, two
Kipp/Zonen-CM21 (0.3-2.8 µm, 0.7-2.8 µm) pyranometers, one
Kipp/Zonen-CH1 (0.3-2.8) pyrheliometer, two Eppley-PIR (4.0-50 µm)
pyrgeometers, one Kipp/Zonen-CG4 (4.0-50 µm) pyrgeometer, one Kipp/Zonen
solar tracker, one Yankee-MFR (0.42, 0.50, 0.61, 0.67, 0.87, and 0.94 µm
narrowband and 0.3-1.0 µm broadband) total/diffuse radiometer, one
Yankee-TSP (0.3-2.8 µm) radiometer, one Yankee-TSI sky imager, one
Cimel-318 (0.34, 0.38, 0.44, 0.50, 0.67, 0.87, 0.94, and 1.02 µm
narrowband) sunphotometer, one ASD-FR (0.35-2.5 µm) spectrometer, one
Bomem-AERI (3-20 µm) interferometer, one GSFC-SMiR scanning microwave
(23, 24 and 37 GHz) radiometer with two surface moisture probes, one
SESI-MPL (0.52 µm) micropulse lidar, and a meteorological weather
station for atmospheric pressure, temperature, humidity, wind speed and
direction. Our strategy is to configure these instruments to:

-   measure the broadband downwelling/upwelling shortwave and longwave
    radiation to derive albedo/emissivity, and to investigate the direct
    radiative forcing through collocated satellite and surface remote
    sensing
    measurements![](media/image7.pct){width="5.929861111111111in"
    height="3.7729166666666667in"}

-   acquire, compare, and synthesize radiometric measurements of the
    broadband total/diffuse (MFR), total shortwave (PSP/TSP/CM21 with
    WG295 filter), and shortwave-infrared (PSP/CM21 with RG695 filter)
    radiation, and correlate these data with satellite measurements for
    radiation budget studies

-   acquire, compare, and synthesize radiometric measurements from the
    sun photometer (Cimel narrowband transmitted and sky radiances),
    narrowband total/diffuse irradiance (MFR), transmitted and reflected
    spectral radiance (FR), emitted spectral radiance (AERI), and 22-36
    GHz microwave emission (SMiR), focusing on the retrieval of
    atmospheric parameters (e.g., column water vapor amount, aerosol
    loadings, aerosol size distribution, etc.) and to seek the
    correlation between the broadband and narrowband radiation
    measurements

-   measure the lidar backscattering intensity to infer the vertical
    profiles of aerosols and cloud parameters for comparison with the
    retrieved results from radiometric measurements, and to initialize
    the atmospheric aerosol profile in the forward computation of
    radiation models

This SMART can be easily transported to field sites of interest
throughout the duration of Terra and Aqua, and has been collocated with
the EOS Validation site in Skukuza, South Africa, during Austral Spring
2000 (SAFARI 2000 period, see below), as well as other EOS-related field
campaigns.

Currently, the Eppley-PSP, Kipp/Zonen-CM21, Eppley-PIR, Kipp/Zonen-CG4,
solar tracker, sky imager, Yankee-TSP radiometers, Yankee-MFR
total/diffuse radiometer, Cimel-318 sunphotometer, GSFC-SMiR scanning
microwave radiometer, and the SESI micropulse lidar, and meteorological
weather station are operational and continuously operated at all times.
In most field deployments, this system requires two operators to set up
and field calibrate the system within about 8 working hours. Then, it
can run in automatic mode, but requires data to be downloaded about once
or twice a month for the radiometers and micro-pulse lidar. The
sunphotometer has satellite-link transmission (like all AERONET
systems). Only the ASD-FR spectrometer is operated. The AERI is
currently under construction and will be in operation in the fall of
2001. A brief summary of the instrument specifications and processing
status is given in Table 4.

Table 4. Summary of surface remote sensing and radiation measurements in
the SMART.

+----------------+--------------+-------------------------------------+
| **             | #### *Spe    | ***Processing Status***             |
| *Instrument*** | cifications* |                                     |
+----------------+--------------+-------------------------------------+
| PSP Shortwave  | a\. 0.3 -    | Calibrated for dark current and     |
|                | 2.8 µm       | off-set corrections                 |
| Radiometers    |              |                                     |
|                | b\. 0.7 -    |                                     |
|                | 2.8 µm       |                                     |
+----------------+--------------+-------------------------------------+

Table 4. Summary of surface remote sensing and radiation measurements in
the SMART (continued).

+----------------+--------------+-------------------------------------+
| **             | #### *Spe    | ***Processing Status***             |
| *Instrument*** | cifications* |                                     |
+----------------+--------------+-------------------------------------+
| CM21 Shortwave | a\. 0.3 -    | Calibrated for dark current and     |
|                | 2.8 µm       | off-set corrections                 |
| Radiometers    |              |                                     |
|                | b\. 0.7 -    |                                     |
|                | 2.8 µm       |                                     |
+----------------+--------------+-------------------------------------+
| TSP Shortwave  | 0.3 - 2.8 µm | Calibrated for dark current and     |
| Radiometer     |              | off-set corrections                 |
+----------------+--------------+-------------------------------------+
| PIR Longwave   | 4.0 - 50 µm  | Calibrated for dark current,        |
|                |              | off-set and temperature             |
| Radiometer     |              | (preliminary) corrections           |
+----------------+--------------+-------------------------------------+
| CG4 Longwave   | 4.0 - 50 µm  | ###### Calibrated for               |
|                |              | dark current and offset corrections |
| Radiometer     |              |                                     |
+----------------+--------------+-------------------------------------+
| MFR/7          | 0.4-1.0 µm,  | Calibrated against laboratory light |
| Shadowband     | 416, 502,    | source and constant field           |
| Radiometer     | 616, 674,    | monitoring with Li-Cor calibrator   |
|                | 869, and 938 | light source                        |
|                | nm           |                                     |
+----------------+--------------+-------------------------------------+
| ASD-FR         | 0.35 - 2.5   | Calibrated against laboratory       |
| Shortwave      | µm           | Mercury-Argon source with 0.1 nm    |
| Spectrometer   |              | bandpass monochromator              |
+----------------+--------------+-------------------------------------+
| Cimel          | 340, 380,    | Calibrated against laboratory light |
| Sunphotometer  | 440, 670,    | source, Langley plot, and           |
|                | 870, 940,    | thin-cloud screening                |
|                | and 1020 nm  |                                     |
+----------------+--------------+-------------------------------------+
| Micropulse     | 0.52 µm      | Calibrated for dead-time,           |
| lidar          |              | after-pulse, and overlap            |
|                |              | corrections                         |
+----------------+--------------+-------------------------------------+
| Scanning       | 22.8, 23.8,  | Cryogenic calibration, 77 K to      |
| Microwave      | 37.0 GHz     | ambient temperature                 |
| Radiometer     |              |                                     |
+----------------+--------------+-------------------------------------+

*3.4 Raman Lidar*[]{#__RefHeading___Toc388510092 .anchor}

The Department of Energy ARM program has developed and operates a Raman
lidar system at the SGP CART site. Rich Ferrare leads an investigation
that uses data from this lidar system as well as the Goddard Scanning
Raman lidar to evaluate the aerosol retrieval algorithms used by MODIS
and MISR and, in particular, to investigate over an extended period of
time the (i) vertical variability of aerosol extinction and
backscattering, (ii) relative humidity effects on aerosol properties,
and (iii) effects of clouds on aerosol properties. The SGP CART Raman
lidar system operates in the vertical mode only, and is in nearly
continuous operation (\~60% of the time since March 1998). A 3-phase
uninterruptible power source (UPS), which was installed in February
1999, should significantly increase the operating time of this Raman
lidar. It measures vertical profiles of aerosol extinction and
backscatter (355 nm), depolarization (355 nm), and water vapor mixing
ratio (from Raman shifted backscattering at 408 nm); these profiles of
aerosol extinction and water vapor mixing ratio are integrated to derive
measurements of aerosol optical thickness and precipitable water vapor.

Figure 7a shows a correlation with CART Raman lidar analyses of aerosol
optical thickness and nearly coincident Cimel sunphotometer measurements
and Fig. 7b shows a correlation with CART Raman lidar analyses of
precipitable water vapor and coincident microwave radiometer
measurements. These figures show the capabilities for validation of
aerosol optical thickness and precipitable water vapor. Perhaps more
importantly, Raman lidar also provides detailed information on the
vertical distribution of aerosols and water vapor that cannot be
obtained from either the sunphotometer or microwave radiometer alone. In
addition, this lidar provides information on the correlation between
aerosol extinction and relative humidity.

*3.5 Radar and Lidar Measurements*[]{#__RefHeading___Toc388510093
.anchor}![](media/image8.pct){width="5.006944444444445in"
height="3.1145833333333335in"}![](media/image9.pct){width="5.022916666666666in"
height="3.1840277777777777in"}

Between September 1997 and September 1998, a ground-based millimeter
wave radar and depolarization lidar were operated on the Arctic ice pack
as a part of the National Science Foundation Surface Heat Budget of the
Arctic (SHEBA) program. This activity was closely coordinated with the
FIRE-ACE program described in section 4.1.6. In addition, an identical
millimeter wave radar began operation in Barrow, Alaska in March 1998 as
part of the ARM NSA site (Section 3.0) with an anticipated operation
period of at least 10 years. Some of the specific Arctic cloud
properties that can be validated with these radars are the determination
of cloudy vs. clear pixels, cloud top heights, and cloud base heights.
When used in combination with surface based radiometers, additional
retrieved parameters can be derived, for example phase, particle size,
optical thickness, emittance, and integrated water path.

In using surface-based data as validation sets for satellite, several
important issues need to be addressed. First, the absolute accuracy of
the surface data sets must be determined before they can be useful. For
instance, it has been shown that radar and lidar often do not agree on
observed cloud boundaries (Uttal et al. 1995). Also, radar-radiometer
retrieval methods for detecting cloud microphysical properties utilize a
number of assumptions (such as the shape of the particle size
distribution) that have yet to be tested extensively with in situ data.
The FIRE-ACE program conducted over 30 research flights over the SHEBA
surface site, which will allow detailed comparisons between in situ
cloud properties and those remotely detected with the radars. Given the
difficulties that satellites have in determining cloud properties over
cold snow and ice surfaces in the Arctic, it is expected that these
measurements will be invaluable for testing the ground-based data sets
that will be used to evaluate the performance of MODIS over the Arctic.
Figure 8 shows an example of a time height cross-section of the
often-complex cloud structure over the SHEBA ice station during one of
the FIRE-ACE aircraft campaigns.

After some knowledge is obtained about the accuracy of the cloud
retrievals for Arctic clouds, the radar and radiometer data from the ARM
NSA site will be processed through an automated cloud processing
procedure to generate a continuous ground-based cloud data set for the
lifetime of the Terra satellite. Because Terra is a high altitude
satellite, and Barrow, Alaska is a high latitude site, it is projected
that there will be on the order of 7-8 overpasses/day. Over a 3 year
period this will allow 8 × 365 × 3 = 8760 coincident satellite-surface
intercomparisons and, therefore, cloud properties from the surface site
will be calculated on at least an hourly basis. Comparisons will be made
based on point-by-point measurements where a short time average of
surface data will be compared to the closest coincident satellite pixel
(cf. Fig. 9). In addition, weekly, monthly and annual statistics will be
computed to test for any systematic biases in the frequency distribution
of such quantities as cloud top heights, detection of cloudy vs. clear
pixels, cloud particle sizes and so forth.

4.0 Pre-launch Activities[]{#__RefHeading___Toc388510094
.anchor}![](media/image10.pct){width="5.4840277777777775in"
height="4.397916666666666in"}

*4.1 Field Experiments*[]{#__RefHeading___Toc388510095 .anchor}

Pre-launch validation will be accomplished using MAS, AVIRIS, CLS
(Spinhirne et al. 1989), CAR (King et al. 1986) and HIS data already
gathered in various field campaigns; data that are especially valuable
in combination with nearly coincident airborne in situ microphysical
measurements. A sampling of data sets already in-hand, along with key
sensors and responsible investigators are given in Table 5.

In addition, numerous data sets obtained with the CAR of the internal
scattered radiation field in smoke (Kuwait oil fires), clouds (FIRE
marine stratocumulus experiment, ARMCAS), and many additional
measurements of the bidirectional reflectance function of natural
surfaces such as tundra, sea ice, oceans, smoke, cerrado, rainforest,
desert, and the Great Dismal Swamp, add immeasurably to a data set on
the surface reflectance characteristics of natural surfaces. The
analyses of these data sets are being conducted by Michael King, Si-Chee
Tsay, Tom Arnold, Peter Soulen, and Jason Li. Examples of BRDF
measurements thus far obtained can be found in King (1992), Tsay et al.
(1998), Curry et al. (2000), Soulen et al. (2000), and Arnold et al.
(2001). An example of BRDF measurements obtained over tundra in early
spring (subfreezing) conditions and late spring (melting) conditions can
be found in Fig. 10 (for three CAR bands). Spectral albedo has also been
computed for each BRDF case and compared to the nadir reflectance.

***Table 5. Pre-launch Field Experiments involving MODIS atmosphere
investigator***s.![](media/image11.pct){width="5.195138888888889in"
height="4.25in"}

+---------+---------+------+----------+------------+----------------+
| ##      | ####    | ***  | **       | ***R       | #### *Pr       |
| # *Expe | *Dates* | Plat | *Primary | esponsible | imary Purpose* |
| riment* |         | form | Se       | Invest     |                |
|         |         | s*** | nsors*** | igators*** |                |
+---------+---------+------+----------+------------+----------------+
| ARMCAS  | June    | ER-2 | MAS,     | Si-Chee    | arctic stratus |
|         | 1995    |      | CLS,     | Tsay       | clouds over    |
|         |         | C-   | AVIRIS,  |            | sea ice;       |
|         |         | 131A | CAR,     | Michael    | multi-layer    |
|         |         |      | micr     | King       | clouds;        |
|         |         |      | ophysics |            | surface        |
|         |         |      |          | Steve      | bidirectional  |
|         |         |      |          | Platnick   | reflectance    |
|         |         |      |          |            | (sea ice &     |
|         |         |      |          | Steve      | tundra)        |
|         |         |      |          | Ackerman   |                |
+---------+---------+------+----------+------------+----------------+
| SCAR-B  | Aug-Sep | ER-2 | MAS,     | Yoram      | smoke, clouds  |
|         | 1995    |      | CLS,     | Kaufman    | and radiation  |
|         |         | C-   | AVIRIS,  |            | from biomass   |
|         |         | 131A | CAR,     | Lorraine   | burning in the |
|         |         |      | micro    | Remer      | cerrado,       |
|         |         | sur  | physics, |            | Pantanal, and  |
|         |         | face | AERONET  | Michael    | Amazon         |
|         |         |      |          | King       | rainforest;    |
|         |         |      |          |            | BRDF           |
|         |         |      |          | Si-Chee    |                |
|         |         |      |          | Tsay       |                |
|         |         |      |          |            |                |
|         |         |      |          | Elaine     |                |
|         |         |      |          | Prins      |                |
+---------+---------+------+----------+------------+----------------+
| SUCCESS | Apr-May | ER-2 | MAS,     | Steve      | mid-latitude   |
|         | 1996    |      | CLS,     | Ackerman   | cirrus clouds  |
|         |         | sur  | HIS,     |            | over           |
|         |         | face | AERI     | Paul       | continents     |
|         |         |      |          | Menzel     |                |
|         |         |      |          |            |                |
|         |         |      |          | Si-Chee    |                |
|         |         |      |          | Tsay       |                |
+---------+---------+------+----------+------------+----------------+

Table 5. Pre-launch Field Experiments involving MODIS atmosphere
investigators (continued).

+---------+---------+------+----------+------------+----------------+
| **      | ####    | ***  | **       | ***R       | ***Primary     |
| *Experi | *Dates* | Plat | *Primary | esponsible | Purpose***     |
| ment*** |         | form | Se       | Invest     |                |
|         |         | s*** | nsors*** | igators*** |                |
+---------+---------+------+----------+------------+----------------+
| TARFOX  | July    | ER-2 | MAS,     | Didier     | sulfate        |
|         | 1996    |      | LASE,    | Tanré      | aerosols,      |
|         |         | C-   | CAR,     |            | water vapor,   |
|         |         | 131A | AERONET  | Yoram      | and radiative  |
|         |         |      |          | Kaufman    | forcing of     |
|         |         | sur  |          |            | aerosols;      |
|         |         | face |          | Lorraine   | surface BRDF   |
|         |         |      |          | Remer      |                |
|         |         |      |          |            |                |
|         |         |      |          | Si-Chee    |                |
|         |         |      |          | Tsay       |                |
+---------+---------+------+----------+------------+----------------+
| WINCE   | Jan-Feb | ER-2 | MAS,     | Paul       | cloud          |
|         | 1997    |      | HIS, CLS | Menzel     | detection and  |
|         |         |      |          |            | properties     |
|         |         |      |          | Steve      | over snow/ice  |
|         |         |      |          | Ackerman   | covered land   |
|         |         |      |          |            | and lakes      |
|         |         |      |          | Chris      |                |
|         |         |      |          | Moeller    |                |
|         |         |      |          |            |                |
|         |         |      |          | Dorothy    |                |
|         |         |      |          | Hall       |                |
+---------+---------+------+----------+------------+----------------+
| FIRE    | M       | ER-2 | MAS,     | Michael    | arctic stratus |
| ACE     | ay-June |      | CLS,     | King       | clouds over    |
|         | 1998    | CV   | AirMISR, |            | sea ice,       |
|         |         | -580 | MIR,     | Steve      | surface BRDF   |
|         |         |      | SSFR,    | Platnick   | (tundra & sea  |
|         |         | sur  | AMPR,    |            | ice)           |
|         |         | face | HIS,     | Steve      |                |
|         |         |      | CAR,     | Ackerman   |                |
|         |         |      | micr     | Si-Chee    |                |
|         |         |      | ophysics | Tsay       |                |
+---------+---------+------+----------+------------+----------------+
| C       | Dec     | ER-2 | MAS,     | Paul       | underfly       |
| ALVEX-N | 1998    |      | MIR,     | Menzel     | NOAA-15 as a   |
|         |         | sur  | S-HIS,   |            | calibration/   |
|         |         | face | AERI     | Chris      | validation     |
|         |         |      |          | Moeller    | exercise       |
+---------+---------+------+----------+------------+----------------+
| WINTEX  | Mar     | ER-2 | MAS,     | Paul       | cloud          |
|         | 1999    |      | S-HIS,   | Menzel     | detection and  |
|         |         | sur  | NAST-I,  |            | properties     |
|         |         | face | NAST-M,  | Steve      | over snow/ice  |
|         |         |      | AERI     | Ackerman   | covered land   |
|         |         |      |          |            | and lakes      |
|         |         |      |          | Chris      |                |
|         |         |      |          | Moeller    |                |
+---------+---------+------+----------+------------+----------------+

Limited validation will also be carried out using collocated HIRS and
AVHRR data sets by Paul Menzel and Steve Ackerman, focusing on surface
emissivity effects. This data set has the advantage of global coverage,
but the spatial scale is far removed from that of MODIS, with spectral
bandwidths that are much wider and off-center from those of MODIS.

*4.1.1 ARMCAS*[]{#__RefHeading___Toc388510096 .anchor}

The Arctic Radiation Measurements in Column Atmosphere-surface System
(ARMCAS) experiment was conducted in the Beaufort Sea, Alaska during
June 1995, with the goal of determining the radiative properties of
Arctic stratus clouds over sea ice in the summertime Arctic. Special
emphasis was placed on acquiring MAS data that could be used to test the
MODIS cloud mask for distinguishing clouds, clear sky, sea ice, and
tundra, and to use these data to retrieve cloud radiative and
microphysical properties over highly reflecting surfaces. These
measurements, acquired from the ER-2 aircraft along with the CLS, were
intercompared with in situ microphysical measurements obtained from the
University of Washington C-131A. Bidirectional reflectance measurements
were also made of sea ice, tundra, and clouds (cf. Fig. 10). Further
details of the spectral bidirectional reflectance and plane albedo of
snow, sea ice, and tundra, can be found in Arnold et al. (2000).

*4.1.2 SCAR-B*[]{#__RefHeading___Toc388510097
.anchor}![](media/image12.pct){width="5.997916666666667in"
height="6.6930555555555555in"}

The Smoke, Clouds, and Radiation--Brazil (SCAR-B) experiment was
conducted in Brazil during August-September 1995, with the goal of
studying biomass burning, emphasizing measurements of surface biomass,
fires, smoke aerosol and trace gases, clouds, and radiation. It was
conducted using both the NASA ER-2 high altitude research aircraft as
well as the University of Washington C-131A, and was coordinated with
numerous AERONET sunphotometer sites throughout Brazil. From this
experiment we found that MODIS, simulated by the MAS on the ER-2
aircraft, proved to be very useful for monitoring smoke, water vapor,
cloud, fire, and surface properties, and the impacts of smoke on
radiation and climate (cf. Kaufman et al. 1998 for further details).

*4.1.3 SUCCESS*[]{#__RefHeading___Toc388510098 .anchor}

The Subsonic Aircraft Contrail and Cloud Effects Special Study (SUCCESS)
field experiment was conducted in April-May 1996, with the goal of
determining the radiative properties of cirrus contrails, and of
contrasting them with naturally occurring cirrus. To assess the
radiative impact of these clouds required a well-calibrated set of
radiation measurements and "ground (or in situ) truth" observations. We
acquired MAS and HIS multispectral observations along with CLS cloud
height measurements from the NASA ER-2 aircraft by coordinating over
flights of the ER-2 with in situ aircraft and ground based measurements.
The MAS and HIS measurements were employed to address the very important
relationship between cirrus radiative properties and the thermodynamic
environment (atmospheric temperature and moisture conditions) wherein
cirrus clouds form and are maintained. The HIS provided accurate
measurements of the thermodynamical properties supporting the cirrus
life cycle and the MAS measured the cirrus areal extent and radiative
properties. Additional emphasis was placed on developing and validating
methods of detecting upper tropospheric clouds and defining their areal
extent with infrared (e.g., 13.9 µm) and near infrared (e.g., 1.88 µm)
channels; these being similar to the MODIS channels and hence the MAS
cirrus detection, thereby having direct relevance to the MODIS cloud
mask algorithm. The CLS data were used to validate MAS upper
tropospheric clouds.

Several studies have demonstrated the sensitivity of spectral radiances
to cloud particle size and shape distributions. The MAS and HIS
instruments provide accurate spectral measurements that can be used to
assess differences in the radiative signatures between contrails and
naturally occurring cirrus clouds. One difficulty in assessing the
impact of high-altitude subsonic aircraft on cirrus formation and
modification is the natural variability of the atmosphere and the
potentially small signal of the radiative perturbation. Variations in
the atmospheric spectral properties for contrail and natural cirrus
conditions will be assessed with the two ER-2 instruments in conjunction
with in situ and ground-based observations.

*4.1.4 TARFOX*[]{#__RefHeading___Toc388510099 .anchor}

The Tropospheric Aerosol Radiative Forcing Observational Experiment
(TARFOX, 10-31 July 1996) campaign measured atmospheric aerosols
emanating from industrial centers in North America transported over the
Atlantic Ocean. Their extent, radiative properties, and transport
mechanisms were studied from satellite, aircraft, ship, and ground-based
sensors. The MAS on the NASA ER-2 aircraft and the GOES Imager on GOES-8
were the primary sensors of interest to the atmosphere group (Didier
Tanré, Yoram Kaufman, Paul Menzel). The monitoring effort focused on the
corridor extending from Wallops Island, Virginia to Bermuda. In situ
aircraft (UK Meteorological Office C-130H, University of Washington
Convair C-131A, Naval Postgraduate School Pelican, and NASA ER-2),
ground measurements (Lidar, AEROCE, AERONET), and satellite observations
(AVHRR, ATSR-2, GOES) were used to measure the direct effects of
tropospheric aerosols on regional radiation budgets of the cloud-free
ocean-atmosphere system, while simultaneously measuring the chemical,
physical, and optical properties of the predominant aerosols. The suite
of measurements made during TARFOX and subsequent collaborative analyses
will provide a better understanding of the impact of these aerosols over
the US eastern seaboard and western Atlantic Ocean and will be used to
assess the degree of closure (consistency) between aerosol radiative
forcing calculations and various measurements of aerosol properties and
other satellite-derived parameters. This study provided a means to
validate current and future satellite remote sensing methods and
products (aerosol optical thickness τa, aerosol size distribution nc(r),
and earth radiation budgets), as demonstrated by Tanré et al. (1999).
Observations were also acquired of the spectral bidirectional
reflectance of the Atlantic Ocean and Great Dismal Swamp, as reported in
Soulen et al. (2000).

*4.1.5 WINCE and WINTEX*[]{#__RefHeading___Toc388510100 .anchor}

The Winter Cloud Experiment (WINCE; January-February 1997) and Winter
Experiment (WINTEX; March 1999) investigated the difficulties in
detecting clouds and estimating their properties in winter conditions
(Steve Ackerman, Paul Menzel, Chris Moeller). These field campaigns were
conducted from Madison, WI. During WINCE, cirrus and thin clouds over
frozen tundra and lakes in the northern US and Canada were measured with
the MAS, HIS, and CLS, along with the GOES-8, AVHRR, and surface AERI
measurements. One of the missions investigated the product stability
under nighttime conditions (infrared only). In addition, two ground
sites in New England measured snow and ice cover during MAS/HIS
overflights in clear sky condition (in collaboration with Dorothy Hall
and George Riggs working on the MODIS snow/ice product). Examples of the
MAS cloud mask were distributed to science team members so that they can
determine its effect on their MODIS products.

During WINTEX, the ER-2 will be instrumented with MAS, S-HIS, NAST-I,
NAST-M, and a camera system (RC-10), which will overfly ground-based
instrumentation that includes uplooking lidar, AERI, and radiosondes.
During WINTEX, the ER-2 will fly from six to eight missions over snow,
bare ground, and vegetated surfaces with and without partial cloud
cover. Day and night flights will be conducted. Plans also include
collecting cloud microphysical profiles (dropped from aircraft) to
supplement the ER-2 measurements of clouds. The ER-2 measurements will
be used to continue assessment of the MODIS cloud mask and cloud top
properties as well as evaluate absolute calibration of the
interferometers (S-HIS, NAST-I); S-HIS and NAST-I will be used to
validate MODIS IR calibration after the Terra launch in July 1999.
WINTEX missions will be flown in southern Canada (winter scenes), over
the Great Lakes (snow and bare ground), and around the Midwest (spring
greening phase) including the SGP CART site (calibration validation).

*4.1.6 FIRE Arctic Cloud Experiment*[]{#__RefHeading___Toc388510101
.anchor}

FIRE, the First ISCCP (International Satellite Cloud Climatology
Project) Regional Experiment, has previously conducted four successful
field missions focused on cloud remote sensing and modeling studies as
they relate to climate. FIRE Phase III (Arctic Cloud Experiment, or ACE)
was conducted in the Arctic between April and July 1998, with the
component of interest to the MODIS science team being confined to a 5
week period consisting of the University of Washington CV-580 along with
a 3 week period of high-altitude ER-2 overflights (cf. Curry et al. 2000
for further details). The CV-580 was equipped with an extensive set of
PMS cloud microphysics probes, a Gerber PVM-100A liquid water content
and effective radius probe, Johnson-Williams and King hot wire probes, a
newly developed Gerber cloud integrating nephelometer (g-meter) for
measuring extinction coefficient and asymmetry factor, thermodynamic
state variable measurements, and a SPEC Inc. Cloud Particle Imager
(CPI). In addition, the ER-2 participated as the upper level aircraft
from May 18-June 6, with the MAS, CLS, HIS, a multispectral along-track
scanning radiometer (AirMISR), a Microwave Imaging Radiometer (MIR), a
Solar Spectral Flux Radiometer (SSFR), and an Advanced Microwave
Precipitation Radiometer (AMPR). All of these ER-2 sensors are of
interest to Goddard Space Flight Center (Michael King, Si-Chee Tsay,
Steve Platnick), as well as the CAR on the CV-580 and numerous in situ
microphysics probes that will be invaluable in accessing the accuracy of
cloud retrievals of the microphysical and radiative properties of Arctic
stratus clouds over a bright (sea ice) surface. This valuable data set
is being used by the University of Wisconsin group for testing the cloud
mask algorithm for readiness at-launch. The suite of measurements made
during FIRE ACE and subsequent collaborative analyses will provide a
means to validate current and future satellite remote sensing methods
and products (cloud optical thickness τc, effective radius re, single
scattering albedo ω0), the cloud mask, and precipitable water.

*4.1.7 CALVEX-N*[]{#__RefHeading___Toc388510102 .anchor}

CALVEX-N, the Calibration/Validation Experiment -- NOAA-15, was an early
effort in a series of ER-2 activities focused on validating infrared
band calibration of MODIS. CALVEX-N, conducted in December 1998 from
Dryden Flight Research Center (DFRC), used NOAA-15 underflights to
exercise the instrumentation and strategies that will be used to
validate MODIS IR calibration during WISC-T2000 (first effort
underflying MODIS on Terra in March 2000). The ER-2 instrumentation
included MAS, S-HIS, MIR, and a high-resolution video camera system.
During the three NOAA-15 missions (December 18, 21, and 22), the ER-2
flew along the sub-orbital track of NOAA-15 collecting scenes of clear
ocean and uniform low stratus cloud. To characterize the atmosphere,
special radiosonde launches along the southern California coast were
coordinated with the ER-2 flights. A ground-based AERI instrument
capable of upward and downward viewing was deployed on Rogers dry
lakebed at Edwards AFB for comparisons with the ER-2 instrumentation.
The MAS and S-HIS data sets are highly complementary. The high spectral
resolution S-HIS provides accurately calibrated IR radiances for each
nominal 2 km spatial resolution footprint; MAS provides the high spatial
resolution data in the visible through thermal infrared. Through this
synergism, data to characterize spatial (MAS) and spectral (S-HIS)
dependencies of MODIS calibration were collected. The data collection
phase of CALVEX-N was successfully completed. Of particular interest,
the MIR 183 GHz radiances will be compared to the AMSU-B 183 GHz
observations to gain insight into AMSU-B absolute calibration and the
calibration dependence on scan angle. Finally, and perhaps most
importantly, the CALVEX-N data analysis will be used to expose any
weaknesses in the instrumentation or data collection procedures, and
will provide insight into how one might address special MODIS IR
calibration challenges (e.g. PC band crosstalk correction) during
WISC-T2000 activities.

*4.2 Operational Surface Networks*[]{#__RefHeading___Toc388510103
.anchor}

Data from various surface observing networks are incorporated into
pre-launch validation activities, as well as selected data from the ARM
site. These are collected and archived daily at the University of
Wisconsin on the Man-computer Interactive Data Access System (McIDAS),
which is connected to Paul Menzel's Science Computing Facility (SCF). In
addition, data from the AERONET are archived at Goddard Space Flight
Center by Brent Holben and are invaluable to the aerosol pre-launch
validation activities of Yoram Kaufman and Didier Tanré.

*4.3 Existing Satellite Data*[]{#__RefHeading___Toc388510104 .anchor}

All of the MODIS pre-launch studies at the University of Wisconsin (Paul
Menzel) rely on AVHRR, HIRS, and GOES data for field experiment support
and validation. These data are archived at the University of Wisconsin
and accessible on McIDAS. Additional AVHRR data collected in support of
MAST, ARMCAS, and various other pre-launch validation activities are
available to Goddard Space Flight Center through the archive appropriate
to that experiment (Langley Research Center DAAC, Goddard Space Flight
Center DAAC, or the Naval Postgraduate School). Finally, pre-launch
activities that are coordinated with Landsat-TM data sets are archived
at Goddard by Yoram Kaufman.

*4.4 Instrument Development*[]{#__RefHeading___Toc388510105 .anchor}

New improvements/additions to the SMART suite of surface radiation and
remote sensing instruments are currently under development:

-   a sun-sky-surface (S3) photometer to replace the Cimel
    sunphotometer, consisting of 14 spectral channels (without
    filter-wheel) ranging from 0.30 to 2.5 µm (including linear
    polarization) and using Si (UV-Vis-NIR) and InGaAs (SWIR, not
    cooled) detectors

-   collocated measurements of surface moisture and temperature with
    SMiR brightness temperature (22.8. 23.8 and 37.0 GHz) of the surface
    are sought for characterizing various types of surface emissivity. A
    future addition of tunable frequencies at 60 GHz (oxygen resonance
    line) for retrieving temperature profiles is also planned

-   an upgrade of solar power for passive SMART instruments and larger
    data logging memory are planned

The prototypes of S3 have already produced preliminary data sets during
the SAFARI 2000 campaign (August-September 2000) and during the ACE-Asia
campaign (March-May 2001). Analyses of these data are progressing and
refining the instrument is currently underway for future operations in
remote areas.

5.0 Post-launch Activities[]{#__RefHeading___Toc388510106 .anchor}

The validation strategies and experience gained from pre-launch
activities will guide the activity after launch. The pre-launch field
activities have been used most effectively for testing MODIS-like
algorithms in a wide variety of atmospheric and environmental
conditions. Post-launch validation will apply these lessons to future
field work and to the intercomparisons with actual MODIS data, both from
Terra and Aqua (2001 and beyond).

*5.1 Field Campaigns*[]{#__RefHeading___Toc388510107 .anchor}

In the first three years following the launch of Terra, we anticipate
collecting numerous data sets for the purpose of validating MODIS
algorithms and data products through direct intercomparisons of MODIS
data with in situ and airborne remote sensing data sets. These campaigns
will be EOS-targeted campaigns as well as participation in broader
community research and analysis programs of direct relevance to assuring
the accuracy of MODIS-derived data products. These post-launch field
campaigns are summarized in Table 6.

Table 6. Post-launch Field Experiments involving MODIS atmosphere
investigators.

+---------+----------+--------+---------+------------+--------------+
| **      | ####     | ***    | ***     | ***R       | ***Primary   |
| *Experi |  *Dates* | Platfo | Primary | esponsible | Purpose***   |
| ment*** |          | rms*** | Sen     | Invest     |              |
|         |          |        | sors*** | igators*** |              |
+---------+----------+--------+---------+------------+--------------+
| WIS     | Mar 2000 | ER-2   | MAS,    | Steve      | overflights  |
| C-T2000 |          |        | S-HIS,  | Ackerman   | of the SGP   |
|         |          | Ci     | CLS,    |            | ARM site;    |
|         |          | tation | AERI,   | Paul       | mid-latitude |
|         |          |        | CPI,    | Menzel     | cirrus over  |
|         |          | L      | Raman   |            | continents,  |
|         |          | earjet | lidar,  | Chris      | clear sky    |
|         |          |        | mi      | Moeller    | radiance;    |
|         |          | Twin   | crowave |            | ice crystal  |
|         |          | Otter  | sensors | Gerald     | habit        |
|         |          |        |         | Mace       |              |
|         |          | s      |         |            |              |
|         |          | urface |         | Rich       |              |
|         |          |        |         | Ferrare    |              |
|         |          |        |         |            |              |
|         |          |        |         | A.         |              |
|         |          |        |         | Heymsfield |              |
+---------+----------+--------+---------+------------+--------------+

Table 6. Post-launch Field Experiments involving MODIS atmosphere
investigators. (continued).

+---------+----------+--------+---------+------------+--------------+
| **      | ####     | ***    | ***     | ***R       | ***Primary   |
| *Experi |  *Dates* | Platfo | Primary | esponsible | Purpose***   |
| ment*** |          | rms*** | Sen     | Invest     |              |
|         |          |        | sors*** | igators*** |              |
+---------+----------+--------+---------+------------+--------------+
| PRIDE   | Jun-Jul  | N      | Che     | Si-Chee    | Transport of |
|         | 2000     | avajo, | mistry, | Tsay       | Saharan dust |
|         |          |        | microp  |            | across the   |
|         |          | s      | hysics, | Lorraine   | Atlantic     |
|         |          | urface | SMART,  | Remer      | ocean to the |
|         |          |        | A       |            | Caribbean    |
|         |          |        | ERONET, | Yoram      |              |
|         |          |        | mi      | Kaufman    |              |
|         |          |        | crotops |            |              |
+---------+----------+--------+---------+------------+--------------+
| SAFARI  | Aug-Sep  | ER-2   | MAS,    | Michael    | smoke,       |
| 2000    | 2000     |        | S-HIS,  | King       | clouds and   |
|         |          | CV-580 | A       |            | radiation    |
|         |          |        | irMISR, | Steve      | from biomass |
|         |          | s      | CPL,    | Platnick   | burning;     |
|         |          | urface | SSFR,   |            | marine       |
|         |          |        | LAS,    | Si-Chee    | s            |
|         |          |        | CAR,    | Tsay       | tratocumulus |
|         |          |        | A       |            | off Namibia; |
|         |          |        | ERONET, | Steve      | surface BRDF |
|         |          |        | micro   | Ackerman   |              |
|         |          |        | physics |            |              |
+---------+----------+--------+---------+------------+--------------+
| Ant     | Jan-Dec  | s      | P-AERI  | Von Walden | clear sky    |
| arctica | 2001     | urface |         |            | radiances    |
|         |          |        |         | Paul       | for MODIS IR |
|         | Dec      |        |         | Menzel     | cold scene   |
|         | 02-Jan   |        |         |            | radiance     |
|         | 03       |        |         | Chris      | validation   |
|         |          |        |         | Moeller    | over         |
|         | Dec      |        |         |            | Antarctica   |
|         | 03-Jan   |        |         |            |              |
|         | 04       |        |         |            |              |
+---------+----------+--------+---------+------------+--------------+
| TX-2001 | Mar-Apr  | ER-2   | MAS,    | Chris      | atmospheric  |
|         | 2001     |        | S-HIS,  | Moeller    | profiles,    |
|         |          | s      | AERI,   | Paul       | Level-1b,    |
|         |          | urface | Raman   | Menzel     | inwater      |
|         |          |        | lidar,  |            | turbidity    |
|         |          |        | mi      | Steve      | (Terra)      |
|         |          |        | crowave | Ackerman   |              |
+---------+----------+--------+---------+------------+--------------+
| A       | Mar-May  | s      | Che     | Si-Chee    | Transport of |
| CE-Asia | 2001     | urface | mistry, | Tsay       | Gobi desert  |
|         |          |        | SMART,  |            | dust across  |
|         | Mar-Apr  |        | A       | Michael    | the polluted |
|         | 2004     |        | ERONET, | King       | industrial   |
|         |          |        | mic     |            | area to the  |
|         |          |        | rotops, | Lorraine   | Pacific      |
|         |          |        | micro   | Remer      | Ocean        |
|         |          |        | physics |            |              |
|         |          |        |         | Yoram      |              |
|         |          |        |         | Kaufman    |              |
+---------+----------+--------+---------+------------+--------------+
| CLAMS   | Jul-Aug  | ER-2   | MAS,    | Lorraine   | clear sky    |
|         | 2001     |        | A       | Remer      | radiances,   |
|         |          | CV-580 | irMISR, |            | ocean BRDF,  |
|         |          |        | LAS,    | V. Martins | surface SW   |
|         |          | s      | CPL,    |            | radiation    |
|         |          | urface | CAR,    | Charles    | fluxes,      |
|         |          |        | BSRN,   | Gatebe     | cirrus cloud |
|         |          |        | A       |            | properties,  |
|         |          |        | ERONET, | Tom        | aerosol over |
|         |          |        | A       | Charlock   | ocean, cloud |
|         |          |        | ATS-14, |            | mask         |
|         |          |        | micro   | Ralph Kahn |              |
|         |          |        | physics |            |              |
+---------+----------+--------+---------+------------+--------------+
| CAMEX 4 | Aug-Sep  | ER-2   | MAS,    | A.         | cloud        |
|         | 2001     |        | micro   | Heymsfield | properties   |
|         |          | DC-8   | physics |            | (hurricanes  |
|         |          |        |         | Cynthia    | & cirrus),   |
|         |          | s      |         | Twohy      | m            |
|         |          | urface |         |            | icrophysical |
|         |          |        |         | Paul       | properties   |
|         |          |        |         | Lawson     |              |
|         |          |        |         |            |              |
|         |          |        |         | Bryan Baum |              |
+---------+----------+--------+---------+------------+--------------+
| CL      | Spring   | ER-2   | MAS,    | Paul       | cloud        |
| AP-2002 | 2002     |        | S-HIS,  | Menzel     | properties,  |
|         |          | s      | CPL,    |            | atmospheric  |
|         |          | urface | AERI,   | Steve      | profiles,    |
|         |          |        | Raman   | Ackerman   | Level-1b     |
|         |          |        | lidar,  |            | (Aqua)       |
|         |          |        | mi      | Chris      |              |
|         |          |        | crowave | Moeller    |              |
+---------+----------+--------+---------+------------+--------------+

Table 6. Post-launch Field Experiments involving MODIS atmosphere
investigators. (continued).

+---------+----------+--------+---------+------------+--------------+
| **      | ####     | ***    | ***     | ***R       | ***Primary   |
| *Experi |  *Dates* | Platfo | Primary | esponsible | Purpose***   |
| ment*** |          | rms*** | Sen     | Invest     |              |
|         |          |        | sors*** | igators*** |              |
+---------+----------+--------+---------+------------+--------------+
| CRYST   | Jun-Aug  | ER-2   | MAS,    | Michael    | subtropical  |
| AL-FACE | 2002     |        | S-HIS,  | King       | cirrus       |
|         |          | s      | CPL,    |            | clouds in    |
|         |          | urface | A       | Steve      | Florida area |
|         |          |        | irMISR, | Platnick   |              |
|         |          |        | SMART,  |            |              |
|         |          |        | micro   | Si-Chee    |              |
|         |          |        | physics | Tsay Steve |              |
|         |          |        |         | Ackerman   |              |
|         |          |        |         |            |              |
|         |          |        |         | A.         |              |
|         |          |        |         | Heymsfield |              |
+---------+----------+--------+---------+------------+--------------+
| Cal     | Dec 2002 | ER-2   | MAS,    | Steve      | marine       |
| ifornia |          |        | CPL,    | Platnick   | s            |
|         | Jul 2003 | CV-580 | A       | Michael    | tratocumulus |
|         |          |        | irMISR, | King       | and valley   |
|         |          |        | LAS,    |            | fog          |
|         |          |        | CAR,    | Si-Chee    |              |
|         |          |        | micro   | Tsay       |              |
|         |          |        | physics |            |              |
+---------+----------+--------+---------+------------+--------------+
| CRYS    | Jul-Aug  | ER-2   | MAS,    | Michael    | cirrus       |
| TAL-TWP | 2004     |        | S-HIS,  | King       | clouds in    |
|         |          | WB-57F | CPL,    |            | western      |
|         |          |        | LAS,    | Steve      | tropical     |
|         |          |        | micro   | Platnick   | Pacific      |
|         |          |        | physics |            |              |
|         |          |        |         | Si-Chee    |              |
|         |          |        |         | Tsay Steve |              |
|         |          |        |         | Ackerman   |              |
|         |          |        |         |            |              |
|         |          |        |         | A.         |              |
|         |          |        |         | Heymsfield |              |
+---------+----------+--------+---------+------------+--------------+

*5.1.1 WISC-T2000*[]{#__RefHeading___Toc388510108 .anchor}

The first EOS-targeted campaign after the December 18, 1999 launch of
Terra was the WISC-T2000 (Wisconsin Snow and Cloud Experiment -- Terra
2000) experiment. The MODIS-related objectives of WISC-T2000 included
snow cover detection, cloud detection, cloud height, and
top-of-atmosphere (TOA) radiance determination, all for the purpose of
validating MODIS Level-1B (L1B) and Science Products. L1B validation
results will be shared with the MODIS Characterization Support Team
(MCST). The field exercise was conducted between February 24 and March
13, 2000, and included an ER-2 (based in Madison, WI) equipped with the
MAS, S-HIS, CLS and AirMISR instruments, ground-based AERI, lidar, and
radar measurements (at the CART SGP site), and class-sonde balloon
launches (CART SGP and Madison). Flights over the Western Great Lakes,
snowfields in the Upper Midwest and New England, and over the Southern
Great Plains (SGP) ARM site in Oklahoma were conducted to meet the
objectives. Flights over the SGP ARM site were coordinated with Cloud
IOP 2000 (UND Citation, Learjet, ground-based radars, lidars) and ARESE
II (Twin Otter) activities, which collected in situ and radiometric
measurements to enhance the data set for cloud validation. S-HIS and MAS
measurements will be used to assess MODIS emissive band L1B calibration
accuracy and Atmospheric Profiles science product. CLS and MAS will be
used together to assess the MODIS Cloud Mask, Snow Mask, and Cloud Top
Properties science products. AirMISR and MAS will be used to assess MISR
instrument performance. The Terra satellite was underflown on each of
the eight science missions, plus on the ferry mission back to California
by the ER-2 (total of nine underflights) to provide a range of satellite
view angles and surface/atmospheric conditions. Of the eight science
underflights of Terra, three were in clear skies, three were with broken
or overcast low cloud, and two were with patchy high cloud. The three
flights over the SGP ARM site take advantage of a broad suite of
instruments at SGP. Lidar and radar observations of cloud boundaries
will be used to validate the presence of cloud as well as cloud top
pressure. Raman lidar will be used to derive aerosol and water vapor
profiles, precipitable water vapor, and aerosol optical thickness under
clear sky conditions. Class-sonde and AERI measurements will be used to
define atmospheric temperature and moisture profiles. Whole sky imagers
can be compared to ER-2 and Terra observations of cloud amount. Finally,
optical depth measurements derived from lidar will aid in specifying the
limit of thin cirrus detection in the cloud mask algorithm.

*5.1.2 PRIDE*[]{#__RefHeading___Toc388510109 .anchor}

The field experiment on and near the island of Puerto Rico, known as
PRIDE (Puerto Rico Dust Experiment), was designed to measure the
properties of Saharan dust transported across the Atlantic Ocean to the
Caribbean. PRIDE, conducted between June 26 and July 24, 2000, was a
collaborative endeavor with the Office of Naval Research and the
University of Miami. It involved a twin engine Piper Navajo aircraft
carrying the Ames Airborne Tracking Sunphotometer (AATS), SSFR, and
microphysics probes (PCASP and FSSP), a well-equipped ground-based site
containing the SMART radiometer and lidar system, and the University of
Puerto Rico oceanographic research vessel that included both
sunphotometer measurements of the aerosol and bio-optical observations
of sea water and chlorophyll.

In the summer months moderate quantities of desert dust are observed in
the Caribbean. AVHRR and AERONET typically report mid-visible aerosol
optical thicknesses of 0.2-0.7 in July. Puerto Rico is the first
significant landfall for the dust traveling across the ocean from
Africa. Dust arriving on the eastern end of the island should not be
affected by other types of man made aerosol pollutants. This will
minimize complications in the analysis.

We will determine the extent to which properties of dust particles and
the spectral surface reflectance of the ocean need to be known before
remote sensing systems can accurately determine optical thickness and
radiative flux. Currently our knowledge of transported Saharan dust is
extremely limited. Even basic characteristics of gross physical
properties such as horizontal and vertical extent and horizontal
homogeneity have never been measured on the western side of the
Atlantic. We also have a poor understanding of dust optical,
microphysical and chemical properties. Dust single scattering albedo and
nonsphericity are especially significant parameters.

Our main purpose in PRIDE was to make simultaneous aerosol optical
thickness and precipitable water vapor measurements from the SMART,
oceanographic research vessel, and low flying aircraft at the time of
the MODIS overpass, and then to compare these ground-based observations
with the MODIS retrievals over both land and ocean. PRIDE gives us the
opportunity to test the MODIS ocean aerosol optical thickness
algorithms. The inclusion of flux measurements allows for the
determination of dust aerosol radiative flux in addition to measurements
of loading and optical thickness. We intend to use the data acquired in
PRIDE to develop new, experimental, MODIS remote sensing algorithms
including retrieval of dust single scattering albedo.

*5.1.3 SAFARI 2000*[]{#__RefHeading___Toc388510110 .anchor}

![](media/image13.jpeg){width="6.051388888888889in"
height="2.9256944444444444in"}

*5.1.4 Antarctica*[]{#__RefHeading___Toc388510111 .anchor}

As part of a National Science Foundation program, Dr. Von P. Walden of
University of Idaho has deployed a surface-based Polar-AERI (P-AERI) to
the Amundsen-Scott South Pole Station for one year beginning January
2001. P-AERI measurements include downward looking surface measurements
from a platform about 5 m above the snow surface as well as uplooking
measurements to document the clear sky downwelling radiance. During
MODIS overpasses the downward-looking and upward-looking measurements
can be collected for the angular viewing geometry of MODIS. Balloon
launches will be used to complete the atmospheric characterization.
These measurements are useful to evaluate MODIS (on Terra) emissive band
calibration accuracy at temperatures typical of cold clouds. Clear sky
Antarctica top-of-atmosphere (TOA) brightness temperatures are
comparable to tropospheric cloud top temperatures in the mid-latitudes
and tropics.

In addition to the 2001 activity at the Amundsen-Scott South Pole
Station, a P-AERI is planned for deployment to the Dome Concordia
(74.5°S, 123°E) station for 3 weeks during December 2002 - January 2003
and then again in December 2003 - January 2004. This EOS-funded activity
will continue the assessment of MODIS calibration on Terra, and provide
new measurements for assessing MODIS cold scene calibration on Aqua. The
activity is based at Dome C because that location allows MODIS viewing
of the ground site from a broad range of mirror scan angles. This will
provide insight on angular dependence in the MODIS cold scene
calibration. During MODIS overpasses the downward-looking and
upward-looking measurements will be collected for the angular viewing
geometry of MODIS. An NCAR class-sonde system will be used to complete
the atmospheric characterization.

*5.1.5 TX-2001*[]{#__RefHeading___Toc388510112 .anchor}

The ARM SGP CART site and Gulf of Mexico region were the targets of
choice for the Terra eXperiment -- 2001 (TX-2001) from March 15 -- April
5, 2001. This activity was timed to occur about one year after MODIS
first light, after the transfer from pre-launch to post-launch
calibration has been completed. A NASA ER-2 aircraft equipped with the
MAS and S-HIS instruments flew seven science missions under the Terra
satellite to evaluate MODIS top-of-atmosphere (TOA) level-1b calibration
at various mirror scan angles, to evaluate the MODIS atmospheric water
vapor product, and to assess coastal inwater turbidity impact on MODIS
cloud products as well as testing MODIS as a turbidity estimator. Three
missions were flown in clear sky over deepwater in the Gulf of Mexico
for level-1b assessment. Each of these flights underflew MODIS at a
different view angle (one each for begin-of-scan side, nadir, and
end-of-scan side). These flights provide insight on scan angle
dependencies in the MODIS level-1b calibration. A flight over the ARM
SGP CART site in Oklahoma captured clear sky conditions just after
frontal passage for evaluating water vapor gradients in the MODIS water
vapor product. The CART site ground based data sets (Raman Lidar, AERI,
MWR, class-sonde, tower-based measurements) define the atmospheric water
vapor content during the MODIS and ER-2 simultaneous overpass. A flight
along the Louisiana coast captured inwater turbidity patterns during
MODIS overpass for direct comparisons between MODIS and MAS reflectance.
A boat team from Louisiana State University collected in situ water
samples during the ER-2 mission. The in situ measurements relate the
remote sensing measurements to inwater suspended matter. There was also
one mission flown with scattered popcorn cumulus clouds over the Gulf of
Mexico when MODIS overflew the target. This mission will be used to
evaluate spatial resolution effects on MODIS data..

*5.1.6 ACE-Asia*[]{#__RefHeading___Toc388510113 .anchor}

The International Global Atmospheric Chemistry program has organized a
series of aerosol characterization experiments (ACE) to acquire data
sets needed for assessing aerosol effects in major regions of the globe.
ACE-Asia is designed to study the compelling variability in spatial and
temporal scale of both pollution-derived and naturally occurring
aerosols, which often exist in high concentrations over eastern Asia and
along the rim of the western Pacific. The phase-I of ACE-Asia was
conducted from March-May 2001 in the vicinity of the Gobi desert, east
coast of China, Yellow Sea, Korea, and Japan, along the pathway of Kosa
(severe events that blanket East Asia with yellow desert dust, peaked in
the Spring season).

Although central Asia is one of the most important dust sources, its
climatic impact is less well studied than that of Saharan dust. Asian
dust typically originates in desert areas far from polluted urban
regions. During transport, dust layers can interact with anthropogenic
sulfate and soot aerosols from heavily polluted urban areas. Added to
the complex effects of clouds and natural marine aerosols, dust
particles reaching the marine environment can have drastically different
properties than those from the source. Thus, understanding the unique
temporal and spatial variations of Asian dust is of special importance
in regional-to-global climate issues such as radiative forcing, the
hydrological cycle, and primary biological productivity in the
mid-Pacific Ocean.

In collaboration with international chemists, our main goals in ACE-Asia
were to measure continuously aerosol optical/radiative properties,
column precipitable water amount, and surface reflectivity over
homogeneous areas from the SMART. The inclusion of flux measurements
permits the determination of dust aerosol radiative flux in addition to
measurements of loading and optical thickness. At the time of the MODIS
overpass, these ground-based observations can provide valuable data to
compare with MODIS retrievals over land. Even without dust events, the
homogeneity of the Gobi desert serves as an excellent calibration
target. Thus, SMART observations in the vicinity of dust sources are our
first priority.

Finally, AERONET was deployed at 12 sites in this campaign, including
two in the Gobi desert, three in China, 2 in Korea, 3 in Japan, 2 in
Taiwan, and one at Midway Island. These measurements will complement
SMART in characterizing the aerosol optical properties during the
springtime dust events that blanketed the region in 2001.

*5.1.7 CLAMS*[]{#__RefHeading___Toc388510114 .anchor}

The Chesapeake Lighthouse and Aircraft Measurements for Satellites
(CLAMS) is planned between July and August 2001, and its focus is on
verifying cloud-free aerosol retrievals over an ocean background. It is
coordinated with the CERES MISR, and MODIS teams, and centers on the
Chesapeake Bay Lighthouse located about 25 km off the coast of Virginia
Beach, Virginia. The primary purpose of this CERES validation site is to
collect Baseline Surface Radiation Network (BSRN) measurements of
downwelling shortwave radiation at the ocean surface, together with
AERONET sunphotometer measurements and a micropulse lidar, all over as
uniform and dark a background as possible. This will allow careful
discrimination of surface and atmospheric radiation, and to assess the
impact of clouds, aerosols, water vapor, and surface temperature on the
radiation budget. CERES funds the Chesapeake Bay Lighthouse from its EOS
validation funds. The focus of the CLAMS field campaign is to coordinate
MODIS, CERES, and MISR observations of aerosol over the ocean with
coincident surface measurements from the Lighthouse and airborne
measurements from the NASA ER-2, University of Washington CV-580,
Langley Research Center OV-10, and a Cessna aircraft operated by the
Goddard Institute for Space Studies. The ER-2 payload of interest for
this experiment is the MAS, AirMISR, CPL, and the recently developed
LAS, which enables studies of the MODIS cloud mask, aerosol retrieval
over ocean, and surface reflectance characterization. The CV-580 should
contain both its standard in situ aerosol microphysics (PCASP, FSSP,
etc.) and chemistry measurements, and carry the CAR instrument for
bidirectional reflectance measurements over the ocean and nearby land
surfaces, and the AATS-14 instrument from Ames Research Center for
spectral aerosol optical thickness measurements.

*5.1.8 CAMEX 4* []{#__RefHeading___Toc388510115 .anchor}

The Convection and Moisture Experiments (CAMEX 4) field campaign will be
conducted during the months of August and September 2001. This
experiment is directed towards severe tropical convection with emphasis
on the energetics of hurricanes and how their investigation can lead to
better landfall forecasts, including precipitation. A concurrent
experiment is KAMP (Keys Area Microphysics Project), with the goal of
evaluating radar retrievals of cloud microphysical properties using
aircraft microphysical measurements.

Measurements of particle size distributions, from several µm to cm size,
direct measurements of the liquid and ice contents, and detailed ice
particle shape information will be acquired from the NASA DC-8 aircraft.
Observations will be made from as high as 13 km to as low as 5 km using
"bow-tie" flight patterns, augmented with Lagrangian spiral descents
through stratiform cloud layers. The NASA ER-2 aircraft, with MAS, a
lidar and a precipitation radar will make remote sensing measurements
over the DC-8. Observations in a variety of cloud types, from hurricanes
to anvils and stratiform precipitation, will be used in the development
of sub-tropical cirrus radiative transfer models for MODIS.

*5.1.9 CLAP-2002* []{#__RefHeading___Toc388510116 .anchor}

The Cloud and Atmospheric Profiles - 2002 {CLAP-2002) experiment is
slated for Spring, 2002. The objectives of CLAP-2002 are validation of
MODIS cloud, atmospheric properties, and emissive band level-1b products
on both the Terra (MODIS) and Aqua (MODIS and AIRS) platforms. This
activity is timed to occur within the first year after Aqua launch. The
ER-2 payload of MAS, S-HIS, and CPL provides accurate high spectral
resolution emissive band calibration and thin cloud definitions for
radiometric, cloud top property, and atmospheric profile validation. All
flights (SGP ARM and Gulf of Mexico) will be coordinated with MODIS (on
Terra and Aqua) overpasses. Overflights of SGP CART will take advantage
of ongoing ground-based measurements (class-sonde, AERI, tower
measurements of temperature and moisture, microwave moisture
measurements, uplooking lidar and radar observations, whole sky imagers)
for cloud and atmospheric profiles validation. An important goal of
CLAP-2002 will be to measure atmospheric profiles under varying
atmospheric moisture content. Clear sky flights over the Gulf of Mexico
provide a uniform background for MODIS and AIRS radiometric evaluation.
With CPL included in the ER-2 payload, thin single and multi-layer cloud
scenes will be sought for cloud top properties and cloud mask validation
over the Gulf. CLAP-2002 will be evenly weighted between validating the
more mature MODIS science product algorithms of Terra and fine-tuning
the science product algorithms of Aqua. To the extent possible,
underflights of Terra and Aqua on single ER-2 missions will be used to
expand synergism between Terra and Aqua.

*5.1.10 CRYSTAL-FACE and CRYSTAL-TWP*[]{#__RefHeading___Toc388510117
.anchor}

The Cirrus Regional Study of Tropical Anvils and Cirrus Layers (CRYSTAL)
experiment, planned to be conducted in two phases, the Florida Area
Cloud Experiment (FACE) in July 2002 and the Western Tropical Pacific
(TWP) in August 2004, focuses on modeling and observing tropical cirrus
cloud systems and their role in regional and global climate. As with the
previous FIRE field experiments, remote sensing is a central theme of
both CRYSTAL-FACE and CRYSTAL-TWP, and validation of satellite
retrievals of cloud properties is a focus of CRYSTAL. The validation
approach is to combine the satellite observations with in situ
observations from field campaigns and continuous ground-based
observations. Comparison of the satellite observations with the
continuous ground-based data provides a statistical meaningful sample
for assessing the retrievals over a long time period. Comparisons with
in situ measurements provide detailed case studies with cloud
macrophysical and microphysical properties to assess cloud algorithm
performance. This experiment includes observations from the NASA ER-2
aircraft as well as in situ aircraft. Primary instruments of interest
for the ER-2 are the MAS, S-HIS, and CPL. This experiment will focus on
the validation of cirrus properties including: detection by cloud mask
algorithm, cloud top height, cloud water phase, and cloud microphysical
properties (especially of ice clouds). An emphasis will be on retrieving
the properties of thin cirrus often observed near the tropopause.

*5.1.11 California Stratus and Valley
Fog*[]{#__RefHeading___Toc388510118 .anchor}

Once the MODIS is in orbit and returning regular data, we envision two
focused periods of ER-2 overflights, to be coordinated with the Terra
and Aqua orbits, in California (the first one over valley fog in the
central valley of California in December 2002, and the second over
marine stratocumulus clouds located over the ocean between Monterey and
San Diego in July 2003). These experiments would entail ER-2 flights
from home base in California (Dryden Flight Research Center), and would
consist, once again, of the MAS, AirMISR, LAS, and CPL, together with
coordinated underflights by the University of Washington CV-580 with its
in situ microphysics probes. This data set, of special interest to
Goddard Space Flight Center (Michael King, Steve Platnick, Si-Chee Tsay)
would help to validate the cloud optical thickness and effective radius
between MODIS and the smaller spatial resolution airborne sensors on the
ER-2.

*5.2 Other Satellite Data*[]{#__RefHeading___Toc388510119 .anchor}

MODIS retrievals will be compared to those determined from in situ
radiosonde measurements, the NOAA HIRS operational retrievals, the GOES
sounder operational retrievals, NCEP analysis of all available data, and
retrievals from the Atmospheric Infrared Sounder (AIRS) on the Aqua
platform. Total ozone will be compared to Total Ozone Mapping
Spectrometer (TOMS) measurements as well as the operational NOAA ozone
estimates from HIRS. In addition, MODIS aerosol retrievals will be
compared to POLDER and ILAS II on ADEOS II.

*5.3 In Situ Measurement Needs at Calibration/Validation
Sites*[]{#__RefHeading___Toc388510120 .anchor}

In order to assure that the cloud, aerosol and water vapor products are
properly derived from our satellite-based data processing algorithms, it
is necessary to coordinate many of the above-specified airborne
campaigns with in situ cloud microphysics, aerosol sampling, and
meteorological sampling. For this purpose we plan to make use of our
long-standing collaboration with Prof. Peter V. Hobbs, University of
Washington, and coordinate many of our cloud and aerosol campaigns from
the ER-2 and Terra with in situ sampling from the University of
Washington CV-580 aircraft.

In addition, we envision intercomparing MODIS-derived column aerosol
products with AERONET and ARM sites, as appropriate. These are all
surface networks with *long-term* measurements that will enable
long-period assessments of our derived products through intercomparisons
with these data (aerosol optical thickness, aerosol size distribution,
and precipitable water). Precipitable water vapor will be also validated
using ground-based microwave radiometers, WMO radiosonde sites, Raman
lidar systems, and a global network of GPS stations (\~2000 worldwide).
Some AERONET sites will be collocated with micropulse lidar sites to
measure the vertical distribution of aerosol and their optical
properties (extinction coefficient and backscattering).

*5.4 Needs for Instrument Development*[]{#__RefHeading___Toc388510121
.anchor}

We envision no need to further develop any airborne or in situ
instruments, but rather to continually assess the performance of the
MODIS Airborne Simulator and to perform regular calibration and
characterization experiments, upgrading the spectrometer as required.
Based on recent operational experience, we anticipate a number of
improvements, enhancements of capability, and laboratory tests.
Laboratory measurements planned for the near future include: (i)
measuring and characterizing the polarization sensitivity of the MAS as
a function of wavelength, (ii) measuring the modulation transfer
function and point spread function of the MAS, (iii) viewing a
calibrated high-temperature blackbody source, and (iv) characterizing
the spectral response functions periodically with high spectral
resolution interferometric sources. Finally, we plan to improve the
stability of the 20 in integrating hemisphere that accompanies the MAS
on field deployments by incorporating variable power supplies that are
servo-controlled by reference detectors.

Similarly, the CAR and HIS will be upgraded or have parts replaced, as
necessary, to maintain their usefulness in field validation activities.
For example, the HIS has been upgraded to have scanning capability in
1998 (S-HIS), and the CAR has recently had the following improvements:
(i) addition of two UVA channels at 0.34 and 0.38 µm (like TOMS), and
(ii) upgrading the data system from 10 bit to 16 bit with the higher
data rate necessary to support the additional channels.

In addition to the airborne radiometers mentioned above, there is a
continuing need to develop new instruments for better measurements of
the aerosol single scattering albedo, CCN spectrum, size dependent
chemical composition, and phase function of ice crystals. We plan to
collaborate with other groups in this regard, and not to initiate or
develop this capability ourselves.

*5.5 Geometric Registration Site*[]{#__RefHeading___Toc388510122
.anchor}

None are required for validating MODIS atmosphere algorithms.

*5.6 Intercomparisons*[]{#__RefHeading___Toc388510123 .anchor}

Post-launch validation of the thermal infrared cloud phase product with
other MODIS products will consist of close inspection of sections of
data representing differing cloud regimes and surface types, including
cross checks during the day mode with the visible reflection function
technique of King et at. (ATBD-MOD-05) and consistency with the cloud
top properties results.

Validation of the MODIS cloud products will also be done post-launch
using products derived from the ASTER, CERES, and MISR sensors on the
Terra, CERES on TRMM, and GLI on ADEOS II.

The MODIS Cloud Mask will be compared to the ASTER Polar Cloud Mask to
ensure consistent classification of polar cloud cover. The ASTER product
will include a classification for each pixel poleward of 60°N or 60°S
using a bit map with the following bit flags: unknown, ice cloud, water
cloud, shadow, land, ice, wet ice, and water. The high spatial
resolution of the ASTER data (30 m at nadir) will help to ensure that
sub-pixel effects are properly accounted for in the MODIS data. Because
the ASTER channels are mostly located in atmospheric window regions,
however, it will still be difficult to correctly identify thin cirrus
clouds over Polar Regions from ASTER data. The 1.38 µm MODIS channel
should easily allow the detection of thin cirrus clouds over Polar
Regions during the daytime, and this comparison with the ASTER cloud
mask may prove invaluable to both algorithms.

The MODIS Cloud Mask and Cloud Properties (cloud top pressure and
temperature, effective emissivity, cloud optical thickness,
thermodynamic phase, and effective radius) products will be compared to
the CERES Single Satellite Footprint (SSF) and Single Satellite Gridded
products that include cloud top pressure and temperature, cloud optical
thickness, effective radius and phase, and cloud category (lower,
lower-middle, upper-middle, high). CERES is heavily dependent on MODIS
observations for cloud detection, so it will be important to ensure that
the MODIS and CERES-derived cloud products are consistent. The MODIS
Cloud Mask and Cloud Properties (cloud top pressure) products will also
be compared to the MISR Top-of-Atmosphere/Cloud Product. The components
of the MISR product that will be used are Reflecting-Level Reference
Altitude (retrieved using MISR stereo imagery), Angle-by-angle cloud
masks, Cloud shadow mask, and Altitude-binned cloud fraction. The MODIS
and MISR Cloud Masks will be compared to ensure consistency of cloud
identification, and the MISR stereo cloud heights will be compared with
the MODIS cloud top heights to geometrically validate the MODIS
radiometrically derived cloud height data.

We plan to compare the MODIS aerosol product with the aerosol product
derived from MISR (MIS05). Using field experiments as well as AERONET,
we plan to investigate the relative success of each of these techniques
as a function of aerosol type, season, surface cover, and view and
illumination directions. This will be an ongoing research endeavor
involving Yoram Kaufman and Didier Tanré of the MODIS Team, and Jim
Conel, Ralph Kahn, John Martonchik, and Dave Diner of the MISR Team.

For atmospheric water vapor, the MODIS precipitable water product will
be compared to column water vapor derived from numerous AERONET
sunphotometers worldwide, as well as with precipitable water estimates
derived from passive microwave and Raman lidar measurements at the SGP
(Oklahoma) ARM CART site, North American radiosondes, microwave
measurements from the SMART, and surface GPS soundings, where available.

*5.7 Modeling Studies and Quality
Assessment*[]{#__RefHeading___Toc388510124 .anchor}

Steve Platnick and Robert Pincus plan to use model calculations and
analysis of MODIS and MAS data to assess the accuracy of the MODIS Cloud
Product retrievals of liquid water cloud optical thickness and effective
radius (MOD06). A component of their investigation includes the effects
of vertical stratification of cloud droplet sizes on the retrievals,
especially discrepancies between retrieved effective radii made from
reflection measurements in different near-infrared bands. They also plan
to develop forward models for assessing biases due to horizontal
inhomogeneity. Results should provide quantitative guidance for the
assessment of quality assurance parameters for the MODIS cloud retrieval
algorithm. It is anticipated that in situ cloud measurements from
planned MODIS Atmosphere Team validation campaigns will help in the
modeling of realistic clouds, as well as for retrieval validation. They
are especially interested in sufficient vertical profiling of clouds and
long horizontal legs. To this end, relatively plane-parallel like marine
stratus clouds are desired as a starting point in their investigation.
This includes the California stratus and valley fog deployments, as well
as opportunities such as Namibian stratus off the coast of Namibia
during SAFARI 2000.

In situ validation of cloud optical thickness fields is quite limited
with current surface and aircraft capabilities. We need some validation
tools for the optical thicknesses retrieved from MODIS data at locales
where no other in situ measurements are available. Alexander Marshak
proposes to use a theoretical method that should provide important
information on how accurate operational cloud retrievals are. As a first
step, this investigation will be applied to stratocumulus clouds. This
method will help to better understand errors related to cloud horizontal
inhomogeneity in operational cloud retrievals and set error bounds on
the inferred values, especially for the 250 m pixel data. The method is
based on estimating the effect of photon horizontal fluxes on the
retrieval of cloud optical properties. To validate this theoretical
model, microwave radiometer data at two ARM sites (SGP and TWP) will be
used. Marshak will also use 15 m spatial resolution ASTER VNIR (visible
and near-infrared) data set of stratocumulus clouds over these two ARM
sites.

6.0 Implementation of Validation Results[]{#__RefHeading___Toc388510125
.anchor}

*6.1 Approach*[]{#__RefHeading___Toc388510126 .anchor}

The MODIS atmosphere algorithms for the cloud mask, cloud properties,
aerosol properties, precipitable water, and atmospheric profiles will
evolve in the first year after MODIS is launched in accordance with
seasonal changes and validation results. Thereafter, algorithm changes
will be restrained to occur once per year as needed. We anticipate
assessing the performance of the MODIS algorithms initially by
processing every other day, as well as specific time periods with
validation experiments of special relevance, as outlined above. After
initially looking at one year's data, consistency checks, quality
assessment flags, validation campaigns, as appropriate, and
intercomparisons with other instruments (especially on Terra), we will
begin whole-scale reprocessing, including every month. This initial
stage may take in excess of one year, during which time the MODIS
calibration algorithm will likely undergo additional refinement.
Continual refinement of the MODIS "operational" algorithms will largely
be conducted at the Team Members SCF as well as at the Atmosphere
Group's Computing Facility (Windhoek), as many of the algorithms are
dependent on results from other algorithms (like calibration). Only
periodically (after say 1.5 years following launch), the first
reprocessing at the MODIS Data Processing System (MODAPS) will be
initiated.

*6.2 Role of EOSDIS*[]{#__RefHeading___Toc388510127 .anchor}

To investigate algorithm changes, past data sets (covering different
seasons and ecosystems) will be necessary. We expect that these data
will be archived and available through EOSDIS. The bulk of the data sets
that we have thus far obtained (including CAR, MAS, and University of
Washington C-131A in situ data) have been archived and are available
through relevant EOS DAACs. The specific point of data distribution for
MAS data is clearly identified as part of the browse imagery archive
portion of the MAS World Wide Web site (see Section 9).

*6.3 Archival, Processing, and Distribution of Validation
Data*[]{#__RefHeading___Toc388510128 .anchor}

The MODIS Science Team plans to gather all relevant data from various
validation exercises and make these data available through EOSDIS. We
anticipate that the MODIS home page on World Wide Web will soon
establish a link to the location and availability of all MODIS-related
validation data sets.

7.0 Summary[]{#__RefHeading___Toc388510129 .anchor}

This plan represents the thought process of all MODIS atmosphere team
members, associates, and close collaborators. This discipline group has
considerable field campaign experience as well as experience with other
satellite and surface network data. Putting all such validation
schedules together, we obtain the "master schedule" for MODIS atmosphere
validation activities, for the focused, field-campaign portion of this
plan (cf. Figure 12).

Table 7 summarizes the validation methods being used by atmosphere data
product.

Table 7. Summary of validation measurements by data product.

+------------------+-------------+-------------------------------------+
| ***MODIS         | ## *Val     | # *Validation Measurements*         |
| Product***       | idation PI* |                                     |
+------------------+-------------+-------------------------------------+
| Cloud mask       | Steve       | Aircraft and ground based lidar and |
|                  | Ackerman    | radar, MAS, ASTER, MISR, AVHRR      |
|                  |             | cloud masks, Level-3 product        |
|                  |             | statistics                          |
+------------------+-------------+-------------------------------------+
| Cloud top        | Paul Menzel | Aircraft and ground based lidar,    |
| pressure,        |             | radar, GOES stereo cloud heights,   |
| temperature, and |             | HIRS, MISR and CERES cloud heights  |
| effective        |             |                                     |
| emissivity       |             |                                     |
+------------------+-------------+-------------------------------------+

Table 7. Summary of validation measurements by data product (continued).

+------------------+-------------+-------------------------------------+
| ***MODIS         | ## *Val     | # *Validation Measurements*         |
| Product***       | idation PI* |                                     |
+------------------+-------------+-------------------------------------+
| Cloud optical    | Michael     | In situ aircraft, remote sensing    |
| thickness and    | King        | aircraft and ground based lidar and |
| effective radius |             | radar, MISR and CERES cloud optical |
|                  |             | thickness                           |
+------------------+-------------+-------------------------------------+
| Cloud particle   | Steve       | Aircraft based in situ              |
| phase            | Ackerman    | measurements, dual polarization     |
|                  |             | lidar, CERES cloud phase            |
+------------------+-------------+-------------------------------------+
| Cirrus           | Bo-Cai Gao  | Aircraft remote sensing with        |
| reflectance in   |             | multispectral radiometers (MAS and  |
| the visible      |             | AVIRIS)                             |
+------------------+-------------+-------------------------------------+
| Aerosol optical  | Yoram       | Surface measurements from AERONET   |
| thickness and    | Kaufman     | sun/sky radiometers, aircraft based |
| size             |             | in situ measurements                |
| distribution     | Didier      |                                     |
|                  | Tanré       |                                     |
+------------------+-------------+-------------------------------------+
| Atmospheric      | Paul Menzel | CART microwave and in situ          |
| moisture and     |             | measurements, CIGSN network of      |
| temperature      |             | Australian soundings, North         |
| gradients        |             | American radiosonde network, HIRS,  |
|                  |             | GOES, AIRS, LASE                    |
+------------------+-------------+-------------------------------------+
| Atmospheric      | Paul Menzel | CART temperature and moisture       |
| stability        |             | measurements, CIGSN network of      |
|                  |             | Australian soundings, North         |
|                  |             | American radiosonde network, HIRS,  |
|                  |             | GOES, AIRS                          |
+------------------+-------------+-------------------------------------+
| Precipitable     | Bo-Cai Gao  | AERONET, CART microwave and Raman   |
| water            |             | lidar measurements, North American  |
|                  | Yoram       | radiosonde network, ground-based    |
|                  | Kaufman     | GPS network, SMiR                   |
|                  |             |                                     |
|                  | Paul Menzel |                                     |
+------------------+-------------+-------------------------------------+
| Gridded level-3  | All         | Intercomparisons and consistency    |
| atmosphere       |             | checks                              |
| product          |             |                                     |
+------------------+-------------+-------------------------------------+

Additional contributions from the wider scientific community are
described in this document. Other opportunities are widely sought.

8.0 References[]{#__RefHeading___Toc388510130 .anchor}

Arnold, G. T., S. C. Tsay, M. D. King, J. Y. Li and P. F. Soulen, 2001:
Airborne spectral measurements of surface-atmosphere anisotropy for
Arctic sea ice and tundra. *Int. J. Remote Sens*., in press.

Curry, J. A. P. V. Hobbs, M. D. King, D. A. Randall, P. Minnis, G. A.
Isaac, J. O. Pinto, T. Uttal, A. Bucholtz, D. G. Cripe, H. Gerber, C. W.
Fairall, T. J. Garrett, J. Hudson, J. M. Intrieri, C. Jakob, T. Jensen,
P. Lawson, D. Marcotte, L. Nguyen, P. Pilewskie, A. Rangno, D. C.
Rodgers, K. B. Strawbridge, F. P. J. Valero, A. G. Williams, and D.
Wylie, 2000: FIRE Arctic Clouds Experiment. *Bull. Amer. Meteor. Soc*.,
**81**, 5--30.

Holben, B. N., T. F. Eck, I. Slutsker, D. Tanré, J. P. Buis, A. Setzer,
E. Vermote, J. A. Reagan, Y. J. Kaufman, T. Nakajima, F. Lavenu, I.
Jankowiak and A. Smirnov, 1998: AERONET---A federated instrument network
and data archive for aerosol characterization. *Remote Sens. Environ.*,
**66**, 1--16.

Kaufman, Y. J., P. V. Hobbs, V. W. J. H. Kirchhoff, P. Artaxo, L. A.
Remer, B. N. Holben, M. D. King, D. E. Ward, E. M. Prins, K. M. Longo,
L. F. Mattos, C. A. Nobre, J. D. Spinhirne, Q. Ji, A. M. Thompson, J. F.
Gleason, S. A. Christopher and S. C. Tsay, 1998: The Smoke, Clouds, and
Radiation---Brazil (SCAR-B) experiment. *J. Geophys. Res*., **103**,
31783--31808.

King, M. D., 1992: Directional and spectral reflectance of the Kuwait
oil-fire smoke. *J. Geophys. Res*., **97**, 14545--14549.

King, M. D., M. G. Strange, P. Leone and L. R. Blaine, 1986:
Multiwavelength scanning radiometer for airborne measurements of
scattered radiation within clouds. *J. Atmos. Oceanic Technol.,* **3**,
513--522.

King, M. D., Y. J. Kaufman, W. P. Menzel and D. Tanré, 1992: Remote
sensing of cloud, aerosol, and water vapor properties from the Moderate
Resolution Imaging Spectrometer (MODIS). *IEEE Trans. Geosci. Remote
Sens.*, **30**, 2--27.

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. Osterwisch, 1996: Airborne scanning spectrometer for
remote sensing of cloud, aerosol, water vapor and surface properties.
*J. Atmos. Oceanic Technol*., **13**, 777--794.

Prata, A. J., 1994: Land surface temperatures derived from AVHRR and
ATSR. II: Experimental results and validation of AVHRR algorithms. *J.
Geophys. Res*., **99**, 13025--13058.

Sassen, K., 1994: Advances in polarization diversity lidar for cloud
remote sensing. *Proc. IEEE, Remote Sensing Instruments for
Environmental Research*, **82**, 1907--1914.

Sassen, K., 1997: The lidar dark band: An oddity of the radar bright
band analogy. *Geophys. Res. Lett*., **22**, 3505--3508.

Sassen, K., and T. Chen, 1995: Contrail cirrus and their potential for
regional climate change. *Bull. Amer. Meteor. Soc*., **78**, 1885--1903.

Smith, W. L., H. E. Revercomb, R. O. Knuteson, F. A. Best, R. Dedecker,
H. B. Howell and H. M. Woolf, 1995: Cirrus cloud properties derived from
the high spectral resolution infrared spectrometry during FIRE II. Part
I: The High Resolution Interferometer Sounder (HIS) system.. *J. Atmos.
Sci.*, **52**, 4238--4245.

Soulen, P. F., M. D. King, S. C. Tsay, G. T. Arnold and J. Y. Li, 2000:
Airborne spectral measurements of surface-atmosphere anisotropy during
the SCAR-A, Kuwait oil fire, and TARFOX experiments. *J. Geophys. Res*.,
**105**, 10203--10218.

Spinhirne, J. D., R. Boers and W. D. Hart, 1989: Cloud top liquid water
from lidar observations of marine stratocumulus. *J. Appl. Meteor*.,
**28**, 81--90.

Tanré, D., L. A. Remer, Y. J. Kaufman, S. Mattoo, P. V. Hobbs, J. M.
Livingston, P. B. Russell, A. Smirnov, 1999: Retrieval of aerosol
optical thickness and size distribution over ocean from the MODIS
Airborne Simulator during TARFOX. *J. Geophys. Res*., **104**,
2261--2278.

Tsay, S. C., M. D. King, G. T. Arnold, and J. Y. Li, 1998: Airborne
spectral measurements of surface anisotropy during SCAR-B. *J. Geophys.
Res*., **103**, 31943--31954.

Uttal, T., E. E. Clothiaux, T.P. Ackerman, J. M. Intrieri and W.L.
Eberhard, 1995: Cloud boundary statistics during FIRE II. *J. Atmos.
Sci*., **52**, 4276--4284.

Vane, G., R. O. Green, T. G. Chrien, H. T. Enmark, E. G. Hansen, and W.
M. Porter, 1993: The Airborne Visible/Infrared Imaging Spectrometer
(AVIRIS). *Remote Sens. Environ*., **44**, 127--143.

9.0 World Wide Web Links for Validation[]{#__RefHeading___Toc388510131
.anchor}

AERI <http://cimss.ssec.wisc.edu/aeriwww/aeri>

AERONET <http://aeronet.gsfc.nasa.gov:8080/>

AirMISR [http:// www-misr.jpl.nasa.gov/mission/]()

[air.html](../his.html)

AVIRIS <http://makalu.jpl.nasa.gov/aviris.html>

CAR <http://ltpwww.gsfc.nasa.gov/CAR>

CIGSN [http://www.dar.csiro.au](http://www.dar.csiro.au/)

CLS <http://virl.gsfc.nasa.gov/er2cls.html>

CPL <http://virl.gsfc.nasa.gov/cpl>

EOS Project Science Office
[http://eospso.gsfc.nasa.gov](http://eospso.gsfc.nasa.gov/)

HIS <http://sponge.ssec.wisc.edu/~dennyh/his/>

[his.html](../his.html)

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

MODIS [http://ltpwww.gsfc.nasa.gov/MODIS/MODIS.
html](http://ltpwww.gsfc/)

MODIS Atmosphere
[http://modis-atmos.gsfc.nasa.gov](http://modis-atmos.gsfc.nasa.gov/)

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

10.0 Acronyms[]{#__RefHeading___Toc388510132 .anchor}

AATS Ames Airborne Tracking Sunphotometer (NASA Ames Research Center)

ACARS ARINC (Aeronautical Radio Inc.) Communications, Addressing and
Reporting System

ACE Arctic Cloud Experiment (Chukchi Sea, Alaska, May-June 1998)

ACE-Asia Aerosol Characterization Experiment-Asia (East Asia, March-May
2001)

ADEOS Advanced Earth Observing Satellite (Japan)

AERI Atmospheric Emitted Radiation Interferometer

AEROCE Aerosol/Ocean Chemistry Experiment

AERONET Aerosol Robotic Network

AirMISR Airborne MISR

AIRS Atmospheric Infrared Sounder

AMPR Advanced Microwave Precipitation Radiometer

AMSU Advanced Microwave Sounding Unit

ARM Atmospheric Radiation Measurement Program

ARMCAS Arctic Radiation Measurements in Column Atmosphere-surface System
(Beaufort Sea, Alaska, June 1995)

ASTER Advanced Spaceborne Thermal Emission and Reflection radiometer

ATBD Algorithm Theoretical Basis Document

ATSR Along Track Scanning Radiometer

AVHRR Advanced Very High Resolution Radiometer

AVIRIS Airborne Visible/Infrared Imaging Spectrometer

BRDF Bidirectional Reflectance Distribution Function

BSRN Baseline Surface Radiation Network

CALVEX-N Calibration Validation Experiment - NOAA-15 (California,
December 1998)

CAMEX Convection and Moisture Experiment

CAR Cloud Absorption Radiometer

CART Clouds and Radiation Testbed

CEPEX Central Equatorial Pacific Experiment (Fiji, February-March 1993)

CERES Clouds and the Earth's Radiant Energy System

CIGSN Continental Integrated Ground Site Network (Australia)

CLAMS Chesapeake Lighthouse and Aircraft Measurements for Satellites
(Virginia, July-August 2001)

CLAP-2002 Clouds and Atmospheric Profiles -- 2002 (Texas and Oklahoma,
Spring 2002)

CLS Cloud Lidar System

CPI Cloud Particle Imager

CPL Cloud Physics Lidar

CRYSTAL Cirrus Regional Study of Tropical Anvils and Cirrus Layers

DAAC Distributed Active Archive Center

EOS Earth Observing System

EOSDIS EOS Data and Information System

ETO Extended Time Observation

FACE Florida Area Cloud Experiment (Florida, June-August 2002)

FARS Facility for Remote Sensing (University of Utah)

FIRE First ISCCP Regional Experiment (California, June-July 1987;
Beaufort Sea, Alaska, April-June, August 1998)

FSSP Forward Scattering Spectrometer Probe

GLAS Geoscience Laser Altimeter System

GLI Global Imager

GOES Geostationary Operational Environmental Satellite

GPS Global Positioning System

HIS High-spectral resolution Interferometer Sounder

HIRS High Resolution Infrared Radiation Sounder

HSB Humidity Sounder from Brazil

ILAS Improved Limb Atmospheric Spectrometer

ISCCP International Satellite Cloud Climatology Project

KAMP Keys Area Microphysics Project

LAS Leonardo Airborne Simulator

LASE Lidar Atmospheric Sensing Experiment

LBA Large Scale Biosphere-Atmosphere Experiment in Amazonia

LEADEX Lead Experiment (Beaufort Sea, Alaska, April 1992)

M-AERI Marine-Atmospheric Emitted Radiation Interferometer

MAS MODIS Airborne Simulator

MAST Monterey Area Ship Tracks Experiment (Monterey and nearby Pacific
Ocean, June 1994)

McIDAS Man-computer Interactive Data Access System

MISR Multi-angle Imaging SpectroRadiometer

MODAPS MODIS Data Processing System

MODIS Moderate Resolution Imaging Spectroradiometer

MOPITT Measurement of Pollution in the Troposphere

MOPITT-A Airborne MOPITT

NASA National Aeronautics and Space Administration

NAST NPOESS Atmospheric Sounder Testbed

NAST-I NAST-Interferometer

NAST-I NAST-Microwave

NCAR National Center for Atmospheric Research

NCEP National Centers for Environmental Prediction (NOAA)

NOAA National Oceanic and Atmospheric Administration

NPOESS National Polar Orbiting Environmental Satellite System

NSA North Slope of Alaska

PCASP Passive Cavity Aerosol Spectrometer Probe

POLDER Polarization and Directionality of Earth's Reflectances

PRIDE Puerto RIco Dust Experiment (Puerto Rico, June-July 2000)

PRF Pulse Repetition Frequency

PSR Polarimetric Scanning Radiometer

S3 Sun-Sky-Surface photometer

SAVE South Africa Validation of EOS

S-HIS Scanning HIS

SAFARI 2000 Southern African Regional Science Initiative 2000 (Southern
Africa, August-September 2000)

SCAR-A Sulfate, Clouds, and Radiation--Atlantic (Delmarva Peninsula and
nearby Atlantic Ocean, July 1993)

SCAR-B Smoke, Clouds, and Radiation--Brazil (Brazil, August-September
1995)

SCAR-C Smoke, Clouds, and Radiation--California (Pacific Northwest,
September 1994)

SCF Science Computing Facility

SeaWiFS Sea-viewing Wide Field-of-view Sensor

SGP Southern Great Plains

SHEBA Surface Heat Budget of the Arctic Ocean

SMART Surface Measurements for Atmospheric Radiative Transfer

SMiR Scanning Microwave Radiometer

SSFR Spectral Solar Flux Radiometer (NASA Ames Research Center)

SST Sea Surface Temperature

SUCCESS Subsonic Aircraft Contrail and Cloud Effects Special Study
(April-May 1996)

SW Shortwave

TARFOX Tropospheric Aerosol Radiative Forcing Observational Experiment
(Delmarva Peninsula and nearby Atlantic Ocean, July 1996)

TLCF Team Leader Computing Facility

TM Thematic Mapper

TOMS Total Ozone Mapping Spectrometer

TRMM Tropical Rainfall Measuring Mission

TWP Tropical Western Pacific (July-August 2004)

TX-2001 Terra Experiment-2001 (Texas and Oklahoma, March-April 2001)

UPS Uninterruptible Power Source

VNIR Visible and Near-Infrared

WINCE Winter Cloud Experiment (Upper Midwest, Canada, and New England,
January-February 1997)

WINTEX Winter Experiment (Upper Midwest, March 1999)

WISC-T2000 Wisconsin Snow and Cloud Experiment -- Terra 2000 (Oklahoma
and Upper Midwest, March 2000)

WMO World Meteorological Organization