EOS

1997

D ATA

H ANDBOOK

EARTH OBSERVING S YSTEM (EOS)

NASA - G ODDARD SPACE F LIGHT CENTER

MTPE

MISSION TO PLANET EARTH (MTPE)

P RODUCTS

Volume 1

TRMM &

AM-1
Data Products Handbook Vol. I i

Editors

Stephen W. Wharton

Monica Faeth Myers

Acknowledgments

For Additional Copies:

Code 902

NASA/Goddard Space Flight Center

Greenbelt, MD 20771

Phone: (301) 614-5321

Internet: myers@daac.gsfc.nasa.gov

This document could not have been developed without

the commitment and support of the EOS instrument

science teams who contributed their time and

resources to providing detailed information about their

respective products. Additional support was provided

by the EOS Project Science Office and the ESDIS

Science Office. Members of the EOS Project at the

Goddard Space Flight Center and scientists of the EOS

Investigators Working Group (IWG) are recognized

for their assistance in reviewing and verifying the

content of this document.

Special thanks to the individuals who contributed to

the writing and production of this document, including

Suraiya Ahmad, Paul Anuta, Mary James, Michael

Kirshner, and Kathy Pedelty.
Data Products Handbook Vol. I iii

How to Use This Document

This document is designed to provide you with information about the products available through the EOSDIS

system in the TRMM and AM-1 timeframes. If you already know the product name, the name may be found in

the summary List of Products beginning on page ix by either the Product ID or the Data Set Name . Both the

Product ID and the Data Set Name will direct you to the page number on which you will find the corresponding

Product Description and other information.

If you are beginning your research into what products are available through MTPE, you may choose to

investigate the twelve science chapters in this document beginning with the Radiance and Imagery products.

Data sets are grouped by these classifications, which are derived from the twenty-four EOS critical science

measurements. Each category provides a framework for understanding the science objectives as well as

providing a thumbnail sketch of what the product is designed to do. Investigators may also cross-reference

products by their source instrument and archive. That information, as well as page numbers, is provided in the

List of Products.

Investigators and educators should realize that additional information about the products will emerge

as the time grows closer to launch of the TRMM and AM-1 missions. An electronic version of the Data

Products Handbook will be available online at the EOS Project Science Office Home Page

(http://eospso.gsfc.nasa.gov/eospso_homepage.html).

This document also contains information about the EOSDIS Distributed Active Archive Centers (DAACs) and

the EOS instrument teams. Access information about the DAACs is contained in Chapter Two. Information

about the TRMM and AM-1 instrument teams is contained in Chapter Three.
Data Products Handbook Vol. I v

Abstract

The MTPE/EOS Data Products Handbook provides high level descriptions of the

science and instrument data sets that will be produced from the Tropical Rainfall

Measuring Mission (TRMM) and the first Earth Observing System (EOS)

satellite (AM-1). These data sets, which will provide essential information for

science and policy research in the areas of global change and Earth system

science, will be distributed to the user community through the EOS Data and

Information System (EOSDIS). The data set descriptions in the MTPE/EOS Data

Products Handbook include product descriptions, research and application

information, information about precursors to the EOS data, suggested reading,

and summary information on product coverage, resolution, and contacts. The

MTPE/EOS Data Products Handbook is a companion to the MTPE/EOS

Reference Handbook (Asrar and Greenstone, 1995) and EOS: Science Strategy

for the Earth Observing System (Asrar and Dozier, 1994).
Data Products Handbook Vol. I vii

Table of Contents

List of Products ...................................................................................................................... ix

Chapter One Introduction............................................................................................................................. 1

Chapter Two Background ............................................................................................................................. 7

Chapter Three Instrument Data Processing Overview ................................................................................. 15

Chapter Four Radiance and Imagery Products............................................................................................ 41

Chapter Five Precipitation and Atmospheric Humidity ............................................................................. 73

Chapter Six Cloud and Aerosol Properties and Radiative Energy Fluxes .............................................. 103

Chapter Seven Lightning............................................................................................................................. 143

Chapter Eight Tropospheric Chemistry...................................................................................................... 149

Chapter Nine Atmospheric Temperatures................................................................................................. 157

Chapter Ten Winds .................................................................................................................................. 167

Chapter Eleven Pressure and Heights........................................................................................................... 181

Chapter Twelve Surface Temperatures of Land and Oceans, Fire Occurrence, and Volcanic Effects......... 187

Chapter Thirteen Vegetation Dynamics, Land Cover, and Land Cover Change............................................ 199

Chapter Fourteen Phytoplankton and Dissolved Organic Matter .................................................................... 213

Chapter Fifteen Snow and Sea Ice Cover ..................................................................................................... 225

Appendices

Appendix A Points of Contact................................................................................................................. 233

Appendix B Acronyms ............................................................................................................................ 241

Appendix C References........................................................................................................................... 247

Figures

Figure 1. Geographic Distribution of EOSDIS DAACs .......................................................................................................... . 11

Figure 2. TRMM Instrument Algorithms and Products .......................................................................................................... . 18

Figure 3. Flow Diagram Showing Interaction of TRMM Ground-Based Radar Validation Products .................................... . 19

Figure 4. LIS Data Flow .......................................................................................................................................................... . 21

Figure 5. ASTER Data Product Archite cture .......................................................................................................................... . 23

Figure 6. Simulated ASTER Images of Cuprite, Nevada ........................................................................................................ . 24

Figure 7. CERES Data Flow.................................................................................................................................................... . 26

Figure 8. Flow Diagram Showing Interrelationship of MODIS Atmosphere, Land, and Ocean Products.............................. 31

Figure 9. Flow Diagram Showing Interrelationship of MODIS Atmosphere Products ........................................................... 33

Figure 10. Flow Diagram Showing Interrelationship of MODIS Land Products...................................................................... . 34

Figure 11. Flow Diagram Showing Interrelationship of MODIS Ocean Products ................................................................... . 35

Figure 12. Typical 6-Hour Cycle of the GEOS DAS ................................................................................................................ . 39

Figure 13. RGB Composite, January 1989 ................................................................................................................................ . 52

Figure 14. RGB Composite, July 1989 ...................................................................................................................................... . 52

Figure 15. Satellite-Gauge Estimate of Precipitation for July 1994 (in mm/mo)................................................... ................... . 85

Figure 16. Total Precipitable Water........................................................................................................................................... . 94

Figure 17. Aerosol Optical Thickness ...................................................................................................................................... .108

Figure 18. Cloud Optical Thickness and Effective Radius....................................................................................................... .110

Figure 19. HIRS Probability of Cirrus...................................................................................................................................... .111

Figure 20. Cloud Mask ............................................................................................................................................................. .113

Figure 21. Longwave Radiation from CERES ERBE-Like Processing ................................................................................... .120

Figure 22. CERES TOA Longwave Flux ................................................................................................................................. .122

Figure 23. Longwave and Shortwave Heating Rates ............................................................................................................... .125

Figure 24. Hurricane Opal ........................................................................................................................................................ .145

Figure 25. Annual Summary (Lightning) ................................................................................................................................. .147

Figure 26. Simulation of MOPITT Level-2 carbon monoxide data product

for one day of observations for January climatological conditions ........................................................................ .151

Figure 27. Simulation of MOPITT Level-2 methane data product

for one day of observations for January climatological conditions ........................................................................ .154

Figure 28. MODIS Atmospheric Profiles ................................................................................................................................. .166
Data Products Handbook Vol. I viii

Figures

Figure 29. Annual Average Surface Streamline and Vector Magnitude Patterns

for the Western Tropical Pacific ........................................................................................................... 170

Figure 30 NOAA/AVHRR Detected High Temperature Sources: June 25, 1992................................................ 194

Figure 31 Global Infrared Sea Surface Temperature ............................................................................................ 196

Figure 32 Changes in Pacific SST Due to El Niño ............................................................................................... 196

Figure 33 Global Distribution of the Six Canopy Structure Based Vegetation Classes Required for

Carbon, Water and Climate Modeling, Derived from AVHRR Pathfinder Data ................................. 203

Figure 34 Global Leaf Area Index Estimated with the NDVI-LAI Relationships Derived from the

Radiative Transfer Model and Applied to the Advanced Very High Resolution Radiometer

(AVHRR) Pathfinder NDVI Data Set .................................................................................................. 206

Figure 35 Global Net Primary Production During 1987 ....................................................................................... 209

Figure 36 Growing Season Average NDVI of Asia Derived from the

Advanced Very High Resolution (AVHRR) Pathfinder Data Set........................................................ 211

Figure 37 East Coast Ocean Color Image ............................................................................................................. 217

Figure 38 Detection and Identification of Algal Blooms...................................................................................... 219

Figure 39 Global Ocean Phytoplankton Biomass ................................................................................................. 224

Figure 40 Result of Applying the SNOMAP Algorithm ...................................................................................... 227

Tables

Table 1. The 24 Critical EOS Science Measurements............................................................................................ 2

Table 2. Data Set Processing Levels....................................................................................................................... 4

Table 3. Coverage Characteristics of the Instruments on TRMM and AM-1 ........................................................ 9

Table 4. Access Information for the EOSDIS Distributed Active Archive Centers ............................................. 12
Proc Instru- 

Product ID Data Set Name Lvl ment Chapter 

* MTPE is evaluating a federated, fully competed EOSDIS at the time of this document’s development. The product/DAAC rel

Spring 1996 but is subject to change.

TRMM

TRMM 1B-01 VIRS Calibrated Radiances 1B VIRS Radiance and ImagerTRMM 1B-11 TMI Calibrated Brightness Temperatures 1B TMI Radiance and ImagerTRMM 1B-21 PR Radar Total Power, Noise 1B PR Radiance and ImagerTRMM 2A-12 TMI Surface Rainfall & Vertical Structure 2A TMI Precipitation and Atm

Humidity

TRMM 2A-21 PR Surface Cross-Section as Function of Scan Angle 2A PR Precipitation and Atm

Humidity

TRMM 2A-23 PR Rain Occurrence and Rain Type & Bright Band Height 2A PR Precipitation and Atm

Humidity

TRMM 2A-25 PR Range Profiles of Rain and Water Content 2A PR Precipitation and Atm

Humidity

TRMM 2B-31 TRMM Combined Surface Rainfall Rate & Vertical Structure 2B comb. Precipitation and Atm

Humidity

TRMM 3A-11 TMI Surface Rainfall 3A TMI Precipitation and Atm

Humidity

TRMM 3A-25 PR Accumulated Rainfall & Vertical Structure 3A PR Precipitation and Atm

Humidity

TRMM 3A-26 Combined Accumulated Surface Rainfall 3A comb. Precipitation and Atm

Humidity

TRMM 3B-31 Combined Accumulated Rainfall and Vertical Structure

(TRMM combined)

3B comb. Precipitation and Atm

Humidity

TRMM 3B-42 Combined Instrument Rain Calibration 4 comb. Precipitation and Atm

Humidity

TRMM 3B-43 Combined Instrument 1-Degree Rainfall 4 comb. Precipitation and Atm

Humidity

TRMM GV2A-53 GV Radar Site Rain Map 2A GV Precipitation and Atm

Humidity

TRMM GV2A-54 GV Radar Site Convective/Stratiform Map 2A GV Precipitation and Atm

Humidity

TRMM GV2A-55 GV Radar Site Reflectivities 2A GV Precipitation and Atm

Humidity
Proc Instru- 

Product ID Data Set Name Lvl ment Chapter 

* MTPE is evaluating a federated, fully competed EOSDIS at the time of this document’s development. The product/DAAC rel

Fall 1996 but is subject to change.

TRMM GV3A-53 GV 5 Day Site Rain Map 3 GV Precipitation and Atm

Humidity

TRMM GV3A-54 GV 30 Day Site Rain Map 3 GV Precipitation and Atm

Humidity

TRMM GV3A-55 GV Monthly 3-D Structure 3 GV Precipitation and Atm

Humidity

ASTER

AST 01 ASTER Reconstructed, Unprocessed Instrument Data 1A ASTER Radiance and ImagerAST 03 ASTER Registered Radiance at Sensor 1B ASTER Radiance and ImagerAST 04 ASTER Brightness Temperature at Sensor 2 ASTER Radiance and ImagerAST 05 ASTER Surface Emissivity 2 ASTER Surface Temp. Land 

Fire Occurrence and Effects

AST 06 ASTER Decorrelation Stretch 2 ASTER Radiance and ImagerAST 07 ASTER Surface Reflectance 2 ASTER Radiance and ImagerAST 08 ASTER Surface Kinetic Temperature 2 ASTER Surface Temp. Land 

Fire Occurrence and Effects

AST 09 ASTER Surface Radiance 2 ASTER Radiance and ImagerAST 13 ASTER Polar Surface and Cloud Classification 2 ASTER Cloud & Aerosol Pro

Radiative Energy Flu

AST 14 ASTER Digital Elevation Models (DEMs) 2 ASTER Radiance and Imager

CERES

CER/BDS Bi-Directional Scan Product (BDS) 1B CERES Radiance and ImagerCER/ES-8 ERBE-Like Product, Unfiltered Radiances, TOA Fluxes (ES-8) 2 CERES Cloud & Aerosol Pro

Radiative Energy Flu

CER/ES-9 Earth Radiation & Atmospheric Data ERBE-Like Product (ES-9) 3 CERES Cloud & Aerosol Pro

Radiative Energy Flu

CER/ES-4 Gridded Fluxes - ERBE-Like Product (ES-4) 3 CERES Cloud & Aerosol Pro

Radiative Energy Flu

CER/ES4-G Gridded Fluxes - ERBE-Like Product (ES-4G) 3 CERES Cloud & Aerosol Pro

Radiative Energy Flu
Proc Instru- 

Product ID Data Set Name Lvl ment Chapter 

* MTPE is evaluating a federated, fully competed EOSDIS at the time of this document’s development. The product/DAAC rel

Spring 1996 but is subject to change.

CER/SSF Single Satellite Footprint TOA and Surface Flux and Clouds (SSF) 2 CERES Cloud & Aerosol Pro

Radiative Energy Flu

CER/CRS Single Satellite CERES Footprint Radiative Fluxes and Clouds

(CRS)

2 CERES Cloud & Aerosol Pro

Radiative Energy Flu

CER/FSW Hourly Gridded Single-Satellite Fluxes and Clouds (FSW) 3 CERES Cloud & Aerosol Pro

Radiative Energy Flu

CER/SYN Synoptic Radiative Fluxes and Clouds (SYN) 3 CERES Cloud & Aerosol Pro

Radiative Energy Flu

CER/AVG Regional Average Data Product (AVG) 3 CERES Cloud & Aerosol Pro

Radiative Energy Flu

CER/ZAVG Zonal and Global Monthly Average Data Product (ZAVG) 3 CERES Cloud & Aerosol Pro

Radiative Energy Flu

CER/SFC Gridded Single Satellite Fluxes and Clouds (SFC) 3 CERES Cloud & Aerosol Pro

Radiative Energy Flu

CER/SRBAVG Monthly TOA and Surface Radiation Budget Averages (SRBAVG) 3 CERES Cloud & Aerosol Pro

Radiative Energy Flu

LIS

LIS 01 LIS Pulse/Background 1A LIS Radiance and ImagerLIS 02 LIS Image Attributes 1B LIS Precipitation and Atm

Humidity

LIS 03 LIS Event Statistics 1B LIS Precipitation and Atm

Humidity

LIS 04 LIS Group Statistics 1B LIS Precipitation and Atm

Humidity

LIS 05 LIS Flash Statistics 2 LIS Precipitation and Atm

Humidity

LIS 06 LIS Area Statistics 2 LIS Precipitation and Atm

Humidity

LIS 07 LIS Flash Density 2 LIS Precipitation and Atm

Humidity

LIS 08 LIS 2.5 deg × 2.5 deg Equal Angle Monthly Grid 2 LIS Precipitation and Atm

Humidity

LIS 09 LIS 500 km × 500 km Equal Area Monthly Grid 2 LIS Precipitation and Atm

Humidity
Proc Instru- 

Product ID Data Set Name Lvl ment Chapter 

* MTPE is evaluating a federated, fully competed EOSDIS at the time of this document’s development. The product/DAAC rel

Fall 1996 but is subject to change.

LIS10 LIS Orbit Attributes 3 LIS Precipitation and Atm

Humidity

LIS 11 LIS Threshold Attributes 3 LIS Precipitation and Atm

Humidity

LIS 12 LIS Browse Area 3 LIS Precipitation and Atm

Humidity

LIS 13 LIS Vector Statistics 3 LIS Precipitation and Atm

Humidity

LIS 14 LIS Metadata Description 3 LIS Precipitation and Atm

Humidity

LIS 15 LIS Summary Data 3 LIS Precipitation and Atm

Humidity

LIS 16 LIS Flash Rate Data 3 LIS Precipitation and Atm

Humidity

LIS 17 LIS Ephemeris Data 3 LIS Precipitation and Atm

Humidity

LIS 18 LIS Event Rate Sets 3 LIS Precipitation and Atm

Humidity

MISR

MIS 01 MISR Reformatted Annotated Product 1A MISR Radiance and ImagerMIS 02 MISR Radiometric Product 1B MISR Radiance and ImagerMIS 03 MISR Geo-rectified Radiance Product 1B MISR Radiance and ImagerMIS 04 MISR TOA and Cloud Product 2 MISR Cloud & Aerosol Pro

Radiative Energy Flu

MIS 05 MISR Aerosol and Surface Product 2 MISR Cloud & Aerosol Pro

Radiative Energy Flu

MIS 06 MISR Global Radiation Product 3 MISR Cloud & Aerosol Pro

Radiative Energy Flu

MIS 07 MISR Global Cloud Product 3 MISR Cloud & Aerosol Pro

Radiative Energy Flu

MIS 08 MISR Global Aerosol Product 3 MISR Cloud & Aerosol Pro

Radiative Energy Flu

MIS 09 MISR Global Surface Product 3 MISR Cloud & Aerosol Pro

Radiative Energy Flu
Proc Instru- 

Product ID Data Set Name Lvl ment Chapter 

* MTPE is evaluating a federated, fully competed EOSDIS at the time of this document’s development. The product/DAAC rel

Spring 1996 but is subject to change.

MIS 10 MISR Ancillary Geographic Product 1B MISR Radiance and Imager

MIS 11 MISR Ancillary Radiometric Product 1B MISR Radiance and ImagerMIS 12 MISR Aerosol Climatology Product 2 MISR Cloud & Aerosol Pro

Radiative Energy Flu

MODIS

MOD 01 MODIS Level-1A Radiance Counts 1A MODIS Radiance and ImagerMOD 02 MODIS Level-1B Calibrated, Geolocated Radiances 1B MODIS Radiance and ImagerMOD 03 MODIS Geolocation Data Set 1B MODIS Radiance and ImagerMOD 04 MODIS Aerosol Product 2 MODIS Cloud & Aerosol Pro

Radiative Energy Flu

MOD 05 MODIS Total Precipitable Water 2 MODIS Precipitation and Atm

Humidity

MOD 06 MODIS Cloud Product 2 MODIS Cloud & Aerosol Pro

Radiative Energy Flu

MOD 07 MODIS Atmospheric Profiles 2 MODIS Atmospheric TemperMOD 08 MODIS Gridded Atmospheric Product 3 MODIS Cloud & Aerosol Pro

Radiative Energy Flu

MOD 09 MODIS Surface Reflectance; Atmospheric Correction Algorithm

Products (also called Spectral Reflectance)

2 MODIS Radiance and Imager

MOD 10 MODIS Snow Cover 2 MODIS Snow and Sea Ice CoMOD 11 MODIS Land Surface Temperatures (LST) and Emissivity 2&3 MODIS Surface Temp. Land & 

Fire Occurrence and Effects

MOD 12 MODIS Land Cover/Land Cover Change 3 MODIS Radiance and ImagerMOD 13 MODIS Vegetation Indices: The Normalized Difference Vegetation

Index (NDVI) and an Enhanced, Modified Vegetation Index (MVI)

2 MODIS Veg. Dynamics, Land 

Land Use Change

MOD 14 MODIS Thermal Anomalies – Fires and Biomass Burning 2&3 MODIS Surface Temp. Land 

Fire Occurrence and Effects

MOD 15 MODIS Leaf Area Index (LAI) & Fraction of Photosynthetically

Active Radiation (FPAR)

4 MODIS Veg. Dynamics, Land 

Land Use Change
Proc Instru- 

Product ID Data Set Name Lvl ment Chapter 

* MTPE is evaluating a federated, fully competed EOSDIS at the time of this document’s development. The product/DAAC rel

Fall 1996 but is subject to change.

MOD 16 MODIS Evapotranspiration 3 MODIS Veg. Dynamics, Land 

Land Use Change

MOD 17 MODIS Vegetation Production and Net Primary Productivity (NPP) 4 MODIS Veg. Dynamics, Land 

Land Use Change

MOD 18 MODIS Normalized Water-Leaving Radiance 2&3 MODIS Radiance and ImagerMOD 19 MODIS Pigment Concentration 2&3 MODIS Phytoplankton and D

Organic Matter

MOD 20 MODIS Chlorophyll Fluorescence 2&3 MODIS Phytoplankton and D

Organic Matter

MOD 21 MODIS Chlorophyll a Pigment Concentration 2&3 MODIS Phytoplankton and D

Organic Matter

MOD 22 MODIS PAR (Photosynthetically Active Radiation) 2&3 MODIS Radiance and ImagerMOD 23 MODIS Suspended-Solids Concentration 2&3 MODIS Phytoplankton and D

Organic Matter

MOD 24 MODIS Organic Matter Concentration 2&3 MODIS Phytoplankton and D

Organic Matter

MOD 25 MODIS Coccolith Concentration 2&3 MODIS Phytoplankton and D

Organic Matter

MOD 26 MODIS Ocean Water Attenuation Coefficient 2&3 MODIS Phytoplankton and D

Organic Matter

MOD 27 MODIS Ocean Primary Productivity 2&3 MODIS Phytoplankton and D

Organic Matter

MOD 28 MODIS Sea Surface Temperature (SST) 2&3 MODIS Surface Temp. Land 

Fire Occurrence and Effects

MOD 29 MODIS Sea Ice Cover 2&3 MODIS Snow and Sea Ice CoMOD 31 MODIS Phycoerythrin Concentration 2&3 MODIS Phytoplankton and D

Organic Matter

MOD 32 MODIS Processing Framework and Match-up Database 2 MODIS Radiance and ImagerMOD 33 MODIS Snow Cover 3 MODIS Radiance and ImagerMOD 34 MODIS Gridded Vegetation Indices: The Maximum Value

Composited NDVI and the BRDF-Adjusted MVI

3 MODIS Veg. Dynamics, Land 

Land Use Change

MOD 35 MODIS Cloud Mask 2 MODIS Cloud & Aerosol Pro

Radiative Energy Flu
Proc Instru- 

Product ID Data Set Name Lvl ment Chapter 

* MTPE is evaluating a federated, fully competed EOSDIS at the time of this document’s development. The product/DAAC rel

Spring 1996 but is subject to change.

MOD 36 MODIS Total Absorption Coefficient 2&3 MODIS Phytoplankton and D

Organic Matter

MOD 37 MODIS Ocean Aerosol Properties 2&3 MODIS Radiance and ImagerMOD 39 MODIS Clear-Water Epsilon 2&3 MODIS Radiance and ImagerMOD 40 MODIS Gridded Thermal Anomalies 3 MODIS Surface Temp. Land 

Fire Occurrence and Effects

MOD 42 MODIS Sea Ice Cover 3 MODIS Snow and Sea Ice CoMOD 43 MODIS Surface Reflectance, BRDF/Albedo Parameter 3 MODIS Radiance and ImagerMOD 44 MODIS Vegetation Cover Conversion 3 MODIS Radiance and Imager

MOPITT

MOP 01 MOPITT Level 1B Radiance and Associated Engineering Data 1B MOPITT Radiance and ImagerMOP 02 MOPITT CO and CH4 Data Set 2 MOPITT Tropospheric Chemis

DAS

DAS 01 DAS Assimilated Land/Water/Ice Flags 4 DAS Radiance and ImagerDAS 02 DAS Assimilated Soil Moisture 4 DAS Precipitation and Atm

Humidity

DAS 03 DAS Assimilated Surface Moisture 4 DAS Precipitation and Atm

Humidity

DAS 04 DAS Assimilated Surface Evaporation 4 DAS Precipitation and Atm

Humidity

DAS 05 DAS Assimilated Moisture Profile 4 DAS Precipitation and Atm

Humidity

DAS 06 DAS Assimilated Moisture Changes 4 DAS Precipitation and Atm

Humidity

DAS 07 DAS Assimilated Moisture Flux 4 DAS Precipitation and Atm

Humidity

DAS 08 DAS Assimilated Precipitable Water 4 DAS Precipitation and Atm

Humidity

DAS 09 DAS Assimilated Precipitation 4 DAS Precipitation and Atm

Humidity

DAS 10 DAS Assimilated Snow Depth 4 DAS Snow and Sea Ice Co
Proc Instru- 

Product ID Data Set Name Lvl ment Chapter 

* MTPE is evaluating a federated, fully competed EOSDIS at the time of this document’s development. The product/DAAC rel

Fall 1996 but is subject to change.

DAS 11 DAS Assimilated Cloud Fraction 4 DAS Cloud & Aerosol Pro

Radiative Energy Flu

DAS 12 DAS Assimilated Cloud Mass Flux 4 DAS Cloud & Aerosol Pro

Radiative Energy Flu

DAS 13 DAS Assimilated Heating Rates 4 DAS Cloud & Aerosol Pro

Radiative Energy Flu

DAS 14 DAS Assimilated Surface Sensible Heat Flux 4 DAS Cloud & Aerosol Pro

Radiative Energy Flu

DAS 15 DAS Assimilated Outgoing Longwave Radiation 4 DAS Cloud & Aerosol Pro

Radiative Energy Flu

DAS 16 DAS Assimilated Outgoing Shortwave Radiation 4 DAS Cloud & Aerosol Pro

Radiative Energy Flu

DAS 17 DAS Assimilated Incident Shortwave Radiation 4 DAS Cloud & Aerosol Pro

Radiative Energy Flu

DAS 18 DAS Assimilated Surface Radiation 4 DAS Cloud & Aerosol Pro

Radiative Energy Flu

DAS 19 DAS Assimilated Surface Albedo 4 DAS Cloud & Aerosol Pro

Radiative Energy Flu

DAS 20 DAS Assimilated Temperature Profiles 4 DAS Upper Air TemperatuDAS 21 DAS Assimilated Temperature Changes 4 DAS Upper Air TemperatuDAS 22 DAS Assimilated Temperature Flux 4 DAS Upper Air TemperatuDAS 23 DAS Assimilated Tropopause Temperature & Pressure 4 DAS Upper Air TemperatuDAS 24 DAS Assimilated Surface Winds 4 DAS Winds DAS 25 DAS Assimilated Wind Profiles 4 DAS Winds DAS 26 DAS Assimilated Vertical Velocity 4 DAS Winds DAS 27 DAS Assimilated Vertically Integrated Winds 4 DAS Winds DAS 28 DAS Assimilated Turbulence Profiles 4 DAS Winds DAS 29 DAS Assimilated Surface Wind Stress 4 DAS Winds DAS 30 DAS Assimilated Surface Roughness 4 DAS Winds DAS 31 DAS Assimilated Surface Drag Coefficients 4 DAS Winds DAS 32 DAS Assimilated Planetary Boundary Layer Depth 4 DAS Winds DAS 33 DAS Assimilated Surface Pressure 4 DAS Pressure & Heights DAS 34 DAS Assimilated Geopotential Heights 4 DAS Pressure & Heights DAS 35 DAS Assimilated Ground Temperature 4 DAS Surface Temp Land & 
Chapter One –

Introduction
Introduction

2 • Chapter One: Introduction Data Products Handbook Vol. I

Overview

The MTPE/EOS Data Products Handbook (DPH)

provides a brief description of the science data

products that will be available from the Earth

Observing System Data and Information System

(EOSDIS). The objective of the DPH is to promote a

broader understanding of how the EOS data

products will contribute to science research in the

understanding, analysis, and monitoring of global

climate change. Table 1 provides a listing of the 24

critical science measurements as identified by the

EOS Project Science Office and the instruments that

relate to them. This volume describes data products

that will be produced from instruments onboard the

Tropical Rainfall Measuring Mission (TRMM)

satellite and the Earth Observing System (EOS)

morning satellite (AM-1).

The information in this document was compiled

from many sources: principally, the Algorithm

Theoretical Basis Documents (ATBDs), which were

written by the science teams; abstracts and

information from the product generation engineering

effort known as the Ad Hoc Working Group on

Production (AHWGP); and preliminary information

compiled by the Science Processing Support Office

(SPSO) of the Earth Science Data and Information

System (ESDIS) Project. The data descriptions in

this reference have been reviewed by the science

team contacts for accuracy. Readers should be aware

that this reference is only the “tip of the iceberg” of

information available on the EOS data products.

DPH Volume I Organization

Chapter 1 provides a roadmap through the document

itself. Chapter 2 provides additional background on

Mission to Planet Earth (MTPE) mission objectives,

a table of key characteristics for the TRMM and

AM-1 instruments, and information on the EOSDIS

and the EOS Distributed Active Archive Centers

(DAACs).

Chapter 3 provides an overview (for each

instrument) of the process by which the data

products are derived from the instrument

observations.

Table 1. The 24 Critical EOS Science

Measurements

ATMOSPHERE

Cloud Properties

(amount, optical properties, height)

MODIS, GLAS, AMSR, MISR,

AIRS, ASTER, EOSP, SAGE III

Radiative Energy Fluxes

(top of atmosphere, surface)

CERES, ACRIM, MODIS,

AMSR, GLAS, MISR, AIRS,

ASTER, SAGE III

Precipitation AMSR

Tropospheric Chemistry

(ozone, precursor gases)

TES, MOPITT, SAGE III, MLS,

HIRDLS, LIS

Stratospheric Chemistry

(ozone, ClO, BrO, OH, trace gases)

MLS, HIRDLS, SAGE III,

ODUS, TES

Aerosol Properties

(stratospheric, tropospheric)

SAGE III, HIRDLS, MODIS,

MISR, EOSP, GLAS

Atmospheric Temperature AIRS/AMSU, HIRDLS, TES,

MODIS

Atmospheric Humidity AIRS/MHS, MLS, SAGE III,

DFA/MR, MODIS, TES

Lightning

(events, area, flash structure)

LIS

SOLAR RADIATION

Total Solar Irradiance ACRIM

Ultraviolet Spectral

Irradiance

SOLSTICE

LAND

Land Cover & Land Use

Change

ETM+/LATI, MODIS, MISR,

ASTER

Vegetation Dynamics MODIS, MISR, ETM+/LATI,

ASTER

Surface Temperature ASTER, MODIS, AIRS, ETM+

Fire Occurrence

(extent, thermal anomalies)

MODIS, ASTER, ETM+

Volcanic Effects

(frequency of occurrence, thermal

anomalies, impact)

MODIS, ASTER, MISR

Surface Wetness AMSR

OCEAN

Surface Temperature MODIS, AIRS, AMSR

Phytoplankton & Dissolved

Organic Matter

MODIS

Surface Wind Fields SeaWinds, AMSR, DFA/MR

Ocean Surface Topography

(height, waves, sea level)

DFA/MR

CRYOSPHERE

Ice Sheet Topography &

Ice Volume Change

GLAS

Sea Ice

(extent, concentration, motion,

temperature)

AMSR, DFA/MR, MODIS

Snow Cover

(extent, water equivalent)

MODIS, AMSR, ASTER,

ETM+/LATI
Introduction

Data Products Handbook Vol. I Chapter One: Introduction • 3

Chapters 4 through 14 contain the data set

descriptions. Chapter 4 covers imagery, and raw

instrument data sets, as well as a few instrument

ancillary data sets that are the basis for numerous

other data sets with broad application to Earth

science research. Chapters 5 through 14 emphasize

the 24 science themes that have been identified as

critical measurements of the EOS program. Three

chapters — upper air temperature, winds, and

pressure and heights — include assimilated products

which do not map directly to the twenty-four critical

science EOS measurements but derive from EOS

products which map to these categories.

The purpose of this document’s grouping is to

provide a first-order indication of the kinds of

science research that the data products can be used

to support. The science themes are intended both to

focus upon the mission objectives during the early

EOS time frame and to represent a subset of the

overall MTPE science objectives, which encompass

a larger number of platforms and hence have a much

broader scope.

The data set description chapter titles are as follows:

• Radiance and Imagery Products;

• Precipitation and Atmospheric Humidity;

• Cloud and Aerosol Properties and Radiative

Energy Fluxes;

• Lightning;

• Tropospheric Chemistry;

• Upper Air Temperatures;

• Winds;

• Pressure and Heights;

• Surface Temperature of Land and Oceans,

Fire Occurrence and Volcanic Effects;

• Vegetation Dynamics, Land Cover, Land

Use Change, and Snow Cover;

• Phytoplankton and Dissolved Organic

Matter; and

• Snow Cover and Sea Ice.

Readers looking for derived geophysical data (e.g.,

Sea Surface Temperature) will be most interested in

the data described in the topical sections (e.g.,

Surface Temperature of Land and Oceans), while

readers interested in dealing with large-volume data

sets such as uncalibrated instrument counts and

temperatures, calibrated radiances, and reflectivities

can refer to the Imagery and Radiances section.

It is important to understand that the science themes

given for the data products are by no means all -

inclusive. The applications envisioned today for

products will change as new uses are discovered and

as the products themselves are improved. These

improvements are likely to come after launch

through algorithm refinement, or enhancements to

input data needed for product development. Many

data sets are applicable to more than one theme.

Where more than one application is appropriate, a

listing of key science applications is provided for the

data set within its product summary description.

Data Product Description

The data set descriptions contain the following

information (Level 1 and radiance products contain

a subset of this information):

• product description;

• research and applications;

• data set evolution;

• suggested reading; and

• product summary information

– coverage

– resolution

– wavelengths (Level 1 products only)

– key science applications (optional)

– key (geophysical if applicable)

parameters (Level 2, 3, 4 products)

– processing level and availability

– product type and production mode

– science team contact.

The Product Description contains summary

information about what is contained in the data set

and a few details about the resolution, derivation,

and accuracy of the data. Research and

Applications briefly describes some of the science

questions and applications for which the data set

will be useful. Data Set Evolution provides

information on precursor data sets and highlights

ways in which the data sets improve upon current
Introduction

4 • Chapter One: Introduction Data Products Handbook Vol. I

Table 2. Data Set Processing Levels

(from the EOS Reference Handbook)

Data Level Description

Level 0 Reconstructed unprocessed instrument/payload data at full resolution; any and all communications

artifacts (e.g., synchronization frames, communications headers) removed.

Level 1A Reconstructed unprocessed instrument data at full resolution, time-referenced, and annotated with

ancillary information, including radiometric and geometric calibration coefficients and georeferencing

parameters (i.e., platform ephemeris) computed and appended, but not applied, to the Level 0 data.

Level 1B Level 1A data that have been processed to sensor units (not all instruments will have a Level 1B

equivalent).

Level 1C TRMM specific for quality content of Level 1B precipitation radar and ground validation data

Level 2 Derived geophysical variables at the same resolution and location as the Level 1 source data.

Level 3 Variables mapped on uniform space-time grid scales, usually with some completeness and consistency.

Level 4 Model output or results from analyses of lower level data (i.e., variables derived from multiple

measurements).

capabilities. Suggested Reading provides several

references for more information. An effort has been

made to include more recent publications which are

generally available to the broader science

community.

Please note that the terms data set and data product

are used interchangeably throughout this document.

The data described in this document will be

distributed through the EOSDIS, and the EOSDIS

science processing support office has assigned EOS

identifiers (EOS-IDs) for each data product. These

EOS-IDs are used throughout this document. These

product IDs should not be confused with ATBD

numbers assigned by the Project Science Office to

identify each unique Algorithm Theoretical Basis

Document which may contain multiple data

products.

Note that CERES products use instrument IDs

which do not use a numbering scheme.

Product Summary Information

The product summary information contains an

overview of the processing level, coverage,

resolution, and contact information for each data set.

Processing Level – Data set processing level is

referred to throughout this document. The

definitions of processing levels are shown in

Table 2. Level 1 data (e.g., Volts, Counts,

Brightness Temperatures, etc.) require knowledge,

agreed-upon algorithms, and computer processing

capability for conversion to Level 2. Level 2 data

measure the biogeophysical properties of Planet

Earth. When a satellite instrument/algorithm is

relatively new, the Level 2 product is heavily used.

When the instrument/ algorithm is mature, many

users may prefer the Level 3 product.

Level 3 (mapped/gridded) and Level 4 (modeled)

products can be used by interdisciplinary scientists

to combine data from different areas of knowledge

without necessarily having to know the details or

undertake all of the processing that would otherwise

be necessary (to process Level 1 to Level 3 or 4).

The availability of Level 3 and 4 data is a major

advantage of EOSDIS in promoting data usage from

outside the science field in which the data were

generated. Some data sets are being produced and

archived in both a Level 2 (unmapped) and Level 3

(mapped and/or temporally sampled) form.

The differences in products allow researchers to

choose products available at a particular level,

enabling them to work in coarser resolution to

model global processes and events or in finely

detailed resolution which allows for investigations at

targeted regions.

Coverage – The AM-1 satellite is in a Sunsynchronous polar orbit and views the globe,

daytime and nighttime, at the same time every day.

TRMM, however, is in a precessing, inclined orbit

and views the Earth from 38°N latitude to 38°S
Introduction

Data Products Handbook Vol. I Chapter One: Introduction • 5

latitude. Many instruments have large swaths and

view the entire globe every day. Some of the higher

resolution instruments have a small swath width and

as such only view the complete Earth’s surface over

a period of several days or weeks. For each data set

described in this reference, a summary of the

coverage characteristics (day/night, global/local,

etc.) is provided.

Processing Level and Availability – Most of the

data sets in this reference are EOS “standard data

products.” This means the products will be routinely

processed for every applicable data acquisition. The

goal of EOS is to begin producing and providing

products immediately after launch and instrument

check-out. The science teams have identified the

best available algorithms and methods for producing

these data sets given current understanding of

physics and instrument performance; these are the

“at-launch” data products. Some products, such as

seasonal products or products describing variance

from seasonal norms, cannot be implemented until

some time after launch. In these cases, the data sets

are referred to as “post-launch.” All Level 1 data

sets are standard at-launch products, while some

Level 2, 3, and 4 data sets will be phased in after

launch.

It is expected that great improvements in the

derivation of geophysical products will be made

during the first months and years of the satellite

operation, and the EOSDIS has been sized to enable

reprocessing of standard products periodically as

improved methods and instrument characterization

are available.

Contacts For Further Information – To learn

more about the data sets described in this guide, the

ATBDs can be acquired from the EOS Project

Science Office (PSO) World Wide Web (WWW)

site at http://spso.gsfc.nasa.gov/spso_homepage.html. In

addition, a science team contact is listed for each

data set described in this volume, and the phone,

fax, and e-mail information for these investigators

can be found in the EOS Directory, available from

the PSO Web page, and in Appendix A of this

document.

Data Set Reference Evolution

Some of the details of product definition are still

being determined. For example, for many mapped

Level 3 data sets, the standard grids or map

projections have not been finalized, and there are

still issues associated with the scope and timing of

recompeting the EOSDIS DAACs as this document

is being written. These are just a few of the issues

being addressed prior to launch. It is anticipated that

this reference will be updated or superseded by

detailed, online data set guides and catalogues,

accessible through the EOSDIS, prior to the launch

of the TRMM and AM-1 platforms.
Chapter Two –

Background
Background

8 • Chapter Two: Background Data Products Handbook Vol. I

MTPE

NASA’s Mission to Planet Earth (MTPE) program

provides measurement systems and research

initiatives to further the acquisition and synthesis of

environmental data to address scientific issues related

to global change and Earth system science. MTPE

programs include basic scientific research, field

campaigns and experiments such as the Boreal

Ecosystem-Atmosphere Study (BOREAS), state-ofthe-art data set development through the Pathfinder

program, development of Earth- and space-based

observing systems including the EOS series of

satellites, and data access and distribution systems.

Throughout all of these activities, MTPE carries a

strong commitment to make high-quality, welldocumented, Earth science data easily available to

the global community to further environmental and

global change research needs. The most pressing

issues for global change researchers are described in

the EOS Science Plan, EOS Science Strategy for the

Earth Observing System, and Our Changing Planet:

The FY 1996 U.S. Global Change Research

Program.

EOS

The Earth Observing System (EOS) is a series of

Earth-orbiting satellites which will provide global

observations of the land surface, atmosphere, and

oceans over 15 years or more, with the first satellites

being launched in the late 1990s. Complete

information and details on the satellites and

instruments included in EOS can be found in the

MTPE/EOS Reference Handbook. This document can

be directly accessed at the following Internet address:

http://eospso.gsfc.nasa.gov/eos_reference/TOC.html.

EOS is complemented by missions and instruments

from international partners, including Japan, Brazil,

the European Space Agency (ESA), and ESA

member countries. For example, TRMM is a joint

NASA/Japanese mission. Together, these NASA and

international programs form the basis for a

comprehensive International Earth Observing System

(IEOS).

The EOS satellites will carry two classes of

instruments: Facility Instruments supplied by NASA

in response to general mission requirements, and

Principal Investigator (PI) Instruments selected

through a competitive process and aimed at the

focused research interests of the selected

investigators. Two EOS instruments — Clouds and

the Earth’s Radiant Energy System (CERES) and

Lightning Imaging Sensor (LIS) — are being flown

on the TRMM satellite, in addition to non-EOS

instruments — Precipitation Radar (PR), TRMM

Microwave Imager (TMI), and Visible Infrared

Scanner (VIRS). However, data from all of the

instruments on TRMM are part of the MTPE research

and data development program and will be

distributed through the EOSDIS archives.

Consequently, this data set reference handbook

includes descriptions of the data sets from all of the

TRMM instruments. Table 3 provides an overview of

the instruments which are providing data described in

this reference handbook.

The AM-1 platform is in a polar orbit with a

descending node equator crossing time of 10:30 a.m.

The TRMM satellite is in an inclined, equatorial orbit

and only views the Earth from roughly 38°N to 38°S.

Most of the AM-1 and TRMM instruments operate in

both daylight and darkness, however some of the

optical instruments will not collect visible data over

areas of darkness.

The instruments have variable scanning and coverage

characteristics. Depending on the swath width, some

instruments provide global data daily, while some of

the higher resolution instruments have smaller swaths

and only cover a portion of the day. Within a repeat

cycle of a few days, however, most of these

instruments also provide global imaging. In the case

of TRMM, the orbital processing is 30 days for the

geographic and diurnal repeat, and for AM-1, the

repeat cycle is roughly 9 days.

Table 3 provides an overview of the coverage

characteristics of the instruments on TRMM and

AM-1.

Data Sets

Data and data sets define mission success for the

EOS. In more than 25 years of Earth observation,

scientists have begun only recently to routinely use

global data sets of moderate to coarse resolution
Background

Data Products Handbook Vol. I Chapter Two: Background • 9

Table 3.

Coverage Characteristics of the Instruments on TRMM and AM-1

Satellite Instrument Description

TRMM PR – Precipitation Radar An electronically scanning radar operating at 13.8 GHz; 4.3 km

instantaneous field-of-view at nadir; 220-km swath. Provided by

NASDA (Japan).

TRMM TMI – TRMM Microwave

Imager

A nine-channel conical scanning passive microwave imager making

measurements from 10 to 85 GHz, 37 to 4.6 km resolution

respectively, covering 760 km swath.

TRMM VIRS – Visible Infrared Scanner A five-channel cross-track imaging radiometer (0.63, 1.6, 3.75, 10.7,

and 12.0 μm) with nominal 2-km resolution at nadir and 720-km

swath.

TRMM &

AM-1

CERES – Clouds & the Earth’s

Radiant Energy System

A three-channel radiometer (0.3 to > 50 μm, 0.3 to 5 μm, 8 to 12 μm).

TRMM will have one cross-track scanning radiometer and AM-1 will

have two instruments, one operating in cross-track mode and one in

rotating azimuth plane mode. The spatial resolution will be x from

TRMM orbit and y from the highest AM-1 orbit.

TRMM LIS – Lightning Imaging Sensor Staring telescope/filter imaging system (0.777 μm) with 5-km spatial

resolution and 2-msec temporal resolution over an imaging area of

600 × 600 km.

AM-1 ASTER – Advanced Spaceborne

Thermal Emission & Reflection

Radiometer

A three-radiometer sensor package with three vis/near-IR, six shortwave, and five thermal infrared channels with 15, 30, and 90-m

resolution, respectively, and a 60-km swath. Provided by MITI

(Japan).

AM-1 MISR – Multi-angle Imaging

Spectroradiometer

Thirty-six channel instrument; nine pushbroom cameras with discrete

view angles (to ± 70°) in four spectral bands (0.443 to 0.865 μm) with

resolution of 275 m to 1.1 km.

AM-1 MODIS – Moderate Resolution

Imaging Spectroradiometer

Thirty-six channel imaging radiometer (0.7 μm to 14.3 μm) with

250-m, 500-m, or 1-km resolution and 2,300-km swath width.

AM-1 MOPITT – Measurement of

Pollution in the Troposphere

Eight-channel cross track scanning gas correlation radiometer

operating at three wavelengths (2.2, 2.3, and 4.7 μm).

(10 -100 km) for global change research. It has taken

computational advances, lowering of data cost,

relaxation of data access restrictions, and proactive

data set generation efforts (such as the Pathfinder

programs of NOAA and NASA) to enable the

research community to effectively use global,

remotely sensed data sets. In the next decade, the

EOS data sets and interdisciplinary investigations

will advance the state-of-the-art use of global Earth

observation data to high to moderate resolution data

sets (100 - 1000 m). These data sets present an

unprecedented opportunity and challenge for the

Earth science community.

The data sets for global change research that are

being produced from the TRMM and AM-1

instruments have been selected by NASA through

peer review. Investigators developing these data sets

may be part of instrument teams or interdisciplinary

science investigation teams.

The unprecedented science goals and volumes of

these data sets drive several critical issues for the

EOSDIS and its ground segment which will produce,

archive, and distribute the data. The issues of

producing and distributing these data sets fall into

two basic categories: science and engineering. In the

years prior to the launch of the first EOS instruments,

there will be continual refinement, improvement, and

even compromise in both these areas. The science

issues are being addressed in part through the

development of ATBDs and the computation and

engineering issues of data set production, archive and
Background

10 • Chapter Two: Background Data Products Handbook Vol. I

distribution are being addressed by the EOSDIS

developers.

ATBDs

Efforts defining the data sets’ theoretical basis and

science algorithms are taking place among the

instrument and investigator teams under the

coordinating direction of the EOS Senior Project

Scientist. Each EOS instrument team for the AM-1

and TRMM (LIS, CERES) platform has presented for

peer review an ATBD which provides detailed

descriptions of the data set science algorithm. These

documents include

• Overview and Technical Background;

• Science Rationale;

• Algorithm Theoretical Description;

– Physics of the Problem

– Mathematical Description

– Uncertainty & Errors

• Practical Considerations;

– Science Software Considerations

– Calibration and Validation

– Quality Control

– Data Dependencies

– Product Definition; and

• Implementation Schedule (pre- and postlaunch).

Much of the information in this Reference Handbook

comes from the ATBDs. In the years prior to launch,

these and subsequent documents will describe

improvements and refinements to the science

algorithms. ATBDs are available over the Internet at

the following address:

http://eospso.gsfc.nasa.gov/atbd/pg1.html.

EOSDIS

Critical to the generation and distribution of the EOS

data sets is the EOSDIS. The EOSDIS provides

traditional flight operations, mission support, and

data processing roles for EOS spacecraft and

instruments and provides archiving and distribution

services for EOS data and a variety of global change

and Earth observation data.

The EOSDIS is taking advantage of recent advances

in computational hardware, systems engineering, and

national and international networking capabilities to

implement a system of unprecedented capacity, user

service, and openness.

Federated Approach to EOSDIS

The scope and role of EOSDIS and the EOSDIS

DAACs is under evaluation at the time of this

document’s development. A National Research

Council (NRC) review recommended a federated,

fully competed EOSDIS. The MTPE program has

formed a tiger team to prepare NASA’s response to

the NRC recommendation. The team is led by

Dr. Dixon Butler and includes members from NASA

Headquarters, Goddard Space Flight Center, other

U.S. agencies, and the Earth Science research

community. The team is currently finalizing the

process for NASA’s response.

EOSDIS DAACs

Key elements of EOSDIS are the DAACs, the

facilities responsible for producing, archiving and

distributing the data sets. The EOSDIS links these

archives through high speed networks and an open

architecture to integrate and provide data services to

the research community. “Open Architecture” means

that the user community will be able to access the

system directly through their own customized

interfaces as well as through standard data access

interfaces.

There are currently seven DAACs representing a

wide range of Earth science disciplines, with an

eighth DAAC acting as a link between the EOS

Program and the socioeconomic and educational user

community. The geographic distribution of the

DAACs is illustrated in Figure 1. Table 4 provides

access information for the nine DAACs. These

institutions are custodians of EOS mission data, and

ensure that data will be easily accessible to users.

Acting in concert, DAACs provide reliable, robust

services to users whose needs may cross traditional

discipline boundaries, while continuing to support the

particular needs of their respective discipline

communities. DAAC assignments were based

primarily on the current distribution of scientific
Background

Data Products Handbook Vol. I Chapter Two: Background • 11

ASF

Sea Ice and

Polar Processes Imagery

EDC

Land Processes 

Imagery

LaRC

Radiation Budget,

Aerosols, and

Tropospheric Chemistry

GSFC

Upper Atmosphere,

Atmospheric Dynamics,

and Global Biosphere

ORNL

Ground-Based Data Relating

to Biogeochemical Dynamics

CIESIN

Socio-Economic 

Applications

JPL

Ocean Circulation and

Air-Sea Interaction

NSIDC

Cryosphere 

(non-SAR)

Figure 1. Geographic Distribution of EOSDIS DAACs.

expertise, institutional heritage, and capability. Each

DAAC has a working group of users to provide

recommendations on priorities for scientific data,

levels of service and the needed capabilities. The

DAACs actively participate in the design,

implementation, and operation of EOSDIS.

The EOSDIS online data ordering service provides

information and ordering of data and documentation

at the DAACs using a menu-driven interface. The

service is open to the public with access to restricted

data by authorization. The ordering service is

graphical and text-based and allows users to identify,

browse, and then select data by time and location,

geophysical parameter, and processing level. The

service is accessible through a guest account via the

Internet or dial-in phone line. Access information for

online and user support services for each of the

DAACs is listed in Table 4.

Data Set Distribution

The EOS data policy is to provide data to the global

research community with few restrictions and at no

cost or at most no more than the marginal cost of

filling an order. Details of the EOS data policy can be

found in the 1995 MTPE/EOS Reference Handbook.

EOS data are not copyrighted; however, it is

requested that all researchers who publish data or

results using EOS data sets include the following

acknowledgment:

Data used in this research include data produced

through the funding of NASA’s Mission to Planet

Earth (MTPE) Earth Observing System (EOS)

Program.

In addition, for publications using EOSDISprovided data the following acknowledgment should

be used:

Data used in this research include data provided to

the authors by the NASA-funded EOS Data and

Information System archive at [insert name of

archive center providing data].
Background

12 • Chapter Two: Background Data Products Handbook Vol. I

Table 4. Access Information for the EOSDIS Distributed Active Archive Centers

Alaska SAR Facility

(ASF)

DAAC Mission: Sea ice and polar processes

Home Page URL: http://www.asf.alaska.edu

EOSDIS VO IMS: telnet eosims.asf.alaska.edu 12345

User Services Address: P.O. Box 757320, University of Alaska, Fairbanks, AK

99775-7320

Voice: (907) 474-6166 Fax: (907) 474-5195

E-mail: asf@eos.nasa.gov

EROS Data Center

(EDC)

DAAC Mission: Land processes

Home Page URL: http://edcwww.cr.usgs.gov/landdaac/landdaac.html

EOSDIS VO IMS: telnet eosims.cr.usgs.gov 12345

User Services Address: EROS Data Center, Sioux Falls, SD 57198

Voice: (605) 594-6116 Fax: (605) 594-6963

E-mail: edc@eos.nasa.gov

Goddard Space Flight

Center (GSFC)

DAAC Mission: Upper atmosphere, atmospheric dynamics, global

biosphere and geophysics

Home Page URL: http://daac.gsfc.nasa.gov

EOSDIS VO IMS: telnet eosims.gsfc.nasa.gov 12345

User Services Address: NASA/GSFC, Code 902.2, Greenbelt, MD 20771

Voice: (301) 286-3209 Fax: (301) 286-0268

E-mail: gsfc@eos.nasa.gov

Jet Propulsion

Laboratory for Physical

Oceanography DAAC

(PO.DAAC)

DAAC Mission: Ocean circulation and air-sea interactions

Home Page URL: http://podaac-www.jpl.nasa.gov

EOSDIS VO IMS: telnet eosims.jpl.nasa.gov 12345

User Services Address: PO.DAAC JPL, MS 300-320, 4800 Oak Grove Dr.,

Pasadena, CA 91109

Voice: (818) 354-9890 Fax: (818) 393-2718

E-mail: jpl@eos.nasa.gov

Langley Research

Center (LaRC)

DAAC Mission: Radiation budget, aerosols, and tropospheric chemistry

Home Page URL: http://eosdis.larc.nasa.gov

EOSDIS VO IMS: telnet eosims.larc.nasa.gov 12345

User Services Address: LaRC DAAC, MS 157D, NASA/LaRC,

Hampton, VA 23681-0001

Voice: (804) 864-8656 Fax: (804) 864-8807

E-mail: larc@eos.nasa.gov

National Snow and Ice

Data Center (NSIDC)

DAAC Mission: Cryosphere

Home Page URL: http://www-nsidc.colorado.edu/NASA/GUIDE

EOSDIS VO IMS: telnet eosims.colorado.edu 123454

User Services Address: NSIDC, University of Colorado,

Boulder, CO 80309-0449

Voice: (303) 492-6199 Fax: (303) 492-2468

E-mail: nsidc@eos.nasa.gov
Background

Data Products Handbook Vol. I Chapter Two: Background • 13

Table 4. Access Information for the EOSDIS Distributed Active Archive Centers

(Continued)

Oak Ridge National

Laboratory (ORNL)

DAAC Mission: Biogeochemical processes

Home Page URL: http://www-eosdis.ornl.gov

EOSDIS VO IMS: telnet eosims.ornl.gov 12345

User Services Address: ORNL DAAC, P.O. Box 20008, MS 6497,

Bldg. 1507, ORNL, Oak Ridge, TN 37831-6407

Voice: (615) 241-3952 Fax: (615) 574-4665

E-mail: ornl@eos.nasa.gov

Socioeconomic Data

and Applications

Center (SEDAC)

DAAC Mission: Socio-economic data

Home Page URL: http://www.ciesin.org

User Services Address: CIESIN/SEDAC, 2250 Pierce Road, University Center,

MI 48710

Voice: (517) 797-2727 Fax: (517) 797-2622

E-mail: sedac@eos.nasa.gov

Reproducibility & Longevity

Since global change studies require access to Earth

observation data over decades, one of the key

directives of the EOSDIS is that all data archived

and distributed will be sufficiently documented such

that researchers looking at data 10 or 20 years after

the launch of the spacecraft will be able to

understand and read the data. Related to this is the

principle that all software, documentation, ancillary

data, etc. be available to users so that any data set

will be reproducible by anyone in the research

community. In addition to ensuring easy and open

access, this will aid in the scientific peer review and

validation process.

References

Asrar, G. and R. Greenstone, Eds., 1995.

Asrar, G. and J. Dozier, 1994.

Barry, R.G. and A.L. Varani, Eds., 1995.

Sellers, P.J., et al., 1995.
Chapter Three

Instrument Data

Processing Overview
Instrument Data Processing Overview

16 • Chapter Three: Instrument Data Processing Overview Data Products Handbook Vol. I

The EOS system is designed to foster

interdisciplinary use of Earth science data sets in

ways that have been previously impossible. The

science strategy identified early for MTPE was to

group instruments to exploit their capabilities for

synergism in surface imaging and in determining

related properties of the atmosphere. Crosscalibration of the instruments and validation of their

resulting data products will further enhance this

synergism. Through EOSDIS, efforts to provide

better data utilization include standardization of

formats and media, consistency in standards and

processing, and a platform of standard analysis tools

to support investigation of the data.

Within the full EOS instrument suite, sets of

instruments complement one another to produce

desired observations. In some instances, certain

instrument suites must generate correlative data;

therefore, they must make their measurements

simultaneously. For example, measurement of ocean

circulation requires global altimeter data, precise

orbit determination, and water vapor amounts. These

observations also contain signals resulting from

ocean tide deformation, atmospheric pressure and

ocean surface interactions, and sea-level change,

including long-term average sea level as well as

interannual hemispherical variations. Such sets of

observations dictate some minimum payload

groupings.

The EOS science program will combine the

measurements from the assembled suite of

instruments, using some instruments to refine the

measurements of others or to provide data products

derived from more than one instrument. This strategy

permits the extraction of parameters that a single

instrument cannot measure reliably. This flow of

data, the product of one data set used as input to

another, is critical to understanding the complex

interrelationships of the Earth’s system. Global and

regional data assimilation models that function as

part of EOSDIS will use the observations from the

EOS platforms to provide continuous calculations of

the principal fluxes between components.

All EOS instruments have their own primary

characteristics, and all fit together to complement

each other. For example, compared with Moderate

Resolution Imaging Spectroradiometer (MODIS),

Advanced Spaceborne Thermal Emission and

Reflection Radiometer (ASTER) has higher spatial

resolution, but lower average duty cycle (8 percent

day, 16 percent night). The Multi-Angle Imaging

Spectroradiometer (MISR) is similar to MODIS in

the 250-m bands. MODIS data can be used for

atmospheric correction for ASTER and cloud

screening for Clouds and the Earth’s Radiant Energy

System (CERES). Similarly, there are synergies

between MISR and MODIS in characterizing

aerosols over oceans. ASTER with its pointing,

stereo mapping, and multi-spectral thermal infrared

(IR) radiometer, will provide synergism with MODIS

and MISR, and will acquire essential data for the

study of volcanoes and surface climate. ASTER will

also provide simultaneous multispectral, highresolution detail to support global mapping of surface

vegetation by MODIS and MISR. Simultaneous data

from ASTER are essential to understand the subpixel

variability of MODIS and MISR data.

The synergy provided by EOS instruments may be

used to obtain and apply atmospheric corrections to

allow quantitative, rather than qualitative, remote

sensing science (such atmospheric attenuation and

cloud-screening corrections are applicable to multiple

sensors, MODIS cloud cover for CERES and

MOPITT, MODIS total-water vapor for ASTER).

Also, they may be used to validate data product

algorithms and maintain instrument calibrations over

a 15-year period, leading to support of a broad range

of interdisciplinary investigations describing the

Earth’s carbon, energy, and water cycles, and our

impact on them.

References:

Earth Observing System: Science and Mission

Requirements Working Group Report.

Hobish, M.K., et al., 1994.

Acknowledgments

The instrument descriptions for the TRMM

instruments were provided by the TRMM science

team. All other instrument summaries were adapted

from the 1995 MTPE/EOS Reference Guide.
Instrument Data Processing Overview

Data Products Handbook Vol. I Chapter Three: Instrument Data Processing Overview • 17

TRMM

Tropical Rainfall Measuring Mission

TRMM is a small Earth probe designed specifically

to monitor rainfall and its associated latent heating

in the tropics. The vertical structure of latent heating

is particularly important because the atmosphere

gets three-fourths of its heat energy from the release

of latent heat in the process of precipitation. The

location at which this energy is released affects the

weather around the world. Despite its fundamental

importance to global climate, today the total rainfall

in the tropics is not known to within a factor of two

and the latent heating can only be estimated

imprecisely. Although the sensors on TRMM have

utility beyond the primary rainfall parameters, only

those products which were critically relevant to

monitoring rainfall and its vertical structure have

been retained so as not to overwhelm the data

system.

Figure 2 shows how the various sensors and

algorithms will interact in order to provide the best

rain products. The algorithms are coded by the

“team” which has the responsibility to develop

products. As an example, the passive microwave

(TMI) team has responsibility for generating code to

produce TMI calibrated brightness temperatures

(1B -11), TMI rainfall structure products (2A-12)

and TMI monthly surface rainfall maps (3A-11).

This type of in-team algorithm development can be

seen for each of the three instrument teams.

The in-team processing has a prominent role in the

data processing scheme because of its long heritage.

The key to TRMM’s success, however, is the

combined algorithms. These are divided into

“Combined” which refers to algorithms applicable

to TRMM measurements, and “TRMM & Other

Sensors” which refers to algorithms that take the

best answer from TRMM and use it to calibrate

other available data in order to reduce the sampling

bias. The lack of heritage with these products makes

these less certain than the in-team algorithms at the

onset of the mission.

The TRMM mission recognizes that in the past new

sensors have led to significant improvements in

interpretation of remotely sensed data and quality of

products. To ensure that any improvements in the

products are quickly passed on to the research

community, TRMM will reprocess data annually if

better algorithms become available. To provide the

stable long time series of products needed for Earth

system research, TRMM will always reprocess data

beginning the first day of data availability.

Additionally, the TRMM mission employs a

concerted validation effort consisting of 10 groundbased radar sites which will supply high-quality

rainfall information throughout the lifetime of the

TRMM mission. Because of the importance of these

data and their usefulness independent of the satellite,

these products are considered part of the TRMM

mission and are processed and archived in the same

fashion as the satellite data. A flow diagram of these

products and how they interact is shown in Figure 3.

All ground-based radar products are generated by

the TRMM Ground Validation Team using identical

methodologies at each of the sites.

To date, the TRMM mission has plans and

agreements to generate rainfall products from the

following sites: Southern Florida (4 NEXRAD

radars), Australia (Darwin), Southeastern Texas (4

NEXRAD sites), Hawaii, Kwajalein Atoll, Taiwan,

Thailand (Chiang Mai), Brazil (São Paolo), Guam,

and Israel (Jerusalem). TRMM will consider

additional radars subject to the approval of the

TRMM Project Scientists, the TRMM office

(responsible for the implementation of the validation

plan) and the TRMM Ground Validation Team

(responsible for the scientific integrity of the

validation data).
Instrument Data Processing Overview

18 • Chapter Three: Instrument Data Processing Overview Data Products Handbook Vol. I

VIRS

Level 1A

1B-01

VIRS

Radiances

Level 1

Radiances

Level 2

Instantaneous Geophysical

Parameters

Level 3

TRMM

Products

30 day 5° x 5°

Level 4

TRMM & Other Satellite

Products 5 day 1° x 1°

1B-11

TMI Calibrated

Antenna

Temperatures

1C-21

PR

Reflectivities

1B-21

PR

Power

2A-23

PR Rain Occurrence,

Storm, & Bright

Band Height

3A-26

PR 30 Day

Surface Rainfall

3A-25

PR 30 Day

Rainfall Structure

3B-42

Combined Instrument

Rain Calibration

3B-43

Global

Rainfall

3A-44

GPI

3A-46 SSM/I

Gridded Rain

3A-45 GPCP

Gridded Rain

3A-11

TMI 30 Day

Surface Rainfall

PR

Level 1A

TMI

Level 1A

NASA – TSDIS / NASDA – EOC

2A-12

TMI

Rainfall Structure

2A-25

PR Rain and Water

Content Properties

2B-31

Combined

Rainfall Structure

2A-21

PR

σo

3B-31

Combined 30 Day

Rainfall Structure

Figure 2. TRMM Instrument Algorithms and Products.
Instrument Data Processing Overview

Data Products Handbook Vol. I Chapter Three: Instrument Data Processing Overview • 19

3A-55

Monthly

3-D Structure

Level 1

Radiances

Level 2

Instantaneous Geophysical

Parameters

Level 3

TRMM

Products

30 day 2 km

Level 4

TRMM & other satellite

products 5 day 2 km

1B-51

Radar

Reflectivities

2A-52

Rain

Existence

2A-55

Gridded 3-D

Reflectivity

2A-53

Radar Site

Rain Map

3A-53

5-Day

Rainfall Map

3A-54

Monthly

Rainfall Map

Radar

Tapes

GV Sites

2A-54

Radar Site

Conv/Srat

1C-51

Qual. Controlled

Radar Reflect.

Figure 3. Flow Diagram Showing Interaction of TRMM Ground-Based Radar Validation Products.
Instrument Data Processing Overview

20 • Chapter Three: Instrument Data Processing Overview Data Products Handbook Vol. I

Key LIS Facts

• EOS-funded instrument selected for flight

on TRMM-1

• Staring telescope/filter imaging system

that detects the rate, location, and radiant

energy of lightning flashes

• Investigates the distribution and

variability of lightning over the Earth with

storm scale spatial resolution

• 90 percent detection efficiency under

both day and night conditions using

background remover and event processor

• LIS Home Pages:

http://www.ghcc.msfc.nasa.gov/lightning/

http://wwwghcc.msfc.nasa.gov/lisotd.html#lis

LIS

Lightning Imaging Sensor

The calibrated optical LIS will investigate the global

incidence of lightning, its correlation with rainfall,

and its relationship with the global electric circuit.

Conceptually, LIS is a simple device, consisting of a

staring imager optimized to locate both intracloud

and cloud-to-ground lightning with storm-scale

resolution over a large region of the Earth’s surface,

to mark the time of occurrence, and to measure the

radiant energy. It will monitor individual storms

within the field-of-view (FOV) for 80 seconds, long

enough to estimate the flashing rate of many storms.

Location of lightning flashes will be determined to

within 5 km over a 600 × 600-km FOV.

The LIS design uses an expanded-optics wide-FOV

lens combined with a narrow-band interference filter

that focuses the image on a small, high-speed,

charge-coupled-device focal plane. The signal is read

out from the focal plane into a real-time data

processor for event detection and data compression.

The particular characteristics of the sensor design

result from the requirement to detect weak lightning

signals during the day when the background

illumination, produced by sunlight reflecting from

the tops of clouds, is much brighter than the

illumination produced by the lightning.

A combination of four methods is used to take

advantage of the significant differences in the

temporal, spatial, and spectral characteristics between

the lightning signal and the background noise. First,

spatial filtering is used to match the instantaneous

FOV of each detector element in the LIS focal-plane

array to the typical cloud-top area illuminated by a

lightning event (about 5 km). Second, spectral

filtering is applied, using a narrow-band interference

filter centered about the strong singly ionized oxygen

emission line (OI [1]) multiplet in the lightning

spectrum at 777.4 nm. Third, temporal filtering is

applied.

The lightning pulse duration is of the order of

400 μsec, whereas the background illumination tends

to be constant on a time scale of seconds. The

lightning signal-to-noise ratio improves as the

integration time approaches the pulse duration.

Accordingly, an integration time of 2 msec is chosen

to minimize pulse splitting between successive

frames and to maximize lightning detectability.

Finally, a modified frame-to-frame background

subtraction is used to remove the slowly varying

background signal from the raw data coming off the

LIS focal plane. If after background removal the

signal for a given pixel exceeds a specified threshold,

that pixel is considered to contain a lightning event.

LIS investigations will increase further understanding

of processes related to, and underlying, lightning

phenomena in the Earth/atmosphere system. These

processes include the amount, distribution, and

structure of deep convection on a global scale, and

the coupling between atmospheric dynamics and

energetics as related to the global distribution of

lightning activity. The investigations will contribute

to several important EOS mission objectives,

including cloud characterization and hydrologic cycle

studies. Lightning activity is closely coupled to storm

convection, dynamics, and microphysics, and can be

correlated to the global rates, amounts, and

distribution of precipitation, to the release and

transport of latent heat, and to the chemical cycles of

carbon, sulfur, and nitrogen. LIS standard products

will be locations, times of occurrence, and intensities

of lightning events.
Instrument Data Processing Overview

Data Products Handbook Vol. I Chapter Three: Instrument Data Processing Overview • 21 Raw Inputs Ephemeris Conversion Conversion to Level 1 Pixel Space Filtering

Raw 

Event 

Data

Raw 

Background

Data

Summary 

of Raw 

Data

Raw 

Data 

Statistics

Raw Data

Threshold

Changes

Raw 

Data 

Errors

Conversion to Level 1 Data

Pixel 

Filtered 

Event 

Data

Pixel 

Filtered 

Background

Data

Summary 

of Pixel 

Filtered 

Data

Pixel 

Filtered 

Data

Statistics

Pixel 

Filtered

Data

Errors

Pixel Space Filtering

Geo-Location

GeoLocated

Event 

Data

GeoLocated

Background

Data

GeoLocated

Data

Statistics

Summary 

of GeoLocated 

Data

GeoLocation

Data

Errors

Geo-Location of Pixel Space Data

1 sec

Ephemeris

Data

F

Equatorial

Crossing

Times

Ephemeris Conversion 

Routine

TRMM/LIS 

Raw Data

TRMM/LIS 

Ephemeris/

Attitude Data

Filtered 

Data

Threshold

Changes

B

A E G H

D

C

Geo-Location

Based Filtering

GeoLocation

Filtered

Event 

Data

Summary 

of GeoLocation

Filtered 

Data

GeoLocation

Filtered

Data

Statistics

Geo-Location Based Filtering

1 2

Figure 4. LIS Data Flow.
Instrument Data Processing Overview

22 • Chapter Three: Instrument Data Processing Overview Data Products Handbook Vol. I

1 2

HDF File

Components

Viewtime 

Calculation

B

GeoLocated

Background

Data

GeoLocation

Filtered

Event 

Data

A C

GeoLocated

Data

Statistics

D E

Summary 

of GeoLocation

Filtered 

Data

G

GeoLocation

Filtered

Data

Statistics

F

Filtered 

Data

Threshold

Changes

H

TRMM/LIS 

Ephemeris/

Attitude Data

Background

Images

Image

Attributes

Event Group Flash Area

Event

Stats

Group

Stats

Flash

Stats

Flashrate

Data

Area

Stats

Vector

Stats

Flash

Density

Stats

2.5 deg

Grid

500 km

Grid

Browse

Stats

Viewtime

Data

Ephemeris

Data

Summary

Data

Orbit

Attributes

Orbit

Summary

Event

Rate

Sets

Threshold

Values

Orbit Division/HDF File Creation Routines

Point-byPoint 

Viewtime

Data

Total

Viewtime

Data

Geo-Location

Filtered

Viewtime

Statistics

True Instrument FOV Calculations

Summary

Component

Geophysical

Component

QA/QC

Component

data

flow

Data Conversion Routine

1 sec

Ephemeris

Data

Equatorial

Crossing

Times

KEY

Figure 4. LIS Data Flow (Continued).
Instrument Data Processing Overview

Data Products Handbook Vol. I Chapter Three: Instrument Data Processing Overview • 23

Key ASTER Facts

• Selected for flight on EOS AM-1

• Heritage: MESSR and OPS

• Imaging radiometer

• Provides high-resolution images of the

land surface, water, ice and clouds

• Earth-orbit stereo capability

• 14 multispectral bands from visible

through thermal infrared

• ASTER Home Page:

http://asterweb.jpl.nasa.gov

Level 

1A

Level 

1B Level 2

Decommutated Data 

With Appended Info

Level 3

VNIR

Decommutated Data 

With Appended Info

SWIR

Decommutated Data 

With Appended Info

Calibrated, Registered 

Radiance at Sensor

Decorr Stretch — VNIR, SWIR, and TIR

(Includes Coarse Cloud Classification)

Radiance at Surface (VNIR, SWIR) 

and Surface Reflectance2

Brightness

Temperature

ASTER 

DEM

Radiance at 

Surface (TIR)

Surface Emissivity 

and Surface 

Kinetic Temperature

Fine Cloud 

Classification Processor3

MODIS

DEM

Polar Cloud Map MODIS

NESDIS

MODIS

DEM

MODIS

MISR

DAO

NCEP

TOMS

SAGE

DEM

Calibrated, Registered 

Radiance at Sensor

Calibrated, Registered 

Radiance at Sensor

TIR

1

1This list contains a variety of alternate input sources, used when the primary input source is not available; 2Computed simultaneously with Radiance at Surface; 

3Produces a cloud mask that is incorporated into other products.

MODIS

MISR

DAO

NCEP

TOMS

SAGE

1

Figure 5. ASTER Data Product Architecture.

ASTER

Advanced Spaceborne Thermal Emission and

Reflection Radiometer

ASTER is a facility instrument provided for the EOS

AM-1 platform by the Japanese Ministry of

International Trade and Industry (MITI). It will

provide high spatial resolution (15-to-90 m)

multispectral images of the Earth’s surface and

clouds in order to better understand the physical

processes that affect climate change. ASTER will

provide data at a scale that can be directly related to

detailed physical processes. These data will bridge

the gap between field observations and data acquired

by coarse spatial resolution instruments such as

MODIS and MISR, and between local-process

models and regional models.
Instrument Data Processing Overview

24 • Chapter Three: Instrument Data Processing Overview Data Products Handbook Vol. I

Figure 6. Simulated ASTER images of Cuprite, Nevada. Linearly (2%) stretched (top row) and decorrelation

stretched (bottom row) color composites of image data. The left images, with 15 m pixel size, display VNIR

bands 3, 2, and 1 in red, green and blue, respectively; the center images, with 30 m pixel size, display SWIR

bands 4, 6, and 9 in RGB; and the right images, with 90 m pixel size, display TIR bands 13, 12, and 10 in

RGB. The simulated data set was created by coregistering airborne AVIRIS and TIMS data, then combining

channels to produce the ASTER band passes. The 3x5 km area (full ASTER scenes will be approximately

60x60 km) exposes a range of unaltered and hydrothermall altered volcanic rocks, alluvial fans derived from

these rocks, and a dry lake bed. The VIR data highlight the presence and oxidation state of iron-bearing

minerals; the SWIR data separate cla-bearing and hydrous minerals; and the TIR data show differences due

to silica content. By combining information from the three wavelength regions, ASTER data allow a detailed

and accurate geologic and alteration map to pe produced.

Clouds are one of the most important variables in the

global climate system. With its high spatialresolution, broad spectral coverage, and stereo

capability, ASTER will provide essential

measurements of cloud amount, type, spatial

distribution, morphology, and radiative properties.

climate and/or forecast models. ASTER data will also

be used for long-term monitoring of local and

regional changes on the Earth’s surface, which either

lead to or are in response to global climate change

(e.g., land use, deforestation, desertification, lake and

playa water level changes) and other changes in

vegetation communities, glacial movement, and

volcanic processes.

ASTER will provide radiative (brightness)

temperature, and the multispectral thermal infrared

(TIR) data can be used to derive surface kinetic

temperature and spectral emissivity. Radiative

temperature is an element in the surface heat balance.

Surface kinetic temperature can be used to determine

elements of surface process models, sensible heat

flux, latent heat flux, and ground heat conduction.

Surface temperatures are also related to

thermophysical properties (such as thermal inertia),
Instrument Data Processing Overview

Data Products Handbook Vol. I Chapter Three: Instrument Data Processing Overview • 25

vegetation health, soil moisture, temporal land

classification (e.g., wet vs. dry, vegetated vs. bare

soil) and evapotranspiration.

ASTER will operate in three visible and near-infrared

(VNIR) channels between 0.52 and 0.86 μm, with 15-

m resolution; six shortwave infrared (SWIR)

channels between 1.60 and 2.43 μm, with 30-m

resolution; and five TIR channels between 8.13 and

11.65 μm, with 90-m resolution. The instrument will

acquire data over a 60-km swath whose center is

pointable (cross-track ± 8.55° in the SWIR and TIR,

with the VNIR pointable out to ± 24°). An additional

VNIR telescope (aft pointing) covers the wavelength

range of Channel 3. By combining these data with

those for Channel 3, stereo views can be created,

with a base-to-height ratio of 0.6. ASTER’s pointing

capabilities will be such that any point on the globe

will be accessible at least once every 16 days in all

14 bands and once every 5 days in the three VNIR

channels.

ASTER data products will exploit combinations of

VNIR, SWIR, and TIR for cloud studies, surface

mapping, soil and geologic studies, volcano

monitoring, and for surface temperature, emissivity,

and reflectivity determination. VNIR and SWIR

bands will be used for investigation of land use

patterns and vegetation, VNIR and TIR combinations

for the study of coral reefs and glaciers, and VNIR

for digital elevation models (DEMs). TIR channels

will be used for study of evapotranspiration, and land

and ocean temperature. The stereoscopic capability

will yield local topography, cloud structure, volcanic

plumes, and glacial changes.
Instrument Data Processing Overview

26 • Chapter Three: Instrument Data Processing Overview Data Products Handbook Vol. I ERBE-Like Surface/TOA Surface/TOA/ Atmosphere

BDS

Level 1 9 Param

1B Radiance

SSF

Level 2 52 Param

Single Sat. Footprint

TOA, Sur. Flux Clouds

CRS

Level 2 68 Param

Rad. Flux & Clouds

Tuned, Adjusted

ES8,ES9

Level 2,3 18,72 Param

ERBE-Like Product

SFC

Level 3 36 Param

Gridded Sing. Sat.

Fluxes & Clouds

FSW

Level 3 89 Param

Hourly Gridded Fluxes

Clouds SARB

ES4,ES4G

Level 3 59,58 Param

ERBE-Like Product

SRBAVG

Level 3 67 Param

Monthly TOA & Sur.

Radiation Budget Avgs.

SYN

Level 3 63 Param

Synoptic Rad.

Flux & Clouds

AVG/ZAVG

Level 3 112,109 Par.

Regional Monthly Avg

Rad Flux Clouds

Global & Zonal

Figure 7. CERES Data Flow.

CERES

Clouds and the Earth’s Radiant Energy System

The U.S. Global Change Research Program classifies

the role of clouds and radiation as its highest

scientific priority (CEES, 1994). There are many

excellent summaries of the scientific issues (IPCC,

1992; Hansen, et al., 1993; Ramanathan, et al., 1989;

Randall, et al., 1989, Wielicki, et al., 1995)

concerning the role of clouds and radiation in the

climate system. These issues naturally lead to a

requirement for improved global observations of both

radiative fluxes and cloud physical properties. The

CERES Science Team, in conjunction with the EOS

Investigators Working Group representing a wide

range of scientific disciplines from oceans, to land

processes, to atmosphere, has examined these issues

and proposed an observational system with the

following objectives:

1. For climate change analysis, provide a

continuation of the ERBE record of

radiative fluxes at the top of the atmosphere

(TOA), analyzed using the same algorithms

that produced the existing ERBE data.

2. Double the accuracy of estimates of

radiative fluxes at TOA and the Earth's

surface.

3. Provide the first long-term global

estimates of the radiative fluxes within

the Earth's atmosphere.

4. Provide cloud property estimates which

are consistent with the radiative fluxes

from surface to top of the atmosphere.

To accomplish these goals, the CERES data products

are divided into three major categories as shown in

Figure 7:

ERBE-Like Products Top Row

CERES TOA/Surface Products Middle Row

CERES TOA/Surface/Atmosphere Bottom Row

The ERBE -Like products address the first objective

of long-term continuity of the ERBE TOA fluxes.

The TOA/Surface Products address the second

objective and attempt to provide the most direct tie

between surface radiative flux estimates and TOA

flux measurements. The TOA/Surface/Atmosphere

products focus on the last two objectives and attempt

to derive an internally consistent set of atmosphere,

cloud, and surface to TOA radiative fluxes, all within

the context of a state of the art radiative transfer

model. In this last case, the observed TOA fluxes are

used as a direct constraint on the model calculations
Instrument Data Processing Overview

Data Products Handbook Vol. I Chapter Three: Instrument Data Processing Overview • 27

in order to implicitly account for non-plane parallel

and other poorly modeled atmospheric radiative

effects. As in ERBE, CERES radiative fluxes will be

separately determined for both clear and cloudy sky

conditions.

One of the major advances of CERES over ERBE is

the availability of high spatial and spectral resolution

cloud imagers for cloud masking, cloud height, and

cloud optical property determination (i.e. VIRS on

TRMM, MODIS on EOS AM/PM). A second major

advance is the use of one CERES scanner in a

crosstrack mode (global spatial coverage) and a

second scanner in a rotating azimuth plane mode

(complete angular sampling of viewing zenith and

azimuth). The rotating azimuth plane scanner will be

combined with nearly simultaneous cloud imager

data to develop a new set of improved empirical

models of the SW and LW anisotropy of radiation as

a function of surface and cloud type. While it is

estimated to take 2 years of this data to develop new

angular models, they are expected to reduce

instantaneous TOA flux errors by a factor of 3 from

the ERBE levels.

Detailed listing of the CERES data products

and their individual parameters can be found

in the CERES Data Products Catalog (http://

asd-www.larc.nasa.gov/DPC/DPC.html). Documentation

of the Release 1 CERES analysis algorithms can be

found in the CERES Algorithm Theoretical Basis

Documents (ATBDs) Volumes 0 through 12,

where each volume covers one of the CERES Data

Products and its associated technical algorithms

(http://eospso.gsfc.nasa.gov/atbd/cerestables.html). A

summary of the CERES experiment can be found in

Wielicki, et al., Bulletin of the American

Meteorological Society, May, 1996.

Suggested Reading:

Barkstrom, 1984.

Hansen, et al., 1993.

IPPC, 1992.

Ramanathan, et al., 1989.

Wielicki, et al., 1995.

Key CERES Facts

• Selected for flight on TRMM, EOS AM,

and EOS PM series

• Heritage: ERBE

• Two broadband, scanning radiometers:

One cross-track mode, one rotating plane

(bi-axial scanning)

• Measures Earth’s radiation budget and

atmospheric radiation from the top of the

atmosphere to the surface

• First instrument (cross-track scanning) will

essentially continue the ERBE mission; the

second (biaxially scanning) will provide

angular radiance information that will

improve the accuracy of angular models

used to derive the Earth’s radiative balance

• Single scanner on TRMM mission (1997)

• Dual scanners on EOS AM-1 (1998),

PM-1 (2000), and single thereafter

• CERES Home Page:

http://asd-www.larc.nasa.gov/ceres/docs.html
Instrument Data Processing Overview

28 • Chapter Three: Instrument Data Processing Overview Data Products Handbook Vol. I

MISR

Multi-Angle Imaging Spectroradiometer

MISR will routinely provide multiple angle

continuous sunlight coverage of the Earth with high

spatial resolution. The instrument will obtain multidirectional observations of each scene within a time

scale of minutes, essentially under virtually the same

atmospheric conditions. MISR uses nine individual

charge-coupled device (CCD)-based pushbroom

cameras to observe the Earth at nine discrete view

angles: one at nadir, plus eight other symmetrical

views at 26.1, 45.6, 60.0 and 70.5° forward and

aftward of nadir. Images at each angle will be

obtained in four spectral bands centered at 443, 555,

670, and 865 nm. Each of the 36 instrument data

channels (four spectral bands × nine cameras) is

individually commandable to provide ground

sampling of 275 m, 550 m, or 1.1 km.

The swath width of the MISR imaging data is

360 km, providing global multi-angle coverage of the

entire Earth in nine days at the equator, and two days

at the poles.

The instrument design and calibration strategies have

the goal of maintaining absolute radiometric

uncertainty to less than ± 3 percent over bright

surfaces and ± 6 percent over dark surfaces, with

smaller uncertainties in relative band-to-band and

angle-to-angle radiances. These objectives will be

met through the use of state-of-the-art detector-based

calibration, using an on-board calibrator on a

monthly basis; the calibrator consists of deployable

solar diffuser panels and an array of radiationresistant and high-quantum-efficiency diodes.

Semiannual field calibration exercises are planned to

provide a ground-truth verification of the diode

performance.

MISR images will be acquired in two observing

modes: global and local. Global mode provides

continuous planet-wide observations, with most

channels operating at moderate resolution and

selected channels operating at the highest resolution

for cloud screening and classification, image

navigation, and stereo-photogrammetry. Local mode

provides data at the highest resolution in all spectral

Key MISR Facts

• Selected for flight on EOS AM series

• Heritage: Galileo, Wide-Field/Planetary

Camera

• Provides top-of-atmosphere, cloud, and

surface angular reflectance functions

• Provides global maps of planetary and

surface albedo and of aerosol and

vegetation properties

• MISR Home Page:

http://www-misr.jpl.nasa.gov/

bands and all cameras for selected 360 km × 300 km

regions. In addition to data products providing

radiometrically calibrated and geo-rectified images,

global mode data will be used to generate two

standard Level 2 science products during ground data

processing: the Top-of-Atmosphere (TOA)/Cloud

Product and the Aerosol/Surface Product.

The purpose of the TOA/Cloud Product is to enable

study, on a global basis, of the effects of different

types of cloud fields (classified by their heterogeneity

and altitude) on the solar radiance and irradiance

reflected to space, and to determine their effects on

Earth’s climate. Additionally, this product provides a

cloud screen for MISR aerosol and surface retrievals.

The aerosol parameters contained with the

Aerosol/Surface Product will enable study, on a

global basis, of the magnitude and natural variability

in space and time of sunlight absorption and

scattering by aerosols in the Earth’s atmosphere,

particularly in the troposphere. In addition, these

parameters will help to determine the aerosols’

effects on climate; to improve our knowledge of the

sources, sinks, and global budgets of natural and

anthropogenic aerosols; and to provide atmospheric

correction inputs for surface imaging data acquired

by MISR and other instruments (e.g., MODIS and

ASTER) that are simultaneously viewing the same

position of the Earth. The surface parameters within

the Aerosol/Surface Product are designed to enable

improved measure of land surface radiative

characteristics, particularly bidirectional and
Instrument Data Processing Overview

Data Products Handbook Vol. I Chapter Three: Instrument Data Processing Overview • 29

hemispherical reflectances, on a global basis; to

provide, in conjunction with MODIS, improved

measures of land surface classification, dynamics,

canopy photosynthesis, transpiration rates over

vegetated terrain; and to supplement MODIS studies

of the biogeochemical cycle in the tropics by

providing atmospherically-corrected ocean color

data.

The DPH describes three ancillary products (MIS 10,

MIS 11, and MIS 12) that are produced at the MISR

Science Computing Facility (SCF) and used in the

generation of other MISR products. These products

are available from the DAAC as aids in interpreting

MISR data.
Instrument Data Processing Overview

30 • Chapter Three: Instrument Data Processing Overview Data Products Handbook Vol. I

MODIS

Moderate Resolution Imaging Spectroradiometer

MODIS is an EOS facility instrument designed to

measure biological and physical processes on a

global basis every 1 to 2 days. Slated for both the

EOS AM and PM satellite series, the instrument will

provide long-term observations from which to derive

an enhanced knowledge of global dynamics and

processes occurring on the surface of the Earth and in

the lower atmosphere. This multidisciplinary

instrument will yield simultaneous, congruent

observations of high-priority atmospheric (cloud

cover and associated properties), oceanic (sea surface

temperature and chlorophyll), and land surface

features (land cover changes, land surface

temperature, and vegetation properties). The

instrument is expected to make major contributions

to understanding of the global Earth system,

including interactions between land, ocean, and

atmospheric processes.

The MODIS instrument employs a conventional

imaging radiometer concept, consisting of a crosstrack scan mirror and collecting optics, and a set of

linear detector arrays with spectral interference filters

located in four focal planes. The optical arrangement

will provide imagery in 36 discrete bands from 0.4 to

14.5 μm selected for diagnostic significance in Earth

science. The spectral bands will have spatial

resolutions of 250 m, 500 m, or 1 km at nadir; signalto-noise ratios of greater than 500 at 1-km resolution

(at a solar zenith angle of 70°); and absolute

irradiance accuracies of ± 5 percent from 0.4 to 3 μm

(2 percent relative to the Sun) and 1 percent or better

in the thermal infrared (3 to 14.5 μm). MODIS

instruments will provide daylight reflection and

day/night emission spectral imaging of any point on

the Earth at least every two days, operating

continuously.

MODIS will provide specific global data products,

which include the following:

• Surface temperature with 1-km resolution, day

and night, with absolute accuracy of 0.2 K for

oceans and 1K for land;

• Ocean color, defined as ocean-leaving

spectral radiance within 5 percent from 415 to

653 nm, enabled by atmospheric correction

from near-infrared sensor channels;

• Chlorophyll fluorescence within 50 percent at

surface water concentrations of 0.5 mgm-3;

• Concentrations of chlorophyll a within 35%;

• Vegetation/land surface cover, conditions, and

productivity

– Net primary productivity, leaf area index,

and intercepted photosynthetically active

radiation

– Land cover type, with change detection and

identification

– Vegetation indices corrected for atmosphere,

soil, and directional effects;

• Snow cover and sea ice cover;

• Land surface reflectance (atmospherically

corrected) with bidirectional reflectance and

albedo;

• Cloud cover with 250-m resolution by day and

1,000-m resolution at night;

• Cloud properties characterized by cloud droplet

phase, optical thickness, droplet size, cloud-top

pressure, and emissivity;

• Aerosol properties defined as optical thickness,

particle size, and mass transport;

• Fire occurrence, size, and temperature;

• Global distribution of precipitable water; and

• Cirrus cloud cover.

Key MODIS Facts

• Selected for flight on EOS AM and

PM series

• Heritage: AVHRR, HIRS, Landsat TM and

NIMBUS-7, and CZCS

• Medium-resolution, multi-spectral,

cross-track scanning radiometer

• Measures biological and physical processes

• MODIS Home Page:

http://ltpwww.gsfc.nasa.gov/MODIS/MODIS.html
Instrument Data Processing Overview

Data Products Handbook Vol. I Chapter Three: Instrument Data Processing Overview • 31 Level 1

MODIS Processing

 MOD02

Calibrated L1B Radiance

MOD03

Geolocation Fields

Level 0

MOD01

Level 1A

Level 2/Level 2 Gridded (L2G)

MOD07

• Total Ozone Burden

• Stability Indices

• Temperature and 

Moisture Profiles

• Precipitable Water (IR)

Grid Pointer

L2G Land 

Pointer Files

MOD35

Cloud Mask

 MOD02 MOD03

MOD02, MOD03, MOD07, MOD35, Grid Pointer, Grid Angular

 Level 1, Level 2 Atmosphere, Level 2 Ocean, Grid Angular, Grid Pointer

Ocean Color 

(see Oceans Chart)

Ocean Color

(L3 prior week)

MOD28

Sea Surface 

Temperature

MOD28

(L3 prior week)

Grid Angular

L2G Land 

Angular Data

Atmos

Land

Oceans

L2, L2G, L3, Legend:

MOD05

Precip. Water 

Product

MOD04

Aerosol Product

MOD06

Cloud Product

MOD11

L2G Land Temp.

MOD21

Chlorophyll Conc.

MOD33

Snow Cover 

(prior week)

Instrument Packet Data

MOD02, MOD03, MOD04, MOD07, MOD35, Grid Pointer, Grid Angular

Figure 8. Flow Diagram Showing Interrelationship of MODIS Atmosphere, Land, and Ocean Products.
Instrument Data Processing Overview

32 • Chapter Three: Instrument Data Processing Overview Data Products Handbook Vol. I

MOD28

Sea Surface 

Temperature 

(day, wk, 3-wk)

MOD27

Ocean Productivity 

(day, wk, 3-wk)

Ocean Color

(day, wk, 3-wk)

MODIS Processing

(Continued)

Level 2 Gridded (L2G)

MOD08

Atmosphere 

Joint Product

(1-, 8-day, mn)

Level 3 Level 4

MOD43

BRDF/Albedo 

(16-day)

MOD10

Snow Cover

(1-, 10-day, mn)

MOD13

Vegetation

(8-, 16-day, mn)

MOD12

Land Cover 

(96-day)

MOD11

Land Surface 

Temp. & Emis.

(8-day, mn)

MOD13

Vegetation 

Indices

MOD12

(L3 prior period or

At-launch Land Cover)

MOD4

(L4 Land Cover Change)

MOD11

(L3 prior period)

MOD10

Snow Cover

MOD11

Land Surface 

Temp. & Emis.

MOD09

Surface 

Reflectance

MOD29

Sea Ice

Level 2 Atmosphere, Level 2 Ocean, Level 2G Land

MOD17

Vegetation Production (8-day, year)

MOD16

Evapotranspiration (8-day, year)

MOD15

LAI and FPAR 

(day, 8-day, mn)

MOD29

Sea Ice Cover 

(1-, 10-day, mn)

MOD14

Thermal Anom. 

(1-, 8-, 16-day, mn)

MOD43

(prior period)

 Level 1, Level 2 Atmosphere, Level 2 Ocean , L2G Grid Pointer, L2G Grid Angular

Atmos

Land

Oceans

L2G, L3, L4 Legend:

MOD12

(L3 prior period or

At-launch Land Cover)

MOD44

 (L4 Land Cover 

Change)

MOD14

Thermal 

Anomalies

MOD44

Land Cover 

Change (8-day)

MOD40

Burn Scars 

(8-day)

Figure 8. Flow Diagram Showing Interrelationship of MODIS Atmosphere, Land, and Ocean Products (Continued).
Instrument Data Processing Overview

Data Products Handbook Vol. I Chapter Three: Instrument Data Processing Overview • 33 Level 1

 MOD02

Calibrated L1B Radiance

MOD03

Geolocation Fields

Level 0

MOD01

Level 1A

 MOD02 MOD03

Level 2

MOD07

Atmospheric Profiles

• Total Ozone Burden

• Stability Indices

• Temp & Moisture Profiles

• Precipitable Water (IR)

MOD05

Precipitable

Water Product

• Solar Algorithm

• IR Algorithm

MOD35

Cloud Mask

MODIS 

Atmosphere Processing

MOD04 

Aerosol Product

• Aerosol Optical 

Thickness

• Aerosol Size 

Distribution

MOD06

Cloud Product

• Cloud Top Properties 

(temperature, height, 

emissivity)

• Cloud Particle Phase

• Cloud Fraction

• Cloud Optical Properties 

(optical thickness and 

particle size)

Level 3

MOD03, Level 2 Atmosphere Products

MOD02, MOD03, MOD07, MOD35

MOD11 

L2G Land Temp.

MOD21

L2 Chlorophyll Conc.

MOD33 

Snow Cover

(prior period)

MOD43

BRDF/Albedo

(16-day)

MOD08

L3 Atmosphere Joint Product:

• Aerosol • Water Vapor

• Clouds • Stability Indices

• Ozone

(daily, 8-day, monthly)

Instrument Packet Data

Figure 9. Flow Diagram Showing Interrelationship of MODIS Atmosphere Products.
Instrument Data Processing Overview

34 • Chapter Three: Instrument Data Processing Overview Data Products Handbook Vol. I

MODIS 

Land Processing

MOD09

Surface Reflectance 

MOD13

Vegetation Indices

MOD14

Thermal Anomalies

MOD05

Precipitable 

Water

MOD35

Cloud Mask

MOD04

LI Aerosol

 MOD29

Sea Ice Max. 

Extent

MOD10

Snow Cover

Level 2 Gridded (L2G) Level 1

 MOD02

Calibrated L1B Radiance

MOD03

Geolocation Fields

Instrument Packet Data

Level 0

MOD01

Level 1A

MOD02, Grid Pointer, Grid Angular

MOD11

Land Surface 

Temp. & Emis.

MOD35

Cloud Mask

Level 2G

MOD43

BRDF/Albedo 

(16-day)

MOD10

Snow Cover 

(1-,10-day, mn)

MOD13

Vegetation 

(8-, 16-day, mn)

MOD12

Land Cover 

(96-day)

MOD11

Land Surface 

Temp. & Emis. 

(8-day, mn)

MOD17

Vegetation Production 

(8-day, year)

MOD16

 Evapotranspiration 

(8-day, year)

MOD15

LAI and FPAR 

(day, 8-day, mn)

MOD29

Sea Ice Cover 

(1-, 10-day, mn) MOD14

Thermal Anom. 

Level 3

(1-, 8-, 16-day, mn)

Level 4

Grid Pointer

Product

Grid Angular

Data Product

MOD12 

(L3 prior period or 

At-launch Land Cover)

MOD11 

(L3 prior period)

MOD12 

(L3 prior period or

At-launch Land Cover)

MOD43

BRDF/Albedo 

(LI prior period)

MOD43

BRDF/Albedo 

(LI prior period)

MOD44

Land Cover 

Change (8-day)

MOD40

Burn Scars 

(8-day)

MOD04

LI Aerosol

Figure 10. Flow Diagram Showing Interrelationship of MODIS Land Products.
Instrument Data Processing Overview

Data Products Handbook Vol. I Chapter Three: Instrument Data Processing Overview • 35 Level 2 Level 3 Level 4

MODIS 

Ocean Processing

MOD04

Sea Aerosol 

Product

MOD28

Sea_Surface 

Temperature

MOD28

Sea_Surface 

Temperature

(day, 1-, 3-week)

MOD35

Cloud Mask

MOD04

Aerosol 

Product

MOD27

Ocean 

Productivity 

(yearly)

Ocean Color:

MOD18 Water/Leaving Rad.

MOD19 Pigment Conc.

MOD20 Chlorophyll Flour.

MOD21 Chlorophyll Conc.

MOD22 PAR/Interim PAR

MOD23 Suspended Solids Conc.

MOD24 Organic Matter Conc. 

MOD25 Coccolith Conc.

MOD26 Water Attenuation Coef.

MOD31 Phycoerithrin Conc.

MOD36 Absorption Gelbstof

MOD37 Aerosol Properties

MOD39 Clear Water Epsilon 

MOD05

Precip. Water

MOD28

L3 Weekly 

(prior period)

Level 0 Level 1

 MOD02

Calibrated L1B Radiance

MOD03

Geolocation Fields

MOD01

Level 1A

 MOD02 MOD03

Ocean Color

(day, 1-, 3-week)

Ocean Color

L3 Weekly 

(prior period)

Instrument Packet Data

Figure 11. Flow Diagram Showing Interrelationship of MODIS Ocean Products.
Instrument Data Processing Overview

36 • Chapter Three: Instrument Data Processing Overview Data Products Handbook Vol. I

MOPITT

Measurements of Pollution in the Troposphere

The MOPITT experiment is provided under a

Memorandum of Understanding with the Canadian

Space Agency (CSA). MOPITT will measure thermal

emission in the 4.6-μm band of carbon monoxide

(CO) and reflected solar radiation in the 2.3-μm band

of CO and 2.2-μm band of methane (CH4).

Tropospheric CO profile and total columns CO and

CH4, will be derived from those measurements in the

retrieval process.

Key MOPITT Facts

• Selected for flight on EOS AM-1

• Heritage: Pressure-modulated cell

elements used in the PMR, SAMS, and

ISAMS instruments, using similar

correlation spectroscopy techniques

• Eight-channel correlation radiometer

with cross-track scanning

• Canadian Space Agency to provide the

instrument

• MOPITT Home Page:

http://eos.acd.ucar.edu/mopitt/home.html

CO is one of the most important trace species in

tropospheric chemistry. The full range of the effects

of the increased/decreased concentration of CO is not

fully understood at the present time, but it is known

that CO is photochemically active and plays a major

part in the concentration of hydroxyl (OH) radicals in

the troposphere. Increased CO may deplete

tropospheric OH radicals, thereby reducing the yearly

removal of many natural and anthropogenic trace

species. In particular, this effect may add to the

increase of CH 4, which in turn could further reduce

OH concentration. Increased CO may also indirectly

intensify global warming and perturb the

stratospheric ozone layer by increasing the lifetime of

trace gases such as CH4, methyl chloride (CH3Cl),

and chlorofluorocarbons (CFCs). CH4 is also a very

effective greenhouse gas, and the change of its

concentration due to human activities will impact

global climate change. Measurements of CO and

CH4 will enhance our knowledge of the chemistry of

the troposphere, and particularly how it interacts with

the surface/ocean/biomass systems, atmospheric

transports, and the carbon cycle.

MOPITT operates on the principle of correlation

spectroscopy, a non-dispersive spectroscopy

technique. It uses the target gas itself contained in a

gas cell as the filter to achieve high spectral

resolution as well as high signal to noise. Both

pressure modulation cells (PMC) and length

modulation cells (LMC) are used in MOPITT. By

cycling the amount of gas in the absorption cell

between two states (low and high pressure in the case

of the PMC, and long and short path in the case of

the LMC), the detector will be alternately looking

through two different filters that exactly correlate

with the target gas spectra. The difference of the two

signals will be identical to the output of a system in

which the gas cell and its modulator are replaced by

an optical filter with non-zero transmission only at

the spectral line positions of the target gas (CO and

CH4 for MOPITT). Therefore, the difference signal is

most sensitive to the contributions by the target gas

and is not sensitive to the contributions of other

gases. The average of the two signals can also be

obtained. It has the property that its transmittance is

near unity away from the lines in the cell, but it

reduces the signals at the centers of the lines. Thus, it

is sensitive to other gases, and especially to the

surface contribution to the upwelling radiation in the

spectral regions of interest.

MOPITT is a cross-track scanning instrument which

collects data with a scan line of four adjacent 22-km

square pixels at nadir and at 14 “stare” positions on

either side of nadir. The pixels are arranged in the

along-track direction. The stare positions are

interleaved so as to produce nearly contiguous

coverage in the cross-track and along-track

directions. For each orbit, a swath ~ 640 km wide is

sampled. With the orbit planned for the for the AM-1

platform, this allows complete sampling of the

Earth’s surface about every three days.

MOPITT Level 2 products include: (1) tropospheric

CO profiles with a resolution of 22 km horizontally,

4 km vertically and with an accuracy of 10 percent

throughout the troposphere; (2) total column of CO
Instrument Data Processing Overview

Data Products Handbook Vol. I Chapter Three: Instrument Data Processing Overview • 37

with a 10 percent accuracy and 22-km horizontal

resolution; and (3) total column of CH4 with a

precision of 1 percent and horizontal resolution of

22 km. The column amounts of CO and CH4 will

only be available on the sunlit side of the orbit. Level

3 products to be developed postlaunch will include

gridded three-dimensional global maps of CO, and

global maps of CH 4 total column.
Instrument Data Processing Overview

38 • Chapter Three: Instrument Data Processing Overview Data Products Handbook Vol. I

The Data Assimilation Office

DAS Processing Overview

The Data Assimilation Office (DAO) develops

research-quality 4-dimensional atmosphere-oceanland data assimilation products for EOS. The data

assimilation combines all available data into a

dynamically consistent depiction of the Earthatmosphere system, and has the unique ability to

produce estimates of unobserved quantities.

Data assimilation approaches are used throughout

science and engineering. Most simply, data

assimilation is the use of a mathematical-physical

model to analyze observations, while at the same

time quantifying the shortcomings of the model

formulation.

In Earth science, a data assimilation system is

effectively an Earth-system model constrained by

Earth-system observations. The data assimilation

system (1) organizes the observations from many

diverse instrument sources and measurement times

into a single useful data product; (2) complements the

observations by propagation of information from

observed into unobserved regions and times; (3)

provides estimates of expected values of the

observations with which to assess data and

instrument quality; (4) provides superior products for

environmental assessment studies; and

(5) supplements the observations by providing

estimates of quantities that are difficult or impossible

to observe.

Support for EOS Science Goals

The goals of the DAO during the EOS timeframe are

to produce assimilation analyses to ensure that the

maximum information is gained from the EOS and

other observations, and to set the foundation for

future Earth system models. Continual research into

numerical and theoretical techniques and associated

Earth science process studies will ensure that the

developed system meets the needs of EOS.

Analyzing historical and new data sets available in

the pre-launch phase of EOS will serve as the basis to

confront Earth system problems, and the successes

and failures of these diagnostic and interpretive

studies will define the evolution of the data

assimilation system. The assimilated data produced

in the pre-launch period will be made available to the

broad science community to assure infusion of

relevant diverse applications into the development

process. A commitment is made to continued

reanalysis of these data sets to minimize the impact

of algorithm changes on interannual signals.

Current Activities

The investigation performs research into all aspects

of 4-dimensional data assimilation, including satellite

retrievals, data quality control, error propagation,

objective analysis, and all component models of the

Earth system. Data from satellites such as the Upper

Atmosphere Research Satellite (UARS), TRMM, and

Advanced Earth Observing System (ADEOS) will be

used to assess the utility of new data types, such as

wind and constituent data. Initial studies concentrate

on meteorology, with an emphasis on the

hydrological cycle and seasonal and interannual

variability. Near-term development will emphasize

land-surface and ocean-surface processes. A strong

emphasis will also be placed on global transport

processes and atmospheric chemistry.

For TRMM and AM-1 the Goddard EOS Data

Assimilation System (GEOS DAS) will provide

Level 4 products of land-surface and atmospheric

quantities. The process works as follows:

Cycling. The GEOS DAS operates on a 6-hour cycle;

it gathers observations in 6-hour blocks and uses

them to correct the General Circulation Model’s

(GCM’s) estimates for that same time period.

Figure 5 shows a typical cycle.

First Guess. Each 6-hour cycle begins with a restart

file from the previous cycle. Using this for its initial

conditions, and boundary conditions from other

sources (e.g., sea surface temperature, terrain

elevation, etc.), the GCM integrates 3 hours into the

future to produce the first guess (FG). (Note: The

restart file is not permanently archived.)

Objective Analysis. Observations (EOS and nonEOS) are gathered for 6-hour intervals surrounding

the valid time of the FG. For example, if the FG valid

time is 06 Universal Time, Coordinated (UTC), then

observations from 03 to 09 UTC are used. An

objective analysis system compares these

observations with the first guess and produces a set
Instrument Data Processing Overview

Data Products Handbook Vol. I Chapter Three: Instrument Data Processing Overview • 39

Objective

Analysis

GCM GCM

Increments

(FG)

(09 - 12 UTC)

(IAU)

(03 - 09 UTC)

σ -> p

Observations

(all levels)

(03-09 UTC)

(06 UTC)

(p)

(06 UTC)

Restart

File

(09 UTC)

First Guess

(06 UTC)

(p)

σ -> p

GCM

(IAU) (σ)

(21 - 03 UTC)

Analysis

(06 UTC)

(p)

(σ)

(σ) (σ) (σ)

Restart

File

(03 UTC)

GCM

(FG) (σ)

(03 - 06 UTC)

p -> σ

Assimilation

(p)

(06 UTC) (06 UTC)

(σ)

Assimilation

00 UTC 06 UTC Cycle

Cycle

12 UTC

Cycle

(σ)

Assimilation

(single level)

(06, 09 UTC)

Boundary

Conditions

(06 UTC)

Figure 12. Typical 6-hour cycle of the GEOS DAS. Processes are rectangles and data files ovals. Times (UTC) are shown

at the top of each process or data file. The vertical coordinate (pressure (p) or sigma (σ)) is shown at the bottom or lower

right corner. In the lower left corner, the GCM integrations are identified as either an incremental analysis update (IAU) or

first guess (FG). Vertical interpolation steps between sigma and pressure levels are shown in the small rectangles.

of gridded corrections. These corrections are called

analysis increments. One assimilation technique is to

apply these increments all at once to the FG

producing the analysis in Figure 12.

IAU. The GEOS DAS uses another method called

the incremental analysis update (IAU). Rather than

putting the increments in all at once at 06 UTC, IAU

goes back and reruns the GCM from 03 to 09 UTC,

gradually inserting the analysis increments at each

model time step. This has several benefits including

the ability to produce assimilations at much higher

temporal resolution. Thus, even though data are

gathered in 6-hour blocks, each single-level product

is provided every 3 hours through the IAU process.

Multi-level (pressure and sigma) assimilations are

still archived every 6 hours, but are based on the

same IAU process. Furthermore, a first-look analysis

(not shown in Figure 12) uses IAU to provide

selected fields at very high temporal resolution

(every few minutes) along the satellite subtrack for

use by TRMM and AM-1 instrument retrievals. The

same boundary conditions that went into the first

guess are also used for the IAU.

Mandatory Pressure vs. Sigma Levels. The World

Meteorological Organization (WMO) has established

the following mandatory pressure levels:

1000 mb 250 mb 20 mb

925 mb 200 mb 10 mb .7 mb

850 mb 150 mb 7 mb .5 mb

700 mb 100 mb 5 mb .4 mb

500 mb 70 mb 3 mb

400 mb 50 mb 2 mb

300 mb 30 mb 1 mb
Instrument Data Processing Overview

40 • Chapter Three: Instrument Data Processing Overview Data Products Handbook Vol. I

The objective analysis is done at these mandatory

pressure levels while the GCM operates on model

sigma levels. The GCM sigma levels are based on the

following formula

σ = ( p - pt ) / ( ps - p t ),

where p is pressure of the sigma level, pt is pressure

at the top of the GCM, and ps is surface pressure.

The GEOS DAS GCM uses approximately 70 sigma

levels from the surface to .1 mb. Interpolation steps

between sigma and pressure levels are indicated in

Figure 12.

Continuing the Cycle. The next 6-hour segment

begins by creating a new restart file at 09 UTC and

then extending the GCM integration 3 more hours to

12 UTC. The FG integration is done without analysis

increments.

DAS Products. The table of contents at the

beginning of this document lists the GEOS DAS

products for TRMM and AM-1. Since all of these

fields are inherent to the GEOS DAS, the marginal

cost of providing a given field is essentially limited

to its storage cost. In fact, many other fields within

the GEOS DAS are not archived.

Selecting a Product: The GEOS DAS provides the

user with many product options. The assimilations on

pressure surfaces are compatible with a wide variety

of other data sets since they are on the WMO

mandatory pressure levels. However, for the user

who demands higher vertical resolution or wants to

avoid the final sigma-to-pressure interpolation, the

assimilations on sigma surfaces are the best choice.

The boundary conditions, observations, and

increments are available for users who want to delve

deeper into the sources of the assimilated products.

Finally, the first guess and analysis products are

more for internal monitoring the GEOS DAS

performance and are not recommended for detailed

climatic studies.
Chapter Four

Radiance and

Imagery Products
TRMM, AM-1

Radiance and Imagery Products

42 • Chapter Four: Radiance and Imagery Products Data Products Handbook Vol. I

Relationship to Global Change

The instruments on TRMM and EOS AM-1 detect

electromagnetic radiation in the visible, infrared,

and microwave wavelengths. Each wavelength

provides different information about the Earthatmosphere system. Because there are dozens of

geophysical parameters such as atmospheric

composition, surface properties, and biological

attributes, many independent wavelength

measurements are required to monitor the Earth

system. Radiances may be thought of as the intensity

or brightness of the radiation. Accurate radiances are

a fundamental and essential requirement for deriving

correct values for all the higher level geophysical

parameters that MTPE is monitoring. To ensure

correct geophysical measurements are made, it is

crucial to have well calibrated radiances within

spectral bands of individual instruments, across

instruments, and across platforms, all for time

intervals from days to decades.

In addition to the radiances, there are a number of

ancillary data products such as cloud cover, surface

reflectivity and topography, aerosols, and scene type

that are important input data sets in the derivation of

other geophysical quantities. These ancillary data

sets are used for data processing within a single

instrument or are transferred from one instrument to

another as part of their input data streams. For our

purposes we can call these ancillary geophysical

data sets “imagery products.” In addition, many of

the land and ocean radiance products are grouped

here. In this section, we review some of the radiance

and imagery data products from nine satellite

sensors on two satellite platforms and explain how

the products support the overall global change

program.

Product Overview

RADIANCES: Electromagnetic energy detected by

satellites arises in the Earth-atmosphere system by

one of four processes: 1) reflection of solar radiation

off the land and oceans or from the atmosphere,

2) thermal infrared to microwave radiation by

surface and atmosphere, 3) non-thermal emission of

radiation, most prominently by lightning discharges,

and 4) return of radiation emitted by the sensor

(such as a radar). Detecting each of these processes

requires a completely different instrument design.

The first three techniques involve passive sensors

which take advantage of electromagnetic radiation

already present. The last technique uses active

sensors that generate their own radiation and

monitor the return signals from echoes off the

surface and atmosphere. TRMM uses all four

techniques. The EOS AM-1 platform concentrates

on the first two methods with high spectral and

spatial resolutions.

On the EOS AM-1 platform, radiances are derived

using the MODIS, ASTER, MOPITT, CERES, and

MISR sensors. The reflected solar and thermal

spectrum is very sensitive to changes in water vapor,

ozone, and aerosol concentrations, to surface

reflectivity, emissivity, and temperature, and to

cloud properties such as droplet size.

TRMM has five sensors: 1) the Precipitation Radar,

2) the Microwave Imager, 3) the Visible Infrared

Scanner, 4) the CERES, and the LIS. Information on

tropical precipitation, the radiation budget, and the

Earth’s lightning climatology is an important goal

for this satellite.

Imagery Data: In the List of Products, there are a

number of geophysical parameters that can be

classified as imagery products which are used as

input in the derivation of higher level geophysical

data products. These imagery products include

masking type data such as cloud cover and scene

type. They include intermediate type products such

as water leaving radiances used in the derivation of

oceanic biological activity and photosynthetically

active radiation (PAR) used in biological studies.

And they include geolocation data allowing

comparisons between instruments of the same

location or across time.

Input Data to Other Instruments: Radiance and

imagery data from one instrument are often used as

input for processing data on another instrument. For

example, the ASTER calibration uses atmospheric

absorbing and scattering properties derived from

simultaneous MISR and MODIS observations made

on the AM-1 platform. In part then, the calibration

of the ASTER radiances may have a dependence on

the MISR and MODIS radiances.
TRMM, AM-1

Radiance and Imagery Products

Data Products Handbook Vol. I Chapter Four: Radiance and Imagery Products • 43

Product Interdependency and Continuity

with Heritage Data

The radiance values are the first physical quantity

derived from the raw instrument counts. The

accuracy and self-consistency of the higher level

geophysical products are dependent on the accuracy

of the radiances and hence upon the calibration of

the sensors. Image products from one instrument

often influence the data processing paths used by

other instruments. For example, CERES data

processing uses MODIS scene classification input.

The ASTER calibration may have a dependency

upon the MODIS calibration. Topographic

information such as MISR’s digital elevation model

(MIS 10) is used in the interpretation of satellite

radiances. In addition, a rough topography will have

a different reflectivity in different directions

(referred to as the bi-directional reflectance

distribution function or BRDF) than will a smooth

topography. The topographic information is a

required input for the interpretation of MISR and

many other types of satellite observations.

The detection of changes in the Earth’s environment

requires accurate and self-consistent observations of

physical phenomena over many years. The EOS and

TRMM programs have placed much emphasis on

making well-calibrated measurements. Calibrating

the measurements over many years is complicated

because many satellites are involved which may or

may not have overlapping observations, the

instruments themselves are changing (because of

degradation, for example) and there are numerous

wavelength bands from the visible to the microwave

requiring different calibration methods.

Radiation flux and budget observations were started

by the Earth Radiation Budget (ERB) and Earth

Radiation Budget Experiment (ERBE) and will

continue with the CERES instruments on TRMM

and AM-1. Calibrated radiances from all these

instruments are fundamentally important for the

documentation of subtle changes in the Earth’s

radiation budget. ASTER advances the technology

of imagers such as Landsat and France’s Systeme

pour l’Observation de la Terre (SPOT). MODIS

provides improved spectral resolution compared to

the Advanced Very High Resolution Radiometer

(AVHRR), the Coastal Zone Color Scanner (CZCS),

and earlier scanning spectroradiometers. MOPITT,

MISR, and the TRMM instruments represent

significant advances over their predecessors as well.

A significant portion of these improvements can be

directly attributed to the increased emphasis on

calibration of all the sensors coming online in the

EOS era.

Suggested Reading

Brest, C.L., and S.N. Goward, 1987.

Christian, H.J., et al., 1989.

Gillaspie, A.R., et al., 1986.

Goodman, A.H. and A. Henderson-Sellers, 1988.

Gordon, H.R., 1978.

Price, J.C., 1984.

Slater, P.N., et al., 1987.
TRMM VIRS 1B-01

VIRS Calibrated Radiances

44 • Chapter Four: Radiance and Imagery Products Data Products Handbook Vol. I

Product Description

This Level 1B data set contains calibrated radiances

in mW/(cm2 • μm • sr) from the 5 channels of the

VIRS instrument. Geolocation, data quality, and solar

and scan geometry information are appended to the 5

channels. Data are collected in day and night modes

over land and ocean. While the Earth is in darkness,

the instrument operates in night mode collecting data

only from channels 3-5 (3.75, 10.80, and 12.00 μm).

Radiometric calibration is performed using ground

testing and in-flight data. In-flight calibration of

channels 1 and 2 is via an onboard solar diffuser and

occasional views of the moon. Channels 3-5 use an

onboard blackbody and a space view to update

calibration parameters before each scan line.

Processing to compensate for undesirable sensor

effects is performed, but the data is not geometrically

corrected or resampled.

The VIRS instrument is similar to the AVHRR

instrument and as such the radiance data may be used

for a variety of atmospheric, terrestrial, and

oceanographic research. This product is an input to

TRMM 3B-42 which generates surface rainfall

derived from geostationary satellites and calibrated

by the data from the TRMM sensors.

VIRS 1B-01

PRODUCT SUMMARY

Coverage:

16 orbits of 720 km each precessing

through diurnal cycle

Resolution:

2.11 km

Wavelengths:

0.63, 1.61, 3.75, 10.80, 12.00 μm

Product Type:

standard, at-launch

Science Team Contact:

W. Barnes
TRMM TMI 1B-11

TMI Calibrated Brightness Temperatures

Data Products Handbook Vol. I Chapter Four: Radiance and Imagery Products • 45

Product Description

This Level 1B data set contains calibrated brightness

temperatures, in degrees Kelvin, from the high- and

low-resolution TMI channels. Pixel geolocation,

calibration and instrument pointing and location

information are also included.

The TMI is similar to the SSM/I instrument flown on

the DMSP satellites with two key differences: (1) the

addition of vertically and horizontally polarized

10-GHz channels; and (2) the scan geometry is the

same for every scan instead of alternating between an

A scan and a B scan. The TMI Level 1B data format

is similar to the SSM/I Wentz Antenna Temperature

data, however some changes were made due to

instrument characteristics and feedback from the

investigator community.

Aside from the rainfall parameters derived as part of

the TRMM mission, TMI data may also be used to

derive sea surface wind speed, atmospheric water

vapor content and cloud liquid water content over the

ocean. Over land, passive microwave frequencies

found on TMI can be useful for monitoring soil

moisture and vegetation.

TMI 1B-11

PRODUCT SUMMARY

Coverage:

16 orbits of 760 km each precessing

through diurnal cycle

Resolution:

4.6 - 37 km

Wavelengths:

10.65 (V, H), 19.35 (V, H), 21.3 (H),

37.0 (V, H), 85.5 (V, H) GHz

Product Type:

standard, at-launch

Science Team Contact:

J. Shiue
TRMM 1B-21, 1C-21

PR – Radar Total Power, Noise, and Reflectivity

46 • Chapter Four: Radiance and Imagery Products Data Products Handbook Vol. I

Product Description

The Level 1B Precipitation Radar (1B-21) data set

contains returned power and noise information, along

with satellite information such as navigation

(spacecraft position and velocity) and radar

calibration information. The Level 1C PR data

(1C -21) are radar reflectivity derived from the

Level 1B data where data are excluded if no

significant echoes are found (i.e., little or no rain

activity) and noisy data are suppressed and set to

predefined values.

PR 1B-21, 1C-21

PRODUCT SUMMARY

Coverage:

36°N - 36°S; 16 orbits at 220 km each

precessing through diurnal cycle

Resolution:

4.4 km (horiz.) 250 m (vertical)

Wavelengths:

13.8 GHz

Product Type:

standard, at-launch

Science Team Contact:

K. Okamoto
TRMM LIS 01

LIS Pulse/Background

Data Products Handbook Vol. I Chapter Four: Radiance and Imagery Products • 47

Product Description

This Level 1A product consists of 8 parameters,

including pulse/background, Calibration Matrix,

Navigation Data, Continuity Data, Error List, QA

Data, and Correction Data. It is the raw data stream

from the LIS instrument including all instrument

housekeeping information along with the lightning

pulses and background images. The purpose of this

product is to provide a “snapshot” of the LIS field of

view when the lightning data volume is low. The data

from the background images are transmitted as part

of the data stream when there is space available. The

pulse and background data from the instrument will

be processed from a set of products with many

components or categories.

LIS 01

PRODUCT SUMMARY

Coverage:

global, 35°N - 35°S

Resolution:

3.5 km × 3.5 km, daily (2 ms)

Key Measurements:

Pulse/background, calibration matrix,

navigation data, continuity data, error list,

QA data, and correction data

Processing Level:

1A

Product Type:

standard, at-launch

Science Team Contact:

H. Christian, S. Goodman
AM-1 MOD 01

MODIS Level-1A Radiance Counts

48 • Chapter Four: Radiance and Imagery Products Data Products Handbook Vol. I

Product Description

This Level 1A data set contains counts for 36

MODIS channels, along with raw instrument

engineering and spacecraft ancillary data. The

Level-1A data are used as input for geolocation,

calibration, and processing. Quality indicators are

added to the data to indicate missing or bad pixels

and instrument modes. Visible, SWIR, and NIR

measurements are made during daytime only, while

radiances for TIR are measured during both the day

and the night portions of the orbit. This product

includes all MODIS data in digitized (counts) form

for all bands, all spatial resolutions, all time tags

(converted), all detector views (Earth, solar diffuser,

SRCA, black body, and space view), and all

engineering and ancillary data. MOD 01

PRODUCT SUMMARY

Coverage:

global, daily (daytime and nighttime)

Resolution:

0.25, 0.5, and 1 km

Wavelengths:

20 channels 0.4-3.0 μm, 16 channels

3-15 μm

Product Type:

standard, at-launch

Science Team Contact:

V. Salomonson
AM-1 MOD 02

MODIS Level-1B Calibrated, Geolocated Radiances

Data Products Handbook Vol. I Chapter Four: Radiance and Imagery Products • 49

The Level 1B data set contains calibrated and

geolocated at-aperture radiances for 36 bands

generated from MODIS Level 1A sensor counts

(MOD 01). The radiance units are in

W m-2 μm-1 sr-1). In addition, Earth BRDF may be

determined for the solar reflective bands (1-19, 26)

through knowledge of the solar irradiance

(e.g., determined from MODIS solar diffuser data,

and from the target illumination geometry).

Additional data are provided including quality flags,

error estimates and calibration data.

Visible, SWIR, and NIR measurements are made

during daytime only, while radiances for TIR are

measured continuously. Only Channels 1 and 2 have

250-m resolution, Channels 3-7 have 500-m

resolution, and the rest have 1-km resolution. MOD 02

PRODUCT SUMMARY

Coverage:

global, daily (daytime and nighttime)

Resolution:

0.25, 0.5, and 1 km

Wavelengths:

20 channels 0.4-3.0 μm, 16 channels

3-15 μm

Product Type:

standard, at-launch

Science Team Contact:

H. Montgomery
AM-1 MOD 03

MODIS Geolocation Data Set

50 • Chapter Four: Radiance and Imagery Products Data Products Handbook Vol. I

Product Description

The MODIS Geolocation Product contains geodetic

coordinates, ground elevation, and solar and satellite

zenith, and azimuth angle for each MODIS 1-km

sample. These data are provided as a ‘companion’

data set to the Level 1B calibrated radiances and the

Level 2 data sets to enable further processing. These

geolocation fields are determined using the spacecraft

attitude and orbit, the instrument telemetry, and a

digital elevation model.

MOD 03

PRODUCT SUMMARY

Coverage:

global, daily (daytime and nighttime)

Resolution:

 1 km

Wavelengths:

n/a

Product Type:

standard, at-launch

Science Team Contact:

V. Salomonson
AM-1 MOD 09

MODIS Surface Reflectance;

Atmospheric Correction Algorithm Products

(also called Spectral Reflectance)

Data Products Handbook Vol. I Chapter Four: Radiance and Imagery Products • 51

Product Description

The MODIS surface reflectance product, MOD 09

(#2015), is a seven-band product computed from the

MODIS Level 1B land bands 1, 2, 3, 4, 5, 6, and 7

(centered at 648 nm, 858 nm, 470 nm, 555 nm,

1240 nm, 1640 nm, and 2130 nm, respectively). The

product is an estimate of the surface spectral

reflectance for each band as it would have been

measured at ground level if there were no

atmospheric scattering or absorption.

The correction scheme includes corrections for the

effect of atmospheric gases, aerosol, and thin cirrus

clouds; it is applied to all noncloudy MOD 35

(#3660) Level 1B pixels which pass the Level 1B

quality control. The correction uses band 26 to detect

cirrus cloud, water vapor from MOD 05 (#1874),

aerosol from MOD 04 (#2293), and ozone from

MOD 07 (#1333); best available climatology is used

if the MODIS water vapor, aerosol, or ozone

products are unavailable. Also, the correction uses

MOD 43 (#3669), BRDF without topography, from

the previous 16-day time period for the atmosphereBRDF coupling term.

Research & Applications

The surface reflectance product is the input for

product generation for several land products:

Vegetation Indices (VIs), BRDF, thermal anomaly,

snow/ice, and Fraction of Photosynthetically Active

Radiation/Leaf Area Index (FPAR/LAI). It is,

therefore, an important and essential product. The atlaunch version will be fully operational.

Data Set Evolution

Because of the novelty of the at-launch MODIS

aerosol product (MOD 04) over land and the cirrus

detection, the inclusion of the correction for aerosol

and thin cirrus clouds will be implemented following

at launch thorough evaluation of these products.

MOD 09

PRODUCT SUMMARY

Coverage:

global land surface (Level 2)

Spatial/Temporal Characteristics:

bands 1 and 2: 250 m

bands 3 - 7: 500 m

daylight data only

Key Science Applications:

global climate modeling, regional climate

modeling, surface energy balance

modeling, land cover characterization

Key Geophysical Parameters:

surface reflectance

Processing Level:

2

Product Type:

standard, at-launch

Science Team Contact:

E. Vermote

Suggested Reading

Vermote, E.F., et al., 1996.
AM-1 MOD 09

MODIS Surface Reflectance;

Atmospheric Correction Algorithm Products

(also called Spectral Reflectance)

52 • Chapter Four: Radiance and Imagery Products Data Products Handbook Vol. I

Figure 13. RGB composite (Red=3.75μm , Green=0.87 μm, Blue=0.67 μm) January 89

Figure 14. RGB composite (Red=3.75μm , Green=0.87 μm, Blue=0.67 μm) July 89. These images

were generated from AVHRR reflectances in channels 1 (0.67 μm), 2 (0.87 μm) and 3 (3.75 μm);

channels 1 and 2 were corrected for atmospheric effects from rayleigh scattering, ozone (using TOMS

gridded ozone product) and water vapor (using DAO total precipitable water). They reflect the state of

land cover for January and July 89 : the green color produced by low values in AVHRR channels 1

and 3 and high values in channel 2 represents vegetated areas. Blue color in the January composite is

produced for snow and residual clouds. The blue color over the desert in the July composite is due to

the saturation of AVHRR channel 3 which leads to erroneous values of reflectance at 3.75 μm
AM-1 MOD 43

MODIS Surface Reflectance;

BRDF/Albedo Parameter

Data Products Handbook Vol. I Chapter Four: Radiance and Imagery Products • 53

Product Description

The BRDF/Albedo Parameter provides

(1) coefficients for mathematical functions that

describe the BRDF of each pixel in the seven

MODIS “Land” bands (1-7); and (2) albedo measures

derived simultaneously from the BRDF for bands 1-7

as well as three broad bands (0.4-0.7; 0.7-3.0, 0.4-3.0

μm). Because deriving BRDF and albedo requires

merging multiple looks at each pixel, the

BRDF/Albedo parameter is provided every 16 days.

Its spatial resolution is 1 km, gridded to Level 3. A

thirty-day summary albedo product at one-quarter

degree spatial resolution is also provided.

Research & Applications

The BRDF functions provided by the BRDF/Albedo

parameter (1) allow normalization of MODIS data to

standard viewing and illumination angles, thus

removing geometric effects from multidate images;

(2) quantify the directional information in the

remotely-sensed signal, which is related to ground

cover type; and (3) provide a surface radiationscattering model for boundary layer parameterization

in regional and global climate modeling. The BRDF

is also used in extraction of surface reflectances at

Level 2. Two albedo measures are provided: “blacksky” albedo (directional-hemispherical reflectance),

and “white-sky” albedo (bi-hemispherical

reflectance). These are intrinsic surface properties,

independent of atmospheric state. They describe the

upward scattering of the solar beam and of uniform

diffuse irradiance, respectively, and may be used as

input to global and regional climate models.

Data Set Evolution

The BRDF/Albedo algorithm combines gridded,

multidate, multiband surface reflectance data from

EOS MODIS and MISR instruments to produce

BRDF functions and derived albedo measures. For

each grid cell, all cloud-free observations in a 16-day

period are assembled and fit to a suite of semiempirical models that describe the BRDF as a linear

function of basic BRDF shapes. The shapes are

derived by simplifying physical models that describe

MOD 43

PRODUCT SUMMARY

Coverage:

global land surface

Spatial/Temporal Characteristics:

1 km, 16-day; 1/4° 30-day

Key Science Applications:

biogeochemical cycle modeling, net

primary productivity estimation, global

climate models

Key Geophysical Parameters:

bidirectional reflectance, spectral albedo

Processing Level:

3

Product Type:

standard, at-launch

Science Team Contact:

A. Strahler, J.-P. Muller

volume scattering and surface scattering of land

surface covers. In addition, a single empirical model

(modified Walthall) is also fitted. The algorithm

outputs (1) the model and parameters that best fit the

observations, (2) modified Walthall parameters,

(3) black-sky and white-sky spectral and broadband

albedos, (4) an extensive series of quality flags, and

(5) information on model fits and surface structure

inference.

Suggested Reading

Barnsley, M.J. and J.-P. Muller, 1991.

Brest, C.L., and S.N. Goward, 1987.

Wanner, W., et al., 1995.

Wanner, W., et al., 1995.
AM-1 MOD 18, MOD 37

MODIS Normalized Water-Leaving Radiance

54 • Chapter Four: Radiance and Imagery Products Data Products Handbook Vol. I

Product Description

This Level 2 and Level 3 product contains ocean

water-leaving radiances for 7 of the 36 wavelengths/

spectral bands (Band 8 through 14, 412 through

681 nm) of MODIS. These are the “ocean” bands; the

water leaving radiances in these bands are used to

derive nearly all of the MODIS ocean products. In

addition, another key parameter generated by the

algorithm is provided as Product MOD 37: Aerosol

Optical Depth (Param. 2344). The Level 2 product is

provided daily at 1-km resolution for cloud-free

pixels. The product constraints are that only cloudfree pixels are used (with Sun glitter below a

threshold), and all valid pixels are outside a distance

threshold from land. The product availability is atlaunch.

Research & Applications

Normalized Water-Leaving Radiance is the radiance

that would exit the ocean in the absence of the

atmosphere if the Sun were at the zenith. It is used in

the bio-optical algorithms to estimate chlorophyll a

concentration and ocean primary productivity on a

global scale. The algorithm evolved from experience

from the CZCS experiment which proved the

feasibility of measuring ocean color from space.

Extensive testing of the algorithm will be conducted

using SeaWiFS imagery. This is the fundamental

product for recovering most of the MODIS ocean

products.

Data Set Evolution

Inputs to the algorithm are the Level 1 radiances in

Bands 8-14, screened for clouds and land, and

estimates of the surface wind speed, atmospheric

pressure, and ozone concentration derived from

National Centers for Environmental Prediction

(NCEP, formerly the NMC). The success of the

algorithm depends on the accurate characterization of

the atmospheric effect which for water typically

constitutes 90 percent of the at-satellite radiance. The

algorithm includes single scattering, multiple

scattering effects, and whitecap removal, and uses six

types of ancillary data. There are three major sources

of uncertainty in the product: (1) The N candidate

aerosol models chosen to describe the aerosol may be

MOD 18, MOD 37

PRODUCT SUMMARY

Coverage:

global ocean surface, clear-sky only

Spatial/Temporal Characteristics:

1 km/daily, weekly

Key Science Applications:

ocean chlorophyll, ocean productivity

Key Geophysical Parameters:

water-leaving radiances in the ocean

bands, aerosol optical depth

Processing Level:

2, 3

Product Type:

standard, at-launch

Science Team Contact:

H.R. Gordon

unrepresentative of the natural aerosol, (2) there is

uncertainty in the estimate of the whitecap

reflectance/radiance, and (3) there is uncertainty in

the sensor’s radiometric calibration. The product will

be validated by comparing simultaneous surfacebased measurements (including drifting buoy

radiometers) and MODIS-derived values at a set of

locations.

Suggested Reading

Deschamps, P.Y., et al., 1983.

Evans, R.H. and H.R. Gordon, 1994.

Gordon, H.R. and D.K. Clark, 1981.

Gordon, H.R., and A.Y. Morel, 1983.

Gordon, H.R. and M. Wang, 1994.
AM-1 MOD 22

MODIS PAR

(Photosynthetically Active Radiation)

Data Products Reference Guide Vol. I Chapter Four: Radiance and Imagery Products • 55

Product Description

This Level 2 and 3 product consists of four related

parameters which describe the irradiance at the ocean

surface. The first is Downwelling Solar Irradiance

(Param. 5354) which is the incident irradiance just

above the sea surface in each of the visible MODIS

ocean bands (8, 9, 10, 11, 12, 13, 14). The second is

Instantaneous Photosynthetically Active Radiation,

Param. 2266 (IPAR), which is the total downwelling

flux of photons just below the sea surface at the

instant MODIS views the pixel integrated over the

wavelength range of 400 to 700 nm. The third is PAR

(Param. 2330) which is the irradiance averaged over

an entire day. Since measurements are only available

at the time MODIS views the pixel, this parameter is

estimated from IPAR by the algorithm. The product

is produced at 1 km daily for Level 2 and gridded at

Level 3 daily and weekly at 1 km. The fourth is ARP

(absorbed radiation by phytoplankton) averaged over

the first optical depth. This parameter is critical for

calculation of fluorescence efficiency, a product

useful for conditioning phytosynthesis models

(MOD 23).

Research & Applications

This product is critical for determining

photosynthetic rate of growth of phytoplankton and

primary ocean production. Downwelling irradiance is

needed as an input to the chlorophyll a algorithm

(MOD 21) and it is used to convert the normalized

water-leaving radiance values (MOD 18) into

remote-sensing reflectance values. A series of

irradiance models specific for terrigenous aerosols

have been available; however, the aerosol

characteristics of these models differ greatly in size

and optical characteristics from marine aerosols. The

irradiance model of Gregg and Carder (1990) uses a

mixture of marine and terrigenous aerosols and forms

the basis for the algorithm for MODIS PAR product.

Data Set Evolution

Product generation begins with solar irradiance data

at 1 nm resolution which is taken from the revised

Neckel and Labs data and corrected for Earth-Sun

MOD 22

PRODUCT SUMMARY

Coverage:

 global ocean surface, clear-sky only

Spatial/Temporal Characteristics:

 1 km/daily, weekly

Key Science Applications:

ocean chlorophyll, productivity

Key Geophysical Parameters:

photosynthetically available irradiance

Processing Level:

2, 3

Product Type:

Validation, at-launch

Science Team Contact:

K. Carder, D. Tanré,

W. Esaias, M. Abbott

distance for the current day. Atmospheric correction

is made for the effects of scattering, absorption by

ozone, absorption by gas, and water vapor. This

spectrum is then binned and weighted appropriately

to give the irradiance in each of the MODIS ocean

bands. Next, the below-surface values are computed

and summed to give IPAR. Inputs to the algorithm

are Water Leaving Radiance (MOD 18), PAR

(MOD 22) and Absorption Coefficient (MOD 36).

Suggested Reading

Gordon, H.R. and M. Wang, 1994.

Gregg, W.W. and K.L. Carder, 1990.

Iqbal, M., 1983.

Paltridge, G.W., and C.M.R. Platt, 1976.
AM-1 MOD 32

MODIS Processing Framework

and Match-up Database

56 • Chapter Four: Radiance and Imagery Products Data Products Handbook Vol. I

Product Description

This Level 2 product consists of the calibration dataset

(Param. 3303) to be used in the generation of all the

MODIS Oceans products. It consists of a database

which contains in situ observations of ocean parameters

matched with satellite measurement data. This matchup

database will initially be populated with existing ocean

surface data matched temporally and spatially with

CZCS and AVHRR data and with SeaWiFS data as it is

obtained and then MODIS data post-launch. The

product includes the definition of the processing

framework in which all the MODIS ocean product

algorithms will operate. It is produced daily, weekly

and monthly and supports ocean products at 1 km but

the product itself does not have a spatial resolution.

Research & Applications

This is a calibration product for MODIS ocean

processing. It is used through a vicarious calibration

scheme to update MODIS radiometric calibration

coefficients which directly relate MODIS Level 1A raw

counts to Level 2 calibrated water-leaving radiances

and to monitor long-term performance of the MODIS

instrument. MOD 32 as a matchup database is also used

to validate derived ocean geophysical parameters such

as SST and for quality control of the ocean team

retrievals.

Data Set Evolution

Heritage programs provide the basis for MODIS

algorithms. A program developed for the Pathfinder

ocean SST product forms the framework for analyzing

AVHRR derived SST, algorithm development and

validation, and application of the matchup database.

Development for ocean color algorithms is based on

experience gained in transition from CZCS to SeaWiFS

algorithms. Development of the SeaWiFS program

involves integration of algorithms generated by H.

Gordon (atmospheric correction) and K. Carder

(chlorophyll). In addition, daily validity tests are being

developed through collaboration of the SeaWiFS

CAL/VAL team and our group. The in situ records are

first temporally matched against the AVHRR

extractions. AVHRR data for the match-up database

were extracted for 3 × 3 pixel boxes centered at each in

situ sea surface temperature measurement location. Sea

MOD 32

PRODUCT SUMMARY

Coverage:

global ocean surface

Spatial/Temporal Characteristics:

 1 km/daily, weekly, monthly

Key Science Applications:

ocean chlorophyll, ocean productivity, all

ocean products

Key Geophysical Parameters:

matchup databases, water leaving

radiances, in situ measurements

Processing Level:

2

Product Type:

standard, at-launch

Science Team Contact:

R. Evans, H.R. Gordon

surface observations are from two main types of

platforms: moored buoys and drifting buoys. After the

SeaWiFS and Pathfinder programs are validated using

actual sensor data, they will be converted from a scalar

to a parallel implementation and coded using the

FORTRAN 90 language and EOS coding standards.

Code for the MODIS algorithms initially will be based

on SeaWiFS and Pathfinder programs. These

implementations will evolve to support product

production for the MODIS ocean investigators and will

be coded using a combination the C and FORTRAN 90

programming languages and EOS coding standards.

Suggested Reading

Evans, R.H. and H.R. Gordon, 1994.

Gordon, H.R., 1987.

Gordon, H.R., and A.Y. Morel, 1983.

Slater, P.N., et al., 1987.

Smith, R.C. and W.H. Wilson, 1981.
AM-1 MOD 39

MODIS Clear-Water Epsilon

Data Products Reference Guide Vol. I Chapter Four: Radiance and Imagery Products • 57

Product Description

This product provides a single parameter (3707). The

parameter is the ratio of clear water-leaving radiance

at 531 nm to that at 667 nm and is called the clearwater epsilon [ε(531,667)]. This quantity relates

directly to the iron content of aerosols over clear

waters mostly in the ± 35o latitude range. The Level

2 product is produced daily and the Level 3 product

is produced daily and weekly, all at 1 km spatial

resolution.

Research & Applications

The primary purpose of this algorithm is to estimate

aerosol iron content over ocean waters. The aerosol

iron influences the validity of other MODIS products.

The secondary objective is to flag instances when

normalized water-leaving radiance retrievals may

need adjustment due to aerosol absorption at blue and

green wavelengths. Such errors will affect

chlorophyll a calculations. The third objective is to

provide a check on the Angstrom exponent derived at

red and infrared wavelengths. The algorithm is valid

for pigment concentrations up to 2 mg/m3. When

pigment concentrations are larger than this the

algorithm can no longer be applied. A research

algorithm is planned post-launch that will use a

coupled ocean-atmosphere radiance model to

interactively address aerosols in high-pigment water.

Data Set Evolution

The algorithm is based on methods developed for

obtaining clear-water epsilon values from CZCS data

(Gordon, 1981). Modifications for the MODIS

algorithm include extension of the clear-water

concept to include waters with higher pigment

concentrations and modification of the values of the

normalized water-leaving radiance at 520, 550 and

670 nm for CZCS to the slightly different MODIS

bands by means of the water absorption curve.

Product validation will use SeaWiFS data in the prelaunch and MODIS data post-launch. Scattering and

optical thickness data plus ship data of water-leaving

radiances data will be acquired to test the clear-water

radiance assumptions. This is an interim validation

product which may not be archived.

MOD 39

PRODUCT SUMMARY

Coverage:

global ocean surface, clear-sky only

Spatial/Temporal Characteristics:

1 km/daily, weekly

Key Science Applications:

aerosol iron estimation, water-leaving

radiance correction

Key Geophysical Parameters:

clear-water epsilon, aerosol iron content

Processing Level:

2, 3

Product Type:

validation, at-launch

Science Team Contact:

K. Carder

Suggested Reading

Carder, K.L., et al., 1991.

Gordon, H.R., 1978.

Gordon, H.R. and D.K. Clark, 1981.

Gordon, H.R., and A.Y. Morel, 1983.

Gordon, H.R. and M. Wang, 1994.
AM-1 MOD 32

MODIS Reformatted Annotated Product

58 • Chapter Four: Radiance and Imagery Products Data Products Handbook Vol. I

Product Description

The Level 1A Reformatted Annotated Product

contains raw MISR data which are decommutated,

reformatted (12-bit L0 data shifted to byte

boundaries, reversal of square-root encoding applied,

and converted to 16-bits) and annotated (e.g., with

time information, etc.). The MISR Level 1A data set

is the primary archive of MISR instrument ChargeCoupled Device (CCD) science data, instrument

engineering data (e.g., instrument temperatures,

electrical status, command verification, etc.),

calibration data, motor data, and navigation data (raw

spacecraft position and attitude). These data are used

by the Level 1B1 processing algorithm to generate

calibrated radiances. The science data output

preserves the spatial sampling rate of the Level 0 raw

MISR CCD Science data.

CCD data are collected during routine science

observations of the sunlit portion of the Earth. Each

product represents one ‘granule’ of data. A ‘granule’

is defined to be the smallest unit of data required for

MISR processing. Also included in the Level 1A

product will be pointers to calibration coefficient

files provided for Level 1B processing.

MIS 01

PRODUCT SUMMARY

Coverage:

daytime, 378-km swath width (nadir),

413 km swath width (off nadir),

providing global coverage in 9 days.

Resolution:

The spatial sampling of the nadir viewing

camera is 250 m (cross-track) × 275 m

(along-track), while for the 8 off-nadir

cameras the sampling is 275 m in both

directions. On-board averaging up to

1.1 km is selectable by ground command.

Wavelengths:

443, 555, 670, 865 nm

Product Type:

standard, at-launch

Science Team Contact:

C. Bruegge
AM-1 MIS 02

MISR Radiometric Product

Data Products Reference Guide Vol. I Chapter Four: Radiance and Imagery Products • 59

Product Description

The MISR Radiometric Product (MIS 02) is

produced during Level 1B1 processing. It contains

spectral radiances for all MISR channels (four

spectral bands and nine cameras), given in units of

W/(m 2 • μm • sr). Each radiance value represents the

incident radiance averaged over the sensor’s total

band response. Processing includes both radiance

scaling and conditioning steps. Radiance scaling

converts the Level 1A data from digital counts to

radiances, using coefficients derived in combination

with the On-Board Calibrator (OBC) and vicarious

calibrations. The OBC contains Spectralon

calibration panels which are deployed monthly and

reflect sunlight into the cameras. The OBC detector

standards then measure this reflected light to provide

the calibration. Vicarious field campaigns are

conducted less frequently but provide an independent

methodology useful for reducing systemic errors.

Radiance conditioning removes undesirable

instrument effects. Image enhancement is provided

by deconvolving the scene with the sensor’s pointspread-function. Additionally, in-band scaling adjusts

the reported radiances to correspond to a nominal

band response profile. This frees the Level 2 software

from the need to correct for detector element nonuniformities. No out-of-band correction is done for

this product, nor are the data geometrically corrected

or resampled.

MIS 02

PRODUCT SUMMARY

Coverage:

daytime, 378-km swath width (nadir),

413-km swath width (off nadir),

providing global coverage in 9 days.

Resolution:

The spatial sampling of the nadir viewing

camera is 250 m (cross-track) × 275 m

(along-track), while for the 8 off-nadir

cameras the sampling is 275 m in both

directions. On-board averaging up to

1.1 km is selectable by ground command.

Wavelengths:

443, 555, 670, 865 nm

Product Type:

standard, at-launch

Science Team Contact:

C. Bruegge
AM-1 MOD 32

MISR Geo-rectified Radiance Product

60 • Chapter Four: Radiance and Imagery Products Data Products Handbook Vol. I

Product Description

The Geo-rectified Radiance Product (GRP) consists

of parameters that have had geometric corrections

applied and have been projected to a Space-Oblique

Mercator (SOM) map grid. Included in this product is

the surface-projected TOA radiance which is the

calibrated radiance from the Level 1B1 data (MIS 02)

that has had a geometric correction applied to remove

spacecraft position and pointing knowledge errors as

well as effects due to topography. The radiance is

then orthorectified on a reference ellipsoid at the

surface. Also part of the GRP is the ellipsoidprojected TOA radiance which uses supplied

spacecraft position and pointing and is not corrected

for topography, but is resampled at surface reference

ellipsoid. In addition, geometric parameters such as

solar and view zenith and azimuth angles are

included.

Resampling of MISR data at Level 1B2 is critical

because the pushbroom images from the nine

cameras are obtained at widely separated locations

along the subspacecraft track. However, derivation of

geophysical products requires that the multiangle,

multispectral radiances for any single ground target

be coregistered.

MIS 03

PRODUCT SUMMARY

Coverage:

daytime, 378-km swath width (nadir),

413-km swath width (off nadir),

providing global coverage in 9 days.

Resolution:

The resampled data are provided on a

275 m × 275 m Space Oblique Mercator

grid in certain channels, and a 1.1 km ×

1.1 km grid in the remaining channels, as

established by the instrument observing

configuration.

Wavelengths:

443, 555, 670, 865 nm

Product Type:

standard, at-launch

Science Team Contact:

D. Diner
AM-1 MIS 10

MISR Ancillary Geographic Product

Data Products Reference Guide Vol. I Chapter Four: Radiance and Imagery Products • 61

Product Description

The MISR Ancillary Geographic Product (AGP)

contains terrain data (generated from a high

resolution DEM), referenced to the WGS84 reference

ellipsoid and mapped onto a SOM grid. It is an

archival product generated once preflight at the

MISR SCF, but which can be distributed to the

scientific community through the DAAC as an aid in

interpreting MISR retrievals. The AGP is used as

input to Level 1B2 and Level 2 processing. Its

contents include latitude, longitude, scene elevation

(average and standard deviation), topographic

shadow and obscuration mask, surface-normal zenith

angle (average and standard deviation), surfacenormal azimuth angle, and a land/ocean/inland

water/ephemeral water/coastline mask. All

parameters are given on 1.1-km centers.

MIS 10

PRODUCT SUMMARY

Coverage:

global, one-time only.

Resolution:

 1.1 km

Wavelengths:

n/a. Parameters include latitude,

longitude, elevation, obstruction mask,

and land & water classification.

Product Type:

internal, on-request

Science Team Contact:

D. Diner
AM-1 MOD 32

MISR Ancillary Radiometric Product

62 • Chapter Four: Radiance and Imagery Products Data Products Handbook Vol. I

Product Description

The MISR Ancillary Radiometric Product (ARP)

contains coefficients and data variables which are

used in the Level 1B1 processing. Updated ARP

parameters include the sensor radiometric calibration

coefficients, uncertainties in calibration, signal-tonoise ratios, pixel data quality indicators, and quality

assessment threshold parameters. Static ARP

parameters include spectral response parameters,

point-spread-functions (PSF), fields-of-view,

passband-weighted solar irradiance values, and PAR

integration weights. The ARP is regenerated

periodically at the MISR SCF to update the

instrument performance report. The ARP is used as

input to Level 1B1 as well as Level 2 processing.

MIS 11

PRODUCT SUMMARY

Coverage:

n/a, generated periodically.

Resolution:

 per pixel

Parameters:

radiometric calibration coefficients,

uncertainties, signal-to-noise ratios,

spectral response parameters, PSF, fieldsof-view, band averaged solar irradiance

and PAR weights

Product Type:

internal

Science Team Contact:

C. Bruegge
AM-1 AST 01

ASTER Reconstructed, Unprocessed

Instrument Data

Data Products Reference Guide Vol. I Chapter Four: Radiance and Imagery Products • 63

Product Description

The ASTER Level 1A raw data (AST 01) are

reconstructed, unprocessed instrument digital counts

with ground resolution of 15 m, 30 m, and 90 m for 3

VNIR (0.52-0.86 μm), 6 SWIR (1.60-2.43 μm), and 5

TIR (8.13-11.65 μm) channels. This product contains

depacketized, demultiplexed, and realigned

instrument image data with geometric correction

coefficients and radiometric calibration coefficients

appended but not applied. This includes correcting

for SWIR parallax as well registration within and

between telescopes. The spacecraft ancillary and

instrument engineering data are also included. This

product is the responsibility of Japan.

The radiometric calibration coefficients consisting of

offset and sensitivity information is generated from a

database for all detectors. The geometric correction is

the coordinate transformation for band-to-band

coregistration.

AST 01

PRODUCT SUMMARY

Coverage:

regional up to 780 60x60 km scenes

per day (daytime for all channels,

daytime and nighttime TIR channels,

nighttime SWIR and TIR channels for

volcano observation)

Resolution: 

15, 30, 90 m (VNIR, SWIR, TIR,

respectively)

Wavelengths:

VNIR - 0.52 - 0.86 μm (3 channels + 1

stereo channel), SWIR - 1.60 - 2.43 μm

(6 channels), TIR - 8.13 - 11.65 μm

(5 channels)

Product Type:

standard, at-launch

Science Team Contact:

H. Fujisada
AM-1 MOD 32

ASTER Registered Radiance at Sensor

64 • Chapter Four: Radiance and Imagery Products Data Products Handbook Vol. I

Product Description

This Level 1B product (AST 03) contains

radiometrically calibrated and geometrically

coregistered data for all ASTER channels. This

product is created by applying the radiometric and

geometric coefficients to the Level 1A data. The

bands have been coregistered both between and

within telescopes, and the data have been resampled

to apply the geometric corrections. As for the Level

1A product, these Level 1B radiances are generated

at 15-m, 30-m, and 90-m resolutions corresponding

to the VNIR, SWIR, and TIR channels. Calibrated,

at-sensor radiances are given in W/(m2 • μm • sr).

This product serves as input to derived geophysical

products.

AST 03

PRODUCT SUMMARY

Coverage:

regional up to 310 60 km × 60 km

scenes per day (daytime and nighttime)

Resolution:

15, 30, 90 m (VNIR, SWIR, TIR,

respectively)

Wavelengths:

VNIR - 0.52 - 0.86 μm (3 channels +

1 stereo channel), SWIR - 1.60 - 2.43 μm

(6 channels), TIR - 8.13 - 11.65 μm

(5 channels)

Product Type:

standard, at-launch

Science Team Contact:

H. Fujisada
AM-1 AST 04

ASTER Brightness Temperature at Sensor

Data Products Reference Guide Vol. I Chapter Four: Radiance and Imagery Products • 65

Product Description

This Level 2 product includes brightness temperature

in degrees Celsius (C) at 90-m resolution for

ASTER’s 5 thermal-infrared channels (8-12 μm).

This is an on-request product and will be generated

only when requested. Brightness temperature is the

apparent temperature of the surface assuming a

surface emissivity of 1.0. Setting the emissivity to

one is equivalent to assuming the target is a black

body, so the brightness temperature is defined as the

temperature a blackbody would be in order to

produce the radiance perceived by the sensor.

Brightness temperature has been used to observe

volcanic ash clouds, detect ice leads in the Arctic,

and to identify anthropogenic and natural fires, to

name a few examples. The ASTER brightness

temperature will be used as an alternate to radiance in

the temperature/emissivity separation algorithm to

report relative cloud top temperature because there

will be no routinely available applicable atmospheric

correction to enable a calculation of exact cloud-top

temperature.

ASTER brightness temperatures can be acquired

during the day or night and over all surface types

(land, water, cloud, etc.).

AST 04

PRODUCT SUMMARY

Coverage:

regional up to 70 64 × 60 km scenes per

day (AM and PM overpass)

Resolution:

90 m

Wavelengths:

brightness temperatures (°C) derived for

each thermal infrared channel

Processing Level

2

Product Type:

standard, at-launch

Science Team Contact:

R. Alley
AM-1 MOD 32

ASTER Browse Data -

Decorrelation Stretch Product

66 • Chapter Four: Radiance and Imagery Products Data Products Handbook Vol. I

Product Description

This Level 2 browse product contains decorrelation

stretch images of ASTER VNIR, SWIR, and TIR

radiance data. These images are produced at pixel

resolutions of 15 m for VNIR, 30 m for SWIR, and

90 m for TIR. Decorrelation stretched images provide

an overview that enhances reflectance and emissivity

variations and subdues variations due to topography

and temperature.

Research & Applications

The decorrelation stretch is a process that is used to

enhance the spectral differences found in a

multispectral image dataset. These images are used as

a visual aid in browsing the ASTER scene data and

making the selection of suitable scenes for further

analysis and research. In particular, a decorrelation

stretched image would show the potential user which

scenes have spectral variations large enough to be

useful for subsequent spectral analysis.

Data Set Evolution

The algorithm used removes the interchannel

correlation found in the input pixels. The

decorrelation-stretched images are color coded

producing a color composite containing the

maximum contrast for the features in the original set

of bands. The user may request as an on-demand

product a decorrelation stretched image generated

from any three ASTER channels that share a single

telescope (VNIR, SWIR, or TIR).

Suggested Reading

Gillaspie, A.R., et al., 1986.

Loeve, M., 1955.

Rothery, D.A., 1987.

Taylor, M.M., 1973.

AST 06

PRODUCT SUMMARY

Coverage:

regional, three images available per scene

Resolution:

15 m (VNIR), 30 m (SWIR), 90 m(TIR)

Wavelengths:

any 3 ASTER channels within a single

telescope for on-demand products

Processing Level

2

Product Type:

standard, at-launch

Science Team Contact:

R. Alley
AM-1 AST 07, AST 09

ASTER Surface Reflectance and

Surface Radiance

Data Products Handbook Vol. I Chapter Four: Radiance and Imagery Products • 67

Product Description

The Level 2 surface reflectance data set (AST 07)

contains surface reflectance for VNIR and SWIR

channels at 15-m and 30-m resolutions, respectively,

derived by applying an atmospheric correction to

observed satellite radiances. The data product is

recorded as percent reflectance. The method uses a

look-up table approach based upon a radiative

transfer model capable of estimating the magnitude

of scattering and absorption given atmospheric

turbidity parameters. The needed section of the lookup table is selected using a set of atmospheric

parameters (e.g., scattering optical depths, columnar

ozone, and water vapor) known at the time and

location of the satellite measurements. The retrieved

surface reflectance is only of known accuracy for

cloud-free pixels because inputs are known for clearsky pixels only.

The Level 2 surface radiance data set (AST 09)

contains surface radiance, in W/(m 2 • μm • sr), for

VNIR, SWIR, and TIR channels at 15-m, 30-m, and

90-m resolutions, respectively. The product is

derived by applying an atmospheric correction to

satellite radiances. The atmospheric correction used

to derive AST 09 for the VNIR and SWIR bands is

similar to that used to drive AST 07. The approach

for atmospheric correction in the thermal infrared

uses a radiative transfer model capable of estimating

the magnitude of atmospheric emission, absorption,

and scattering using a set of atmospheric parameters

(e.g., temperature, water vapor, ozone, profiles, and

scattering optical depths) known at the time and

location of the satellite measurements. The retrieved

surface radiance is only of known accuracy for cloudfree pixels because inputs are known for clear-sky

pixels only.

Both AST 07 and AST 09 are generated only upon

request. The products based on VNIR data are

available only for daytime satellite overpasses. Those

from SWIR data are available for daytime

overpasses, and, in cases of high temperature sources

(e.g., volcanoes or fires), available for nighttime

overpasses. Surface radiance from TIR data are

available for both daytime and nighttime overpasses.

AST 07, AST 09

PRODUCT SUMMARY

Coverage:

regional; 16 days required for global

coverage; 70 scenes per day

Resolution:

15, 30, 90 m (VNIR, SWIR, TIR,

respectively)

Wavelengths:

VNIR - 0.52 - 0.86 μm (4 channels),

SWIR - 1.6 - 2.43 μm (6 channels),

TIR - 8.125 - 11.65 μm (5 channels)

Product Type:

standard, on-request

Science Team Contact:

K. Thome (VNIR, SWIR),

F. Palluconi (TIR)

Research and Applications

The objective of these ASTER products is to provide

estimates of the surface radiance and reflectance. The

thermal infrared radiance includes both surface

emitted and surface reflected components. After

accurate atmospheric correction, seasonal and annual

surface changes can be studied and surface kinetic

temperature and emissivity can be extracted. Surface

radiances and reflectances can also be used for

surface classification, desertification studies, and

surface energy balance work.

Data Set Evolution

This product, as well as similar products for other

AM-1 instruments, mark the first implementations of

operational atmospheric corrections in environmental

satellites. Past work to retrieve surface reflectance

from satellite imagery has focused on small sets of

satellite data for highly studied ground targets. The

primary reason for this is that atmospheric correction

is a computationally intensive process. Methods for
AM-1 MOD 32

ASTER Surface Reflectance and

Surface Radiance

68 • Chapter Four: Radiance and Imagery Products Data Products Handbook Vol. I

reducing the computational requirements include

using look-up tables and making assumptions about

the horizontal homogeneity of the atmosphere.

Research into developing future versions of code to

produce AST 07 and AST 09 will focus on

attempting pixel-by-pixel corrections using pixel-bypixel atmospheric input parameters rather than

assuming horizontal homogeneity. Because of

ASTER’s small ground instantaneous field of view,

future versions of the atmospheric correction will

include corrections for multiple scatter effects from

adjacent pixels of differing reflectance, also known

as the adjacency effect. Look-up tables will also

expanded to include other parameters such as surface

bidirectional reflectance characteristics.

Suggested Reading

Deschamps, P. and T. Phulpin, 1980.

Hilland, J.E., et al., 1985.

McMillin, L.M., 1975.

Prabhakara, C., et al., 1975.

Price, J.C., 1984.
AM-1 AST 14

ASTER Digital Elevation Models (DEMs)

Data Products Reference Guide Vol. I Chapter Four: Radiance and Imagery Products • 69

Product Description

This data set contains topographic information

derived from the along-track, 15-m ASTER optical

stereo data acquired in the near-infrared. These high

spatial resolution DEMs (up to 7-m absolute

horizontal and vertical accuracy with appropriate

ground control, and up to 10-m relative accuracy

without ground control) can be used to derive

absolute slope and slope aspect good to 5 degrees

over horizontal distances of more than 100 m.

ASTER DEM’s should meet 1:50,000 to 1:250,000

map accuracy standards.

This is an on-request product which will be generated

by the Land DAAC at EROS Data Center only when

requested, and at a rate to be determined. Based on

simulations of instrument operations, mission

planning, cloud cover and illumination, the ASTER

digital stereo data with a base/height ration of 0.6

should be acquired for all of the Earth’s land surface

below 85 degrees latitude by the end of the 5-year

mission. These data are appropriate for users

interested in generating DEMs themselves.

Research and Applications

Topographic data as well as derived slope and slope

aspect are basic to all aspects of land surface research

including; cartography, climate modeling,

biogeochemistry, biogeography, geophysics,

geology, geomorphology and soil science. Digital

elevation data are also required for atmospheric and

radiometric correction of most satellite-acquired

observations of the land surface. Digital elevation

data are also used for practical engineering

applications such as studies of drainage and runoff,

site suitability studies for buildings, or waste

containment sites.

Data Set Evolution

Generation of elevation models from stereo

photographic data, now a routine adjunct to standard

surveying methods, has been developed over the past

60 years based on the principles of photogrammetry.

Extensions of these principles to the generation of

DEMs from optical stereo satellite data had been

implemented over the past two decades. Examples of

AST 14

PRODUCT SUMMARY

Coverage:

global

Spatial/Temporal Characteristics:

15 m

Key Geophysical Parameters:

digital topography

Key Science Applications:

cartography, land sciences, correction of

remotely sensed data.

Processing Level:

4

Product Type:

on request, at-launch

Science Team Contact:

H. Lang

these satellite stereo systems include SPOT, JERS-1

OPS, and MOMS. Currently, there are large areas of

the globe for which no consistent, high-resolution,

widely available elevation models exist. ASTER

DEMs will help provide much needed coverage over

many of these remote areas.

Suggested Reading

American Society of Photogrammetry, 1952.

Bohme, R., 1993.

O’Neill, M.A. and I.J. Dowman, 1993.

Welch, R. and H. Lang, 1994.
AM-1 MOD 32

DAS Assimilated Land/Water/Ice Flags

70 • Chapter Four: Radiance and Imagery Products Data Products Reference Guide Vol. I

Product Description

This product contains geolocated Level 1B radiances

for the eight spectral channels of the MOPITT

instrument. Radiances are obtained at three different

wavelengths using two different methods of gas

correlation radiometry. Reflected solar radiation is

measured at 2.2 and 2.3 μm using length modulator

cells to determine the total atmospheric columns of

methane and carbon monoxide respectively. Thermal

radiation at 4.6 μm is measured in four channels

using a combination of length modular and pressure

modulator cells to provide information on the vertical

profile of carbon monoxide. Engineering data

describing the state of the gas cells and calibration

are also provided to support transformation of the

radiances into Level 2 products.

Radiances are acquired by cross track scanning about

the nadir resulting in an interlaced swath of pixels

approximately 640 km wide. All channels are

simultaneously measured on four separate pixels each

with a resolution of 22 × 22 km. Each channel

measures radiance in two states of its correlation cell.

An average and a difference radiance are derived

from these two.

Suggested Reading

MOPITT Algorithm Theoretical Basis Document:

Conversion of MOPITT Digital Counts into

Calibrated Radiances in Carbon Monoxide and

Methane Absorption Bands (Level 0 to Level 1),

MOPITT Algorithm Theoretical Basis Document:

Retrieval of Carbon Monoxide Profiles and

Column Amounts of Carbon Monoxide and

Methane from MOPITT Radiances (Level 1 to

Level 2).

MOP 01

PRODUCT SUMMARY

Coverage:

global

Spatial/Temporal Characteristics:

650 km swath centered at nadir.

interlaced crosstrack scan of 4 pixels

each 22 × 22 km at nadir

Wavelengths:

 wavelength width

2.258 μm center ± 0.355

2.334 μm center ± 0.011

4.627 μm center ± 0.055

Processing Level:

1B

Product Type:

standard, at-launch

Science Team Contact:

J. Drummond
TRMM, AM-1 BDS

Bi-Directional Scan Product (BDS)

Data Products Handbook Vol. I Chapter Four: Radiance and Imagery Products • 71

Product Description

The Level 1 radiance product (BDS) contains filtered

radiances in the three CERES spectral bands:

shortwave (< 5 μm), longwave-window (8-12 μm),

and total (0.3-100 μm). The label “filtered” is used

since the radiances upwelling from the top of the

atmosphere are filtered through the optics of the

CERES instrument and by the spectral response of

the detector for each band. All the calibrated

radiances from one day’s global observations at the

full resolution footprint (20-km nadir) for CERES are

produced and archived as one product. The platform

ephemeris data are appended as housekeeping data in

engineering units and the Level 0 instrument inputs,

allowing this product to be a used for reprocessing in

the case of changes in the calibration coefficients or

other improvements in Level 1 processing.

Research & Applications

These radiance data products are the primary inputs

to all the CERES product algorithms. The spacecraft

ephemeris and sensor telemetry are inputs to this

subsystem which uses instrument calibration

coefficients to convert the spacecraft telemetry inputs

into geolocated filtered radiances and housekeeping

data into engineering units.

Data Set Evolution

The Radiance product algorithm operates on the

Level 0 reformatted raw sensor counts and produces

calibrated radiances in the three channels. The steps

in the processing are: (1) Convert the raw

housekeeping telemetry into engineering units

[temperatures, voltages, etc.]; (2) calculate the

geographical location of the CERES footprints;

(3) merge the raw spacecraft ephemeris and detector

point knowledge telemetry converted housekeeping

data and the raw radiometric detector telemetry;

(4) revise the radiometric detector count conversion

coefficients when required; (5) convert the detector

radiometric signals into filtered radiances; and

(6) archive the BDS standard products.

BDS

PRODUCT SUMMARY

Coverage:

global for AM-1; 40°N - 40°S for TRMM

Spatial/Temporal Characteristics:

20 km - not averaged or binned/ 1 per day

Key Geophysical Parameters:

TOA SW, LW, and window radiances,

geolocation data, surface temperature and

albedo

Processing Level:

0, 1

Product Type:

standard, at-launch

Science Team Contact:

B. Lee

Suggested Reading

Hoffman, L.H., et al., 1987.

Jarecke, P.J., et al., 1991.

Lee III, R.B., et al. , 1996.

Smith, G.L., et al., 1986.
AM-1 MOD 32

DAS Assimilated Land/Water/Ice Flags

72 • Chapter Four: Radiance and Imagery Products Data Products Handbook Vol. I

Product Description

This Level 4 product provides boundary conditions

for the Goddard EOS data assimilation system

(GEOS DAS). It is an estimate of land elevation,

land & water coverage, and permanent and sea ice at

each grid point of the GEOS DAS. This field is

based on the Navy 10 minute × 10 minute

topography dataset and satellite derived estimates of

permanent and sea ice.

Research & Application

This product is for reference by researchers using

GEOS DAS products. It provides elevation

references and insight into the boundary conditions

in the GEOS DAS general circulation model.

Data Set Evolution

Most of this field remains fixed in time. The only

exception is the amount of sea ice.

Suggested Reading

Bloom, S.C., et al., 1995.

Pfaendtner, J., et al., 1995.

Schubert, S.D., et al., 1995.

Schubert, S.D., et al., 1993.

Takacs, L.L., et al., 1994.

DAS 01

PRODUCT SUMMARY

Coverage:

global

Spatial/Temporal Characteristics:

2 × 2.5 deg lat-lon grid, daily at 00, 06,

12, and 18 UTC

Key Geophysical Parameters:

land water ice flags

Processing Level:

4

Product Type:

standard, post-launch

Science Team Contact:

R. Rood, L. Takacs
Chapter Five

Precipitation and

Atmospheric

Humidity
TRMM, AM-1

Precipitation and Atmospheric Humidity

74 • Chapter Five: Precipitation and Atmospheric Humidity Data Products Handbook Vol. I

Relationship to Global Change Issues

The hydrological cycle is one of three key Earth

system processes that are components of global

climate; the carbon and energy cycles are the others.

Monitoring precipitation and atmospheric humidity is

critical to obtaining hydrological-cycle parameters

needed for developing global climate models and

detecting climate change. The processes that generate

rainfall are central to the dynamical, biological, and

chemical processes in the atmosphere, in the oceans,

and on the land surfaces. It is clear that space-based

measurement is the only means to a more accurate

global documentation of tropical rainfall. The

TRMM instruments are designed to provide greatlyimproved measurements of rain rates over both land

and ocean compared to current methods. TRMM is

often referred to as a “flying rain gauge,” with a high

potential contribution to the World Climate Research

Program’s (WCRP’s) rain climatology initiative. This

section describes the TRMM and EOS products that

support the monitoring of rainfall and atmospheric

humidity, including the TRMM ground truth

validation data acquisition.

Precipitation: The majority of global precipitation

occurs in the tropics, between latitudes of 30°N and

30°S. Tropical rain is one of the key parameters that

affects the global climate change, the global heat

balance, and the global water cycle. To understand

and predict large variations in weather and climate,

it is critical to understand the coupling between the

atmosphere and the surface below, which is ocean

over 75 percent of the globe. Atmospheric

circulation transports both energy and water, moving

heat from the tropics to polar regions. Water,

evaporated from the oceans and the land surface,

falls as rain or snow often in places far removed

from its point of origin. There is evidence that

tropical rainfall variability, apparently coupled with

changes in the underlying surface (particularly sea

surface temperature), is associated with significant

alterations in wind patterns and rainfall. Fluctuations

in rainfall amount impact climate on both short-term

and long-term timescales.

Unlike the small-scale general features that appear

on daily weather maps, the long-term, time-averaged

circulation and precipitation fields show large-scale

features associated with the annual evolution of the

monsoons, trade wind systems, and oceanic

convergence zones. Large-scale features are also

evident on a monthly and seasonal scale. Interannual

variability is dominated by tropical Pacific oceanatmosphere interactions. These interactions are

associated with the ENSO cycle, whose major

swings, which occur at irregular intervals of 2-7

years, are associated with pronounced year-to-year

precipitation variations over large areas of the

tropics. The large-scale spatial coherence and

systematic evolution of both intraseasonal and

interannual tropical precipitation anomalies is of

great significance to climate prediction. They are

also important to the development of optimum

sampling strategies for estimating accumulated

precipitation over periods of a few weeks to a few

months.

The characterization of rainfall in tropical regimes is

fundamental to improving techniques of radarrainfall measurements as well. Not only are there

regional and seasonal differences, but significant

differences also occur within tropical rain systems.

These differences can be broadly classified as 1)

morphological, referring to sizes, lifetimes, and

spatial structures of rain producing systems; and 2)

microphysical, referring to raindrop formation

processes, and the raindrop size distribution.

Rainfall rates display a highly variable structure in

both time and space, meaning radar surveillance

systems must take frequent measurements to

accurately assess rainfall totals. Three-dimensional

volume scans (like the WSR-88D radar scans on the

nightly weather reports) every five minutes are

desirable, although scans at 15-minute intervals

provide useful data. The hydrological and severe

weather monitoring requirements of most

operational radar systems satisfy these criteria,

providing invaluable ground-based validation data

with nominal additional expenditure.

Humidity: Atmospheric circulation redistributes

moisture and heat. Water vapor evaporates from the

ocean and land surfaces, then is transported to other

parts of the atmosphere where condensation occurs,

clouds form, and precipitation ensues. There is a net

outflow of atmospheric moisture from the tropics,

where sea-surface temperatures are high and the

warm atmosphere can hold large amounts of water
TRMM, AM-1

Precipitation and Atmospheric Humidity

Data Products Handbook Vol. I Chapter Five: Precipitation and Atmospheric Humidity • 75

vapor, to higher latitudes, where condensation and

precipitation remove the moisture. Current

numerical weather prediction models give

inconsistent results about the future distribution of

precipitation.

Humidity is the amount of water vapor in the

atmosphere. Water vapor concentration depends on

temperature, which determines the total amount of

water that the atmosphere can hold without

saturation. Hence, water vapor amounts decrease

from equator to pole and with increasing altitude.

Generally, water amounts are less over the

continents than over the oceans, and the upper

atmosphere is drier than the near-surface

atmosphere. Superimposed on this general behavior

are smaller variations of water vapor amounts that

determine the formation and properties of clouds

and rainfall. Simultaneous imaging of clouds and

water vapor by TRMM and EOS AM-1 can provide

the data necessary to understand better the

interactions among cloud processes and the largescale flow and distribution of water vapor.

Product Overview

The products included in this section are primarily

produced by the TRMM instruments, with MODIS

from the EOS AM-1 platform providing two

products giving humidity data. The DAS products

included in this section are primarily based on

TRMM data.

The TRMM mission is designed to use the

measurements of multiple instruments flying on its

platform to quantify the distribution of the twothirds of global precipitation estimated to fall in the

tropics and subtropics. The PR will measure the 3-D

rainfall distribution over both land and ocean. More

specifically, this instrument will provide information

about the vertical structure of precipitation, the key

to determining the latent heat input to the

atmosphere. A unique feature is that it will permit

the measurement of rain over land where passive

microwave precipitation estimation techniques have

difficulty.

The fundamental data set to be obtained from the

TRMM program is a 3-year sequence of

instantaneous rainfall rates as well as monthly,

tropical, rainfall accumulations averaged over

geographical grid squares 500 km on a side. These

gridded monthly averages can be used to validate

climate forecasting models. The TRMM data

product sequence begins with the TMI and the

precipitation radar data, which form the basis of the

improved rainfall estimation promised by the

mission. Rainfall estimates from the TMI and the PR

are combined in TRMM product 3B-31, and then

combined with Sun-synchronous low-orbit satellite

microwave data (SSM/I), geostationary satellite IR

data (e.g., GOES) and rain gauge data to produce the

final rainfall product (3B-43).

The MODIS products provide water vapor

measurements (MOD 05) needed for atmospheric

correction algorithms for all surface parameter

retrieval tasks. They are also input to research to

improve understanding of the hydrological cycle,

energy budget, and climate. The atmospheric

stability parameter in MOD 07 gives information on

convection and instability which, along with

temperature and moisture profiles, also contained in

product MOD 07, is needed by the atmospheric

correction algorithms.

A set of TRMM ground validation products from

approximately 10 independent sites is included.

These products provide the rainfall data used to

validate the data obtained from the PR on TRMM.

Included are the instantaneous surface rainfall maps

(2A-53), the radar site Convective/Stratiform Map

(2A-54), and the radar site gridded reflectivities

(2A-55) used to validate the vertical hydrometeor

and latent heating retrievals of the TMI and the PR.

Five-day and 30-day rainfall data at the radar sites

(3A-53, 54) and monthly 3-D radar reflectivity

distributions (3A-55) complete the set of products

that are needed to validate and improve all the

TRMM rainfall products.

Product Interdependency and Continuity

with Heritage Data

The TRMM mission will carry the first quantitative

precipitation radar to be flown in space. Previous

measurements from space of rain rate have used

passive microwave measurements from instruments

on Nimbus and the Defense Meteorological

Satellites, which are in polar orbits and cannot
TRMM, AM-1

Precipitation and Atmospheric Humidity

76 • Chapter Five: Precipitation and Atmospheric Humidity Data Products Handbook Vol. I

capture the diurnal variability of rainfall in the

tropics. TRMM includes a microwave radiometer

(TMI) that provides continuity with these earlier

data and which will be used in conjunction with the

TRMM PR and the heritage instrument data to

provide greatly-improved accuracy in rainfall

mapping. The radiometers on TRMM will give good

measurements of rainfall rates over the oceans, but

such measurements will continue to be less reliable

over land where the surface emissivity

inhomogeneities make interpretation more difficult.

VIRS, which is derived from AVHRR heritage

technology, is also included in the TRMM

instrument complement so that the rainfall

measurements from the radar and passive

microwave instruments can be used to better

interpret VIS/IR measurements from past and future

operational satellites.

The MODIS humidity products will be used by

atmospheric characterization and climate modeling

researchers in conjunction with the rainfall data even

though there is not a direct product dependence

between MODIS and TRMM.

The assimilated data products in this section come

from the GEOS DAS. This system combines a

global general circulation model with standard

meteorological observations, along with TRMM and

AM-1 observations, to produce dynamically

consistent estimates of the fields described.

Suggested Reading

Chiu, L.S., 1988.

Fujita, M., et al., 1985.

Inoue, T. 1987.

Rasmusson, E.M., 1985.

Simpson, J., Ed., 1988.

Simpson, J., et al., 1996.

Theon, J.S., Ed., 1988.

Theon, J.S., et al., Eds., 1992.

Wu, R., J.A. Weinman, and R.T. Chin, 1985.
TRMM PR 2A-21

PR Surface Cross-Section as

Function of Scan Angle

Data Products Handbook Vol. I Chapter Five: Precipitation and Atmospheric Humidity • 77

Product Description

This Level 2 product provides an estimate of the path

attenuation and its reliability by using the surface as a

reference target. Path attenuation is estimated by

comparing the apparent surface cross section

measured in the presence of rain, with the averaged

surface cross section measured in the absence of rain.

A reliability parameter is also provided based on the

variability of the surface cross section in the absence

of rain. The product includes the spatial and temporal

statistics of the surface scattering cross section, and

classification of the cross sections into land/ocean,

rain/no rain categories. Output parameters consist of

attenuation, reliability, surface cross section (sigma

zero).

Research & Applications

One of the ways by which attenuating-wavelength

radars such as TRMM can correct for attenuation is

by means of the surface reference method. In this

method, the path integrated attenuation caused by

rain is estimated by a ratio of the apparent surface

cross section, σ ο, in the raining region to a

temporally or spatially averaged value of this

quantity in rain-free regions.

As the surface cross sections are closely related to the

vegetation and soil moisture conditions over land,

and the wind speed and directions over oceans, a

number of TRMM-related studies can be undertaken

with these data sets.

Data Set Evolution

There have been limited σ° measurements made from

aircraft radars, but systematic global maps have not

previously been compiled.

Relation to Other TRMM Products

This product is used as input for the PR rain profiling

algorithm (2A-25). For areas far away from rainfall,

σ° is a unique product of TRMM.

PR 2A-21

PRODUCT SUMMARY

Coverage:

36°N - 36°S; 16 orbits at 220 km each

precessing through diurnal cycle

Spatial/Temporal Characteristics:

radar swath, ± 17° incidence,

4 km at nadir

Key Geophysical Parameters:

path attenuation, surface cross section

Processing Level:

2

Product Type:

standard, at-launch

Science Team Contact:

K. Okamoto

Suggested Reading

Meneghini. R., and T. Kozu, 1990.
TRMM 2A-23

PR Rain Occurrence and

Rain Type & Bright Band Height

78 • Chapter Five: Precipitation and Atmospheric Humidity Data Products Handbook Vol. I

Product Description

The Level 2 product 2A-23 indicates the presence of

a “bright band,” which in turn indicates the presence

of stratiform rain. If the bright band is detected, its

height is determined. This information is used to

classify the rain type (stratiform type, convective

type or warm rain). TSDIS will supply information as

to the background type: land, ocean, or

indeterminate. The height of the bright band is useful

to the TMI algorithms to identify the height of the

freezing level.

Research & Applications

PR will provide information on the distribution and

characterization of rainfall and latent heating over the

global tropics and subtropics. This information will

be used to advance the Earth system science

objective to understand global energy and water

cycles. In addition, it will provide understanding of

the mechanism through which tropical rainfall and its

variability influence global atmospheric circulation

by improving the modeling of rainfall and

atmospheric circulation interactions. This will enable

the improved prediction of monthly and longer global

circulation and rainfall variability. Finally, it will

improve modeling of convectively-driven

precipitating systems in the tropics, including their

organization on the mesocale and their interactions

with the ocean and the atmosphere.

Data Set Evolution

There have been a number of airborne radars that

have flown on field campaigns over the last few

years. Most notably, ARMAR is a 13.8 GHz radar

flying on the NASA DC8 which was built

specifically to simulate TRMM observations.

PR 2A-23

PRODUCT SUMMARY

Coverage:

16 orbits of 760 km each precessing

through diurnal cycle

Spatial/Temporal Characteristics:

radar swath, ± 17° incidence,

4 km at nadir

Key Geophysical Parameters:

storm and bright-band height, rain type

Processing Level:

2

Product Type:

standard, at-launch

Science Team Contact:

K. Okamoto

Relationship to Other TRMM Products

Product 2A-23 is a qualitative description not

previously available. It will be useful to passive

microwave as well as input to the PR rain algorithm.

Suggested Reading

Furuhama, Y., et al., 1982.

Iguchi, T. and R. Meneghini, 1994.

Theon, J.S., et al., Eds., 1992.
TRMM PR 2A-25

PR Range Profiles of Rain and Water Content

Data Products Handbook Vol. I Chapter Five: Precipitation and Atmospheric Humidity • 79

Product Description

Level 2 product 2A-25 retrieves rain parameters over

each PR resolution cell (4 km × 4 km × 250 m).

Products 1C-21, 2A-21, and 2A-23 are input to this

product. In the presence of rain, these parameters

include rainfall structure, estimated path attenuation,

and its reliability.

Research & Applications

PR will provide information on the distribution and

characterization of rainfall and latent heating over the

global tropics and subtropics. This information will

be used to advance the Earth system science

objective to understand global energy and water

cycles. In addition, it will provide understanding of

the mechanism through which tropical rainfall and its

variability influence global atmospheric circulation

by improving the modeling of rainfall and

atmospheric circulation iterations. This will enable

the improved prediction of monthly and longer global

circulation and rainfall variability. Finally, it will

improve modeling of convectively-driven

precipitating systems in the tropics, including their

organization on the mesocale and their interactions

with the ocean and the atmosphere.

Data Set Evolution

There have been a number of airborne radars that

have flown on field campaigns over the last few

years. Most notably, ARMAR is a 13.8 GHz radar

flying on the NASA DC8 which was built

specifically to simulate TRMM observations.

Relationship to Other TRMM Products

Product 2A-25 provides the best single instrument

rain profile information. Over the narrow swath of

the radar it will be used to improve the passive

microwave algorithm (2A-12), thereby extending the

usefulness of the PR to the wide swath of the TMI.

PR 2A-25

PRODUCT SUMMARY

Coverage:

day and night, 38°N - 38°S

Spatial/Temporal Characteristics:

swath PR resolution cell: 4 km × 4 km,

250 m vertical at nadir

Key Geophysical Parameters:

rain rate profile

Processing Level:

2

Product Type:

standard, at-launch

Science Team Contact:

K. Okamoto

Suggested Reading

Iguchi, T. and R. Meneghini, 1994.

Kozu, T., et al., 1991.

Theon, J.S., et al., Eds., 1992.
TRMM TMI 2A-12

TMI Surface Rainfall and Vertical Structure

80 • Chapter Five: Precipitation and Atmospheric Humidity Data Products Handbook Vol. I

Product Description

This product contains vertical hydrometeor profiles

from the TMI obtained by blending the radiometric

data with dynamical cloud model data. The results

consist of the surface rainfall rate and a confidence

parameter, as well as 3-D results, with five

hydrometeor classes and the associated latent heating

derived at ~ 14 vertical layers. The layer information

must be viewed as approximate since it is inferred

through the cloud model rather than from direct

measurements. This is a Level 2 product which has

not been resampled; scan time, latitude, longitude,

and scan line/pixel number are included for each

pixel in this product.

Research & Applications

The research objectives of this product include

• improve the ability to obtain rainfall and

latent heat release over the global tropics;

• validate and improve models that promote

the understanding of the formation and

organization of convection and their

interactions with the ocean and ambient

atmosphere and the climate system; and

• advance understanding of the Earth’s global

energy and water cycles by determining how

tropical rainfall and its variability influence

global circulation.

Relationship to other TRMM products

This product generates output similar to 2A-25 and

2B-31. Product 2A-25, the PR rainfall vertical

structure has better vertical resolution (250 m), but is

limited to a relatively narrow swath of 220 km as

compared to the 760 km swath of the TMI.

Sensitivity to the integrated ice content is also greater

from the TMI product that the PR product.

Product 2B-31 combines information from both the

radar and radiometer over the narrow swath of the

radar. In this region, 2B-31 should ultimately provide

the best surface rainfall and structure products.

Problems related to co-registration of the sensors, as

well as the lack of PR heritage, make this product

(2B-31) less reliable at the onset of the mission.

TMI 2A-12

PRODUCT SUMMARY

Coverage:

16 orbits of 760 km each precessing

through diurnal cycle

Spatial/Temporal Characteristics:

swath, 10 km resolution

Key Geophysical Parameters:

rainfall, hydrometeors, and latent heating

profile

Processing Level:

2

Product Type:

standard, at-launch

Science Team Contact:

C. Kummerow

Data Set Evolution & Applications

The TMI is similar to several spaceborne microwave

instruments which have flown over the past decade,

including the SSM/I on-board the DMSP satellites

and the SMMR on-board Nimbus-7. The major

changes are the addition of 10 GHz dual-polarizedchannels and increased spatial resolution, which will

reduce the footprint filling uncertainty that affects

passive microwave algorithms.

Suggested Reading

Kummerow, C., and L. Giglio, 1994.

Mugnai, A., et al., 1993.

Smith, E.A., et al., 1994.

Smith, E.A., et al., 1994.
TRMM TMI 2B-31

TRMM Combined Surface Rainfall Rate and

Vertical Structure

Data Products Handbook Vol. I Chapter Five: Precipitation and Atmospheric Humidity • 81

Product Description

This is a Level 2 product which combines

information from more than one sensor. This product

contains vertical hydrometeor profiles obtained by

blending reflectivities obtained by the PR with

brightness temperatures from the TMI instrument.

The results consist of the surface rainfall rate and a

confidence parameter. Also included are the 3-D

results, with five hydrometeor classes and the

associated latent heating derived at the vertical

resolution of the PR (250 m).

This product is designed to improve our ability to

obtain rainfall and latent heat release over the global

tropics. Additionally, it will be used to validate and

improve models that promote the understanding of

the formation and organization of convection and

their interactions with the ocean and ambient

atmosphere and the climate system. Finally, it

advances our understanding of the Earth’s global

energy and water cycles by determining how tropical

rainfall and its variability influence global

circulation.

Research & Applications

The vertical distribution of hydrometeors is an

important part of any surface rainfall rate retrieval

algorithm, because the distribution strongly affects

the microwave radiances. Once problems associated

with different viewing geometries and sensor

resolutions are overcome, this product should

constitute the best product available from the TRMM

mission.

Relationship to Other TRMM Products

This product generates output similar to 2A-12 and

2A-25. Product 2A-12 (the TMI rainfall vertical

structure) has wider coverage (760 km vs. 220 km).

The PR algorithm serves primarily to ensure a robust

solution until problems related to co-registration of

the sensors and different spatial resolutions are

properly solved.

TMI 2B-31

PRODUCT SUMMARY

Coverage:

36°N - 36°S; 16 orbits at 220 km each

precessing through diurnal cycle

Spatial/Temporal Characteristics:

swath 4 km at nadir

Key Geophysical Parameters:

rainfall profile

Processing Level:

2

Product Type:

standard, at-launch

Science Team Contact:

E. Smith

Data Set Evolution & Applications

The simultaneous use of active and passive

microwave sensors can yield dramatic improvements

over either sensor alone. Some limited aircraft studies

have demonstrated this point. The opportunity to

merge radiances from active and passive sensors, as

well as visible and infrared radiances on a global

dataset, is a capability which is unique to the TRMM

satellite. As the algorithm matures, this product will

form the cornerstone of TRMM retrievals and will

serve as the transfer standard to calibrate previous

satellites’ measurements.

Suggested Reading

Schols, J.L. and J.A. Weinman, 1994.

Smith, E.A., et al., 1994.
TRMM TMI 3A-11

TMI Surface Rainfall

82 • Chapter Five: Precipitation and Atmospheric Humidity Data Products Handbook Vol. I

Product Description

This product consists of mean monthly rainfalls over

5° × 5° cells. Retrievals are only possible over

oceanic areas, where data are needed most for

climate model verification. The frequency of

occurrence of rain intensities in different rate

categories over a specified area can be plotted as a

histogram or as a smoothed curve which fits the

histogram. This curve is called the probability density

function (pdf) of the rain rates. In the case of tropical

rainfall, the pdf’s are always highly skewed such that

a large fraction of the total rainfall is concentrated

into a relatively small number of storms of high

intensity or in a small fraction of the time or of the

area in which it is raining. This product is obtained

by considering histograms of 19-GHz, 21-GHz and

19-21-GHz combinations. Monthly rainfall indices

are computed by statistically matching the histograms

with the model calculated pdfs. The main purpose of

this product is to provide a robust baseline of surface

rainfall rates.

Research & Applications

This product is designed to advance understanding of

the Earth’s global energy and water cycles by

determining how tropical rainfall and its variability

influence global circulation. A reliable measurement

of rainfall, particularly over the oceans, will provide

for the first time space-time variability of vertically

integrated latent heating. This, coupled with

information on the nature of synoptic disturbances in

different regions of the tropics, can help determine

the vertical structure of the diabatic heating.

Data Set Evolution

The TMI is similar to several spaceborne microwave

instruments which have flown over the past decade

including the SSM/I on-board the DMSP satellites

and the SMMR onboard Nimbus-7. The major

changes are the addition of 10 GHz polarized

channels and increased spatial resolution, which will

reduce the footprint filling uncertainty that affects

passive microwave algorithms. The surface rainfall

algorithm is very similar to the one developed and

applied as part of the GPCP program for the past 8

years.

TMI 3A-11

PRODUCT SUMMARY

Coverage:

day and night, 40°N - 40°S

over oceans only

Spatial/Temporal Characteristics:

5° × 5°, monthly

Key Geophysical Parameters:

rainfall amount and statistics

Processing Level:

3

Product Type:

standard, at-launch

Science Team Contact:

A. Chang

Relationship to Other TRMM Products

This product generates monthly surface rainfall with

sampling corresponding to the wide swath of the

TMI. This parameter may also be obtained from the

TRMM combined sensor algorithm but is included as

a standard product to serve as a very robust baseline.

Suggested Reading

Wilheit, T.T., et al., 1991.
TRMM 3A-25, 3A-26

PR Accumulated Rainfall and Vertical Structure,

Accumulated Surface Rainfall

Data Products Handbook Vol. I Chapter Five: Precipitation and Atmospheric Humidity • 83

Product Description

Statistics of rain data are provided in this Level 3

product, including probability distributions of rain

rates, to help identify and characterize sampling

errors, and to determine patterns in precipitation

movement and latent heat transfer. The statistical

nature of the rainfall is characterized by the outputs

from the Level 2 radar algorithms. Four categories of

output products will be computed over 5° × 5° × 1

month domains. The four categories are: mean and

variance of the rain rate at five heights above the

surface; fractional rain areas (stratiform, convective,

both); histograms of the rain rates, reflectivity

factors, heights of the bright-band and storm top, and

path attenuation; and correlation coefficients between

rain rates at the various heights.

Research & Applications

This product is designed to provide the temporal

continuity and spatial coverage necessary for routine

evaluation of time-space mean precipitation.

Applications include quantitative estimates of large

scale, time-averaged precipitation over the global

tropics, analyses of the seasonal, interannual and

diurnal variability of precipitation, and the

establishment of multiyear time series data for global

climate change.

Relationship to Other TRMM Products

Inputs to algorithm 3A-25 will be the output products

from the Level 2 PR algorithms. A few of the output

products from 3A-25 and 3A-26 will provide

estimates of the same quantity (e.g., the monthly

mean rain rate at the 5° × 5° boxes over the TRMM

domain). The approach used in 3A-25 is that of a

straightforward sample mean of the individual

measurements obtained during the month whereas

3A-26 uses a somewhat more involved statistical

approach. Comparisons of these products over the

TRMM ground-validation sites are obviously

important. As the TMI 3A-11 algorithm will output

monthly means over the same 5° × 5° cells as will

3A-25 and 3A-26, comparisons of the output

products should be made. To reduce the effects of

sampling errors on 3A-25 and 3A-26, comparisons of

these outputs with those of 3A-11 should also be

made over longer time periods (3 months, 6 months,

and yearly).

TRMM 3A-25, 3A-26

PRODUCT SUMMARY

Coverage:

40°N - 40°S

Spatial/Temporal Characteristics:

5° × 5°, monthly

Key Geophysical Parameters:

rain rate statistics, storm top height, path

attenuation

Processing Level:

3

Product Type:

standard, at-launch

Science Team Contact:

K. Okamoto

Data Set Evolution

Approaches for measuring statistically averaged rain

rates for TRMM have their heritage in the GARP

Atlantic Tropical Experiment (GATE) measured rain

rates experiments, as well as the Florida Area

Cumulus Experiment (FACE). The GATE and FACE

radar rain distributions were the heritage data sets

used to describe some of the key statistics

characteristics of the precipitation which TRMM is

intended to measure. Additionally, by identifying the

ranges of rain rates measured in these experiments,

they served to enable instrument designers to better

specify the parameters of and performance of the

observing system.

Suggested Reading

Atlas, D., et al., 1990.

Meneghini, R. and J.A. Jones, 1993.

Wilheit, T.T., et al., 1991.
TRMM 3B-31

Combined Accumulated Rainfall

and Vertical Structure

84 • Chapter Five: Precipitation and Atmospheric Humidity Data Products Handbook Vol. I

Product Description

This Level 3 product consists of rainfall

accumulations over 5° × 5° areas and monthly time

scales of surface rainfall, as well as the 3-D

hydrometeor structure and measurements of latent

heating. Retrievals are made over both land and

ocean using the sampling characteristics of the TMI

instrument. The product is a calibration of the TMI

profiling algorithm (2A-12), where the calibration is

obtained by comparing the coincident geophysical

products from the TMI 2A-12 with those of the

combined sensors approach (2B-31). The calibration

coefficients are then applied to the entire 2A-12

dataset and monthly accumulations are computed.

The simultaneous use of active and passive

microwave sensors can yield dramatic improvements

over either sensor alone, as demonstrated by earlier

aircraft studies. The opportunity to merge radiances

from active and passive sensors as well as visible and

infrared radiances on a global dataset is a capability

which is unique to the TRMM satellite. As the

algorithm matures, this product will form the

cornerstone of TRMM retrievals and will serve as the

transfer standard to calibrate previous satellites

measurements.

Research & Applications

At present there are no reliable data sets for either

monthly or seasonal mean rainfall or for their

interannual variability to validate the global

atmospheric models. Latent heat derived from

precipitation is the major source of energy within the

atmosphere, and its vertical distribution affects the

stability of the atmosphere and hence the propagation

pattern of Rossby waves, which influence the climate

in regions that are located far from the tropical

storms where the latent energy is released. This

product will allow the measurement of latent heating

to be calculated from condensation. The direct

observation of precipitation, and the cloud systems

which produce it in varying amounts, will permit

researchers to know exactly where the latent heat is

being released. This information can be used as input

to climate models to determine higher latitude effects

and possible interannual changes.

TRMM 3B-31

PRODUCT SUMMARY

Coverage:

day and night, 40°N - 40°S

Spatial/Temporal Characteristics:

5° × 5°, monthly

Key Geophysical Parameters:

rainfall, hydrometeors, land heating

distribution

Processing Level:

3

Product Type:

standard, at-launch

Science Team Contact:

E. Smith

Relationship to Other TRMM Products

This product generates monthly surface rainfall and

latent heating with sampling corresponding to the wide

swath of the TMI. Product 3A-25 generates similar

products but only uses the narrower swath of the PR.

Data Set Evolution

Precipitation distributions have already been retrieved

from spaceborne microwave radiometers, and TMI

builds on that earlier work. The primary instrument

currently used to retrieve volumetric rainfall from

space is the SSM/I passive radiometer system on the

DMSP. The TRMM Combined Monthly Rainfall

product overcomes the limitation of radiometric

measurements, which include difficulty in measuring

light rain and snowfall over land. Combing radar and

radiometric measurements in a common precipitation

retrieval algorithm takes advantage of the strengths of

both types of measurements.

Suggested Reading

Smith, E.A., et al., 1994.

Schols, J.L. and J.A. Weinman, 1994.
TRMM 3B-42, 3B-43

TRMM Combined Instrument Rain Calibration

and Combined Instrument Global 1-Degree Rainfall

Data Products Reference Guide Vol. I Chapter Five: Precipitation and Humidity • 85

TRMM 3B-42, 3B-43

PRODUCT SUMMARY

Coverage:

day and night, 40°N - 40°S

Spatial/Temporal Characteristics:

1° × 1° × 5 day

Key Geophysical Parameters:

rainfall

Processing Level:

4

Product Type:

standard, at-launch

Science Team Contact:

R. Adler

Combined satellite-gauge

precipitation map (mm) – July 1994

Figure 15. Satellite-Gauge Estimate of Precipitation for July 1994 (in mm/mo). This technique for combining low-orbit

microwave, geosynchronous-orbit infrared, and rain gauge data is a prototype of TSDIS Algorithm 3B-43.

Product Description

This pair of products implements the “flying rain

gauge” aspect of TRMM by calibrating other

precipitation estimates with the “best” TRMM

estimate of precipitation to provide improved rainfall

estimates on finer scales than TRMM can provide by

itself. The TRMM Combined Instrument Rain

Calibration (3B-42) uses TRMM estimates of surface

rain and hydrometeor structure to calibrate and adjust

rain estimates made from geosynchronous infrared

observations. The “Rainfall” (3B-43) combines

3B-42 with rain gauge analyses (where available) and

SSM/I estimates — a passive radiometer system on

the Defense Meteorological Satellite Program

(DMSP) satellite, similar to the TMI — to

objectively determine the best estimate of rainfall on

the space and time scales indicated. This includes

adjustments for bias between the satellite and gauge

fields, where feasible, and objective combination

based on the relative errors estimated for each field.

Each product has an associated field of estimated

error that reflects the local sampling of the various

input fields and the reliability of each data source.

Research & Applications

Surface rainfall in many parts of the world is very

poorly documented on the time and space scales that

these products provide. These products will provide

considerably more detail for validation of numerical

weather and climate predictions, accumulations into

seasonal-scale climatologies, and input to surface

hydrology models. As well, these products will

provide systematic insight into the accuracy of the

independent input fields, both regionally and

seasonally, which will motivate fresh research to

improve the precipitation estimation and process.

Relationship to Other TRMM Products

The Surface Rainfall and Vertical Structure (2B-31),

and consequently all of the products antecedent to

3B-31, is key to making 3B-42 (and hence 3B-43) as
TRMM 3B-42, 3B-43

TRMM Combined Instrument Rain Calibration

and Combined Instrument Global 1-Degree Rainfall

86 • Chapter Five: Precipitation and Atmospheric Humidity Data Products Handbook Vol. I

accurate as possible. The TRMM VIRS 11 μm

temperatures (1B-01) provide key data in the

calibration step of 3B-42. The TRMM Global

Rainfall products provide finer space and time scales

than pure TRMM products, but at the expenses of

depending on data sources outside TRMM.

Data Set Evolution

The analysis procedures for 3B-42 and 3B-43 were

developed for rainfall estimates from the SSM/I,

which has flown on selected DMSP polar-orbiting

platforms since mid-1987. The TRMM versions of

both procedures are third-generation code; further

development is expected as researchers gain

experience with the new data sources.

Suggested Reading

Adler, R.F., et al., 1994.

Huffman, G.J., et al., 1995.
TRMM GV2A-53

Radar Site Rain Map

Data Products Handbook Vol. I Chapter Five: Precipitation and Atmospheric Humidity • 87

Product Description

The Level 2A Ground Validation Radar Site Rain

Map (2A-53) product contains instantaneous (highest

temporal, 2-km horizontal resolution) rainmaps

produced from the 1C-51 reflectivity fields. These

require application of an appropriate reflectivity (Z)

to rain (R) transformation, determined separately for

each site on the basis of rain gauge, radar, and

raindrop size information.

Research and Applications

This product is used to determine the intensity,

duration, and spatial extent of rain producing systems

at each ground validation site.

Relationship to Other TRMM Products

This product is necessary to validate instantaneous

rainfall retrievals derived from measurements by

instruments on the TRMM satellite (2A-12, 2A-25;

2B-31).

Data Set Evolution

Rainfall estimation from surface-based radar

observations have been used operationally for several

decades in short-term forecasting and for

hydrological purposes. Recent upgrades to the

operational systems and rainfall algorithms are used

in the TRMM Ground Validation Program.

Suggested Reading

Krajewski, W.F., 1987.

Rosenfeld, D., et al., 1994.

Steiner, M., and R.A. Houze, Jr., 1993.

TRMM GV2A-53

PRODUCT SUMMARY

Coverage:

150 km radius of ground validation radar,

continuous

Resolution:

2 km (minimum)

Key Geophysical parameters:

rainfall

Processing Level:

2

Product Type:

validation, at-launch

Science Team Contact:

R. Houze
TRMM GV2A-54

GV Radar Site Convective/Stratiform Map

88 • Chapter Five: Precipitation and Atmospheric Humidity Data Products Handbook Vol. I

Product Description

The Level 2A Ground Validation Radar Site

Convective/Stratiform Map identifies rainfall from

TRMM 2A-53 as occurring from either convective or

stratiform type clouds and generated from the

reflectivity product 1C-51.

Research and Applications

Characterization of precipitation types as stratiform

or convective is essential for determining the vertical

profile of heating associated with these two major

classifications. Classification methods include

information on the vertical and horizontal structure of

reflectivity fields.

Relationship to Other TRMM Products

Product 2A-54 will be used to validate the

convective/stratiform separation derived by the PR

algorithm 2A-23. The convective stratiform

classification map will be used as input to 3A-55 to

be used in evaluation of the vertical heating profile,

which is of interest to large scale models for

initialization and validation studies.

Data Set Evolution

Convective/stratiform classification methods have

been developed for use in studies of the structure,

dynamics, and evolution of tropical mesoscale

convective systems.

Suggested Reading

Churchill, D.D. and R.A. Houze, Jr., 1984.

Houze, R.A. Jr., 1989.

Rosenfeld, D., et al., 1995.

Steiner, M., et al., 1995.

TRMM GV2A-54

PRODUCT SUMMARY

Coverage:

150 km radius of ground validation site,

continuous

Resolution:

2 km (minimum)

Key Geophysical parameters:

rain type

Processing Level:

2

Product Type:

validation, at-launch

Science Team Contact:

R. Houze
TRMM GV2A-55

GV Radar Site Reflectivities

Data Products Handbook Vol. I Chapter Five: Precipitation and Atmospheric Humidity • 89

Product Description

The Level 2A Ground Validation Radar Site

Reflectivity (2A-55) product contains instantaneous

(highest temporal, 2-km horizontal resolution) 3-D

Cartesian gridded reflectivities, interpolated from

volume scan reflectivities (1C-51). The origin and

outer boundaries of the 3-D Cartesian grid will be

determined by the TRMM Ground Validation Team.

In addition to the 3-D gridded reflectivity data, this

product will include vertical profiles of the mean

reflectivity and the frequency distribution at each

height. The frequency distribution will be provided in

the form of contoured frequency by altitude diagrams

(CFADs). Vertical profiles and CFADs will be

provided for the following categories: total, totalland, total-ocean, total-convective, total stratiform,

total-anvil, land-convective, land-stratiform, landanvil, ocean-convective, ocean-stratiform, and oceananvil.

Research and Applications

The three-dimensional structure of radar reflectivity

is determined by hydrometeor distributions within

cloud systems and is indicative of both microphysical

and dynamical processes within such systems. The

reflectivity structure can be used to simulate satellite

observations of precipitating cloud systems, given

assumptions about the hydrometeor distributions.

Relationship to Other TRMM Products

This product will be used to validate the vertical

hydrometeor and latent heating retrievals of the TMI

(2A-12), PR (2A-25), and combined product (2B-31)

vertical hydrometeor and latent heating retrievals.

Data Set Evolution

Pre-operational tests within TSDIS began in August

1995 and will continue until the launch of TRMM.

Suggested Reading

Yuter, S.E., and R.A. Houze, Jr. 1995.

TRMM GV2A-55

PRODUCT SUMMARY

Coverage:

150 km radius of ground validation site,

continuous

Resolution:

2 km horizontal (minimum); 1.5 km

vertical (minimum)

Key Geophysical parameters:

3-D reflectivity

Processing Level:

2

Product Type:

validation, at-launch

Science Team Contact:

R. Houze
TRMM GV3A-53

GV 5 Day Site Rain Map

90 • Chapter Five: Precipitation and Atmospheric Humidity Data Products Handbook Vol. I

Product Description

5-day, 2 km × 2 km surface rainfall at site.

Research and Applications

Significant variations in tropical rainfall patterns

have been found to occur at periods longer than 5

days. These patterns frequently have large-scale

structure and persistence that could be useful for

weather prediction and validation of general

circulation models.

Relationship to Other TRMM Products

Although TRMM sampling by itself is not sufficient

to accurately represent 5-day rainfall totals,

supplementary observations of proxy rainfall data

observed continuously by other satellites can be

calibrated regionally by the validation product to

globally map and monitor variations in rainfall

patterns occurring at periods in the range of weeks to

a month or two. Directly, this product will validate

the estimates for the TRMM and other satellite

algorithms (3B-42 and 3B-43).

Data Set Evolution

Pre-operational tests within TSDIS began in August

1995 and will continue until the launch of TRMM.

Suggested Reading

Huffman, G.J., et al., 1995.

Rosenfeld, D., et al., 1994.

Steiner, M., and R.A. Houze, Jr., 1993.

TRMM GV3A-53

PRODUCT SUMMARY

Coverage:

150 km radius of ground validation radar,

continuous

Spatial/Temporal Characteristics:

5-day accumulations, 2 × 2 km

Key Geophysical Parameters:

surface rainfall

Processing Level:

3

Product Type:

validation, at-launch

Science Team Contact:

R. Houze
TRMM GV3A-54

GV 30 Day Site Rain Map

Data Products Handbook Vol. I Chapter Five: Precipitation and Atmospheric Humidity • 91

Product Description

30-day, 2 km × 2 km surface rainfall at site.

Research and Applications

This product will be used to validate the satellite

algorithms and to provide independent validation to

climate and forecast models.

Relationship to Other TRMM Products

This product is the principal means of validating all

the TRMM monthly 5° × 5° rainfall estimates.

Data Set Evolution

Rainfall estimation from surface-based radar

observations have been used operationally for several

decades in short term forecasting and for

hydrological purposes. Recent upgrades to the

operational systems and rainfall algorithms are used

in the TRMM Ground Validation Program.

Suggested Reading

Huffman, G.J., et al., 1995.

Rosenfeld, D., et al., 1994.

Steiner, M., and R.A. Houze, Jr., 1993.

TRMM GV3A-54

PRODUCT SUMMARY

Coverage:

150 km radius of ground validation radar,

continuous

Spatial/Temporal Characteristics:

30-day accumulations, 2 × 2 km

Key Geophysical Parameters:

rain vertical structure and latent heating

Processing Level:

3

Product Type:

validation, at-launch

Science Team Contact:

R. Houze
TRMM GV3A-55

GV Monthly 3-D Structure

92 • Chapter Five: Precipitation and Atmospheric Humidity Data Products Handbook Vol. I

Product Description

This product contains vertical profiles of the mean

reflectivity and the frequency distribution at each

height. The frequency distribution will be provided in

the form of CFADs. Vertical profiles and CFADs

will be provided for the following categories: total,

total-land, total-ocean, total-convective, totalstratiform, total-anvil, land-convective, landstratiform, land-anvil, ocean-convective, oceanstratiform, and ocean-anvil.

Research and Applications

The 3-D structure of radar reflectivity is determined

by hydrometeor distributions within cloud systems

and is indicative of both microphysical and

dynamical processes within such systems. The

reflectivity structure can be used to simulate satellite

observations of precipitating cloud systems, given

assumptions about the hydrometeor distributions.

Relationship to Other TRMM Products

This product is necessary to cross-check the vertical

structure retrieved by TRMM satellite instruments

(3A-25, 3A-26; 3B-31).

Data Set Evolution

Pre-operational tests within TSDIS began in August

1995 and will continue until the launch of TRMM.

Suggested Reading

Keenan, T.D., et al., 1988.

Yuter, S.E., and R.A. Houze, Jr. 1995.

TRMM GV3A-55

PRODUCT SUMMARY

Coverage:

150 km radius of ground validation radar,

continuous

Spatial/Temporal Characteristics:

30-day accumulations, 2 × 2 km

Key Geophysical Parameters:

3-D reflectivity

Processing Level:

3

Product Type:

validation, at-launch

Science Team Contact:

R. Houze
AM-1 MOD 05, MOD 08

MODIS Total Precipitable Water

Data Products Handbook Vol. I Chapter Five: Precipitation and Atmospheric Humidity • 93

Product Description

The MODIS Precipitable Water Product (MOD 05)

consists of column water vapor amounts. During the

daytime, a near-infrared algorithm is applied over

clear land areas of the globe and above clouds over

both land and ocean. Over clear ocean areas, water

vapor estimates are provided over the extended glint

area. An infrared algorithm for deriving atmospheric

profiles is also applied both day and night for

Level 2.

Both daily Level 2 (MOD 05) and daily, 8-day, and

monthly Level 3 (MOD 08) gridded averages are

included. The Level 2 data are generated at the 1-km

spatial resolution of the MODIS instrument using the

near-infrared algorithm during the day, and at 5 × 5

1-km pixel resolution both day and night using the

infrared algorithm when at least 9 FOVs are cloud

free. The infrared-derived precipitable water vapor is

generated as one component of product MOD 07, and

simply added to product MOD 05 for convenience.

Level 3 data are computed on 0.5° latitude and

longitude, equal area and equal angle grids.

The solar retrieval algorithm relies on observations of

water vapor attenuation of reflected solar radiation in

the near-infrared MODIS channels so that the

product is produced only over areas where there is a

reflective surface in the near IR.

Research & Applications

The near-infrared total column precipitable water is

very sensitive to boundary layer water vapor since it

is derived from attenuation of reflected solar light

from the surface. This data product is essential to

understand the hydrological cycle, aerosol properties,

aerosol-cloud interactions, energy budget, and

climate. Of particular interest is the collection of

water vapor data above cirrus cloudiness, which has

important applications to climate studies. MODIS

will also provide finer horizontal scale atmospheric

water vapor gradient estimates than are currently

available from the POES.

MOD 05, MOD 08

PRODUCT SUMMARY

Coverage:

global

Spatial/Temporal Characteristics:

varies with retrieval technique;

1 km near-infrared daylight only, and

5 km infrared day and night (Level 2),

0.5° (Level 3)/daily, 8-day, and monthly

Key Science Applications:

hydrological cycle climatology, effect on

aerosol and clouds, atmospheric

correction, characterization of the

atmosphere

Key Geophysical Parameters:

atmospheric total column water vapor

Processing Level:

2, 3

Product Type:

standard, at-launch

Science Team Contact:

B. C. Gao, Y. Kaufman, D. Tanré,

P. Menzel

Data Set Evolution

The solar column water vapor parameter is derived

from the attenuation by water vapor of near IR solar

radiation. Techniques employing ratios of water

vapor absorbing channels 17, 18, and 19 with the

atmospheric window channels 2 and 5 are used. The

ratios remove partially the effects of variation of

surface reflectance with wavelength and result in the

atmospheric water vapor transmittances. The column

water vapor amounts are derived from the

transmittances based on theoretical radiative transfer

calculations and using look-up table procedures.

MODIS is the first space instrument to use near IR

bands together with the traditional IR bands to

retrieve total precipitable water. Experience in this
AM-1 MOD 05, MOD 08

MODIS Total Precipitable Water

94 • Chapter Five: Precipitation and Atmospheric Humidity Data Products Handbook Vol. I

retrieval is based on an AVIRIS instrument aboard an

ER-2 aircraft. Atmospheric water vapor should be

determined with an accuracy of 5-10%.

The thermal column water vapor parameter is derived

by integrating the moisture profile through the

atmospheric column. Other split window methods

also exist. This class of techniques uses the

difference in water-vapor absorption that exists

between channel 31 (11 μm) and channel 32 (12 μm).

Data validation will be conducted by comparing

these data with water vapor measurements from the

NWS radiosonde network, from ground-based

upward-looking microwave radiometers, and from a

ground-based sunphotometer network. Quality

control will be performed in two dimensions. The

first will be comparisons of specific validation sites

across as many different climatic and geographic

regions as possible. The second will be a statistical

analysis of the entire data set.

Suggested Reading

Gao, B.-C. and A.F.H. Goetz, 1990.

Gao, B.-C., et al., 1993.

Green, R.O. and J.E. Conel, 1995.

Jedlovec, G.J., 1987.

Kaufman, Y.J. and B.-C. Gao, 1992.

King, M.D., et al., 1992.

Kleepsies, T.J. and L.M. McMillan, 1984.

Figure 16. Total Precipitable Water (Green and Conel, 1995). The figure on the left represents an AVIRIS false color

image of Rogers Dry Lake, California, while the figure on the right corresponds to derived column water vapor. In the

water vapor image, black represents precipitable water of 1.2 cm and white precipitable water of 1.5 cm. The spatial

variability of water vapor values is up to 20% over this topographically uniform terrain, but is easily detectable from

measurements of solar radiation reflected by the surface in the near-infrared spectral region.
DAS 02

DAS Assimilated Soil Moisture

Data Products Handbook Vol. I Chapter Five: Precipitation and Atmospheric Humidity • 95

Product Description

This Level 4 product consists of 3-hourly estimates

of soil moisture at 3 levels; shallow, root, and deep. It

is produced by the land-surface model of the GEOS

DAS. This model estimates the net balance of water

into and out of the soil considering precipitation,

snowmelt, evaporation, and runoff.

Research & Application

Soil moisture is a crucial part of the Earth’s

hydrologic system. Vegetation depends on soil

moisture. In addition, there is constant moisture

feedback between the solid Earth and atmosphere.

Like the atmosphere, the soil is a vast reservoir of

moisture. An accurate depiction of the Earth’s

hydrologic state must include estimates of soil

moisture.

Data Set Evolution

Soil moisture derived directly from satellites is

sporadic and often inaccurate. Data assimilation

provides an alternative for estimating global soil

moisture. Numerical weather prediction centers like

the European Centre for Medium Range Weather

Forecasts (ECMWF) and NCEP include soil moisture

parameterizations in their forecast models. The

GEOS DAS will extend these techniques beyond the

current state-of-the-art by including new EOS

observations to improve precipitation estimates and

incorporating improved land-surface models.

Suggested Reading

Bloom, S.C., et al., 1995.

Pfaendtner, J., et al., 1995.

Schubert, S.D., et al., 1995.

Schubert, S.D., et al., 1993.

Takacs, L.L., et al., 1994.

DAS 02

PRODUCT SUMMARY

Coverage:

global

Spatial/Temporal Characteristics:

2° × 2.5° lat-lon grid, daily at 00, 03, 06,

09, 12, 15, 18, and 21 UTC

Key Geophysical Parameters:

soil moisture at 3 levels: shallow, root,

and deep

Processing Level:

4

Product Type:

standard, post-launch

Science Team Contact:

R. Rood, A. Molod
DAS 03

DAS Assimilated Surface Moisture

96 • Chapter Five: Precipitation and Atmospheric Humidity Data Products Handbook Vol. I

Product Description

This Level 4 product consists of 3-hourly estimates

of the surface saturation specific humidity produced

by the GEOS DAS. It is based on the assimilation of

pressure, temperature, wind, and moisture

observations. These include conventional

meteorological observations, both in situ and

remotely sensed, as well as TRMM and AM-1 data

such as TMI and MODIS.

Research & Application

An estimate of the surface saturation specific

humidity is needed to determine evaporation rates

over land. Evaporation is a critical element in the

moisture feedback between Earth and atmosphere.

An accurate depiction of the Earth’s hydrologic state

must include estimates of soil moisture.

Data Set Evolution

There are a wide variety of pressure, temperature,

wind, and moisture data available from conventional

meteorological sources. These include surface

observing sites, rawinsondes, satellite soundings, etc.

Yet even with all these data, there are significant

gaps in global coverage. The accuracy of individual

measurements also varies widely. The best way to

combine all these data globally is through data

assimilation which takes into account instrument

errors and bridges the gaps between observations.

New AM-1 and TRMM products will greatly

improve the accuracy and coverage of the data used

to produce this field. In addition, data assimilation

provides dynamically consistent estimates of surface

moisture through its general circulation model that

takes into account global circulation systems,

precipitation, and evaporation.

DAS 03

PRODUCT SUMMARY

Coverage:

global

Spatial/Temporal Characteristics:

2° × 2.5° lat-lon grid, daily at 00, 03, 06,

09, 12, 15, 18, and 21 UTC

Key Geophysical Parameters:

surface saturation specific humidity

Processing Level:

4

Product Type:

standard, post-launch

Science Team Contact:

R. Rood, A. Molod

Suggested Reading

Bloom, S.C., et al., 1995.

Pfaendtner, J., et al., 1995.

Schubert, S.D., et al., 1995.

Schubert, S.D., et al., 1993.

Takacs, L.L., et al., 1994.
DAS 04

DAS Assimilated Surface Evaporation

Data Products Handbook Vol. I Chapter Five: Precipitation and Atmospheric Humidity • 97

Product Description

This Level 4 product consists of 3-hourly estimates

of the surface evaporation produced by the GEOS

DAS. It is based on the assimilation of pressure,

temperature, moisture, and wind observations. These

include conventional meteorological observations,

both in situ and remotely sensed, as well as TRMM

and AM-1 data such as TMI and MODIS. The

calculation of evaporation within the GEOS DAS is

based on the vertical gradient of moisture, the

potential evapotranspiration fraction for the given

surface type (1 over oceans), and the turbulent eddy

exchange coefficient for heat and moisture at the

surface.

Research & Application

Evaporation is a critical element in the moisture

feedback between Earth and atmosphere. An accurate

depiction of the Earth’s hydrologic state must include

surface evaporation.

Data Set Evolution

There are a wide variety of pressure, temperature,

moisture, and wind data available from conventional

meteorological sources. These include surface

observing sites, rawinsondes, satellite soundings, etc.

Yet even with all these data, there are significant

gaps in global coverage. The accuracy of individual

measurements also varies widely. The best way to

combine all these data globally is through data

assimilation which takes into account instrument

errors and bridges the gaps between observations.

New AM-1 and TRMM products will greatly

improve the accuracy and coverage of the data used

to produce this field. In addition, data assimilation

provides dynamically consistent estimates of

evaporation through its general circulation model that

takes into account global circulation systems,

precipitation, and radiation.

DAS 04

PRODUCT SUMMARY

Coverage:

global

Spatial/Temporal Characteristics:

2° × 2.5° lat-lon grid, daily at 00, 03, 06,

09, 12, 15, 18, and 21 UTC

Key Geophysical Parameters:

surface evaporation

Processing Level:

4

Product Type:

standard, post-launch

Science Team Contact:

R. Rood, A. Molod

Suggested Reading

Bloom, S.C., et al., 1995.

Pfaendtner, J., et al., 1995.

Schubert, S.D., et al., 1995.

Schubert, S.D., et al., 1993.

Takacs, L.L., et al., 1994.
DAS 05

DAS Assimilated Moisture Profile

98 • Chapter Five: Precipitation and Atmospheric Humidity Data Products Handbook Vol. I

Product Description

This Level 4 product consists of 6-hourly estimates

of atmospheric moisture produced by the GEOS

DAS. It is based on the assimilation of pressure,

temperature, wind, and moisture observations. These

include conventional meteorological observations,

both in situ and remotely sensed, as well as TRMM

and AM-1 data such as TMI and MODIS. Specific

humidity at 2 and 10 m above the surface are

contained in Q2M and Q10M. Mixing ratios are

available at mandatory pressure levels in MIXR.

Estimates of the error in the assimilated mixing ratios

are given in MIXRE. Relative humidity at mandatory

pressure levels are in RH. Vertical profiles of specific

humidity at both mandatory pressure and model

sigma levels are given in SPHU.

Research & Application

Water vapor is a key greenhouse gas and thus crucial

to global climate change. It is also a critical element

in the Earth’s hydrologic cycle. Determining the

global distribution of water vapor is essential to

global change research.

Data Set Evolution

There are a wide variety of pressure, temperature,

wind, and moisture data available from conventional

meteorological sources. These include surface

observing sites, rawinsondes, satellite soundings, etc.

Yet even with all these data, there are significant

gaps in global coverage. The accuracy of individual

measurements also varies widely. The best way to

combine all these data globally is through data

assimilation which takes into account instrument

errors and bridges the gaps between observations.

New AM-1 and TRMM products such as MODIS

and TMI will greatly improve the accuracy and

coverage of the data used to produce these fields. In

addition, data assimilation provides dynamically

consistent estimates of water vapor through its

general circulation model that takes into account

global circulation systems, precipitation, and

evaporation.

DAS 05

PRODUCT SUMMARY

Coverage:

global

Spatial/Temporal Characteristics:

2° × 2.5° lat-lon grid, 24 mandatory

pressure or 70 model sigma levels, daily

at 00, 06, 12, and 18 UTC

Key Geophysical Parameters:

moisture profiles (mixing ratio, relative

humidity, and specific humidity)

Processing Level:

4

Product Type:

standard, post-launch

Science Team Contact:

R. Rood, A. Hou, D. Ledvina

Suggested Reading

Bloom, S.C., et al., 1995.

Pfaendtner, J., et al., 1995.

Schubert, S.D., et al., 1995.

Schubert, S.D., et al., 1993.

Takacs, L.L., et al., 1994.
DAS 06

DAS Assimilated Moisture Changes

Data Products Handbook Vol. I Chapter Five: Precipitation and Atmospheric Humidity • 99

Product Description

This Level 4 product consists of 6-hourly estimates

of atmospheric moisture changes at a given level of

the atmosphere estimated by the GEOS DAS. It is

based on the assimilation of pressure, temperature,

wind, and moisture observations. These include

conventional meteorological observations, both in

situ and remotely sensed, as well as TRMM and AM1 data such as TMI and MODIS. TURBQ is an

estimate of the moisture change taking place at a

given level as a result of turbulent mixing. MOISTQ

is an estimate of the moisture change taking place at

a given level as a result of convection. Both fields are

available at mandatory pressure and model sigma

levels.

Research & Application

Water vapor is a key greenhouse gas and thus crucial

to global climate change. It is also a critical element

in the Earth’s hydrologic cycle. These fields provide

important insight into the vertical exchange of

moisture taking place at both large scales and subgrid scales.

Data Set Evolution

There are a wide variety of pressure, temperature,

wind, and moisture data available from conventional

meteorological sources. These include surface

observing sites, rawinsondes, satellite soundings, etc.

Yet even with all these data, there are significant

gaps in global coverage. The accuracy of individual

measurements also varies widely. The best way to

combine all these data globally is through data

assimilation which takes into account instrument

errors and bridges the gaps between observations.

New AM-1 and TRMM products such as MODIS

and TMI will greatly improve the accuracy and

coverage of the data used to produce these fields. In

addition, data assimilation provides dynamically

consistent estimates of water vapor changes through

its general circulation model that takes into account

global circulation systems, precipitation, and

evaporation.

DAS 06

PRODUCT SUMMARY

Coverage:

global

Spatial/Temporal Characteristics:

2° × 2.5° lat-lon grid, 24 mandatory

pressure and 70 model sigma levels, daily

at 00, 06, 12, and 18 UTC

Key Geophysical Parameters:

moisture changes from turbulence and

convection

Processing Level:

4

Product Type:

standard, post-launch

Science Team Contact:

R. Rood, A. Molod

Suggested Reading

Bloom, S.C., et al., 1995.

Pfaendtner, J., et al., 1995.

Schubert, S.D., et al., 1995.

Schubert, S.D., et al., 1993.

Takacs, L.L., et al., 1994.
DAS 07

DAS Assimilated Moisture Flux

100 • Chapter Five: Precipitation and Atmospheric Humidity Data Products Handbook Vol. I

Product Description

This Level 4 product consists of 3-hourly estimates

of vertically integrated atmospheric moisture flux

estimated by the GEOS DAS. It is based on the

assimilation of pressure, temperature, wind, and

moisture observations. These include conventional

meteorological observations, both in situ and

remotely sensed, as well as TRMM and AM-1 data

such as TMI and MODIS. The field is separated into

two components, VINTUQ (zonal u-wind) and

VINTVQ (meridional v-wind).

Research & Application

Water vapor is a key greenhouse gas and thus crucial

to global climate change. It is also a critical element

in the Earth’s hydrologic cycle. Changes in moisture

flux can lead to extreme conditions like droughts or

floods.

Data Set Evolution

There are a wide variety of pressure, temperature,

wind, and moisture data available from conventional

meteorological sources. These include surface

observing sites, rawinsondes, satellite soundings, etc.

Yet even with all these data, there are significant

gaps in global coverage. The accuracy of individual

measurements also varies widely. The best way to

combine all these data globally is through data

assimilation which takes into account instrument

errors and bridges the gaps between observations.

New AM-1 and TRMM products such as MODIS

and TMI will greatly improve the accuracy and

coverage of the data used to produce these fields. In

addition, data assimilation provides dynamically

consistent estimates of moisture flux through its

general circulation model that takes into account

global circulation systems, precipitation, and

evaporation.

DAS 07

PRODUCT SUMMARY

Coverage:

global

Spatial/Temporal Characteristics:

2° × 2.5° lat-lon grid, daily at 00, 03, 06,

09, 12, 15, 18, and 21 UTC

Key Geophysical Parameters:

vertically integrated atmospheric

moisture flux

Processing Level:

4

Product Type:

standard, post-launch

Science Team Contact:

R. Rood, A. Molod

Suggested Reading

Bloom, S.C., et al., 1995.

Pfaendtner, J., et al., 1995.

Schubert, S.D., et al., 1995.

Schubert, S.D., et al., 1993.

Takacs, L.L., et al., 1994.
DAS 08

DAS Assimilated Precipitable Water

Data Products Handbook Vol. I Chapter Five: Precipitation and Atmospheric Humidity • 101

Product Description

This Level 4 product consists of 3-hourly estimates

of precipitable water produced by the GEOS DAS. It

is based on the assimilation of pressure, temperature,

wind, and moisture observations. These include

conventional meteorological observations, both in

situ and remotely sensed, as well as TRMM and AM1 data such as TMI and MODIS.

Research & Application

Water vapor is a key greenhouse gas and thus crucial

to global climate change. It is also a critical element

in the Earth’s hydrologic cycle. Precipitable water

provides a quick estimate of the horizontal

distribution of water vapor.

Data Set Evolution

There are a wide variety of pressure, temperature,

wind, and moisture data available from conventional

meteorological sources. These include surface

observing sites, rawinsondes, satellite soundings, etc.

Yet even with all these data, there are significant

gaps in global coverage. The accuracy of individual

measurements also varies widely. The best way to

combine all these data globally is through data

assimilation which takes into account instrument

errors and bridges the gaps between observations.

New AM-1 and TRMM products such as MODIS

and TMI will greatly improve the accuracy and

coverage of the data used to produce these fields. In

addition, data assimilation provides dynamically

consistent estimates of precipitable water through its

general circulation model that takes into account

global circulation systems, precipitation, and

evaporation.

DAS 08

PRODUCT SUMMARY

Coverage:

global

Spatial/Temporal Characteristics:

2° × 2.5° lat-lon grid, daily at 00, 03, 06,

09, 12, 15, 18, and 21 UTC

Key Geophysical Parameters:

precipitable water

Processing Level:

4

Product Type:

standard, post-launch

Science Team Contact:

R. Rood, A. Hou, D. Ledvina

Suggested Reading

Bloom, S.C., et al., 1995.

Pfaendtner, J., et al., 1995.

Schubert, S.D., et al., 1995.

Schubert, S.D., et al., 1993.

Takacs, L.L., et al., 1994.
DAS 09

DAS Assimilated Precipitation

102 • Chapter Five: Precipitation and Atmospheric Humidity Data Products Handbook Vol. I

Product Description

This Level 4 product consists of 3-hourly estimates

of precipitation rates (mm/day) produced by the

GEOS DAS. It is based on the assimilation of

pressure, temperature, wind, and moisture

observations. These include conventional

meteorological observations, both in situ and

remotely sensed, as well as TRMM and AM-1 data

such as TMI and MODIS. Two fields are provided,

total precipitation rate (PREACC) and convective

precipitation rate (PRECON).

Research & Application

Precipitation is a critical element in the Earth’s

hydrologic cycle.

Data Set Evolution

There are a wide variety of pressure, temperature,

wind, and moisture data available from conventional

meteorological sources. These include surface

observing sites, rawinsondes, satellite soundings, etc.

Yet even with all these data, there are significant

gaps in global coverage. The accuracy of individual

measurements also varies widely. The best way to

combine all these data globally is through data

assimilation which takes into account instrument

errors and bridges the gaps between observations.

New AM-1 and TRMM products such as MODIS

and TMI will greatly improve the accuracy and

coverage of the data used to produce these fields. In

addition, data assimilation provides dynamically

consistent estimates of precipitation through its

general circulation model that takes into account

global circulation systems and local convective

processes.

DAS 09

PRODUCT SUMMARY

Coverage:

global

Spatial/Temporal Characteristics:

2° × 2.5° lat-lon grid, daily at 00, 03, 06,

09, 12, 15, 18, and 21 UTC

Key Geophysical Parameters:

precipitation rates (total and convective)

Processing Level:

4

Product Type:

standard, post-launch

Science Team Contact:

R. Rood, A. Molod

Suggested Reading

Bloom, S.C., et al., 1995.

Pfaendtner, J., et al., 1995.

Schubert, S.D., et al., 1995.

Schubert, S.D., et al., 1993.

Takacs, L.L., et al., 1994.
Chapter Six

Cloud and

Aerosol

Properties and

Radiative

Energy Fluxes
TRMM, AM-1

Cloud and Aerosol Properties,

and Radiative Energy Fluxes

104 • Chapter Six: Cloud & Aerosol Properties, and Radiative Energy Fluxes Data Products Handbook Vol. I

Relationship to Global Change

The flow of energy to and from the Earth is

modulated by clouds and aerosols. As such,

measuring and understanding these elements is key to

predicting climate change and to testing global

climate models. The technology for accurately

measuring radiative fluxes, cloud properties, and

aerosol properties from space has advanced to the

point that these observations can be used to greatly

advance our ability to address critical global change

issues. For example: if the global climate changes, do

clouds act as a positive or negative feedback? Do

aerosols offset some of the expected warming from

increasing greenhouse gases? The products described

below provide key global observations needed to

explore these questions.

CLOUDS: A key step in measuring cloud properties

from space is to determine where clouds exist, how

they are distributed through the atmosphere, and what

their dimensions are. Once clouds are identified and

mapped, cloud properties such as effective particle

size, thermodynamic phase (water, ice), cloud top

properties, and optical thickness (which all help

determine how much radiation passes through them)

may be measured. The MODIS, MISR, and ASTER

instruments on the EOS AM-1 platform and the

VIRS instrument on TRMM will provide these key

cloud measurements. The daily observations

provided by these instruments also will provide the

sampling needed to drive and verify climate models.

AEROSOLS: The measurement of aerosols,

suspended particles in the atmosphere (e.g., dust,

sulfate, smoke, etc.), is an important element in

describing energy transmission through the

atmosphere. Aerosols are a significant source of

uncertainty in climate modeling because they affect

cloud microphysics by acting as condensation nuclei,

thereby affecting cloud radiative properties. They

also interact directly with solar radiation, thus

affecting the radiative balance. The location of

anthropogenic aerosols is also an important

consideration in their impact on climate. While

TRMM data will not directly address aerosol

concentrations or properties, aerosol products

described here, derived from MODIS and MISR on

AM-1, will provide new information on aerosol

formation, distribution, and sinks over both the land

and ocean.

RADIATION: The flow of radiation from the Sun to

the Earth, its absorption, reflection, and emission,

determines the energy balance of the Earth, and

hence the nature of the life of its inhabitants. The

information from products describing optical and

physical properties of clouds and aerosols is

combined with broadband radiation measurements

from CERES to provide estimates of solar and

infrared radiative fluxes, which in turn determine the

heating and cooling of the Earth and the atmosphere.

The radiation estimates needed to do this are both

shortwave and longwave fluxes at the surface and at

the top of the atmosphere, both downwelling and

upwelling, so that the net flow can be determined.

Also, fluxes must be distinguished between cloudy

and clear-sky regions, and the fluxes over a series of

elevation intervals are needed to test atmospheric

models.

Product Overview

The physical quantities that must be measured for the

cloud, aerosol, and radiation categories are discussed

in the previous section. In this section we relate the

data products to these measurement needs and

explain how the product groups support the overall

global change program. An evolutionary approach to

product generation for the CERES cloud and

radiation products will be used to implement a

progression from ERBE to CERES algorithms. The

ERBE program has produced a ten-year record of

cloud and radiation budget information and CERES

will continue these measurements.

Starting 6 months after launch (following instrument

and analysis validation) CERES will begin

production of ERBE-Like data products. These TOA

fluxes will use the ERBE analysis algorithms to

allow consistent comparisons to historical ERBE

data. The first 24 months after launch will be used to

develop a new set of CERES angular distribution

models (ADMs). These ADMs are used to convert

broadband CERES radiances (i.e., radiative energy at

a single viewing direction) into estimates of

broadband hemispheric fluxes. The CERES rotating
TRMM, AM-1

Cloud and Aerosol Properties,

and Radiative Energy Fluxes

Data Products Handbook Vol. I Chapter Six: Cloud & Aerosol Properties, and Radiative Energy Fluxes • 105

azimuth plane scan mode will be used along with the

cloud properties derived using VIRS and MODIS

data to derive a new and much more accurate set of

ADMs. Beginning 24 months after launch, these

more accurate CERES estimated TOA fluxes will be

produced in addition to the ERBE-Like data. Finally,

starting 30 months after launch, CERES will

routinely provide estimates of radiative fluxes at the

surface and at a selected number of levels within the

atmosphere. The number of atmospheric levels will

depend on the accuracies determined during postlaunch validation.

All the CERES products except the ERBE-like

products are classed as “post-launch” for the reasons

discussed above. The MODIS, MISR, and ASTER

products are “at-launch” except for the gridded MISR

products (MIS 07, MIS 08) and AST 13. The

following section discusses how these products

satisfy the measurement needs in the three categories.

The cloud products provide the parameters needed

to determine how much solar radiation reaches the

Earth and how much escapes back to space. The

parameters also supply information on factors that

influence cloud formation and forces driving

circulation and global climate. The cloud products

fall into two classes: (1) cloud detection and

delineation , and (2) cloud property retrieval. Cloud

detection and delineation produces masks indicating

where clouds exist and information on layering and

overlap to give a geometric picture of global cloud

coverage. Cloud property retrieval quantifies the

physics of the cloud by means of parameters such as

optical thickness, temperature, liquid water content,

ice water content, particle radius, cloud top altitude,

and phase, which all relate to the radiative

transmission, reflection, and emission of the cloud.

The CERES cloud detection process uses TRMM

VIRS and AM-1 MODIS observations to produce a

global binary mask indicating the presence or

absence of a cloud in each pixel. The MISR cloud

products provide along-track directional reflectance

measurements of clouds that will enable development

of bidirectional models for different cloud types,

especially for horizontally inhomogeneous cloud

fields. Stereo matching of MISR multi-angle imagery

enables cloud top elevations to be obtained. These

parameters are provided in products MIS 04 and

MIS 07. The ASTER cloud product (AST 13) also

provides stereo views of cloud cover, which will

complement the MISR data and will permit cloud top

elevation estimation. The major advantage of the

ASTER cloud product is the high spatial resolution

that allows the study of individual cloud cells. The

disadvantage is very limited spatial and time

coverage. The ASTER products are also tuned to the

difficult polar cloud detection task, which will

include the detection of water clouds and ice clouds.

The MODIS cloud mask (MOD 35) is a global

product that provides a probability that a view of the

Earth is obstructed by cloud and whether cloud

shadow is detected. It also provides information on

the processing path taken by the algorithm (land/sea,

day/night, etc.), along with individual spectral test

results used in the determination of the final cloud

mask product.

Cloud physical properties are obtained using a set of

algorithms operating on multispectral imagery data

from multiple sources including MODIS and VIRS.

CERES cloud properties include cloud layer

mapping, cloud-top and cloud base pressure, infrared

emissivity, liquid water path, particle radius, and

cloud overlap, all derived from MODIS or VIRS data

and remapped onto the broadband CERES fields of

view for studies of the Earth’s atmosphere energy

distribution. The MODIS cloud property parameters

include particle size and phase, optical thickness,

cloud top height, emissivity and temperature, and

cloud fraction in a region, all of which support

specific MODIS science tasks that complement the

radiation studies and products from other

instruments.

Aerosol Properties are provided by MODIS

(MOD 04) and MISR (MIS 05). Aerosols must be

accounted for during the retrieval of surface and

atmospheric parameters. The MODIS aerosol

products will document one of the most elusive and

least understood radiatively active components of the

atmosphere by providing information on aerosol

optical thickness, aerosol size distribution, and

aerosol sources and sinks. MISR provides

unprecedented constraints on aerosol optical depth

over ocean and many land surfaces along with

information about particle size, sphericity, and
TRMM, AM-1

Cloud and Aerosol Properties,

and Radiative Energy Fluxes

106 • Chapter Six: Cloud & Aerosol Properties, and Radiative Energy Fluxes Data Products Handbook Vol. I

comsitional type. Both MODIS and MISR will aid in

understanding how sunlight is absorbed by aerosols,

determining global aerosol budgets, and improving

correction for aerosol effects in the retrieval of

surface parameters.

The Radiant Energy Flux Products provide

estimates of the radiative flux downward and upward

at the top-of-atmosphere, at the Earth’s surface, and

at selected intermediate altitudes, as well as the net

flux, which is used to determine the radiation budget.

This budget determines the overall heating and

cooling of the atmosphere and Earth’s surface and

subsequent changes in climate. These parameters are

generated from CERES data and from the final

averaged flux and radiation budget products AVG,

ZAVG and CRH. The radiation algorithms use the

cloud and aerosol products in the process of

determining how much solar radiation is transmitted,

reflected, and absorbed in the

cloud/atmosphere/Earth surface system, and are thus

critical to the success of the global climate

monitoring task.

Product Interdependency and Continuity

with Heritage Data

The products from these instruments, especially the

cloud products from MODIS, CERES, MISR, and

ASTER, are interrelated on a number of levels. First,

many algorithms require output products from other

instruments as ancillary input data. For example,

CERES cloud parameters (SSF) require MODIS and

TRMM ancillary data products such as the cloud

products (MOD 06) and microwave water path

(MWP). MISR aerosol optical depth products will

benefit from MODIS water vapor measurements, and

MISR cloud albedo retrievals will use MODIS cloud

phase. The MODIS cloud mask may be used in the

viewing decisions of high resolution instruments like

ASTER. In many cases, output data from one

instrument become input ancillary data for another.

This permits the formatting necessary to permit the

generation of higher level products, and does not

represent a duplication of data products.

Second, surface-viewing instruments will use

ancillary data in atmospheric correction algorithms to

reduce contamination of surface retrievals. Similar

products from different instruments may use different

algorithms and are useful for mutual comparison and

validation. Finally, a degree of redundancy for

principal (especially cloud) products used as ancillary

input is necessary to permit alternative sources of

ancillary data in the event of instrument failure or

degradation.

The CERES instruments on TRMM and AM-1 are a

continuation of the ERBE project, extending a

heritage of radiation budget products dating back

more than a decade. Several CERES data products

will use ERBE algorithms to ensure continuity. As

with other EOS-era missions, the data sets produced

by continuing past missions will provide valuable

information on long-term trends. Analysis of the

trends will provide valuable input to long-term

predictive models, and will enable scientists to

interpret better the possible causes or consequences

of changes to clouds, aerosols, and radiation budget

on global climate change.

Suggested Reading

Coakley, J., et al., 1983.

Di Girolamo, L. and R. Davis, 1994.

Diner, D.J., et al., 1991.

Holben, B.N., et al., 1992.

Kaufman, Y.J. and C. Sendra, 1988.

Kaufman, Y.J., et al., 1996.

King, M.D., et al., 1992.

King, M.D., 1987.

Ramanathan, V., 1986.

Ramanathan, V., 1987.

Sohn, B.-J. and F.R. Robertson, 1993.

Tanré, D., et al., 1996.

Wang, M. and H.R. Gordon, 1994.

Wielicki, B.A., et al., 1995.
AM-1 MOD 04, MOD 08

MODIS Aerosol Product

Data Products Handbook Vol. I Chapter Six: Cloud & Aerosol Properties, and Radiative Energy Fluxes • 107

Product Description

The MODIS Aerosol Product (MOD 04) monitors

the ambient aerosol optical thickness and size

distribution globally over the oceans and the moist

parts of the continents. The size distribution is

derived only over the oceans; the aerosol type is

derived over the continents. Daily Level 2 and daily,

8-day, and monthly Level 3 gridded averages are

provided. The Level 2 (MOD 04) data are produced

at the spatial resolution of a 10 × 10 1-km (at nadir)

pixel array. The Level 3 (MOD 08) spatial resolution

is 0.5° latitude and longitude, equal area and equal

angle for both ocean and land, and includes not only

aerosol but also cloud and water vapor properties.

Research & Applications

Aerosols are one of the greatest sources of

uncertainty in climate modeling. Aerosols modify

cloud microphysics by acting as CCN, and as a result

impact cloud radiative properties and climate.

Aerosol scatters back to space and absorbs solar

radiation. The MODIS aerosol product will be used

to study aerosol climatology, sources and sinks of

specific aerosol types (e.g., sulfates and biomass

burning aerosol), interaction of aerosols with clouds,

and atmospheric corrections of remotely sensed

surface reflectance over the land.

Data Set Evolution

Present satellite measurements are limited to

reflectance measurements in one (GOES,

METEOSAT) or two (AVHRR) channels. There has

been no real attempt to retrieve aerosol content over

land on a global scale. Algorithms have been

developed for use only over dark vegetation. The

blue channel on MODIS, not present on AVHRR,

offers the possibility to extend the derivation of

optical thickness over land to additional surfaces.

The algorithms will use MODIS bands 1 through 7

and 22, and require prior cloud screening using

MODIS data. Over the land, the dynamic aerosol

models will be derived from ground-based sky

measurements and used in the net retrieval process.

Over the ocean, 3 parameters that describe the

aerosol loading and size distribution will be

MOD 04, MOD 08

PRODUCT SUMMARY

Coverage:

global over oceans, nearly global over

land

Spatial/Temporal Characteristics:

10 km for Level 2; 0.5° latitude and

longitude, equal area and equal angle

(Level 3)/daily, 8-day, and monthly

Key Science Applications:

aerosol climatology, biomass burning

aerosols, atmospheric corrections, cloud

radiative properties, climate modeling

Key Geophysical Parameters:

atmospheric aerosol optical depth

(global) and aerosol size distribution

(oceans )

Processing Level:

2, 3

Product Type:

standard, at-launch

Science Team Contact:

Y. Kaufman, D. Tanré

retrieved. Pre-assumptions on the general structure of

the size distribution are required in the inversion of

MODIS data and the volume size distribution will be

described with two log-normal modes: a single mode

to describe the accumulation mode particles

(radius < 0.5 μm) and a single coarse mode to

describe dust and/or salt particles (radius > 1.0 μm).

The aerosol parameters we therefore expect to

retrieve are: the ratio between the two modes, the

spectral optical thickness, and the mean particle size

of the each mode.

The quality control of these products will be based

on comparison with ground stations and climatology.
AM-1 MOD 04, MOD 08

MODIS Aerosol Product

108 • Chapter Six: Cloud & Aerosol Properties, and Radiative Energy Fluxes Data Products Handbook Vol. I

Suggested Reading

Holben, B.N., et al., 1992.

Kaufman, Y.J. and L.A. Remer, 1994.

Kaufman, Y.J. and B.N. Holben, 1996.

Kaufman, Y.J. et al., 1996.

Kaufman, Y.J. and C. Sendra, 1988.

King, M.D., et al., 1992.

Rao, C.R.N., et al., 1989.

Remer, L.A. et al., 1996.

Tanré, D. et al., 1996.

Figure 17. Aerosol Optical Thickness. False color Landsat TM image over the North Carolina -Virginia region (12 July

1993). The apparent reflectance of TM 0.47 μm (left) and 0.66 μm channels (right) are shown in the upper

panels. The bottom panels are the corresponding aerosol optical thickness retrieved by the MODIS aerosol

algorithm for the reduced resolution Landsat image.
AM-1 MOD 06, MOD 08

MODIS Cloud Product

Data Products Handbook Vol. I Chapter Six: Cloud & Aerosol Properties, and Radiative Energy Fluxes • 109

Product Description

The MODIS Cloud Product (MOD 06) combines

infrared and visible techniques to determine both

physical and radiative cloud properties. Daily global

Level 2 (MOD06) and daily, 8-day and monthly

Level 3 products (MOD08) are provided. Cloud

particle phase (ice vs. water, clouds vs. snow),

effective cloud particle radius, and cloud optical

thickness are derived using the MODIS visible and

near-infrared channel radiances. An indication of

cloud shadows affecting the scene is also provided.

Cloud top temperature, height, effective emissivity,

phase (ice vs. water, opaque vs. non-opaque), and

cloud fraction are produced by the infrared retrieval

methods both day and night at 5 × 5 1-km pixel

resolution.

Research & Applications

A thorough description of global cloudiness and its

associated properties is essential to the MODIS

mission for two reasons. First, clouds play a critical

role in the radiative balance of the Earth, and must be

accurately described in order to accurately assess

climate and potential climate change. In addition, the

presence or absence of cloudiness must be accurately

determined in order to properly retrieve many

atmospheric and surface parameters. For many of

these retrievals, cloud cover, even thin cirrus,

represents contamination. Key radiative properties of

clouds such as phase, optical depth, and temperature

may be retrieved using MODIS instruments with

unprecedented resolution.

Data Set Evolution

The determination of cloud top properties will

require the use of MODIS bands 29 and 31-36, along

with the cloud mask product (MOD 35), to screen for

clouds. In addition, NCEP or DAO global model

analyses of surface temperature and pressure, profiles

of temperature and moisture, and blended SST

analyses will be required in the calculation of cloud

forcing as a function of atmospheric pressure and

emissivity. The Menzel cloud phase algorithm will

require MODIS bands 29, 31, and 32 and analyses of

surface emissivity.

MOD 06, MOD 08

PRODUCT SUMMARY

Coverage:

global

Spatial/Temporal Characteristics:

varies with parameter; once or twice

per day, at resolutions of 1 km or 5 km

(Level 2) and 0.5° latitude and longitude,

equal area and equal angle (Level 3)/

daily, 8-day, and monthly

Key Science Applications:

cloud parameterization, climate

modeling, climate monitoring, increasing

accuracy of other MODIS retrievals

Key Geophysical Parameters:

cloud particle phase (two algorithms),

cloud particle size and optical depth, and

cloud top temperature, emissivity and

height

Processing Level:

2, 3

Product Type:

standard, at-launch

Science Team Contact:

P. Menzel, M. King

The validation of cloud top heights will be conducted

through comparisons with stereo determinations of

cloud heights from GOES and lidar estimates and

aircraft observations of cirrus heights. Cloud

emissivity will be compared to lidar determined

values. These interim products will be used in

concert with field campaigns with the MAS

instrument. The Menzel cloud phase parameter will

be validated using HIRS/AVHRR data and by

comparison to the King cloud phase parameter.

The King cloud phase algorithm requires product

MOD 02, calibrated multispectral radiances. Cloud

particle size and optical depth require these radiances

plus the cloud top parameters within MOD 06 and
AM-1 MOD 06, MOD 08

MODIS Cloud Product

110 • Chapter Six: Cloud & Aerosol Properties, and Radiative Energy Fluxes Data Products Handbook Vol. I

effective 

radius (μm)

4 8 12 16

LWP

 (g/m2)

5 50 100 175

optical

thickness

3 10 20

2.13μm bidirectional

reflectance

Figure 18. Cloud Optical Thickness and Effective Radius. MODIS Airborne Simulator 2.13 μm

bidirectional reflectance along with retrieved cloud optical thickness, effective radius, and liquid water

path (using MAS visible and 2.13 μm channels) for the southern portion of a ship track imaged off the

coast of California on 29 June 1994. The ship is easily seen as the bright region in the reflectance

panel and as the lower droplet size in the effective radius panel; the track is not obvious in the optical

thickness panel. The last panel is liquid water path which is approximated as being proportional to the

product of optical thickness and effective radius.

the Menzel cloud phase parameter. In addition, these

parameters require MODIS product MOD 09 (surface

reflectance) and the NCEP analyses and profiles

described above. The validation and quality control

of these products will be performed primarily

through the use of in situ measurements obtained

during field campaigns and with the use of the MAS

instrument.

Suggested Reading

King, M.D., et al., 1992.

King, M.D., et al ., 1996.

Nakajima, T.Y. and T. Nakajima, 1994.

Platnick, S., et al., 1996.

Strabala, K.I., et al., 1994.

Wylie, D.P., et al., 1994.
AM-1 MOD 06, MOD 08

MODIS Cloud Product

Data Products Handbook Vol. I Chapter Six: Cloud & Aerosol Properties, and Radiative Energy Fluxes • 111

Figure 19. HIRS Probability of cirrus. Level 3 MODIS cloud product using a combination of the effective

emissivity and cloud top pressure product to determine a geographical distribution of cirrus cloud. HIRS

observations were used to generate this example. Top figure is for June-August, and the bottom figure

December-February. Both panels represent 7 year averages.
AM-1 MOD 35

MODIS Cloud Mask

112 • Chapter Six: Cloud & Aerosol Properties, and Radiative Energy Fluxes Data Products Handbook Vol. I

Product Description

The MODIS Cloud Mask (MOD 35) is a daily, global

Level 2 product generated at the 1-km and 250-m (at

nadir) spatial resolution. The algorithm employs a

series of visible and infrared threshold and

consistency tests to specify confidence levels that an

unobstructed view of the Earth’s surface is observed.

An indication of shadows affecting the scene is also

provided. The 250-m cloud mask flags are based on

the visible channel data only. Radiometrically

accurate radiances are required, so holes in the Cloud

Mask will appear wherever the input radiances are

incomplete or of poor quality.

Research & Applications

A determination of the presence of global cloudiness

is essential to the MODIS mission for two reasons.

First, clouds play a critical role in the radiative

balance of the Earth, and must be accurately

described to assess climate and potential climate

change. Second, the presence of cloudiness must be

accurately determined to properly retrieve many

atmospheric and surface parameters. For many of

these retrieval algorithms even thin cirrus represents

contamination.

Data Set Evolution

The MODIS cloud mask algorithm employs a battery

of spectral tests, which use methodology applied for

APOLLO, International Satellite Cloud Climatology

Project (ISCCP), CLAVR, and SERCAA to identify

cloudy FOVs. From these a clear-sky confidence

level (> 99 percent, > 95 percent, > 66 percent, or

< 1 percent) is assigned to each FOV. For

inconclusive results, spatial and temporal variability

tests are applied. The spectral tests rely on radiance

(temperature) thresholds in the infrared and

reflectance thresholds in the visible and nearinfrared. Thresholds vary with surface type,

atmospheric conditions (moisture, aerosol, etc.), and

viewing geometry. Along with MOD 02 calibrated

radiances, a 1-km land/water mask, DEM, ecosystem

analysis, snow/ice cover map, NCEP analysis of

surface temperature and wind speed, and an estimate

of precipitable water will be required as inputs.

MOD 35

PRODUCT SUMMARY

Coverage:

global

Spatial/Temporal Characteristics:

250 m and 1 km, daily

Key Science Applications:

cloud determination and screening,

climate modeling, climate monitoring,

increasing accuracy of other MODIS

retrievals

Key Geophysical Parameters:

presence of cloud or shadow

Processing Level:

2

Product Type:

standard, at-launch

Science Team Contact:

P. Menzel

Cloud mask validation will be conducted using MAS

data from several field campaigns, all-sky cameras,

and comparison with NOAA operational instruments

and other EOS AM-1 instruments such as ASTER.

Suggested Reading

Gao, B.-C., et al., 1993.

Gustafson, G.B., et al. , 1994.

King, M.D., et al., 1992.

Rossow, W.B. and L.C. Garder, 1993.

Saunders, R.W. and K.T. Kriebel, 1988.

Stowe, L.L., et al., 1991.
AM-1 MOD 35

MODIS Cloud Mask

Data Products Handbook Vol. I Chapter Six: Cloud & Aerosol Properties, and Radiative Energy Fluxes • 113

Figure 20. Cloud Mask. The three panels on the left represent MODIS Airborne Simulator (MAS) images acquired as part

of NASA’s Subsonic Assessment Program (SASS) over Oklahoma on 21 April 1996 at 0.66, 1.88, and 11.02 μm. The

1.88 μm channel is sensitive to water vapor absorption and is therefore similar in principle to the 1.38 μm channel that

will be used by MODIS to detect the presence of high thin clouds. Each of these channels has difficulty in detecting a

certain cloud type. For example, the 1.88 μm channel is least sensitive to the occurrence of low level cloud, while thin

cirrus is difficult to detect using the 0.66 μm channel. The final panel is the resultant cloud mask image. Most of the

scene is classified as either high confident clear (black) or high confident cloudy (white).
AM-1 MIS 04, MIS 06, MIS 07

MISR TOA and Cloud Data

114 • Chapter Six: Cloud & Aerosol Properties, and Radiative Energy Fluxes Data Products Handbook Vol. I

Product Description

The Level 2 Top-of-Atmosphere (TOA)/Cloud

Product (MIS 04) consists of TOA radiation and

cloud information including: finely-sampled (2.2 km)

TOA albedo, coarsely-sampled TOA albedos

projected to 30-km altitude (35.2 km), TOA bidirectional reflectance factor (BRF) at 2.2 km, and

Reflecting Level Reference Altitude (RLRA) for 2.2-

km regions, texture indices at 2.2 km, altitude-binned

(high, middle, and low) and total cloud fractions at

17.6 km. The coarse TOA albedos provided will

include a restrictive albedo, derived from the nine

multiangle observations of a single region, and an

expansive albedo which is calculated including

contributions from surrounding regions, at the

appropriate angles. Nadir cloud masks on 1.1-km

centers for thick and high clouds will also be

included. Data from MIS 04 will be mapped to

produce Level 3 globally gridded products (MIS 06,

MIS 07) postlaunch.

Research & Applications

The main purpose of the MISR TOA/cloud product is

to produce accurate measures of spectral albedo

classified by scene type. These will be used to study

the global effects of different types of cloud fields,

including spatial and temporal dependencies, and to

determine their effects on Earth’s climate. Bidirectional reflectances of clear and cloudy regions

obtained by MISR will be used to develop

anisotropic reflectance models classified by cloud

type, determine the spatial and temporal variability of

cloud albedo, and validate coarse spatial resolution

angular reflectance models generated by other

instruments. Automated stereo matching of

multiangle imagery will be used to estimate cloudtop elevations. In addition, MISR will help to obtain

a better understanding of the nonlinear scaling

between sub-grid and grid scale processes in GCMs

and provide improved parameterization in these

models.

Data Set Evolution

Earlier satellites, notably Nimbus-7 (ERB) and

NOAA-9 Earth Radiation Budget Experiment

(ERBE), pioneered the beginnings of techniques to

directly integrate radiances from the same scene,

MIS 04, MIS 06, MIS 07

PRODUCT SUMMARY

Coverage:

daytime, 9-day for global repeat coverage

Spatial/Temporal Characteristics:

9-day for global coverage with 1.1 km,

2.2 km, 17.6 km and 35.2 km sampling

(various parameters) at Level 2; 16-day,

monthly, and seasonal globally gridded

products at Level 3

Key Geophysical Parameters:

TOA albedo, TOA bi-directional reflectance

factor, reflecting level reference altitude,

cloud screens, altitude-binned cloud fraction

Processing Level:

2, 3

Product Type:

standard, at-launch (MIS 04) post-launch

gridded products (MIS 06, MIS 07)

Science Team Contact:

R. Davies

measured more or less coincidentally at several

different angles, to yield the hemispherical flux. The

Nimbus-7 scanner reduced its FOV to keep the

viewed area about the same size, but had to look at

different scenes across track and build up directional

models. In its alongtrack mode, the NOAA-9 scanner

obtained a very limited data set looking at fixed

regions, but since its scanner had a fixed FOV the

size of the target area changed systematically with

angle. The presently available models have been

painstakingly constructed from Nimbus-7 data, but

cannot accommodate different cloud types, and

define only a few categories of cloud cover amounts.

Suggested Reading

Di Girolamo, L. and R. Davies, 1994.

Goodman, A.H. and A. Henderson-Sellers, 1988.

Rossow, W.B. and L.C. Garder, 1993.

Wielicki, B.A. and L. Parker, 1992.
AM-1 MIS 05, MIS 08, MIS 09

MISR Aerosol and Surface Data

Data Products Handbook Vol. I Chapter Six: Cloud & Aerosol Properties, and Radiative Energy Fluxes • 115

Product Description

The Level 2 Aerosol/Surface Product (MIS 05)

contains a variety of information on the Earth’s

atmosphere and surface. The aerosol data include

tropospheric aerosol optical depth on 17.6-km

centers, archived with a compositional model

identifier and retrieval residuals, ancillary data

including relative humidity (RH), ozone optical

depth, stratospheric aerosol optical depth, and

retrieval flags. The land surface data include

hemispherical directional reflectance factor,

bihemispherical reflectance (i.e., albedo),

bidirectional reflectance factor, directional

hemispherical reflectance, BRF model parameters,

FPAR, and terrain-referenced view and illumination

angles. Ocean data include water-leaving equivalent

reflectance and phytoplankton pigment

concentration. In addition to MIS 05 geophysical

products, the mapped, global, seasonal gridded

aerosol products (MIS 08) and surface products

(MIS 09) also will include tropospheric aerosol

absorbed shortwave energy and mass loading, surface

shortwave albedo, and selected surface biophysical

parameters. The Level 3 globally gridded products

(MIS 08 and MIS 09) will be developed post-launch.

Research & Applications

MIS 05, MIS 08, and MIS 09 products will be used

to study absorption and scattering in the lower

atmosphere and at the surface for use in climate and

biosphere-atmosphere interaction studies. The MISR

aerosol data sets will enable: (1) the study of the

global magnitude and variability of sunlight

absorption and scattering by aerosols in the Earth’s

atmosphere (particularly tropospheric aerosols) and

their effect on climate; (2) improved understanding

of the sources, sinks, and global budgets of aerosols;

and (3) improved atmospheric correction for surface

imaging data acquired by MISR and other

instruments (e.g., MODIS and ASTER).

The MISR surface data sets will enable: (1) the study

of the global magnitude and variability of sunlight

absorption and scattering by the Earth’s surface,

particularly through determination of the surface

hemispherical reflectance (albedo); (2) improved

measures of land surface classification and dynamics;

MIS 05, MIS 08, MIS 09

PRODUCT SUMMARY

Coverage:

daytime

Spatial/Temporal Characteristics:

9-day for global coverage with 1.1 km

and 17.6 km sampling (various

parameters) at Level 2; 16-day, monthly,

and seasonal globally gridded products at

Level 3

Key Geophysical Parameters:

column aerosol optical depth, aerosol

compositional model identifier,

directional hemispherical reflectance

factors, hemispherical reflectances, BRF

model parameters, FPAR, terrainreferenced view and illumination angles,

tropical ocean water-leaving equivalent

reflectance, tropical ocean phytoplankton

pigment concentration

Processing Level:

2, 3

Product Type:

standard, at-launch (MIS 05); post-launch

(MIS 08, MIS 09)

Science Team Contact:

J. Martonchik

and (3) improved observations of ocean color in the

tropics by providing data in equatorial regions to

supplement MODIS observations.

Data Set Evolution

MISR builds on earlier work with satellite and

aircraft optical sensors such as the AVHRR and

ASAS. Surface hemispherical reflectance (spectral

albedo) is retrievable with much higher accuracy

from MISR multi-angle observations than it can be

inferred from solely nadir spectral reflectance factors.

In addition, current aerosol retrievals (e.g., from

AVHRR) are unable to distinguish different particle
AM-1 MIS 05, MIS 08, MIS 09

MISR Aerosol and Surface Data

116 • Chapter Six: Cloud & Aerosol Properties, and Radiative Energy Fluxes Data Products Handbook Vol. I

types, since they are based on measurements at a

single wavelength and angle of view, and the

algorithm to convert observed radiance to aerosol

optical depth assumes particles of a fixed

composition and size. MISR retrievals will be based

on more extensive coverage in both wavelength and

view angle, providing greater ability to distinguish

different particle types based on their physical and

optical properties.

Suggested Reading

Dickinson, R.E., et al., 1990.

Irons, J.R., et al., 1991.

Martonchik, J.V. and D.J. Diner, 1992.

Wang, M. and H.R. Gordon, 1994.
AM-1 MIS 12

MISR Aerosol Climatology Product

Data Products Handbook Vol. I Chapter Six: Cloud & Aerosol Properties, and Radiative Energy Fluxes • 117

Product Description

The Aerosol Climatology Product (ACP) is generated

once, at the MISR SCF, with possible infrequent

updates. It is used for interpretation of the aerosol

data contained in MIS 05. The ACP contains the

physical and optical properties that define common

atmospheric aerosol types. The parameters reported

in the ACP include an aerosol model identifier

(name, number, and composition); a water activity

identifier (hygroscopic or not, and if so, how

hydrophilic); a particle shape identifier (spherical,

polyhedral, or irregular); a grid of relative humidity

values for which all optical properties have been

calculated; particle size distribution parameters;

particle density (volume-weighted for mixtures);

complex index of refraction; scattering and extinction

cross-section; single scattering albedo; scattering

anisotropy parameter; and phase function. It also

includes the definitions of the aerosol mixtures to be

used during generation of MIS 05, along with

climatological likelihood parameters for these

mixtures.

MIS 12

PRODUCT SUMMARY

Coverage:

n/a, one-time only, with possible

infrequent updates

Resolution:

n/a

Wavelengths:

443, 555, 670, and 865 nm plus selected

MODIS wavelengths; parameters include

aerosol optical properties

Product Type:

internal

Science Team Contact:

R. Kahn
TRMM, AM-1 ES-8

ERBE-Like Product

Unfiltered Radiances, TOA Fluxes (ES-8)

118 • Chapter Six: Cloud & Aerosol Properties, and Radiative Energy Fluxes Data Products Handbook Vol. I

Product Description

This Level 2 ERBE-like data set (ES-8) contains a

24-hour collection of scanner fluxes from a single

satellite. ERBE (flown on NOAA-9, 10, and ERBS)

is the heritage instrument for CERES and this

product is tailored to match the characteristics of the

similar ERBE product which was labeled S-8. ES-8

product is expected to be available 6 months after

launch. Fluxes at TOA are produced from unfiltered

radiances by ERBE inversion algorithms and ERBE

angular distribution models (ADMs) to correct for

anisotropies over an observed scene. The ES-8 also

includes the shortwave (SW), longwave (LW),

and window (WN) channel radiometric data; SW,

LW, and WN unfiltered radiance values;

and the ERBE scene identification (clear, partly

cloudy, mostly cloudy, or overcast data over ocean,

coasts, land, snow, and desert). These data are

organized according to the CERES 3.3-sec scan into

6.6-sec records. These records contain only Earthviewing measurements, approximately 450 for

TRMM and 390 for EOS. As long as there is one

valid scanner measurement within a record, the

ES-8 record will be generated. A complete listing

of parameters for this data product can be

found in the CERES Data Products Catalog

(http://asd-www.larc.nasa.gov/DPC/DPC.html).

Research & Applications

This product is a CERES-produced equivalent to the

instantaneous fluxes derived from the ERBE scanner

on ERBS, NOAA-9, and NOAA-10. ES-8 flux data

are directly comparable to ERBE fluxes, and thus

effectively extend the time series of ERBE flux

measurements, allowing global change experiments

to continue using ERBE algorithms without the

errors associated with comparing fluxes estimated by

two different algorithms. Improved TOA flux

estimation algorithms for CERES (using improved

ADMs and VIRS/MODIS cloud identification) are

available in product Single Satellite Footprint (SSF).

Producing the two versions of TOA fluxes will allow

continuity with the previous ERBE data along with

improved accuracy of CERES TOA Flux data (see

Product SSF).

ES-8

PRODUCT SUMMARY

Coverage:

global (for AM-1); 40°N - 40°S for

TRMM

Spatial/Temporal Characteristics:

20 km - not averaged or binned/1 per day

Key Geophysical Parameters:

TOA SW, LW fluxes and radiances,

scene type

Processing Level:

2

Product Type:

standard, at-launch

Science Team Contact:

R. Green

Data Set Evolution

The ERBE-like Inversion Subsystem consists of

algorithms which convert filtered radiometric

measurements to instantaneous flux estimates at

the TOA. The basis for this procedure is the

ERBE Data Management System which produced

TOA fluxes from the ERBE scanning radiometers.

The system consists of algorithms for Spectral

Correction, Observed Scene Type, Radiance-to-Flux

Conversion using Angular Distribution Models,

and finally the Regional Averaging algorithm

which produces regional fluxes. A linear estimation

scheme is used to relate the filtered and unfiltered

radiances. Ancillary parameter files are required

for processing, namely ERBE-Like ADMs and

ERBE Spectral Correction Coefficients for

converting filtered radiances to unfiltered

radiances. Documentation on the ES-8 algorithms

can be found in CERES ATBD Subsystem 2.0

(http://eospso.gsfc.nasa.gov/atbd/cerestables.html).
TRMM, AM-1 ES-8

ERBE-Like Product

Unfiltered Radiances, TOA Fluxes (ES-8)

Data Products Handbook Vol. I Chapter Six: Cloud & Aerosol Properties, and Radiative Energy Fluxes • 119

Suggested Reading

Avis, L.M., et al., 1984.

Barkstrom, B.R. and G.L. Smith, 1986.

Green, R.N. and J.R. Robbins, 1995.

Lee,R.N., et al., 1995.

Smith, G.L., et al., 1986.
TRMM, AM-1 ES-9, ES-4, ES-4G

ERBE-Like Products Gridded Monthly, Regional,

Zonal Earth Radiation & Atmospheric Data

(ES-9, ES-4, ES-4G)

120 • Chapter Six: Cloud & Aerosol Properties, and Radiative Energy Fluxes Data Products Handbook Vol. I

ES-9, ES-4, ES-4G

PRODUCT SUMMARY

Coverage:

global (for AM-1) 40°N - 40°S for

TRMM

Spatial/Temporal Characteristics:

2.5°, 5.0°, 10.0°, global/monthly

(by day and hour)

Key Geophysical Parameters:

TOA shortwave and longwave radiant

flux, scene type, albedo, solar incidence,

cloud condition

Processing Level:

3

Product Type:

standard, at-launch

Science Team Contact:

P. Minnis

Figure 21. Longwave Radiation From CERES ERBE-Like

Processing (plotted from an ES-4 produced at the

Langley DAAC from a full month of ERBE data as

a simulation for CERES).

Product Description

This Level 3 product (ES-9) provides radiative flux

estimates in the ERBE format for 2.5° regions daily

and flux averages over a month. This product is the

ERBE equivalent of CERES product AVG/ZAVG

and provides continuity with the ERBE mission and

experiments. For each region, data are collected by

the local solar hour for each hour of each day in the

month. Recorded data includes the mean estimates of

shortwave and longwave radiant flux at the TOA, the

standard deviations of these estimates, the maximum

and minimum estimate, and scene information or

cloud condition. Similar parameters are determined

for those scanner measurements identified as viewing

clear-sky areas. The daily, monthly hourly, and

monthly averages are also stored and for all

observations summary statistics are provided

including mean, minimum and maximum values,

standard deviation, and number of days with at least

one sample.

The ES-4 data product consists of regional, zonal,

and global averages of scanner TOA fluxes arranged

temporally by local time, day, and month. Data are

averaged spatially to regions, latitude zones, and the

globe. The seven sets of records correspond to regional and zonal (2.5°), nested regional and

zonal (5.0° and 10°), and global averages.

The ES-4G data product contains the same

time and space averages as the ES-4 science

data product, with the difference being the

arrangement of the data. The ES-4G file

presents a gridded data product with all

regions for a given data parameter grouped

together, while ES-4 presents all parameters

for each region.

Research & Applications

This product provides an equivalent of the

ERBE-based radiation balance component of

inputs to global climate models which were

developed as part of or over the same period

as the ERBE program and will allow

continuation of this research while new

algorithms are developed for CERES data.

The collection of products ES-9, ES-4, and
TRMM, AM-1 ES-9, ES-4, ES-4G

ERBE-Like Products Gridded Monthly, Regional,

Zonal Earth Radiation & Atmospheric Data

(ES-9, ES-4, ES-4G)

Data Products Handbook Vol. I Chapter Six: Cloud & Aerosol Properties, and Radiative Energy Fluxes • 121

ES-4G are equivalent to their ERBE analogs (S-9, S4, S-4G), and effectively extend the time series of

ERBE flux measurements through the EOS era.

Various spatial scales are provided to simplify

intercomparisons with earlier NIMBUS-7 and ERBE

non-scanner time series.

Data Set Evolution

These datasets are generated using heritage

ERBE algorithms operating on inputs from

CERES calibrated Level 1 data and ancillary

data. Data processing uses CERES Subsystem 3.0

which temporally interpolates CERES measurements

using linear interpolation over oceans and halfsine curve fit over land and desert regions.

Monthly and monthly-hourly means are then

computed using the combination of observed

and interpolated values. Documentation on the

algorithms used to create the data products can be

found in CERES ATBD Subsystem 3.0

(http://eospso.gsfc.nasa.gov/atbd/cerestables.html).

Suggested Reading

Brooks, D.R., et al., 1986.

Harrison, E.F., et al., 1990.

Harrison, E.F., et al., 1995a.

Ramanathan V., et al., 1989b.
TRMM, AM-1 SSF

Single Satellite Footprint TOA and

Surface Flux and Clouds (SSF)

122 • Chapter Six: Cloud & Aerosol Properties, and Radiative Energy Fluxes Data Products Handbook Vol. I

SSF

PRODUCT SUMMARY

Coverage:

global (AM-1); 40°N - 40°S (TRMM)

Spatial/Temporal Characteristics:

hourly, footprints at full resolution

(20 km at nadir)

Key Geophysical Parameters:

radiance and flux (TOA and Surface);

cloud and clear-sky statistics, optical depth,

infrared emissivity, liquid water path, ice

water path, cloud top pressure, cloud

effective pressure, cloud temperature and

cloud height, cloud bottom pressure, water

and ice particle radius

Processing Level:

2

Product Type:

standard, post-launch

Science Team Contact:

B. Baum

Figure 22. CERES TOA LW Flux (plotted from an SSF produced at the

Langley DAAC using ERBE data as a simulation for CERES

and AVHRR data as a simulation for VIRS/MODIS).

Product Description

This Level 2 product is the Single Satellite Flux and

Cloud Swath data set that contains single satellite

footprint (SSF) geometry and viewing angles;

radiance and radiative flux (TOA and Surface); cloud

and clear-sky statistics, scene type, and imager

viewing angles; footprint statistics for each of four

cloud height categories; visible optical depth;

infrared emissivity; liquid water path; ice water path;

cloud top pressure; cloud effective pressure,

temperature, and height; cloud bottom pressure;

water and ice particle radius; particle phase; vertical

aspect ratio; visible optical depth/IR emissivity (13

percentiles); and cloud overlap conditions (11

conditions). Means and standard deviations are

provided for most parameters. Each SSF covers a

single-hour swath from a single CERES instrument

mounted on one satellite. The SSF is an archival

product that will be run daily in validation mode

starting with the TRMM launch until sufficient data

have been collected and analyzed to produce a

production quality set of CERES broadband angular

models. It is estimated that at TRMM launch plus 24

months, the SSF product will be produced on a

routine basis and will be archived within EOSDIS for

distribution to the science

community.

Research & Applications

This product generates the basic data

on radiation and cloud characteristics

needed by all the hourly, monthly,

gridding, averaging, and radiation

budget algorithms producing all the

CERES output products.

Data Set Evolution

This product is the first step in the

sequence of processes from the 1B

radiances to global radiation budget

estimates.

The SSF analysis uses coarse spatial

resolution broadband radiance data
TRMM, AM-1 SSF

Single Satellite Footprint TOA and

Surface Flux and Clouds (SSF)

Data Products Handbook Vol. I Chapter Six: Cloud & Aerosol Properties, and Radiative Energy Fluxes • 123

from CERES, matched with simultaneous high

spatial resolution narrowband radiance data from

VIRS (TRMM) and MODIS (EOS-AM, PM).

Analysis proceeds in six basic steps:

1. Cloud masking and clear-sky map

update.

2. Cloud height and layering determination.

3. Cloud optical property determination.

4. Convolution of imager pixel level

cloud/surface properties with the CERES

point spread function for each CERES

field of view.

5. Use of convolved cloud/surface

properties to select a broadband

anisotropic model for conversion of

CERES radiance to TOA flux.

6. Estimation of radiative fluxes at the

surface using theoretical/statistical

relationships to TOA fluxes.

The final SSF data product contains the matched

cloud and radiative flux data which will also be used

for later determination of radiative fluxes within the

atmosphere (CRS data product). The algorithms are

described in the CERES ATBD 4.0-4.6

(http://eospso.gsfc.nasa.gov/atbd/cerestables.html).

Suggested Reading

Baum, B.A., et al., 1995a.

Baum, B.A., et al., 1995b.

Green, R.N. and B.A. Wielicki, 1995.

Green, R.N., et al., 1995.

Minnis, P., et al., 1995.

Wielicki, B.A.., et al., 1995.
TRMM, AM-1 CRS

Single Satellite CERES Footprint

Radiative Fluxes and Clouds (CRS)

124 • Chapter Six: Cloud & Aerosol Properties, and Radiative Energy Fluxes Data Products Handbook Vol. I

Product Description

This Level 2 post-launch product contains corrected

and adjusted longwave and shortwave radiative

fluxes for surface, multiple atmosphere levels

(between 2 and 18 levels 2.5 years after launch,

depending on validation results) and TOA for both

clear-sky and total-sky for each CERES footprint

for one hour or a single satellite swath (8-12 percent

of the Earth) from one satellite. Fluxes within the

atmosphere are calculated with a plane parallel

radiative transfer code; atmospheric properties

(such as clouds) are adjusted to produce agreement

with satellite-observed fluxes at the top of the

atmosphere. This product is the second stage of

refinement in a sequence starting with SSF

and ending with AVG/ZAVG (See CERES Data

Flow diagram in Chapter Three, Instrument

Overviews). This data set contains time and location

data, algorithm flags, cloud category properties for

up to four cloud layers (lower, lower middle, upper

middle, and high), and cloud overlap data for

eleven overlap conditions (clear, low (L), lower

middle (LM), upper middle (UM) high (H), H/UM,

H/LM, H/L, UM/LM, UM/L, LM/L). The flux

adjustments for both clear-sky and total sky are also

included. Cloud properties include pressure, optical

depth, water and ice path, and particle radius and

optical depth/IR emissivity frequency distribution.

PAR at the surface is also provided. A complete

listing of parameters for this data product can be

found in the CERES Data Products Catalog

(http://asd-www.larc.nasa.gov/DPC/DPC.html).

Research & Applications

The surface and atmospheric radiation budget

(SARB) is the primary driver of the hydrological

cycle and the general circulation of the atmosphere.

Anthropogenically induced changes in the radiatively

active trace gases and aerosols will affect the SARB

and will therefore force a climate response. The 68

radiance and radiant flux and cloud parameters

provided by this product are inputs to higher level

algorithms which produce gridded, averaged flux and

cloud property products. This product contains a

further enhancement of the basic data on radiation

and cloud characteristics from SSF needed for

CRS

PRODUCT SUMMARY

Coverage:

global for AM-1; 40°N-40°S for TRMM

Spatial/Temporal Characteristics:

hourly, footprints at full resolution

(20 km)

Key Geophysical Parameters:

shortwave and longwave radiant flux at

the surface, multiple atmosphere levels,

and TOA, scene identification, cloud

parameters

Processing Level:

2

Product Type:

standard, post-launch

Science Team Contact:

T. Charlock

determination of the amount of energy passing from

space through the atmosphere to the Earth and

flowing from the Earth back to space which is

provided by products SRBAVG, AVG, and ZAVG. It

is a global product and is used as the input to the

hourly gridded computation of surface, multiple

atmosphere levels and TOA radiative fluxes. The

analysis algorithms are described in the CERES

ATBD Subsystem 5.0 (http://eospso.gsfc.nasa.gov/

atbd/cerestables.html). An example of the radiative

flux profile, expressed in terms of the radiative

diabatic heat rate, is given in Figure 9. The local

scale CERES/ARM/GEWEX Experiment (CAGEX)

provides a prelaunch window to this component

of CERES with on-line access to input data,

calculated fluxes, and validating measurements

(http://snowdog.larc.nasa.gov:8081/cagex.html).

Data Set Evolution

The inputs to this product come from the SSF

product and the cloud parameters are further
TRMM, AM-1 CRS

Single Satellite CERES Footprint

Radiative Fluxes and Clouds (CRS)

Data Products Handbook Vol. I Chapter Six: Cloud & Aerosol Properties, and Radiative Energy Fluxes • 125

Heating Rates (°K/day)

Pressure (mb)

Pixel Latitude: 29.1 Pixel Longitude: 128.9

Mid-level Cloud Scene

Solar Zenith Angle: 62.1°

-10

1000

900

800

700

600

500

400

300

200

100

50

1000

900

800

700

600

500

400

300

200

100

50

-5 0 5 10

Untuned Fu-Liou LW

Tuned Fu-Liou LW

OLR: 292.6 W/m

ERBE OLR: 277.7 W/m

Tuned OLR: 278.9 W/m

TOA Net: 496.2 W/m

ERBE TOA Net: 477.8 W/m

Tuned TOA Net: 479.1 W/m

Untuned SFC Net: 355.8 W/m

Tuned SFC Net: 347.5 W/m

Untuned SFC Net: -37.4 W/m

Tuned SFC Net: -40.0 W/m

Untuned Fu-Liou LW

Tuned Fu-Liou LW

2

2

2

2

2

2

2

2

2

2

Figure 23. Longwage and Shortwave Heating Rates. For LW and SW, fluxes at the TOA (as untuned FL, ERBE

observations, and tuned FL), fluxes at the surface (as untuned FL and tuned FL), and heating rates (untuned are solid

and tuned are dashed). One ERBE footprint containing low clouds.

corrected and adjusted. The TOA and surface values

are corrected for satellite and cloud effects to produce

observed TOA and tuned surface estimates. This data

set is tailored to provide the needed inputs to the

hourly gridded flux and surface radiation budget

Level 3 product, FSW.

Suggested Reading

Charlock, T.P., and T.L. Alberta, 1996

Charlock, T.P., et al., 1993.

Charlock, T.P., et al., 1995.

Fu, Q., and K. Liou, 1993.

Liou, K.-N., 1992.

Whitlock, C.H., et al., 1995.

Wu, M., and L.-P. Chang, 1992.
TRMM, AM-1 FSW

Hourly Gridded Single-Satellite

Fluxes and Clouds (FSW)

126 • Chapter Six: Cloud & Aerosol Properties, and Radiative Energy Fluxes Data Products Handbook Vol. I

Product Description

The Hourly Gridded Single Satellite Fluxes and

Clouds (FSW) Level 3 data product contains hourly

single satellite flux and cloud parameters derived

from the single satellite cloud radiative swath (CRS)

data set averaged over 1° equal angle regions. Each

FSW covers a single-hour swath from a single

CERES instrument mounted on one satellite. The

single satellite case is indicated since the heritage

instrument, ERBE, had up to three satellites

operating and these data were integrated by the final

flux and radiation budget algorithms. The same

integration is part of the CERES system which will

process data from TRMM, EOS-AM, and EOS-PM

satellites. Data presented for each region include

radiative fluxes at TOA, surface, and atmospheric

levels for both clear-sky and total-sky, cloud overlap

conditions, cloud properties for up to four cloud

layers (lower, lower middle, upper middle, and high),

column-averaged cloud properties, angular model

scene classes and adjustment parameters. Summary

statistics (mean, standard deviation, number of

observations) are included for most parameters for

data quality verification.

Research & Applications

This product is an interim step in the generation of

regional and global radiation products from CERES

footprint data. It contains the gridded data on

radiation and cloud characteristics needed by the

synoptic flux and clouds algorithm SYN. The

contribution of this product is the averaging and

gridding operations applied to both the radiative flux

and cloud property data.

Data Set Evolution

The algorithm is based on ERBE algorithms and

carries out aggregation of the high resolution 20 km

(nadir) data products to 1° (110 km) data cells using

weighted averaging and linear interpolation to

produce the flux and cloud data in this grid cell

format chosen for final products. This is an equal

angle grid consisting of regions that are 1° in latitude

by 1° in longitude. The parameter list is essentially

the same as for CRS. The analysis algorithms are

FSW

PRODUCT SUMMARY

Coverage:

global (AM-1); 40°N - 40°S (TRMM)

Spatial/Temporal Characteristics:

1° grid/hourly

Key Geophysical Parameters:

gridded surface, atmosphere layer and

TOA fluxes, clear-sky and total-sky,

cloud parameters in CRS, (e.g., cloud

overlap, cloud pressure, cloud altitude,

cloud temperature, ice water properties,

ice particle properties, liquid water

properties)

Processing Level:

3

Product Type:

standard, post-launch

Science Team Contact:

L. Smith

described in the CERES ATBD Subsystem 6.0

(http://spso.gsfc.nasa.gov/atbd/cerestables.html).

Suggested Reading

Smith, G.L., and T.D. Bess, 1983.

Stowe, et al., 1993.
TRMM, AM-1 SYN

Synoptic Radiative Fluxes and Clouds (SYN)

Data Products Handbook Vol. I Chapter Six: Cloud & Aerosol Properties, and Radiative Energy Fluxes • 127

Product Description

This Level 3 product contains regional LW, SW, and

WN radiative fluxes for surface, selected atmospheric

levels as in CRS, and TOA for 3-hour intervals on a

1° equal angle grid at synoptic times (0, 3, 6, ...

21 UTC), and includes data from up to three CERES

instruments (the product generation system has the

capability of combining data from CERES

instruments on TRMM, EOS-AM, and EOS-PM).

This includes the region data, observed CERES TOA

data for clear-sky and total-sky, cloud properties for

up to four cloud layers (lower, lower middle, upper

middle, and high), cloud top pressure, cloud area

fraction, column averaged cloud properties for five

weighting schemes (TOA SW, TOA LW, LW, LWC

[liquid water] and IWC [ice water]), angular model

scene classes for 12 ERBE scenes types, and

atmospheric flux profile, as well as flux adjustment

for surface and TOA for both clear-sky and total-sky,

adjustment parameters for clear-sky and cloud layers.

Research & Applications

The synoptic format of this product provides data

which is global and simultaneous in time. This is

preferred by climate modelers since the synoptic

times ensure consistency with ground truth

meteorological observations from weather stations

and radiosondes as well as with geostationary

satellites which provide images at synoptic hours.

The production of a CERES synoptic data product is

important because synoptic views provide a basis for

studying the life cycle of cloud systems and for

validating the CERES data processing, and it

provides a regular data structure which simplifies the

design of algorithms and operation of the data

processing system. Synoptic fields of radiation and

clouds are potentially valuable in developing and

understanding the role of clouds in the generation and

dissipation of available potential energy, since the

calculation of this quantity requires integration over

approximately horizontal layers within the

atmosphere.

Data Set Evolution

The CERES time-space averaging and time

interpolation algorithm is based on techniques used

SYN

PRODUCT SUMMARY

Coverage:

global (AM-1); 40°N - 40°S (TRMM)

Spatial/Temporal Characteristics:

1°, 3-hour intervals

Key Geophysical Parameters:

synoptic time interval clear-sky, totalsky fluxes at surface, atmosphere levels

and TOA, cloud parameters including

cloud overlap, cloud pressure, cloud

altitude, cloud temperature, optical depth,

ice water properties, ice particle

properties, liquid water properties,

photosynthetically active radiation

Processing Level:

3

Product Type:

standard, post-launch

Science Team Contact:

T. Wong

in previous Earth radiation budget systems such as

ERBE. The chief input to the time interpolation

process is the gridded SW and LW TOA clear-sky

and total-sky fluxes and cloud information provided

by the FSW product. Data from the three possible

systems which could be simultaneously in orbit

(TRMM, EOS-AM, and EOS-PM) provide up to

six samples per day. This significantly reduces

temporal sampling errors. Estimates of the cloud

properties at synoptic times are also provided.

The cloud mask output defines clear-sky conditions

for the 1° cells for the radiative fluxes which

are averaged over 3 hour periods. Interpolation to the

synoptic times uses geostationary data to assist

in modeling meteorological variations between

times of observations. The analysis algorithms are

described in CERES ATBD Subsystem 7.0

(http://eospso.gsfc.nasa.gov/atbd/cerestables.html).
TRMM, AM-1 SYN

Synoptic Radiative Fluxes and Clouds (SYN)

128 • Chapter Six: Cloud & Aerosol Properties, and Radiative Energy Fluxes Data Products Handbook Vol. I

Suggested Reading

Breigleb and Ramanathan, 1982.

Brooks, D.R., et al., 1986.

Harrison, E.F., et al., 1988.

Minnis, P. and E.F. Harrison, 1984.

Smith, G.L., et al., 1995.

Young, D.F., et al., 1995a.
TRMM, AM-1 AVG, ZAVG

Regional Average Data Product &

Zonal and Global Monthly Average Data Product

Data Products Handbook Vol. I Chapter Six: Cloud & Aerosol Properties, and Radiative Energy Fluxes • 129

Product Description

The regional monthly radiative flux and cloud averages

(AVG) product contains monthly and monthly-hourly

averages of the TOA and surface LW and SW radiative

fluxes, together with LW and SW fluxes at standard

pressure levels in between. This final product also

contains observed cloud and clear-sky properties at the

standard 1° horizontal resolution. Included in this data

set are radiative fluxes for both clear-sky and total-sky

at TOA, cloud categories for four cloud layers (lower,

lower middle, upper middle, and high), columnaveraged cloud properties for five weighting schemes

(TOA SW, TOA LW, surface LW, liquid water, and

ice), overlap data for eleven cloud conditions, angular

model scene classes, atmospheric flux profile, flux

adjustment for surface and TOA for both clear-sky and

total-sky, and adjustment parameters for clear-sky and

cloud layers. Mean, standard deviation, and number of

days or number of observations are given with most

parameters. A complete listing of parameters for this

data product can be found in the CERES Data Products

Catalog. The zonal monthly radiative fluxes and clouds

averages (ZAVG) product is similar to the regional

monthly average data set, however it contains a

summary of the zonal (1° latitudinal zones) and global

averages of the radiative fluxes and cloud properties.

This data set, which is equivalent to the zonal averages

and global averages in the ERBE S-4 product, may be

used as a browse product for the CERES data.

Research & Applications

This product will be used by meteorological

researchers to study climate and improve global climate

models. Zonal quantities are needed for studying

energy transport since averaging on large spatial scales

minimizes the effects of regional-scale anomalies in

studying climate change and global dynamics. Global

averages can be compared with other historical data

sets derived from different regional scales to detect

climate temperature trends and evaluate large-scale

climate anomalies such as the effects of major volcanic

eruptions.

Data Set Evolution

The product algorithm first calculates the means on a

regional (1° equal-angle grid) basis from one month of

synoptic maps from the SYN 3-hourly product.

AVG, ZAVG

PRODUCT SUMMARY

Coverage:

global (for AM-1 and AM-1/TRMM

combined)

Spatial/Temporal Characteristics:

monthly 1° (AVG) and 1° latitudinal

zones (ZAVG)

Key Geophysical Parameters:

averaged clear-sky, total-sky surface,

TOA and standard CERES pressure level

fluxes, cloud parameters include: cloud

overlap, pressure, temperature, optical

depth, ice particle properties, liquid water

properties, PAR

Processing Level:

3

Product Type:

standard, post-launch

Science Team Contact:

T. Wong

Regional means are then combined to obtain zonal and

global averages. The main steps of the monthly

averaging process are: (1) regionally sort the

synoptically-ordered data, (2) linearly average all flux

data to produce monthly and monthly-hourly means,

(3) average the cloud properties using the proper

weighting schemes, and (4) combine and average the

regional means into zonal and global means. Once

regional means are computed for all parameters and all

regions, these means are combined into zonal and

global means. Area weighting factors are used to

correct for the variation of grid box

size with latitude. The analysis algorithms are

described in CERES ATBD Subsystem 8.0

(http://eospso.gsfc.nasa.gov/atbd/cerestables.html).

Suggested Reading

Harrison, E.F., et al., 1990.

Li, Z. and H. Leighton, 1993.

Young, D.F., et al., 1995b.
TRMM, AM-1 SFC

Gridded Single Satellite Fluxes and Clouds (SFC)

130 • Chapter Six: Cloud & Aerosol Properties, and Radiative Energy Fluxes Data Products Handbook Vol. I

Product Description

This is a Level 3 product which takes the cloud and

atmosphere-processed CERES radiance data from the

high resolution (21-km at nadir) footprint format to a

1° (110-km in latitude) equal-angle grid which is

common to all the CERES output products. The data

for each region contain spatially averaged values from

a single hour swath from a single CERES instrument

mounted on one satellite. The major categories of data

output on the SFC are total-sky radiative fluxes at

TOA and surface, clear-sky radiative fluxes at TOA

and surface, column-averaged cloud properties,

angular model scene classes, PAR, and direct/diffuse

ratio. Data are presented for each region with

summary statistics (mean, standard deviation, number

of observations).

This product differs from FSW in the following ways.

First, SW and LW radiative fluxes are provided only

at the TOA and Surface. Second, cloud properties are

only provided as column averages. These first two

changes substantially reduce the data volume. Third,

the surface fluxes are estimated differently than in

FSW. Here, the surface flux estimates rely heavily on

parameterized relationships of fluxes at the surface to

those measured at the TOA. The intent is to use

radiative transfer models only to derive the form of

the parameterized relationship, while simultaneous

TOA and Surface observations are used to confirm the

relationship and if necessary to modify it. The FSW

product focuses on deriving an internally consistent

set of atmosphere, cloud, and radiative fluxes from

surface to top of the atmosphere given the constraint

of a state-of-the-art radiative transfer model.

Research and Applications

This product is a necessary step in the transformation

of raw CERES data to the radiation budget outputs

from product SRBAVG. The averaging and gridding

operations performed on the footprint data serve to

reduce variability by averaging to a 1-hour period and

present the data in a uniform grid which is required

for temporal comparison of flux and cloud parameters.

Data Set Evolution

The parameters generated by this product are derived

from the SSF product which creates all the flux and

cloud parameters. Each of the parameters from

SFC

PRODUCT SUMMARY

Coverage:

global (AM-1); 40°N - 40°S (TRMM)

Spatial/Temporal Characteristics:

1°, hourly (1-hour period summary)

Key Geophysical Parameters:

total-sky, clear-sky and angular model

scene radiative fluxes (TOA and surface),

cloud parameters, including cloud pressure,

cloud altitude, cloud temperature, optical

depth, photosynthetically active radiation,

direct/diffuse ratio

Processing Level:

3

Product Type:

standard, post-launch

Science Team Contact:

D. Kratz, T. Charlock

SSF are averaged to the 1° grid using equal weighting

which results in approximately 50 footprints

being averaged for each geographic cell. These

parameters are all inputs to the SRBAVG final

radiation budget product. The analysis algorithms are

described in CERES ATBD Subsystem 4.6

(http://eospso.gsfc.nasa.gov/atbd/cerestables.html).

Suggested Reading

Barkstrom, B.R., et al., 1995.

Cess, R., et al., 1991.

Gupta, S.K., et al., 1992.

Gupta, S.K., et al., 1995.

Inamdar, A.K. and V. Ramanathan, 1994.

Inamdar, A.K. and V. Ramanathan, 1995.

Kratz, D.P. and Z. Li, 1995.

Li, Z., et al., 1993.
TRMM, AM-1 SRBAVG

Monthly TOA and

Surface Radiation Budget Averages (SRBAVG)

Data Products Handbook Vol. I Chapter Six: Cloud & Aerosol Properties, and Radiative Energy Fluxes • 131

Product Description

The Monthly Level 3 TOA and Surface Radiation

Budget Averages (SRBAVG) final product (CER 06)

contains monthly and monthly-hourly regional,

zonal, and global averages of the TOA and surface

LW and SW fluxes (clear-sky and total-sky), columnaveraged cloud properties, and angular model scene

types for each 1° equal-angle region. This product

differs from the AVG/ZAVG product in three ways.

First, the surface fluxes have been calculated from

the TOA fluxes using statistical parameterizations.

Second, no flux fields are calculated at levels

between TOA and the surface. Third, the regional

fluxes are calculated using two methods: with and

without geostationary data. The monthly TOA/SRB

average data set is produced for each spacecraft and

for each combination of spacecraft. At the TRMM

launch, this product will be produced in a validation

mode for the first 30 months. During these 30

months, the CERES Science Team will derive a

production quality set of angular distribution models

which are needed to produce the LW and SW

instantaneous fluxes.

Research and Applications

The cloud properties as a function of LW and SW

radiative fluxes at TOA and surface are critical to

cloud forcing issues and cloud properties as a

function of liquid water and ice water volume are

critical to cloud dynamics modeling.

Data Set Evolution

This product is obtained from the chain of processes

which start with the 1B radiances and flow through

products BDS, IES, SSF, SFC ultimately producing

the final averaged radiative flux and surface radiation

budget (SRB) product. The regional fluxes are

calculated using two different methods which will

help produce very stable, accurate long-term data sets

and averages for use in detailed regional studies of

radiation and clouds and interdisciplinary studies.

This is a post-launch product. Currently, there is no

adopted method available for producing total-sky

surface LW flux from TOA flux. Data required for

parameterization must be obtained from either MOA

atmospheric data set or from CERES and ISCCP

cloud properties. In addition, data used in this

SRBAVG

PRODUCT SUMMARY

Coverage:

global (AM-1, TRMM and AM-1

combined); 40°N - 40°S (TRMM)

Spatial/Temporal Characteristics:

1°, one per month

Key Geophysical Parameters:

averaged total-sky radiative fluxes

(TOA and surface), net surface

radiative fluxes (surface radiation

budget), averaged cloud parameters

Processing Level:

3

Product Type:

standard, post-launch

Science Team Contact:

T. Charlock, T. Wong

product are limited to days in which there is at least

one observation. The cloud parameters are also

averaged monthly and monthly-daily. The surface

radiation budget is the net radiative flux at the

surface and is computed using the hourly gridded

TOA and surface fluxes from SFC, parameterized

models, atmospheric profiles, cloud parameters,

surface temperature and emissivity to produce the

downward and net shortwave and longwave fluxes at

the surface. The LW algorithm is based on

parameterized equations developed expressly for

computing surface LW fluxes in terms of

meteorological parameters obtained from satellite

and/or other operational sources. The analysis

algorithms are described in CERES ATBD

Subsystem 10.0 (http://eospso.gsfc.nasa.gov/

atbd/cerestables.html).

Suggested Reading

Cess, R., et al., 1991.

Gupta, S.K., et al., 1992.

Harrison, E.F., et al., 1995b.

Li, Z., et al., 1993.

Suttles, J.T., and G. Ohring, 1986.
AST 13

Polar Surface and Cloud Classification

132 • Chapter Six: Cloud & Aerosol Properties, and Radiative Energy Fluxes Data Products Handbook Vol. I

Product Description

This Level 2 product is a polar classification map,

consisting of the following classes: water, wet/slush

ice, snow/ice, land, shadow, water cloud, and ice

cloud. Each pixel is classified using a bit map for

each of the above classes. Therefore, for complex

scenes such as cirrus over broken sea ice, the water,

ice, and ice cloud bits would be set. This product is

produced at 30-m spatial resolution and uses a

combination of visible, near-infrared, and infrared

channels. The polar regions are defined as all land

and water regions lying poleward to 60°N or 60°S.

Both daytime and nighttime classifications will be

available, with the daytime algorithm applied for

solar zenith angles less then 85°, and the nighttime

algorithm applied in all cases. This is an on-request

product.

Research & Applications

Since greenhouse forcings are expected to be

amplified in the polar regions, these regions may act

as early warning indicators of global climate shifts.

Cloud cover can be expected to be altered by

greenhouse forcings, and cloud changes are expected

to have a significant effect on sea ice conditions and

regional ice-albedo feedbacks, especially to the polar

heat balance which directly affects surface melting.

ASTER polar data will be used to monitor changes in

surface conditions, notably temperature, albedo, and

sea ice breakup.

Data Set Evolution

This data set builds on work over the past decade

with data from Landsat TM, AVIRIS, TIMS, and

MAS. The improved spectral coverage, resolution,

and radiometric accuracy will enable ASTER to

provide remotely sensed polar data of unprecedented

accuracy.

The ASTER polar data set will also be used to

validate the global-scale polar data and cloud

property retrievals from MODIS. In particular, this

data set will be used to validate the MODIS cloud

optical thickness and effective particle sizes which

directly impact the Earth’s radiative budget. Only

AST 13

PRODUCT SUMMARY

Coverage:

regional (poleward from 60° N or S)

Spatial/Temporal Characteristics:

30 m over 60 km × 60 km scenes

Key Science Applications:

climate modeling, polar heat balance,

Key Geophysical Parameters:

polar clouds, thick cloud, thin cloud,

shadow, ice, wet ice

Processing Level:

3

Product Type:

standard, on-request

Science Team Contact:

R. Welch

ASTER has the spatial resolution necessary to fully

analyze the cloud 3-D effects.

Suggested Reading

Ebert, E., 1987.

Tovinkere, V.R., et al., 1993.

Welch, R.M., et al., 1990.

Welch, R.M., et al., 1990.
DAS 11

DAS Assimilated Cloud Fraction

Data Products Handbook Vol. I Chapter Six: Cloud & Aerosol Properties, and Radiative Energy Fluxes • 133

Product Description

This Level 4 product consists of 3- and 6-hourly

estimates of cloud fraction produced by the GEOS

DAS. It is based on the assimilation of pressure,

temperature, wind, and moisture observations. These

include conventional meteorological observations,

both in situ and remotely sensed, as well as TRMM

and AM-1 data such as TMI and MODIS. Two fields

are provided, two-dimensional cloud fraction

(CLDFRC) and three-dimensional cloud fraction

(CLROLW).

Research & Application

Clouds play a crucial role in the Earth’s radiative

balance. Accurate estimates of global cloud coverage

are needed to quantify global climate change.

Data Set Evolution

Satellite observations of clouds have been available

for many years. TRMM and AM-1 will provide much

needed improvements to these observations. Cloud

estimates from the GEOS DAS will be consistent

with the dynamics of the assimilation general

circulation model. However, for the near future

assimilated clouds are unlikely to be as accurate as

satellite imagery and thus should be used cautiously.

Suggested Reading

Bloom, S.C., et al., 1995.

Pfaendtner, J., et al., 1995.

Schubert, S.D., et al., 1995.

Schubert, S.D., et al., 1993.

Takacs, L.L., et al., 1994.

DAS 11

PRODUCT SUMMARY

Coverage:

global

Spatial/Temporal Characteristics:

2° × 2.5° lat-lon grid, 1 level for 2-D,

70 model sigma levels for 3-D, daily at

00, 03, 06, 09, 12, 15, 18, and 21 UTC

Key Geophysical Parameters:

cloud fraction (2-dimensional 8

times/day, 3-dimensional 4 times/day)

Processing Level:

4

Product Type:

standard, post-launch

Science Team Contact:

R. Rood, A. Molod
DAS 12

DAS Assimilated Cloud Mass Flux

134 • Chapter Six: Cloud & Aerosol Properties, and Radiative Energy Fluxes Data Products Handbook Vol. I

Product Description

This Level 4 product consists of 6-hourly estimates

of cloud mass flux per unit time from all convective

clouds as estimated by the GEOS DAS. It is based on

the assimilation of pressure, temperature, wind, and

moisture observations. These include conventional

meteorological observations, both in situ and

remotely sensed, as well as TRMM and AM-1 data

such as TMI and MODIS. These fields are generated

by the convective parameterization within the GEOS

DAS GCM. Two fields are provided, cumulative

cloud mass flux from all convective clouds

(CLDMAS) and mass flux at the top of each

convective cloud (DTRAIN).

Research & Application

Convective clouds play a crucial role in the Earth’s

radiation balance. Accurate estimates of cloud mass

flux from convective clouds will help determine the

life cycle of convective cloud systems and thus their

impact on global climate change.

Data Set Evolution

This very specialized data set is not generally

available globally from conventional observations. It

is usually estimated through convective

parameterization schemes within global GCMs. The

GEOS DAS convective parameterization scheme will

have the advantage of additional observational input

from TRMM and AM-1 instruments.

Suggested Reading

Bloom, S.C., et al., 1995.

Pfaendtner, J., et al., 1995.

Schubert, S.D., et al., 1995.

Schubert, S.D., et al., 1993.

Takacs, L.L., et al., 1994.

DAS 12

PRODUCT SUMMARY

Coverage:

global

Spatial/Temporal Characteristics:

2° × 2.5° lat-lon grid, 70 model sigma

levels, daily at 00, 06, 12, and 18 UTC

Key Geophysical Parameters:

cloud mass flux

Processing Level:

4

Product Type:

standard, post-launch

Science Team Contact:

R. Rood, A. Molod
DAS 13

DAS Assimilated Heating Rates

Data Products Handbook Vol. I Chapter Six: Cloud & Aerosol Properties, and Radiative Energy Fluxes • 135

Product Description

This Level 4 product consists of 6-hourly estimates

of heating rates from longwave and shortwave

radiation under clear skies as estimated by the GEOS

DAS. It is based on the assimilation of pressure,

temperature, wind, and moisture observations. These

include conventional meteorological observations,

both in situ and remotely sensed, as well as TRMM

and AM-1 data such as TMI and MODIS. These

fields are generated by the radiation parameterization

within the GEOS DAS GCM. Two fields are

provided, clear-sky heating rate due to shortwave

radiation (SWCLR) and clear-sky heating rate due to

longwave radiation (LWCLR).

Research & Application

Radiative heating of the atmosphere is a significant

component to the global heat balance. Heating rates

are needed to determine atmospheric stability which

drives local convection and to determine horizontal

temperature gradients which modify the

atmosphere’s general circulation.

Data Set Evolution

This very specialized data set is not generally

available globally from conventional observations. It

is usually estimated through radiation

parameterization schemes within global GCMs. The

GEOS DAS radiation parameterization scheme will

have the advantage of additional observational input

from TRMM and AM-1 instruments.

Suggested Reading

Bloom, S.C., et al., 1995.

Pfaendtner, J., et al., 1995.

Schubert, S.D., et al., 1995.

Schubert, S.D., et al., 1993.

Takacs, L.L., et al., 1994.

DAS 13

PRODUCT SUMMARY

Coverage:

global

Spatial/Temporal Characteristics:

2° × 2.5° lat-lon grid, 70 model sigma

levels, daily at 00, 06, 12, and 18 UTC

Key Geophysical Parameters:

clear-sky heating rates from longwave

and shortwave radiation

Processing Level:

4

Product Type:

standard, post-launch

Science Team Contact:

R. Rood, A. Molod
DAS 14

DAS Assimilated Surface Sensible Heat Flux

136 • Chapter Six: Cloud & Aerosol Properties, and Radiative Energy Fluxes Data Products Handbook Vol. I

Product Description

This Level 4 product consists of 3-hourly estimates

of surface sensible heat flux estimated by the GEOS

DAS. It is based on the assimilation of pressure,

temperature, wind, and moisture observations. These

include conventional meteorological observations,

both in situ and remotely sensed, as well as TRMM

and AM-1 data such as TMI and MODIS. This field

is generated by the turbulence parameterization

within the GEOS DAS GCM.

Research & Application

Surface sensible heat flux is a significant component

to the global heat balance. Heating rates near the

Earth’s surface are needed to determine atmospheric

stability (which drives local convection) and to

determine horizontal temperature gradients (which

modify the atmosphere’s general circulation).

Data Set Evolution

This very specialized data set is not generally

available globally from conventional observations. It

is usually estimated through turbulence

parameterization schemes within global GCMs. The

GEOS DAS turbulence parameterization scheme will

have the advantage of additional observational input

from TRMM and AM-1 instruments.

Suggested Reading

Bloom, S.C., et al., 1995.

Pfaendtner, J., et al., 1995.

Schubert, S.D., et al., 1995.

Schubert, S.D., et al., 1993.

Takacs, L.L., et al., 1994.

DAS 14

PRODUCT SUMMARY

Coverage:

global

Spatial/Temporal Characteristics:

2° × 2.5° lat-lon grid, daily at 00, 03,

06, 09, 12, 15, 18, and 21 UTC

Key Geophysical Parameters:

surface sensible heat flux

Processing Level:

4

Product Type:

standard, post-launch

Science Team Contact:

R. Rood, A. Molod
DAS 15

Assimilated Outgoing Longwave Radiation

Data Products Handbook Vol. I Chapter Six: Cloud & Aerosol Properties, and Radiative Energy Fluxes • 137

Product Description

This Level 4 product consists of 3-hourly estimates

of outgoing longwave radiation produced by the

GEOS DAS. It is based on the assimilation of

pressure, temperature, wind, and moisture

observations. These include conventional

meteorological observations, both in situ and

remotely sensed, as well as TRMM and AM-1 data

such as TMI and MODIS. These fields are generated

by the radiation parameterization within the GEOS

DAS GCM. Two fields are provided, net outgoing

longwave radiation (OLR) and net clear-sky outgoing

longwave radiation (OLRCLR).

Research & Application

Outgoing longwave radiation plays a significant role

in to the global heat balance. It is also indicative of

the presence of clouds and snow cover.

Data Set Evolution

Outgoing longwave radiation measurements are

already available from satellite instruments such as

ERBE and will be available from CERES on TRMM

and AM-1. Although the GEOS DAS will not

directly assimilate ERBE and CERES measurements,

the assimilated estimates of outgoing radiation will

be dynamically consistent with other assimilation

products such as precipitation and atmospheric

circulation.

Suggested Reading

Bloom, S.C., et al., 1995.

Pfaendtner, J., et al., 1995.

Schubert, S.D., et al., 1995.

Schubert, S.D., et al., 1993.

Takacs, L.L., et al., 1994.

DAS 15

PRODUCT SUMMARY

Coverage:

global

Spatial/Temporal Characteristics:

2° × 2.5° lat-lon grid, daily at 00, 03,

06, 09, 12, 15, 18, and 21 UTC

Key Geophysical Parameters:

outgoing longwave radiation

Processing Level:

4

Product Type:

standard, post-launch

Science Team Contact:

R. Rood, A. Molod
DAS 16

Assimilated Outgoing Shortwave Radiation

138 • Chapter Six: Cloud & Aerosol Properties, and Radiative Energy Fluxes Data Products Handbook Vol. I

Product Description

This Level 4 product consists of 3-hourly estimates

of outgoing shortwave radiation produced by the

GEOS DAS. It is based on the assimilation of

pressure, temperature, wind, and moisture

observations. These include conventional

meteorological observations, both in situ and

remotely sensed, as well as TRMM and AM-1 data

such as TMI and MODIS. These fields are generated

by the radiation parameterization within the GEOS

DAS GCM. Two fields are provided, net outgoing

shortwave radiation (OSR) and net clear-sky

outgoing shortwave radiation (OSRCLR).

Research & Application

Outgoing shortwave radiation plays a significant role

in to the global heat balance. It is also indicative of

the presence of clouds and snow cover.

Data Set Evolution

Outgoing shortwave radiation measurements are

already available from satellite instruments such as

ERBE and will be available from CERES on TRMM

and AM-1. Although the GEOS DAS will not

directly assimilate ERBE and CERES measurements,

the assimilated estimates of outgoing radiation will

be dynamically consistent with other assimilation

products such as precipitation and atmospheric

circulation.

Suggested Reading

Bloom, S.C., et al., 1995.

Pfaendtner, J., et al., 1995.

Schubert, S.D., et al., 1995.

Schubert, S.D., et al., 1993.

Takacs, L.L., et al., 1994.

DAS 16

PRODUCT SUMMARY

Coverage:

global

Spatial/Temporal Characteristics:

2° × 2.5° lat-lon grid, daily at 00, 03,

06, 09, 12, 15, 18, and 21 UTC

Key Geophysical Parameters:

outgoing shortwave radiation

Processing Level:

4

Product Type:

standard, post-launch

Science Team Contact:

R. Rood, A. Molod
DAS 17

Assimilated Incident Shortwave Radiation

Data Products Handbook Vol. I Chapter Six: Cloud & Aerosol Properties, and Radiative Energy Fluxes • 139

Product Description

This Level 4 product consists of 3-hourly estimates

of incident shortwave radiation at the top of the

atmosphere produced by the GEOS DAS. It is based

on the solar constant, Earth-Sun distance, and Sun

zenith angle.

Research & Application

Incident shortwave radiation plays a significant role

in to the global heat balance.

Data Set Evolution

This field does not require special observations. It is

simply calculated from the Earth’s orbit about the

Sun.

Suggested Reading

Bloom, S.C., et al., 1995.

Pfaendtner, J., et al., 1995.

Schubert, S.D., et al., 1995.

Schubert, S.D., et al., 1993.

Takacs, L.L., et al., 1994.

DAS 17

PRODUCT SUMMARY

Coverage:

global

Spatial/Temporal Characteristics:

2° × 2.5° lat-lon grid, daily at 00, 03,

06, 09, 12, 15, 18, and 21 UTC

Key Geophysical Parameters:

incident shortwave radiation

Processing Level:

4

Product Type:

standard, post-launch

Science Team Contact:

R. Rood, A. Molod
DAS 18

Assimilated Surface Radiation

140 • Chapter Six: Cloud & Aerosol Properties, and Radiative Energy Fluxes Data Products Handbook Vol. I

Product Description

This Level 4 product consists of 3-hourly estimates

of surface radiation produced by the GEOS DAS. It

is based on the assimilation of pressure, temperature,

wind, and moisture observations. These include

conventional meteorological observations, both in

situ and remotely sensed, as well as TRMM and

AM-1 data such as TMI and MODIS. These fields

are generated by the radiation parameterization

within the GEOS DAS GCM. Six fields are provided,

net downward clear-sky shortwave radiation at the

surface (SWGCLR), net downward shortwave

radiation at the surface (RADSWG), net downward

visible radiation at the surface (VISDNG), net

upward clear-sky longwave radiation at the surface

(LWGCLR), net upward longwave radiation at the

surface (RADLWG), and net downward longwave

radiation at the surface (LWDNG).

Research & Application

Surface radiative heat flux is a significant component

to the global heat balance. Heating rates near the

Earth’s surface are needed to determine atmospheric

stability which drives local convection and to

determine horizontal temperature gradients which

modify the atmosphere’s general circulation.

Data Set Evolution

This very specialized data set is not generally

available globally from conventional observations. It

is usually estimated through radiation

parameterization schemes within global GCMs. The

GEOS DAS radiation parameterization scheme will

have the advantage of additional observational input

from TRMM and AM-1 instruments.

DAS 18

PRODUCT SUMMARY

Coverage:

global

Spatial/Temporal Characteristics:

2° × 2.5° lat-lon grid, daily at 00, 03,

06, 09, 12, 15, 18, and 21 UTC

Key Geophysical Parameters:

surface radiation

Processing Level:

4

Product Type:

standard, post-launch

Science Team Contact:

R. Rood, A. Molod

Suggested Reading

Bloom, S.C., et al., 1995.

Pfaendtner, J., et al., 1995.

Schubert, S.D., et al., 1995.

Schubert, S.D., et al., 1993.

Takacs, L.L., et al., 1994.
DAS 19

Surface Albedo

Data Products Handbook Vol. I Chapter Six: Cloud & Aerosol Properties, and Radiative Energy Fluxes • 141

Product Description

This Level 4 product consists of 3-hourly estimates

of surface albedo. It is produced by the land surface

model of the GEOS DAS. This model uses as input

calculations of land surface type, greenness, and leaf

area index based on MODIS measurements. The

model converts these characteristics to albedo using

surface radiation contained within the radiation

parameterization of the GEOS DAS. Four types of

albedo are provided, near-IR diffuse (ANIRDF),

near-IR direct (ANIRDR), visible diffuse (AVISDF),

and visible direct (AVISDR).

Research & Application

Surface albedo is a significant component to the

global heat balance. It determines the heating rate at

the Earth’s surface. Heating rates near the Earth’s

surface are needed to determine atmospheric stability

which drives local convection and to determine

horizontal temperature gradients which modify the

atmosphere’s general circulation.

Data Set Evolution

MODIS will provide improved estimates of surface

the characteristics used as input to the GEOS DAS

albedo calculations. Previously, the GEOS DAS

calculated surface albedo based on monthly mean

values.

Suggested Reading

Bloom, S.C., et al., 1995.

Pfaendtner, J., et al., 1995.

Schubert, S.D., et al., 1995.

Schubert, S.D., et al., 1993.

Takacs, L.L., et al., 1994.

DAS 19

PRODUCT SUMMARY

Coverage:

global

Spatial/Temporal Characteristics:

2° × 2.5° lat-lon grid, daily at 00, 03,

06, 09, 12, 15, 18, and 21 UTC

Key Geophysical Parameters:

surface albedo

Processing Level:

4

Product Type:

standard, post-launch

Science Team Contact:

R. Rood, A. Molod
Chapter Seven

Lightning
Lightning

144 • Chapter Seven Lightning Data Products Handbook Vol. I

Relationship to Global Change Issues

Understanding lightning is a way to understand the

interplay of some key components of atmospheric

dynamics. Lighting is closely coupled to storm

convection dynamics and can be correlated to the

global rates, amounts, and distribution of convective

precipitation. It points to where strong convection is

occurring, when large quantities of water are

growing in the mixed phase regions of storms, and

suggests how latent heat is being released during the

storm’s life cycle. Since the microscales on which

particles interact to generate electricity are coupled

through storm scale processes to synoptic scale

systems, lighting activity should provide

information on the development of the atmosphere

over many scale sizes, especially in the tropics.

The Lightning Imaging Sensor (LIS) on the TRMM

Observatory is designed to detect, locate, and

measure the intensity of lightning for scientific

investigation of the distribution and variability of

total lightning over the Earth and to increase our

understanding of the Earth’s atmosphere system.

LIS will provide a global lightning and thunderstorm

climatology from which changes (even subtle

temperature variations) might be easily detected.

These data will be used in a variety of global change

studies, including cloud characterization, hydrologic

cycle studies, storm convection, microphysics and

dynamics, and the seasonal and interannual

variability of thunderstorms.

LIS represents a significant advance over any

previous satellite-borne lightning detectors. Those

instruments could provide only a relative global

distribution of lightning of a strictly statistical

nature. Additionally, they required collecting data

for long periods of time before they could be

meaningfully interpreted in terms of global

distributions. It was not possible to determine the

lightning frequency of a particular storm with the

earlier estimates. Lightning observations from the

LIS can be readily associated with the thunderstorms

that produce them, and the detection of even a single

discharge is significant and provides important

information (e.g., storm location, precipitation

estimates, storm height, the presence of ice,

lightning frequency, electrical output, etc.).

LIS has been designed to study the distribution and

variability of total lightning on a global basis. Its

staring imager is optimized to locate and detect

lightning with storm scale resolution of 5 - 10 km

over a large region (600 × 600 km) of the Earth’s

surface. The field of view (FOV) is sufficient to

observe a point on the Earth or a cloud for 80

seconds, adequate to estimate the flashing rate of

many storms. This makes it possible to estimate

lightning frequency even for storms with flash rates

as low as 1 - 2 discharges per minute.

Product Interdependency and Continuity

with Heritage Data

During the 1980s, the extensive optical and

electrical observations of lightning were made from

high altitude NASA U-2 aircraft with the primary

objective of defining a baseline design criteria for a

space sensor capable of mapping lightning

discharges from geostationary orbit during day and

night, with high spatial resolution and high detection

efficiency. The results, combined with parametric

trade-off studies and other research, have clearly

established the feasibility of making this kind of

lightning measurement from space with present

state-of-the-art technology.

Global lightning signatures from the Defense

Meteorological Satellite Program (DMSP) Optical

Linescan System (OLS) have been analyzed from

the filmstrip imagery and digital DMSP tapes

archived at the NOAA National Geophysical Data

Center (NGDC) National Snow and Ice Data Center

in Boulder, Colorado. This global lightning database

will be an important reference dataset for LIS.

In the TRMM pre-mission period, space-based

lightning observations are being provided by the

Optical Transient Detector (OTD), launched in 1995

as a secondary payload on a Pegasus launch vehicle.

In a near-polar orbit, OTD (Figure 10) is able to

detect all types of lightning events, including cloudto-ground, cloud-to-cloud, and intra-cloud.

Considered to be a LIS prototype, OTD data will

populate a database of global lightning activity to

which LIS data will be added.
Lightning

Data Products Handbook Vol. I Chapter Seven: Lightning • 145

Figure 24. Hurricane Opal. Data from the OTD sensor shows lightning flashes occurring during

Hurricane Opal, October 4, 1995. The OTD is based on the LIS instrument and can be viewed

as a LIS prototype.

Suggested Reading

Christian, H.J., et al., 1989.

Christian, H.J., et al., 1992.

Guo, C. and E.P. Krider, 1982.
LIS 02, 03, 04, 05, 06, 07, 08, 09, 10, 11, 12, 13, 14, 15, 16, 17, 18

Lightning Imaging Sensor Product

146 • Chapter Seven Lightning Data Products Handbook Vol. I

Product Description

The TRMM LIS generates a single lightning product

with multiple components which describe lightning

strikes spatially and temporally. The Level 1

components provide lightning pulse and background

data with navigation and browse data to provide a

“snapshot” of the LIS field of view. Events are

defined as a pixel value exceeding a threshold for

2 ms and is the basic unit of lightning data. The pixel

location, amplitude, and time of the event are

included. Both lightning pulses which fill more than

one pixel and groups of pulses are placed in a Level 2

group component, which includes size and number of

events in the group. The flash component

corresponds to a classically-defined lightning flash

and includes time, location, duration in the LIS field

of view, and number of groups in the flash. The area

component corresponds to a classically defined

thunderstorm or thunderstorm complex along with

the descriptive parameters. Level 3 summary

information about the data obtained during one orbit

is provided in the vector and browse components,

which includes location, average observation time,

counts of events, groups, flashes, and areas at 2.5°

grid points. The Level 3 flash density data product

holds all data associated with the flash density

parameter recorded during the orbit. It will be used to

produce the equal angle and equal area maps of

monthly lightning activity.

Research and Applications

LIS will view a total area exceeding 600 km ×

600 km at the cloud top using a 128 × 128

photodiode array. Individual storms and storm

systems are monitored for 80 seconds, a period long

enough to obtain a measure of the lightning flash rate

while the storm is in the field of view of the sensor.

This makes it possible to estimate lightning

frequency even for storms with flash rates as low as

1-2 discharges per minute. LIS represents a

significant advance over any previous satelliteborne

lightning detector. Lightning observations from the

LIS can be readily associated with the thunderstorms

that produce them, and the detection of even a single

discharge is significant and provides important

information (e.g., storm location, precipitation

estimates, storm height, the presence of ice, lightning

 LIS 02, 03, 04, 05, 06, 07, 08, 09,

10, 11, 12, 13, 14, 15, 16, 17, 18

PRODUCT SUMMARY

Coverage:

global

Spatial/Temporal Characteristics:

5 km × 5 km pixel at nadir and

2.5° × 2.5° browse grid/daily

Key Science Applications:

thunderstorm distribution and variability,

rainfall mapping and thunderstorm

analysis

Key Geophysical Parameters:

lightning events, groups, flashes, areas

Processing Level:

1, 2, 3, 4

Product Type:

standard, at-launch

Science Team Contact:

H. Christian, S. Goodman, R. Blakeslee

frequency, electrical output, etc.). The earlier

satelliteborne lightning instruments provided only a

relative global distribution of lightning of a strictly

statistical nature; this required collecting data for

long periods of time before it could be meaningfully

interpreted in terms of global distributions. It was not

possible to determine the lightning frequency of a

particular storm with the earlier estimates.

Data Set Evolution

NASA U-2 aircraft lightning detection data were

collected in order to provide a baseline for future

satellite-based lightning instruments. Based on a

combination of the results of U-2 data and advanced

in technology, the OTD (Figure 11) has been able to

begin establishing a database of lightning

observations from a near-polar orbit at a 70°

inclination, at an altitude of 740 km, and with a FOV

of 1300 × 1300 km. LIS data sets will further build

upon OTD with a FOV of 600 × 600 km and a storm

scale resolution of 5-10 km.
LIS 02, 03, 04, 05, 06, 07, 08, 09, 10, 11, 12, 13, 14, 15, 16, 17, 18

Lightning Imaging Sensor Product

Data Products Handbook Vol. I Chapter Seven: Lightning • 147

Figure 25. Annual Summary. Flash summary map for May 1995 - April 1996 shows lightning flashes detected within the field

of view of the OTD. Lightning is closely coupled to storm convection dynamics and can be rrelated to the global

rates, amounts and distribution of convective precipitation.

Suggested Reading

Christian, H.J., et al., 1989.

Guo, C. and E.P. Krider, 1982.
Chapter Eight

Tropospheric

Chemistry
AM-1

Tropospheric Chemistry

150 • Chapter Eight: Tropospheric Chemistry Data Products Handbook Vol. I

Relationship to Global Change Issues

In recent years, increasing concern has focused on

the natural and anthropogenic alteration of the

Earth’s environment and how these activities impact

global change. Human activities are changing the

concentrations of trace constituents in the

troposphere; these in turn affect the radiative

balance, dynamics, and chemistry of the atmosphere.

One of the most pressing global change issues

facing researchers and policymakers today involves

the greenhouse effect and potential global warming.

Carbon dioxide, nitrous oxide, methane, and CFCs

are the principal anthropogenic greenhouse gases

that are responsible for this effect. Of these, carbon

dioxide has the highest concentration and plays the

most significant role. However, the other gases are

more efficient (on a per molecule basis) at trapping

infrared radiation, and their combined effects are

almost as large as that of carbon dioxide, even

though their concentrations are much smaller. Ozone

also proves significant, because the catalytic

chemical process involved in its breakdown in the

stratosphere and its increasing tropospheric

concentrations have a multifaceted influence on the

Earth’s radiative balance.

Within the EOS AM-1 timeframe, the primary

objective of studies in tropospheric chemistry is

understanding the interaction with the surface/ocean/

biomass systems, atmospheric transports and the

carbon cycle. The particular focus is the time

evolution of the distributions of CO and CH4 in the

troposphere. The scientific questions become

convoluted when considering the sources, chemistry

and ultimate sinks of minor atmospheric constituents

and their interrelationships. The planet’s inorganic

and organic matter are the sources of many of these

gases, increasing the importance of land-surface

studies to determine emission rates and locations.

Yet, the large-scale and seasonal distribution of

these sources remains poorly understood. Spacebased remote sensing provides the ideal platform for

global monitoring of trace gas sources.

Scope

Whether the changes are anthropogenic or naturally

occurring, understanding these changes in the trace

chemical makeup of the troposphere is a critical

component in anticipating global climate change. On

EOS AM-1, the MOPITT instrument will investigate

the evolution of tropospheric trace and greenhouse

gases and their interaction with the climate and

biosphere. Along with trace gas measurements,

studies of greenhouse gases also require information

from additional disciplines (e.g., ocean color,

biomass burning, and land vegetation). Data

products associated with those discipline areas are

described in other sections of this document. Finally,

knowledge is required of the regional increases in

tropospheric ozone as well as studies of the decrease

in stratospheric ozone.

Interdisciplinary studies will also advance

knowledge about the chemistry of the troposphere

and the lower stratosphere. The GEOS DAS will

produce the 4-D data assimilation system wind data

(Winds, p. 205) to provide standard global wind data

based on meteorological and constituent inputs. The

model will estimate transport and mixing rates for

atmospheric constituents for use in atmospheric

chemistry models. The effects of trace constituents

on temperature profiles and dynamics differ greatly

between the troposphere and stratosphere. Other 4-D

GEOS DAS models will address the vertical

exchange of constituents in the tropopause region.

They will elucidate mechanisms for transport

between the troposphere and stratosphere through

process studies that use aircraft and balloons to carry

out in situ measurements in the atmosphere.

Product Overview

Key to our understanding of the horizontal and

vertical measurements of the troposphere is the

measurement of CO and CH4.

Carbon monoxide possesses a lifetime (on the order

of 3 to 7 months), such that the CO is moved around

on a regional scale, and thus performs the function

of a chemical tracer. In addition to horizontal

transport, studies have shown that there is

considerable vertical transport of CO, pointing to the

use of 3-D measurements which could be used to

study chemical transport processes in the

troposphere. MOPITT CO data, along with data

from other instruments, will be used in modeling

efforts by several researchers to advance our
Tropospheric Chemistry

Data Products Handbook Vol. I Chapter Eight: Tropospheric Chemistry • 151

Figure 26. Simulation of MOPITT Level-2 carbon monoxide data product for one day of observations for

January climatological conditions.

understanding of global tropospheric chemistry and

its relationship to sources, sinks, and atmospheric

transports.

The full range of the effects of the increased

concentration of CO are not fully understood at the

present time, but it is believed that CO is

photochemically active and plays a major role in the

concentration of OH radicals in the troposphere. The

OH radical acts as a “detergent” in the troposphere,

oxidizing many compounds. Satellite observations

may clarify whether CO is increasing or decreasing.

Increases in CO are likely to be the result of a rise in

human activities. Increases in CO may deplete

tropospheric radicals, thereby reducing the yearly

removal of many natural and anthropogenic trace

species. In particular, this effect may add to the

increase of CH4, which in turn could further reduce

OH concentration. Increased CO may also indirectly

intensify global warming and perturb stratospheric

ozone by increasing the lifetime of other trace

greenhouse gases. Greenhouse gases are those

chemicals that permit the influx of solar radiation to

the surface below, but inhibit to some degree

longwave radiation leaving the Earth, producing a

net warming effect.

The MOPITT data set also addresses the

measurement of CH4. Methane is the most abundant

hydrocarbon in the atmosphere and possesses

several infrared absorption bands, including one at

approximately 7.7 μm, which lies in an atmospheric

window with no significant absorption by other

gases. This wavelength is also close to the peak

radiating temperature of terrestrial thermal radiation

(about 250 K), making the gas a strong contributor

to the so-called “greenhouse effect.” Moreover, CH4

currently appears to be increasing at a rate of

1 percent per year, but the source of this increase is

not certain and there is evidence that it began
AM-1

Tropospheric Chemistry

152 • Chapter Eight: Tropospheric Chemistry Data Products Handbook Vol. I

decelerating during the 1980s. Improved knowledge

of the source strengths and distributions will help to

resolve this uncertainty. Knowledge of the global

column distribution of CH4 taken with dynamical

data may lead to a better quantitative understanding

of its biogeochemical cycles. The Level 2 CH4 data

set may be used to produce Level 3 global grids

which in turn may be used to develop regional and

seasonal averages and other special products.

Measurement of CH 4 from the EOS AM-1 platform

is expected to be extremely useful in observing the

distribution and variation of the sources of CH4.

While ground-based observations tend to generate a

specified accuracy over time, they cannot observe

the spatial and temporal variations of the sources

that are of most interest at present. Studies

conducted in conjunction with other measurements

of meteorological conditions (such as local wind

speed and direction) may determine the source

magnitude from the atmospheric perturbations to the

background amount. The combination of spacebased and in situ measurements can be synthesized

with other databases of gaseous concentrations and

emissions into global climate models for a better

understanding of the overall problem.

In addition to the MOPITT product, Total Ozone

Burden (a parameter in MOD 07) will measure total

column ozone to monitor the potential

environmental hazard of anthropogenic ozone

depletion, and to help develop and use atmospheric

correction models.

Product Interdependency and Continuity

with Heritage Data

The MOPITT instrument heritage includes the

Nimbus-7 Stratospheric and Mesospheric Sounder

(SAMS) instrument and the Improved Stratospheric

and Mesospheric Sounder (ISAMS) instrument on

UARS, which employed correlation spectroscopy

systems similar to that employed by MOPITT.

Currently operating stratospheric instruments such

as TOMS and others onboard UARS are helping

scientists learn about the exchange of trace gases

between the stratosphere and troposphere. Prior to

the launch of EOS AM-1, surface fluxes of some

land- and ocean-derived trace gases will be

estimated using AVHRR and SeaWiFS observations

and 4-D data assimilation. The Japanese

Interferometric Monitor for Greenhouse Gases

(IMG) instrument onboard ADEOS will also supply

global measurements of long-lived trace gases in the

pre-EOS era.

Suggested Reading

Brasseur, G.P. and R.G. Prinn, 1992.

Brenninkmeijer, C.A.M., et al., 1992.

Chicano, R.J. and R.S. Oremland, 1988.

Crutzen, P.J., 1991.

Dlugokencky, E.J., et al., 1994.

Dickerson, R.R., et al., 1987.

Drummond, J.R., 1992.

Novelli, P.C., et al., 1994.

Pan., L.D., et al., 1995.

Reichle, Jr., H.G., et al., 1989.

Reichle, Jr., H.G., et al., 1990.

Steele, L.P., et al., 1992.

Wofsy, S.C., et al., 1972.
AM-1 MOP02

CO Profile, CO Column and CH4 Column Data

Data Products Handbook Vol. I Chapter Eight: Tropospheric Chemistry • 153

Product Description

The carbon monoxide (CO) vertical profile and CO

column data, along with methane (CH4) total column,

make up the three parameters of the MOPITT Level2 product. The parameters are retrieved daily over the

globe with column measurements available only in

the sunlit portion. Horizontal resolution is variable

from 22 km to ~ 100 km, depending on cloud

clearing and pixel averaging, to maintain high signal

to noise. The profile vertical resolution is about 4 km

in the troposphere. CO profile and total column

precision are expected to be 10 percent. Methane

column precision is expected to be 1 percent.

Research & Applications

Global CO measurements are very important because

of the role of the gas in the extremely complicated

chemical balance of the troposphere. Most of the

chemistry in this region of the atmosphere is induced

by the chemical activity of the hydroxyl and similar

radicals. These agents react rapidly with many trace

gases of both natural and anthropogenic origin

preventing their buildup in the troposphere through

the creation of chemical products that are rained out

or lost from the atmosphere by dry deposition.

However, the concentrations of these radicals are

extremely difficult to measure due to their short

lifetimes and low concentrations. The major agents

regulating the concentrations of the radicals are

carbon monoxide, ozone, and nitrogen oxide. Global

CO measurements used in conjunction with

atmospheric chemical models therefore provide an

important measure of assessing the ability of the

atmosphere to “cleanse” itself.

The Level-2 carbon monoxide parameters will also

be used in the future to produce Level-3 global grids

which in turn may be used to develop regional and

seasonal averages and other special products. These

data products will provide a time evolution of the

distribution of CO in the troposphere. From these a

better knowledge of the chemical interactions,

transports, sources and links will be obtained.

Understanding their biogeochemical cycles and the

intimate interrelation with other gases and with the

climate will lead to better predictions of possible

efrfects of anthropogenic activities.

MOP02

PRODUCT SUMMARY

Coverage:

global

Spatial/Temporal Characteristics:

CO profiles retrieved daily, day and

night; column amounts retrieved daily in

sunlit portion of orbits; resolution 22 km

with some degradation depending on

cloud clearing and pixel averaging

Key Geophysical Parameters:

atmospheric total column carbon

monoxide; tropospheric vertical profile of

carbon monoxide; atmospheric total

column methane

Key Science Applications:

biogeochemical modeling

tropospheric chemical modeling

greenhouse gas modeling

Processing Level:

2

Product Type:

standard, at-launch

Science Team Contact:

J.R. Drummond

Measurements from the EOS AM-1 platform are

expected to be extremely useful in resolving the

temporal and spatial variations in CH4 not observable

by ground-based networks. Knowledge of the global

column distribution of methane taken with dynamical

data may lead to a better quantitative understanding

of its biogeochemical cycles. The Level-2 methane

data set may be used in the future to produce Level-3

global grids which in turn may be used to develop

regional and seasonal averages and other special

products.

The global distribution of these profiles and column

amounts will be used in descriptive studies of these

gases on a global basis, providing the first detailed

information on their horizontal, vertical, and
AM-1 MOP02

CO Profile, CO Column and CH4 Column Data

154 • Chapter Eight: Tropospheric Chemistry Data Products Handbook Vol. I

Figure 27. Simulation of MOPITT Level-2 methane data product for one day of observations for January

climatological conditions.

temporal variations, and their relationships to other

activities such as biomass burning, industrial activity,

thunderstorm venting of the boundary layer, etc.

There are also a wide variety of temporal and spatial

scales involved. By observing the sources, their

magnitudes and their variation we could learn a

considerable amount about the interaction of the

surface and the troposphere. By monitoring the

magnitude of the sinks, in conjunction with

atmospheric chemical models, we learn about the

chemistry of the troposphere.

Data Set Evolution

The products are derived from measurements of both

thermal upwelling radiation and the attenuation of

solar radiation by atmospheric carbon monoxide and

methane. Absorption due to CO or CH 4 is

discriminated from other gases by means of length

modulator cells containing known amounts of the

gases. A fast forward radiance model is used by the

data processing algorithm to compute average and

difference signals for known states of the

atmosphere. The algorithm adjusts the CO and CH4

amounts to bring the computed radiances into

agreement with the observed. Ancillary data from the

NCEP (formerly the NMC) are used to specify the

atmospheric temperature and water vapor content in

the calculations.

The particular instrument and algorithm

configuration described here has not been previously

applied. However, the maximum likelihood retrieval

scheme has been successfully utilized in several

space missions remotely measuring atmospheric

chemical species. The use of correlation radiometry

to determine concentrations of CO in the midtroposphere has been successfully demonstrated by

the Measurement of Air Pollution from Satellites

(MAPS) instrument which has been flown aboard the
AM-1 MOP02

CO Profile, CO Column and CH4 Column Data

Data Products Handbook Vol. I Chapter Eight: Tropospheric Chemistry • 155

Space Shuttle several times. The approach of using

reflected solar radiation to determine the total column

amounts of an atmospheric gas has been successfully

used many times. Perhaps the most notable

application has been with NASA’s TOMS, first

deployed on the Nimbus-7 satellite.

Suggested Reading

Brasseur, G.P. and R.G. Prinn, 1992.

Brenninkmeijer, C.A.M., et al., 1992.

Cicerone, R.J. and R.S. Oremland, 1988.

Crutzen, P.J., 1991.

Dickerson, R.R., et al., 1987.

Dlugokencky, E.J., et al., 1994.

Drummond, J.R., 1992.

Granier, C., et al., 1992.

MOPITT Algorithm Theoretical Basis Document:

Conversion of MOPITT Digital Counts into

Calibrated Radiances in Carbon Monoxide and

Methane Absorption Bands (Level 0 to Level 1),

MOPITT Algorithm Theoretical Basis Document:

Retrieval of Carbon Monoxide Profiles and

Column Amounts of Carbon Monoxide and

Methane from MOPITT Radiances (Level 1 to

Level 2).

Muller, J.F. and G.P. Brasseur, 1995.

Novelli, P.C., et al., 1994.

Reichle, Jr., H.G., et al., 1989.

Steele, L.P., et al., 1992.

Wofsy, S.C., et al., 1972.

Zander, R.P., et al., 1989.
Chapter Nine

Atmospheric

Temperatures
Atmospheric Temperatures

158 • Chapter Nine: Atmospheric Temperatures Data Products Handbook Vol. I

Relationship to Global Change Issues

The atmosphere is not physically uniform but has

significant variations in temperature and pressure

with altitude. The regions where temperature is

either increasing or decreasing with height define

the different layers of the atmosphere. The lowest 10

to 15 km of the atmosphere is called the

troposphere, and the region from about 15 to 50 km

is known as the stratosphere. The transitional region

between the troposphere and stratosphere is known

as the tropopause.

Upper air temperature is an important parameter

influencing the hydrological cycle and is a key input

to global climate models. Tropospheric and

stratospheric temperatures are central to the problem

of greenhouse warming because Global Circulation

Models (GCMs) predict that temperature changes

with enhanced concentrations of greenhouse gases

will have a characteristic profile in these layers, with

warming in the mid-troposphere and cooling in

much of the stratosphere. Cooler stratospheric

temperatures is an expected consequence of the

increased trapping of terrestrial radiation in the

troposphere. Most ozone depletion occurs in the

lower part of the stratosphere (15 to 25 km).

Accurate knowledge of the fluctuations in

stratospheric temperature on a global scale is

therefore highly desired to understanding global

warming.

The most valuable data for understanding

atmospheric dynamics and initializing forecast

models are 3-D horizontal wind and temperature

data. These variables are followed in priority by the

3-D moisture distribution, surface wind, surface

pressure, and sea-surface temperature. Problems

with the establishment of 3-D global temperature

data are the notable gaps of data over the oceans.

Also, although sharp temperature inversions exist in

the atmosphere, they are difficult to infer from

satellite.

However, in the absence of satellite coverage of

direct wind observations, the data assimilation

system can provide a dynamically consistent,

temporally frequent, globally covered, and

statistically optimal global dataset.

The data assimilation system organises the

observations from many diverse instrument sources

and measurement times into a single useful data

product, complements the observations by

propagation of information from observed into

unobserved regions and times, and supplements the

observations by providing estimates of quantities

that are difficult or impossible to observe.

The GEOS DAS models with sufficient vertical

resolution are expected to provide consistent

inversion data, improving short-range forecasts.

These forecast improvements will occur when the

data input to the models reaches a vertical resolution

of 2 km, which are the parameters association with

the TRMM/EOS GEOS DAS temperature profiles.

Present infrared sounders have an accuracy of about

2.5° and a vertical resolution of 5 km to 6 km.

Product Overview

MODIS Atmospheric Profiles (MOD 07, 08)

provides atmospheric temperature, as well as total

ozone burden, atmospheric stability, moisture

profiles and atmospheric water vapor. Four

assimilated products will be available in the TRMM

/EOS AM timeframe. Temperature Profiles (DAS

20) provides 3- and 6-hourly estimates of

atmospheric temperatures and includes temperatures

at the surface and 2 and 10 meters above the surface.

Temperatures and mandatory pressure and model

sigma levels are also provided. Temperature changes

(DAS 21) provides 6-hourly estimates of

atmospheric temperature changes at a given level of

the atmosphere. Parameters include estimates of

temperature changes from turbulent mixing,

convection, and long and shortwave radiation.

Temperature Flux (DAS 22) consists of 3-hourly

estimates of integrated atmospheric temperature

flux. Both zonal u-winds and meridional v-winds are

included. All the assimilated temperature products

are classified as level 4 within EOS.

Product Interdependency and Continuity

with Heritage Data

Data assimilation using a numerical weather forecast

model is critical to the success of EOS objectives.

The GOES-1 DAS ingests the global conventional
Atmospheric Temperatures

Data Products Handbook Vol. I Chapter Nine: Atmospheric Temperatures • 159

observations and the temperature retrievals to the

forecasting models to produce assimilated data

products. The present series of atmospheric sounders

(TOVS) aboard NOAA polar satellites are limited

by their vertical resolution in the atmosphere.

NOAA’s operational satellites and the Defense

Meteorological Satellite Program (DMSP)

instruments have been providing the temperature

and humidity profiles and surface products for

ingesting in assimilation models and climate studies.

Three NOAA instruments are: AVHRR since 1981;

the TOVS since 1979; the Geostationary

Operational Environmental (GOES) satellite VISSR

and its VISSR atmospheric sounder version since

1978. Considerable progress has also been made

with the high-resolution interferometer sounder

(HIS/2) deployed on geostationary satellites. Also,

the four channel MSU replaced with 20 channel on

Advanced Microwave Sounding Unit (AMSU), is

presently flown on NOAA-K providing high

resolution weather atmospheric temperature profile.

Suggested Reading

Daley, R., 1991.

Pfaendtner, J., et al., 1995.

Schubert, S.D., et al., 1993.

Takacs, L.L., et al., 1994.
DAS 20

Assimilated Temperature Profiles

160 • Chapter Nine: Atmospheric Temperatures Data Products Handbook Vol. I

Product Description

This Level 4 product consists of 3- and 6-hourly

estimates of atmospheric temperature produced by

the GEOS DAS. It is based on the assimilation of

pressure, temperature, wind, and moisture

observations. These include conventional

meteorological observations, both in situ and

remotely sensed, as well as TRMM and AM-1 data

such as TMI and MODIS. Surface air temperature is

contained in TS. Temperatures at 2 and 10 m above

the surface are contained in T2M and T10M.

Temperatures at mandatory pressure and model

sigma levels are in TMPU.

Research & Application

Variation in atmospheric temperature drives the

Earth’s general circulation. Convective precipitation

is generated by vertical variations in atmospheric

temperature. Specification of atmospheric

temperature is critical to understanding global

climate change.

Data Set Evolution

There are a wide variety of pressure, temperature,

wind, and moisture data available from conventional

meteorological sources. These include surface

observing sites, rawinsondes, satellite soundings, etc.

Yet even with all these data, there are significant

gaps in global coverage. The accuracy of individual

measurements also varies widely. The best way to

combine all these data globally is through data

assimilation which takes into account instrument

errors and bridges the gaps between observations.

New AM-1 and TRMM products such as MODIS

and TMI will greatly improve the accuracy and

coverage of the data used to produce these fields. In

addition, data assimilation provides dynamically

consistent estimates of temperature through its

general circulation model that takes into account

global circulation systems, precipitation, and

evaporation.

DAS 20

PRODUCT SUMMARY

Coverage:

global

Spatial/Temporal Characteristics:

2° × 2.5° lat-lon grid, 24 mandatory

pressure or 70 model sigma levels, daily

at 00, 06, 12, and 18 UTC. Surface

2-m and 10-m data, daily at 00,

03, 06, 09, 12, 15, 18, and 21 UTC

Key Geophysical Parameters:

temperature profiles

Processing Level:

4

Product Type:

standard, post-launch

Science Team Contact:

R. Rood, L. Takacs, D. Lamich

Suggested Reading

Bloom, S.C., et al., 1995.

Pfaendtner, J., et al., 1995.

Schubert, S.D., et al., 1995.

Schubert, S.D., et al., 1993.

Takacs, L.L., et al., 1994.
DAS 21

Assimilated Temperature Changes

Data Products Handbook Vol. I Chapter Nine: Atmospheric Temperatures • 161

Product Description

This Level 4 product consists of 6-hourly estimates

of atmospheric temperature changes at a given level

of the atmosphere estimated by the GEOS DAS. It is

based on the assimilation of pressure, temperature,

wind, and moisture observations. These include

conventional meteorological observations, both in

situ and remotely sensed, as well as TRMM and AM1 data such as TMI and MODIS. TURBT is an

estimate of the temperature change taking place at a

given level as a result of turbulent mixing. MOISTT

is an estimate of the temperature change taking place

at a given level as a result of convection. RADLW

and RADSW are estimates of the temperature

changes taking place at given levels as a result of

longwave and shortwave radiation. All four fields are

available at mandatory pressure and model sigma

levels.

Research & Application

Variation in atmospheric temperature drives the

Earth’s general circulation. Convective precipitation

is generated by vertical variations in atmospheric

temperature. Specification of atmospheric

temperature is critical to understanding global

climate change. This product gives important insight

into the mechanism responsible for local temperature

changes in the atmosphere.

Data Set Evolution

There are a wide variety of pressure, temperature,

wind, and moisture data available from conventional

meteorological sources. These include surface

observing sites, rawinsondes, satellite soundings, etc.

Yet even with all these data, there are significant

gaps in global coverage. The accuracy of individual

measurements also varies widely. The best way to

combine all these data globally is through data

assimilation which takes into account instrument

errors and bridges the gaps between observations.

New AM-1 and TRMM products such as MODIS

and TMI will greatly improve the accuracy and

coverage of the data used to produce these fields. In

addition, data assimilation provides dynamically

consistent estimates of temperature changes through

its general circulation model that takes into account

DAS 21

PRODUCT SUMMARY

Coverage:

global

Spatial/Temporal Characteristics:

2° × 2.5° lat-lon grid, 24 mandatory

pressure and 70 model sigma levels,

daily at 00, 06, 12, and 18 UTC

Key Geophysical Parameters:

temperature changes from turbulence,

convection, and radiation

Processing Level:

4

Product Type:

standard, post-launch

Science Team Contact:

R. Rood, A. Molod

global circulation systems, precipitation, radiation,

and evaporation.

Suggested Reading

Bloom, S.C., et al., 1995.

Pfaendtner, J., et al., 1995.

Schubert, S.D., et al., 1995.

Schubert, S.D., et al., 1993.

Takacs, L.L., et al., 1994.
DAS 22

Assimilated Assimilated Temperature Flux

162 • Chapter Nine: Atmospheric Temperatures Data Products Handbook Vol. I

Product Description

This Level 4 product consists of 3-hourly estimates

of vertically integrated atmospheric temperature flux

estimated by the GEOS DAS. It is based on the

assimilation of pressure, temperature, wind, and

moisture observations. These include conventional

meteorological observations, both in situ and

remotely sensed, as well as TRMM and AM-1 data

such as TMI and MODIS. The field is separated into

two components, VINTUT (zonal u-wind) and

VINTVT (meridional v-wind).

Research & Application

Variation in atmospheric temperature drives the

Earth’s general circulation. Specification of

atmospheric temperature flux is critical to

understanding global climate change.

Data Set Evolution

There are a wide variety of pressure, temperature,

wind, and moisture data available from conventional

meteorological sources. These include surface

observing sites, rawinsondes, satellite soundings, etc.

Yet even with all these data, there are significant

gaps in global coverage. The accuracy of individual

measurements also varies widely. The best way to

combine all these data globally is through data

assimilation which takes into account instrument

errors and bridges the gaps between observations.

New AM-1 and TRMM products such as MODIS

and TMI will greatly improve the accuracy and

coverage of the data used to produce these fields. In

addition, data assimilation provides dynamically

consistent estimates of temperature flux through its

general circulation model that takes into account

global circulation systems, precipitation, and

evaporation.

DAS 22

PRODUCT SUMMARY

Coverage:

global

Spatial/Temporal Characteristics:

2° × 2.5° lat-lon grid, daily at 00, 03,

06, 09, 12, 15, 18, and 21 UTC

Key Geophysical Parameters:

vertically integrated atmospheric

temperature flux

Processing Level:

4

Product Type:

standard, post-launch

Science Team Contact:

R. Rood, A. Molod

Suggested Reading

Bloom, S.C., et al., 1995.

Pfaendtner, J., et al., 1995.

Schubert, S.D., et al., 1995.

Schubert, S.D., et al., 1993.

Takacs, L.L., et al., 1994.
DAS 23

Assimilated Tropopause Temperature and Pressure

Data Products Handbook Vol. I Chapter Nine: Atmospheric Temperatures • 163

Product Description

This Level 4 product consists of 3-hourly estimates

of the tropopause pressure and temperature produced

by the GEOS DAS. It is based on the assimilation of

pressure, temperature, wind, and moisture

observations. These include conventional

meteorological observations, both in situ and

remotely sensed, as well as TRMM and AM-1 data

such as TMI and MODIS. Tropopause pressures are

in TROPP and temperatures in TROPT.

Research & Application

CERES requires tropopause information for its

retrieval algorithms.

Data Set Evolution

There are a wide variety of pressure, temperature,

wind, and moisture data available from conventional

meteorological sources. These include surface

observing sites, rawinsondes, satellite soundings, etc.

Yet even with all these data, there are significant

gaps in global coverage. The accuracy of individual

measurements also varies widely. The best way to

combine all these data globally is through data

assimilation which takes into account instrument

errors and bridges the gaps between observations.

New AM-1 and TRMM products such as MODIS

and TMI will greatly improve the accuracy and

coverage of the data used to produce these fields. In

addition, data assimilation provides dynamically

consistent estimates of temperature through its

general circulation model that takes into account

global circulation systems, precipitation, and

evaporation.

DAS 23

PRODUCT SUMMARY

Coverage:

global

Spatial/Temporal Characteristics:

2° × 2.5° lat-lon grid, daily at 00, 03,

06, 09, 12, 15, 18, and 21 UTC

Key Geophysical Parameters:

tropopause

Processing Level:

4

Product Type:

standard, post-launch

Science Team Contact:

R. Rood, L. Takacs, D. Lamich

Suggested Reading

Bloom, S.C., et al., 1995.

Pfaendtner, J., et al., 1995.

Schubert, S.D., et al., 1995.

Schubert, S.D., et al., 1993.

Takacs, L.L., et al., 1994.
AM-1 MOD 07, MOD 08

MODIS Atmospheric Profiles

164 • Chapter Nine: Atmospheric Temperatures Data Products Handbook Vol. I

Product Description

The MODIS Atmospheric Properties (MOD 07 at

Level 2 and MOD 08 at Level 3) consists of several

parameters; they are Total Ozone Burden,

Atmospheric Stability, Temperature and Moisture

Profiles, and Atmospheric Water Vapor. All of these

parameters are produced day and night for Level 2 at

5 x 5 1-km pixel resolution when at least 9 FOVs are

cloud free; for Level 3, data are generated on a 0.5°

latitude/longitude resolution grid daily, monthly, and

seasonally.

The MODIS Total Ozone Burden is an estimate of

the total column tropospheric and stratospheric ozone

content. The MODIS Atmospheric Stability consists

of three daily Level 2 and monthly Level 3

atmospheric stability indices. The Total Totals (TT),

the Lifted Index (LI), and the K index (K) are each

computed using the infrared temperature and

moisture profile data also derived as part of MOD 07.

The MODIS Temperature and Moisture Profiles are

produced at 20 vertical levels for temperature and 15

levels for moisture. A simultaneous direct physical

solution to the infrared radiative transfer equation in

a cloudless sky is used. The MODIS Atmospheric

Water Vapor product is an estimate of the total

tropospheric column water vapor made from

integrated MODIS infrared retrievals of atmospheric

moisture profiles in clear scenes.

Research & Applications

Total column ozone estimates at MODIS resolution

are required by MODIS investigators developing

atmospheric correction algorithms. This information

is crucial for accurate land and ocean surface

parameter retrievals. Furthermore, strong correlations

have been found to exist between the meridional

gradient of total ozone and the wind velocity at

tropopause levels, providing the potential to predict

the position and intensity of jet streams. Total

column ozone monitoring is also important due to the

potential harm to the environment caused by

anthropogenic ozone depletion.

Atmospheric instability measurements are predictors

of convective cloud formation and precipitation. The

MODIS instrument offers an opportunity to

characterize gradients of atmospheric stability at high

MOD 07, MOD 08

PRODUCT SUMMARY

Coverage:

global, clear-sky only

Spatial/Temporal Characteristics:

5 km (Level 2), 0.5° (Level 3)/daily,

monthly

Key Science Applications:

for ozone: atmospheric correction,

prediction of cyclogenesis, anthropogenic

ozone depletion

for atmospheric stability: atmospheric

correction, prediction of convective

cloudiness and precipitation,

characterization of the atmosphere

for soundings: atmospheric correction

algorithm development and use,

characterization of the atmosphere

for total column water vapor:

atmospheric correction algorithm

development and use, characterization of

the atmosphere

Key Geophysical Parameters:

total column ozone, atmospheric stability

(Total Totals, Lifted Index, and K index),

atmospheric profiles of temperature and

moisture, atmospheric total column water

vapor

Processing Level:

2, 3

Product Type:

standard, routine

Science Team Contact:

P. Menzel

resolution and greater coverage. Radiosonde-derived

stability indices are limited by the coarse spacing of

the point source data too large to pinpoint local

regions of probable convection.
AM-1 MOD 07, MOD 08

MODIS Atmospheric Profiles

Data Products Handbook Vol. I Chapter Nine: Atmospheric Temperatures • 165

Atmospheric temperature and moisture sounding data

at high spatial resolution from MODIS and high

spectral resolution sounding data from AIRS will

provide a wealth of new information on atmospheric

structure in clear skies. The profiles will be used to

correct for atmospheric effects for some of the

MODIS products (e.g., sea surface and land surface

temperatures, ocean aerosol properties, water leaving

radiances, and PAR) as well as to characterize the

atmosphere for global greenhouse studies.

Total column precipitable water estimates at MODIS

resolution are required by MODIS investigators

developing atmospheric correction algorithms. This

information is crucial for accurate land and ocean

surface parameter retrievals. MODIS will also

provide finer horizontal scale atmospheric water

vapor gradient estimates than are currently available

from the POES.

Data Set Evolution

One of two ozone retrieval methods developed using

the HIRS will be chosen as best suited for application

with MODIS data. Both use a first guess perturbation

method and radiances from MODIS channel 30 (9.6

μm) to solve the radiative transfer equation. The

perturbations are with respect to some a priori

conditions that may be estimated from climatology,

regression, or more commonly from an analysis or

forecast provided by a numerical model. The MODIS

cloud mask product (MOD 35) will also be used to

screen for clouds.

Atmospheric stability estimates will be derived from

the MODIS temperature and moisture retrievals

contained in product MOD 07. Layer temperature

and moisture values may be used to estimate the

temperature lapse rate of the lower troposphere and

the low-level moisture concentration.

Temperature and moisture profile retrieval

algorithms are adapted from the International TOVS

Processing Package (ITPP), taking into account

MODIS’ lack of stratospheric channels and far higher

horizontal resolution. The profile retrieval algorithm

requires calibrated, navigated, and coregistered 1-km

FOV radiances from MODIS channels 20, 22-25, 27-

29, and 30-36. The MODIS cloud mask (MOD 35) is

used for cloud screening. The algorithm also requires

NCEP model analyses of temperature and moisture

profiles as a first guess and an NCEP analysis of

surface temperature and pressure.

Several algorithms for determining atmospheric

water vapor, or precipitable water, exist. It is most

directly achieved by integrating the moisture profile

through the atmospheric column. Other split window

methods also exist. This class of techniques uses the

difference in water-vapor absorption that exists

between channel 31 (11 μm) and channel 32 (12 μm).

Data validation will be conducted by comparing

results to in situ radiosonde measurements, NOAA

HIRS operational retrievals, GOES sounder

operational retrievals, NCEP analyses, and retrievals

from the AIRS instrument on the PM platform. A

field campaign using a profiler network in the central

U.S. and the MAS-equipped aircraft will be initiated

in the first year after launch. Quality control will

consist of manual and automatic inspections, with

regional and global mean temperatures at 300, 500,

and 700 hPa monitored weekly, along with 700 hPa

dew point temperatures. For total ozone, data

validation will consist of comparing the TOMS as

well as operational NOAA ozone estimates from

HIRS to the MODIS retrievals.

Suggested Reading

Hayden, C.M., 1988.

Houghton, J.T., et al., 1984.

Jedlovec, G.J., 1987.

Kleepsies, T.J. and L.M. McMillan, 1984.

Ma, X.L., et al., 1984.

Prabhakara, C., et al., 1970.

Shapiro, M.A., et al., 1982.

Smith, W.L. and F.X. Zhou, 1982.

Smith, W.L., et al., 1985.

Sullivan, J. et al., 1993.
AM-1 MOD 07, MOD 08

MODIS Atmospheric Profiles

 166 • Chapter Nine: Atmospheric Temperature Data Products Handbook Vol. 1

253.0

254.0

255.0

256.0

257.0

258.0

259.0

260.0

261.0

262.0

263.0

264.0

265.0

-81

-73 -65

-57

29

33

37

41

10.0

15.0

20.0

25.0

30.0

35.0

40.0

-81

-73 -65

-57

29

33

37

41

Figure 28. MODIS Atmospheric Profiles. Level 2 MODIS atmospheric properties generated by the International TOVS Processing Package (ITPP) using NOAA-12 HIRS data acquired on 15 May 1995 at 12:12

UTC. The upper panel represents temperature at 500 hPa (K), whereas the lower panel represents total precipitable water (mm).
Chapter Ten

Winds
Winds

168 • Chapter Ten: Winds Data Products Handbook Vol. I

Relationship to Global Change Issues

Atmospheric winds provide information essential to

understanding atmospheric circulation and dynamics

in addition to global climate change. Global wind

estimates, for example, provide information about

the sources, sinks and transport mechanisms of

stratospheric trace gases important to resolving

global warming issues. Winds are also a vehicle for

addressing various multidisciplinary questions

involving the global biogeochemical and hydrologic

cycles. The 1991-1992 El Niño was the first El Niño

to be predicted in advance and was based on winds

as the driving force of a global circulation model.

Because wind fields have been primarily measured

by land-based rawinsondes, measurements have

been too irregularly distributed to provide adequate

data for global climate studies. Missing data

includes oceanic areas (covering roughly three

quarters of the Earth’s surface) and some lessdeveloped Southern hemisphere land areas have

been poorly observed. These measurements

historically have been inferred from satellite cloud

tracking imagery or computed from pressure

gradients. Tracking of clouds by satellites provides

estimates of low- and high-level winds over portions

of the Earth, but uncertainties in heights introduce

errors and there are many gaps in the data. The

desired accuracy is ± 10 percent, which corresponds

to about 1 m/s in the lower troposphere and 5 m/s in

the upper troposphere. Even with these data,

however, there are significant gaps in global

coverage.

However, in the absence of satellite coverage of

direct wind observations, the Goddard EOS Data

Assimilation System (GEOS DAS) can provide

dynamically consistent, temporally frequent,

globally covered, and statistically optimal global

datasets. And assimilated wind products in the

TRMM/AM-1 timeframe provide significant

improvements over prior measurements available

today.

Within the EOS AM-1 timeframe, new products

from the TRMM and AM-1 instruments will greatly

improve the accuracy and coverage of the data.

Improvements will come as EOS and TRMM

instrument data fill spatial and temporal gaps in the

observational data used in the GEOS DAS with well

calibrated and high quality atmospheric fields. The

TMI and MODIS instruments will also provide

improved boundary conditions for the forecast

model. The GEOS DAS assimilated products are

classified as Level 4 products in EOS.

Data assimilation is the process of ingesting

observations into a model of the Earth system. The

current product uses meteorological observations

and an atmospheric model. The result is a

comprehensive and dynamically consistent dataset

which represents the best estimate of the state of the

atmosphere at that time. The assimilation process

fills data voids with model predictions and provides

a suite of data-constrained estimates of unobserved

quantities such as vertical motion, radiative fluxes,

and precipitation. The GEOS DAS process not only

combines all the data globally but accounts for

instruments errors and bridges the gap between

observations.

Product Overview

The assimilated wind products are a synthesis of

measurements and short-term model forecasts. In the

GEOS-I system, this is accomplished using an

analysis scheme in which observations of the

following variables are used: geopotential thickness,

zonal (u) and meridional (v) winds, water vapor

mixing ratio, sea level pressure and surface winds

over the ocean. The assimilation process is carried

out at a horizontal resolution of 1° latitude by 1°

longitude at 50 model sigma /21 mandatory upperair pressure levels and at sea level. The analysis

increments (observation minus background forecast)

are computed every 6 hours using observations from

a ± 3-hour data window centered on the analysis

times (00, 06, 12, and 18 UTC). The increments are

then inserted gradually into the global circulation

model (GCM) by rerunning the forecast and adding

a fraction of the increment at each model time step.

Over the 6-hour period centered at the analysis time

the full effect of incrementing is realized. In this

way, the assimilation consists of a continuous GCM

forecast with additional heat, momentum, moisture

and mass source terms updated every 6 hours from

observations. It is the output of this assimilation

procedure that makes up the datasets provided here.
Winds

Data Products Handbook Vol. I Chapter Ten: Winds • 169

GEOS DAS classifies the parameters as prognostic

or diagnostic. Prognostic parameters are

atmospheric state variables which the model

forecasts (e.g., temperature and humidity profiles).

During the assimilation these are the parameters

most directly influenced by the observations.

Prognostic fields are sampled 4 times daily at 00, 06,

12, 18 UTC.

The assimilated products, surface wind speed

(DAS 24), surface stress (DAS 29), surface

roughness (DAS 30), surface drag coefficient

(DAS 31), and planetary boundary layer depth

(DAS 32) are classified as diagnostic parameters.

These parameters are generally not measured, but

are calculated by the model’s physical

parameterizations in a manner consistent with the

observations. Diagnostic surface or other single

level fields are given 8 times daily as time averages

over the three hours-preceding the time stamp (i.e.,

at 00 for 21 (previous day) to 00, at 03 for 00 to 03,

at 06 for 03 to 06 UTC). Diagnostic upper air fields

are given 4 times daily as time averages over 6

hours centered on the times 00, 06, 12, 18 UTC.

The GEOS DAS assimilated wind products include

the following datasets.

• Surface winds (DAS 24) drive ocean currents

and exchange momentum with the oceans and

land. Sea level winds are used to infer wind

stresses affecting ocean circulation. Surface

Wind Stress (DAS 29) plays an important role

in regulating the Earth’s general circulation. It is

also an important forcing function for ocean

circulation. Surface Roughness (DAS 30)

provides estimates to approximate the drag

imposed on low-level winds. This field is

needed to specify iterations in the planetary

boundary layer (PBL). Surface Drag

Coefficients (DAS 31) provides drag

coefficients for temperature, moisture, winds

which are used in calculating surface sensible

heat and moisture flux and in calculating surface

wind stress.

• Wind profiles (DAS 25) define the Earth’s

general circulation. Specification of the

atmosphere’s general circulation is critical to

understanding global climate change. Vertical

Velocity (DAS 26) determines atmospheric

cloud formation and precipitation. Upward

motion produces clouds and precipitation while

downward motion produces clear skies.

Vertically Integrated Winds (DAS 27)

provides angular momentum studies of the

Earth’s atmosphere.

• Turbulence plays an important role in the

atmosphere’s general circulation; its

parameterization consists of elements that

handle vertical diffusion of surface fluxes of

heat, moisture, and momentum. Turbulence

Profiles (DAS 28) provides data on how

turbulence influences the general circulation

model within the GEOS DAS.

Boundary layer processes control the exchange of

momentum, energy and mass across the lower

boundary of the atmosphere. It is critical for

developing efficient coupled ocean-atmosphere

models. Measuring boundary level wind profiles is

also a significant step forward in understanding

regional climate change following alteration of land

surface by overgrazing and deforestation. The

Planetary Boundary Layer Depth (DAS 32) is

generated by the turbulence parameterization with

the GEOS DAS General Circulation model (GCM).

It is defined as the depth at which turbulent kinetic

energy reduces to ten percent of its surface value.

Product Interdependency and Continuity

with Heritage Data

The GEOS DAS assimilated wind products are the

results of ongoing research to track, model, and

validate assimilated fields. The GEOS DAS

incorporates observations from instruments on

satellites JERS-1, ERS-1, SSM/I, TOVS, GOES,

and METEOSATS for improving the assimilation

procedures. The DOA also provides assimilated

wind data sets in support of the Tropical Ocean

Global Atmosphere (TOGA) subproject of the

Global Ocean-Atmosphere Response Experiment

(COARE) and from UARS, which has temperature,

wind and constituent measurements. In many ways

UARS provides a prototype for the EOS effort,

making UARS a very important mission to study

prior to the launch of the EOS satellites.

Future wind scatterometers (NSCAT launched in

1996 on board ADEOS, ERS-2, and the NSCAT
Winds

170 • Chapter Ten: Winds Data Products Handbook Vol. I

Figure 29. Annual Average Surface Streamline and Vector Magnitude Patterns for the

Western Tropical Pacific. Interannual variability can be discerned from the SSM/I surface

wind velocity data set. Each of the seven years of data shown is assumed to begin with July 1

and to end with June 30 of the following year (http://nimbus.gsfc.nasa.gov:8080/DIG/

SpaceBasedSW/SSMI/SurfWindVel/InterAnnualVar/InterAnnualVar.html).

follow-on SeaWinds on ADEOS II) and Radarsat

will provide measurements of the basic wind-forcing

fields and wind stress maintaining the continuity of

the ocean vector wind dataset allowing improved

estimates of critical air-sea fluxes of heat,

momentum. and chemical species.

Suggested Reading

Daley, R., 1991.

Pfaendtner, J., et al., 1995.

Schubert, S.D. and R.B. Rood, 1995.

Schubert, S.D., et al., 1993.

Takacs, L.L., et al., 1994.
DAS 24

Assimilated Surface Winds

Data Products Handbook Vol. I Chapter Ten: Winds • 171

Product Description

This Level 4 product consists of 3-hourly estimates

of the sea level winds and surface wind speed

produced by the GEOS DAS. It is based on the

assimilation of pressure, temperature, wind, and

moisture observations. These include conventional

meteorological observations, both in situ and

remotely sensed, as well as TRMM and AM-1 data

such as TMI and MODIS. Three fields are provided:

u-component sea level winds (SLU), v-component

sea level winds (SLV), and surface wind speed

(WINDS) which includes sea level winds over the

oceans and winds over land.

Research & Application

Accurate sea level wind estimates are needed for

MODIS retrievals over the oceans. Sea level winds

are also needed to infer wind stresses affecting ocean

circulation.

Data Set Evolution

There are a wide variety of pressure, temperature,

wind, and moisture data available from conventional

meteorological sources. These include surface

observing sites, rawinsondes, satellite soundings, etc.

Yet even with all these data, there are significant

gaps in global coverage. The accuracy of individual

measurements also varies widely. The best way to

combine all these data globally is through data

assimilation which takes into account instrument

errors and bridges the gaps between observations.

New AM-1 and TRMM products will greatly

improve the accuracy and coverage of the data used

to produce this field. In addition, data assimilation

provides dynamically consistent estimates of surface

winds through its general circulation model that takes

into account global circulation systems, precipitation,

and evaporation.

DAS 24

PRODUCT SUMMARY

Coverage:

global

Spatial/Temporal Characteristics:

2° × 2.5° lat-lon grid, daily at 00, 03,

06, 09, 12, 15, 18 and 21 UTC

Key Geophysical Parameters:

surface winds

Processing Level:

4

Product Type:

standard, post-launch

Science Team Contact:

R. Rood, L. Takacs, D. Lamich

Suggested Reading

Bloom, S.C., et al., 1995.

Pfaendtner, J., et al., 1995.

Schubert, S.D., et al., 1995.

Schubert, S.D., et al., 1993.

Takacs, L.L., et al., 1994.
DAS 25

Assimilated Wind Profiles

172 • Chapter Ten: Winds Data Products Handbook Vol. I

Product Description

This Level 4 product consists of 3 and 6-hourly

estimates of atmospheric winds produced by the

GEOS DAS. It is based on the assimilation of

pressure, temperature, wind, and moisture

observations. These include conventional

meteorological observations, both in situ and

remotely sensed, as well as TRMM and AM-1 data

such as TMI and MODIS. UWIND and VWIND

contain the u- and v-components of wind at

mandatory pressure and model sigma levels. U2M,

V2M, U10M, and V10M contain the u and vcomponents at 2 m and 10 m above the Earth’s

surface. UWINDE and VWINDE contain the

estimated wind errors at the mandatory pressure

levels.

Research & Application

Atmospheric winds define the Earth’s general

circulation. Specification of the atmosphere’s general

circulation is critical to understanding global climate

change.

Data Set Evolution

There are a wide variety of pressure, temperature,

wind, and moisture data available from conventional

meteorological sources. These include surface

observing sites, rawinsondes, satellite soundings, etc.

Yet even with all these data, there are significant

gaps in global coverage. The accuracy of individual

measurements also varies widely. The best way to

combine all these data globally is through data

assimilation which takes into account instrument

errors and bridges the gaps between observations.

New AM-1 and TRMM products such as MODIS

and TMI will greatly improve the accuracy and

coverage of the data used to produce these fields. In

addition, data assimilation provides dynamically

consistent estimates of temperature through its

general circulation model that takes into account

global circulation systems, precipitation, and

evaporation.

DAS 25

PRODUCT SUMMARY

Coverage:

global

Spatial/Temporal Characteristics:

2° × 2.5° lat-lon grid, 24 mandatory

pressure or 70 model sigma levels, daily

at 00, 06, 12, and 18 UTC plus 2 m and

10 m data daily at 00, 03, 06, 09, 12, 15,

18, and 21 UTC

Key Geophysical Parameters:

wind profiles

Processing Level:

4

Product Type:

standard, post-launch

Science Team Contact:

R. Rood, L. Takacs, D. Lamich

Suggested Reading

Bloom, S.C., et al., 1995.

Pfaendtner, J., et al., 1995.

Schubert, S.D., et al., 1995.

Schubert, S.D., et al., 1993.

Takacs, L.L., et al., 1994.
DAS 26

Assimilated Vertical Velocity

Data Products Handbook Vol. I Chapter Ten: Winds • 173

Product Description

This Level 4 product consists of 6-hourly estimates

of atmospheric vertical velocity produced by the

GEOS DAS. It is based on the assimilation of

pressure, temperature, wind, and moisture

observations. These include conventional

meteorological observations, both in situ and

remotely sensed, as well as TRMM and AM-1 data

such as TMI and MODIS. Vertical velocity is not

measured directly but derived from the continuity

equation within the GEOS DAS general circulation

model.

Research & Application

Atmospheric vertical velocity determines

atmospheric cloud formation and precipitation.

Upward motion produces clouds and precipitation

while downward motion produces clear skies.

Data Set Evolution

There are a wide variety of pressure, temperature,

wind, and moisture data available from conventional

meteorological sources. These include surface

observing sites, rawinsondes, satellite soundings, etc.

Yet even with all these data, there are significant

gaps in global coverage. The accuracy of individual

measurements also varies widely. The best way to

combine all these data globally is through data

assimilation which takes into account instrument

errors and bridges the gaps between observations.

New AM-1 and TRMM products such as MODIS

and TMI will greatly improve the accuracy and

coverage of the data used to produce these fields. In

addition, data assimilation provides dynamically

consistent estimates of temperature through its

general circulation model that takes into account

global circulation systems, precipitation, and

evaporation.

DAS 26

PRODUCT SUMMARY

Coverage:

global

Spatial/Temporal Characteristics:

2° × 2.5° lat-lon grid, 21 mandatory

pressure or 50 model sigma levels, daily

at 00, 06, 12, and 18 UTC

Key Geophysical Parameters:

vertical velocity

Processing Level:

4

Product Type:

standard, post-launch

Science Team Contact:

R. Rood, L. Takacs, D. Lamich

Suggested Reading

Bloom, S.C., et al., 1995.

Pfaendtner, J., et al., 1995.

Schubert, S.D., et al., 1995.

Schubert, S.D., et al., 1993.

Takacs, L.L., et al., 1994.
DAS 27

Assimilated Vertically Integrated Winds

174 • Chapter Ten: Winds Data Products Handbook Vol. I

Product Description

This Level 4 product consists of 3-hourly estimates

of vertically integrated winds estimated by the GEOS

DAS. It is based on the assimilation of pressure,

temperature, wind, and moisture observations. These

include conventional meteorological observations,

both in situ and remotely sensed, as well as TRMM

and AM-1 data such as TMI and MODIS. The field is

separated into two components, UBAR (zonal uwind) and VBAR (meridional v-wind).

Research & Application

Vertically integrated wind estimates are angular

momentum studies of the Earth’s atmosphere.

Data Set Evolution

There are a wide variety of pressure, temperature,

wind, and moisture data available from conventional

meteorological sources. These include surface

observing sites, rawinsondes, satellite soundings, etc.

Yet even with all these data, there are significant

gaps in global coverage. The accuracy of individual

measurements also varies widely. The best way to

combine all these data globally is through data

assimilation which takes into account instrument

errors and bridges the gaps between observations.

New AM-1 and TRMM products such as MODIS

and TMI will greatly improve the accuracy and

coverage of the data used to produce these fields. In

addition, data assimilation provides dynamically

consistent estimates of temperature flux through its

general circulation model that takes into account

global circulation systems, precipitation, and

evaporation.

DAS 27

PRODUCT SUMMARY

Coverage:

global

Spatial/Temporal Characteristics:

2° × 2.5° lat-lon grid, daily at 00, 03, 06,

09, 12, 15, 18, and 21 UTC

Key Geophysical Parameters:

vertically integrated barotropic winds

Processing Level:

4

Product Type:

standard, post-launch

Science Team Contact:

R. Rood, A. Molod

Suggested Reading

Bloom, S.C., et al., 1995.

Pfaendtner, J., et al., 1995.

Schubert, S.D., et al., 1995.

Schubert, S.D., et al., 1993.

Takacs, L.L., et al., 1994.
DAS 28

Assimilated Turbulence Profiles

Data Products Handbook Vol. I Chapter Ten: Winds • 175

Product Description

This Level 4 product consists of 6-hourly estimates

of atmospheric turbulence produced by the

turbulence parameterization of the GEOS DAS. It is

based on the assimilation of pressure, temperature,

wind, and moisture observations. These include

conventional meteorological observations, both in

situ and remotely sensed, as well as TRMM and AM1 data such as TMI and MODIS. QQ is the turbulent

kinetic energy. TURBU and TURBV are the u- and

v-component momentum changes due to turbulence.

Research & Application

Turbulence plays an important role in the

atmosphere’s general circulation. This product

provides information on how turbulence influences

the general circulation within the GEOS DAS.

Data Set Evolution

There are a wide variety of pressure, temperature,

wind, and moisture data available from conventional

meteorological sources. These include surface

observing sites, rawinsondes, satellite soundings, etc.

Yet even with all these data, there are significant

gaps in global coverage. The accuracy of individual

measurements also varies widely. The best way to

combine all these data globally is through data

assimilation which takes into account instrument

errors and bridges the gaps between observations.

New AM-1 and TRMM products such as MODIS

and TMI will greatly improve the accuracy and

coverage of the data used to produce these fields. In

addition, data assimilation provides dynamically

consistent estimates of temperature through its

general circulation model that takes into account

global circulation systems, precipitation, and

evaporation.

DAS 28

PRODUCT SUMMARY

Coverage:

global

Spatial/Temporal Characteristics:

2° × 2.5° lat-lon grid, 24 mandatory

pressure or 70 model sigma levels, daily

at 00, 06, 12, and 18 UTC

Key Geophysical Parameters:

turbulence profiles

Processing Level:

4

Product Type:

standard, post-launch

Science Team Contact:

R. Rood, A. Molod

Suggested Reading

Bloom, S.C., et al., 1995.

Pfaendtner, J., et al., 1995.

Schubert, S.D., et al., 1995.

Schubert, S.D., et al., 1993.

Takacs, L.L., et al., 1994.
DAS 29

Assimilated Surface Wind Stress

176 • Chapter Ten: Winds Data Products Handbook Vol. I

Product Description

This Level 4 product consists of 3-hourly estimates

of surface wind stress estimated by the GEOS DAS.

It is based on the assimilation of pressure,

temperature, wind, and moisture observations. These

include conventional meteorological observations,

both in situ and remotely sensed, as well as TRMM

and AM-1 data such as TMI and MODIS. This field

is generated by the turbulence parameterization

within the GEOS DAS general circulation model

(GCM). UFLUX is the turbulent flux of zonal (ucomponent) momentum from the surface while

VFLUX is the turbulent flux of meridional (vcomponent) momentum from the surface. USTAR is

the surface-stress velocity, or friction velocity.

USTAR is used to calculate surface roughness length,

Z0, over the ocean.

Research & Application

Surface wind stress plays an important role in

regulating the Earth’s general circulation. It is also an

important forcing function for ocean circulation.

Data Set Evolution

This very specialized data set is not generally

available globally from conventional observations. It

is usually estimated through turbulence

parameterization schemes within global GCMs. The

GEOS DAS turbulence parameterization scheme will

have the advantage of additional observational input

from TRMM and AM-1 instruments.

Suggested Reading

Bloom, S.C., et al., 1995.

Pfaendtner, J., et al., 1995.

Schubert, S.D., et al., 1995.

Schubert, S.D., et al., 1993.

Takacs, L.L., et al., 1994.

DAS 29

PRODUCT SUMMARY

Coverage:

global

Spatial/Temporal Characteristics:

2° × 2.5° lat-lon grid, daily at 00, 03, 06,

09, 12, 15, 18, and 21 UTC

Key Geophysical Parameters:

surface wind stress

Processing Level:

4

Product Type:

standard, post-launch

Science Team Contact:

R. Rood, A. Molod
DAS 30

Assimilated Surface Roughness

Data Products Handbook Vol. I Chapter Ten: Winds • 177

 Product Description

This Level 4 product consists of 3-hourly estimates

of surface roughness produced by the GEOS DAS.

Over land, it is based on monthly mean data from

Dorman and Sellers (1989). Over oceans it is based

on surface stress velocity estimated by the GEOS

DAS GCM. Surface stress velocity is based on the

assimilation of pressure, temperature, wind, and

moisture observations from conventional

meteorological sources, both in situ and remotely

sensed, along with other TRMM and AM-1 data such

as TMI and MODIS.

Research & Application

Surface roughness estimates are needed to

approximate the drag imposed on low-level winds.

This field is needed to specify interactions in the

PBL.

Data Set Evolution

Surface roughness estimates over land will not

improve much as a result of TRMM or AM-1.

Surface roughness over the ocean, however, will

improve based on better wind estimates using TMI

and MODIS.

Suggested Reading

Bloom, S.C., et al., 1995.

Dorman, J. L., and P.J. Sellers, 1989.

Pfaendtner, J., et al., 1995.

Schubert, S.D., et al., 1995.

Schubert, S.D., et al., 1993.

Takacs, L.L., et al., 1994.

DAS 30

PRODUCT SUMMARY

Coverage:

global

Spatial/Temporal Characteristics:

2° × 2.5° lat-lon grid, daily at 00, 03, 06,

09, 12, 15, 18, and 21 UTC

Key Geophysical Parameters:

surface roughness

Processing Level:

4

Product Type:

standard, post-launch

Science Team Contact:

R. Rood, A. Molod
DAS 31

Assimilated Surface Drag Coefficients

178 • Chapter Ten: Winds Data Products Handbook Vol. I

Product Description

This Level 4 product consists of 3-hourly estimates

of surface drag coefficients estimated by the GEOS

DAS. It is based on the assimilation of pressure,

temperature, wind, and moisture observations. These

include conventional meteorological observations,

both in situ and remotely sensed, as well as TRMM

and AM-1 data such as TMI and MODIS. This field

is generated by the turbulence parameterization

within the GEOS DAS GCM. CT is the surface drag

coefficient for temperature and moisture. CU is the

drag coefficient for winds. CT is used in calculating

surface sensible heat and moisture flux while CU is

use in calculating surface wind stress.

Research & Application

Surface heat and moisture flux along with surface

wind stress play an important role in determining the

Earth’s general circulation.

Data Set Evolution

This very specialized data set is not generally

available globally from conventional observations. It

is usually estimated through turbulence

parameterization schemes within global GCMs. The

GEOS DAS turbulence parameterization scheme will

have the advantage of additional observational input

from TRMM and AM-1 instruments.

Suggested Reading

Bloom, S.C., et al., 1995.

Pfaendtner, J., et al., 1995.

Schubert, S.D., et al., 1995.

Schubert, S.D., et al., 1993.

Takacs, L.L., et al., 1994.

DAS 31

PRODUCT SUMMARY

Coverage:

global

Spatial/Temporal Characteristics:

2° × 2.5° lat-lon grid, daily at 00, 03, 06,

09, 12, 15, 18, and 21 UTC

Key Geophysical Parameters:

surface drag coefficients

Processing Level:

4

Product Type:

standard, post-launch

Science Team Contact:

R. Rood, A. Molod
DAS 32

Assimilated Planetary Boundary

Layer Depth

Data Products Handbook Vol. I Chapter Ten: Winds • 179

Product Description

This Level 4 product consists of 3-hourly estimates

of PBL depth estimated by the GEOS DAS. It is

based on the assimilation of pressure, temperature,

wind, and moisture observations. These include

conventional meteorological observations, both in

situ and remotely sensed, as well as TRMM and AM1 data such as TMI and MODIS. This field is

generated by the turbulence parameterization within

the GEOS DAS GCM. It is defined as the depth at

which turbulent kinetic energy reduces to 10 percent

of its surface value.

Research & Application

The depth of the PBL plays an important role in the

vertical mixing that takes place in the atmosphere. A

shallow PBL may trap harmful pollutants near the

Earth’s surface.

Data Set Evolution

This very specialized data set is not generally

available globally from conventional observations. It

is usually estimated through turbulence

parameterization schemes within global GCMs. The

GEOS DAS turbulence parameterization scheme will

have the advantage of additional observational input

from TRMM and AM-1 instruments.

Suggested Reading

Bloom, S.C., et al., 1995.

Pfaendtner, J., et al., 1995.

Schubert, S.D., et al., 1995.

Schubert, S.D., et al., 1993.

Takacs, L.L., et al., 1994.

DAS 32

PRODUCT SUMMARY

Coverage:

global

Spatial/Temporal Characteristics:

2° × 2.5° lat-lon grid, daily at 00, 03, 06,

09, 12, 15, 18, and 21 UTC

Key Geophysical Parameters:

planetary boundary layer depth

Processing Level:

4

Product Type:

standard, post-launch

Science Team Contact:

R. Rood, A. Molod
Chapter Eleven

Pressure and Heights
Presssure and Heights

182 • Chapter Eleven: Pressure and Heights Data Products Handbook Vol. I

Relationship to Global Change Issues

Monitoring surface pressure and geopotential

heights is important to the understanding of

atmospheric dynamics on regional and global scales.

These measurements are critical to obtaining

hydrological cycle parameters (wind) needed for

locating the position of storm systems and for

developing atmospheric global climate models

(GCMs) whereas geopotential heights have been

crucial in the understanding of large-scale

circulation changes.

Although pressure is one of the fundamental

atmospheric variables, there are no current remote

sensing techniques for measurement of this pressure

field. Limited maps have been produced either by in

situ measurements or by indirect methods. However,

dynamically consistent global atmospheric fields are

provided from assimilation of available surfacebased observations and satellite retrievals to the

AGCM, constrained by some surface boundary

conditions. The practical aspects of this approach

are enormous. The data assimilation system

complements the observations by propagation of

information from observed into unobserved regions

and times.

Data assimilation is the process of ingesting

observations into a model of the Earth system. The

current product uses meteorological observations

and an atmospheric model. The result is a

comprehensive and dynamically consistent dataset

which represents the best estimate of the state of the

atmosphere at that time. The assimilation process

fills data voids with model predictions and provides

a suite of data-constrained estimates of unobserved

quantities such as vertical motion, radiative fluxes,

and precipitation. The GEOS DAS process not only

combines all the data globally but accounts for

instruments errors and bridges the gap between

observations.

The geopotential height is used for all aerological

reports because of its convenience. It represents the

height of a surface where parcels of air have the

same potential energy (effectively it is gravity

weighted height relative to sea level). Because of

poleward increase of gravity along a constant

geometric-height surface, a given geopotential

surface has a smaller geometric height over the

poles than over the equator. Geopotential height is

used to compute geopotential thickness (differences

in the geopotential heights of two constant pressure

surfaces in the atmosphere) for input to the

assimilating model.

Assimilated values of surface pressure (especially

over oceans) have been shown to improve the

forecasts during the Global Energy and Water Cycle

Experiment (GEWEX) in 1979, especially in the

southern hemisphere. These assimilated data provide

reliable surface reference levels for satellite

measurements and are also used to estimate surface

winds in regions where other measurements are not

available.

The anomalies of the northern hemisphere map of

assimilated geopotential heights of the 500 mb have

been successfully used in the understanding

(Kalnay, et al. , 1983) of the stratospheric sudden

warming events observed during the FGGE period,

winter of 1979 and in the understanding of

atmospheric “blocking” (persistence of large-scale

meridonal flow in the middle troposphere for at least

10 days, and manifesting as quasi-stationary high

amplitude planetary waves in the midtropospheric

geopotential height fields) observed at middle and

high latitudes over the oceanic regions of the

northern hemisphere.

Within the DAS the input observational database are

mostly acquired from NCAR, NCEP, and

NOAA/NESDIS. For global sea level pressure and

near surface wind analysis over the oceans, data

from surface land synoptic reports (sea level

pressure only), ships and buoys are used. The upperair analysis of temperature, height/geopotential

thickness, wind and moisture incorporate the data

from rawinsondes, dropwindsondes, rocketsondes,

aircraft winds, cloud tracked winds, and the TOVS

soundings. The other input for the model is a series

of boundary conditions (land surface type, sea

surface temperature, albedo, sea ice, snow cover)

based on climatology provided either through NCEP

or NCAR. The assimilating forecasting model

generates an improved global gridded dataset of

surface pressure and geopotential heights in addition

to other atmospheric and surface parameters vital to
Pressure and Heights

Data Products Handbook Vol. I Chapter Eleven: Pressure and Heights • 183

future forecasting and other land and atmosphere

studies.

Use of better observations from TRMM and EOS

AM-1 such as TMI and MODIS will improve the

assimilation products and hence global modeling

efforts, numerical weather predictions, investigation

of atmosphere-surface interactions, and monitoring

of climate variations and trends.

Product Overview

The products in this category include assimilated

surface pressure (DAS 33) and assimilated surface

and upper air geopotential heights (DAS 34). These

are EOS Level 4 products.

The product DAS 33 includes surface pressure, sea

level pressure, sea level pressure error, pressure of

the top of the model relative to observed surface

pressure and 3-hour average of surface pressure and

surface pressure tendency. The product DAS 34

consists of 6 hourly estimates of assimilated upper

air geopotential heights and associated analyzed

errors at 24 mandatory pressure levels, and upper air

perturbation geopotential heights at 70 model sigma

levels. Surface geopotential heights included in

DAS 34 and all surface parameters of DAS 33 are

reported at 3-hour intervals.

Product Interdependency and Continuity

with Heritage Data

Prior to the launch of the EOS sensors, new highresolution data from the sensors of present satellite

missions (GOES, DMSP, ERS, UARS,

TOPEX/POSEIDON, SeaWiFS, and ADEOS) along

with other sources such as the Pathfinder and

COADS data sets, will be used to carry out the

validation studies towards the improvement and

enhancement of assimilation algorithms and

prediction models.

Once EOS satellite missions are launched, new data

from advanced sensors aboard NOAA, DMSP,

GOES, ERS, TRMM, EOS AM, ADEOS II, EOS

PM, and CHEM platforms, will be used to improve

the quality of assimilated products that include 4-

dimensional gridded fields of atmospheric

parameters (e.g., wind, temperature, moisture,

geopotential, turbulence, radiative heating/flux,

clouds, precipitation, sensible and latent heat flux,

and planetary boundary layer depth) and surface

land and ocean parameters (e.g., albedo,

temperature, pressure, evaporation, roughness, snow

depth, soil moisture, vegetative moisture, and sea

level height).

In particular, EOS sensors TRMM, MODIS, and

CERES will provide high vertical resolution

temperature, moisture profiles (MOD 07) and cloud

height information (MOD 06 & CER 07), and better

surface temperature (MOD 11) and land cover type

(MOD 12) values to be used in EOS-DAS

assimilation system as input and boundary

conditions.

Suggested Reading

Daley, R., 1991.

Kalnay, E., et al., 1983.

Pfaendtner, J., et al., 1995.

Schubert, S.D., et al., 1993.

Takacs, L.L., et al., 1994.
DAS 33

Assimilated Surface Pressure

184 • Chapter Eleven: Pressure and Heights Data Products Handbook Vol. I

Product Description

This Level 4 product consists of 3-hourly estimates

of the surface pressure humidity produced by the

GEOS DAS. It is based on the assimilation of

pressure, temperature, wind, and moisture

observations. These include conventional

meteorological observations, both in situ and

remotely sensed, as well as TRMM and AM-1 data

such as TMI and MODIS. This product includes

surface pressure (PSFC), sea level pressure (SLP),

sea level pressure error (PRSE), surface pressure

minus pressure at the top of the GEOS DAS general

circulation model (PSPTOP), three-hour average of

surface pressure (PAVE), and surface pressure

tendency (DPDT).

Research & Application

An estimate of the surface pressure is needed by

many of the TRMM and AM-1 instrument retrieval

algorithms. Surface pressure patterns also indicate

the position of storm systems and the general

circulation of the atmosphere.

Data Set Evolution

There are a wide variety of pressure, temperature,

wind, and moisture data available from conventional

meteorological sources. These include surface

observing sites, rawinsondes, satellite soundings, etc.

Yet even with all these data, there are significant

gaps in global coverage. The accuracy of individual

measurements also varies widely. The best way to

combine all these data globally is through data

assimilation which takes into account instrument

errors and bridges the gaps between observations.

New AM-1 and TRMM products will greatly

improve the accuracy and coverage of the data used

to produce this field. In addition, data assimilation

provides dynamically consistent estimates of surface

pressure through its general circulation model that

takes into account global circulation systems,

precipitation, and evaporation.

DAS 33

PRODUCT SUMMARY

Coverage:

global

Spatial/Temporal Characteristics:

2° × 2.5° lat-lon grid, daily at 00, 03, 06,

09, 12, 15, 18 and 21 UTC

Key Geophysical Parameters:

surface pressure

Processing Level:

4

Product Type:

standard, post-launch

Science Team Contact:

R. Rood, L. Takacs, D. Lamich

Suggested Reading

Bloom, S.C., et al., 1995.

Pfaendtner, J., et al., 1995.

Schubert, S.D., et al., 1995.

Schubert, S.D., et al., 1993.

Takacs, L.L., et al., 1994.
DAS 34

Assimilated Geopotential Heights

Data Products Handbook Vol. I Chapter Eleven: Pressure and Heights • 185

Product Description

This Level 4 product consists of 3- and 6-hourly

estimates of atmospheric geopotential heights

produced by the GEOS DAS. It is based on the

assimilation of pressure, temperature, wind, and

moisture observations. These include conventional

meteorological observations, both in situ and

remotely sensed, as well as TRMM and AM-1 data

such as TMI and MODIS. Surface air temperature is

contained in TS. Surface geopotential heights are in

PHIS. Geopotential heights of mandatory pressure

levels are in HGHT and their errors in HGHTE.

Perturbation geopotential heights at model sigma

levels are in PHGHT.

Research & Application

Variation in atmospheric geopotential drives the

Earth’s general circulation.

Data Set Evolution

There are a wide variety of pressure, temperature,

wind, and moisture data available from conventional

meteorological sources. These include surface

observing sites, rawinsondes, satellite soundings, etc.

Yet even with all these data, there are significant

gaps in global coverage. The accuracy of individual

measurements also varies widely. The best way to

combine all these data globally is through data

assimilation which takes into account instrument

errors and bridges the gaps between observations.

New AM-1 and TRMM products such as MODIS

and TMI will greatly improve the accuracy and

coverage of the data used to produce these fields. In

addition, data assimilation provides dynamically

consistent estimates of geopotential heights through

its general circulation model that takes into account

global circulation systems, precipitation, and

evaporation.

DAS 34

PRODUCT SUMMARY

Coverage:

global

Spatial/Temporal Characteristics:

2° × 2.5° lat-lon grid, 24 mandatory

pressure or 70 model sigma levels, daily

at 00, 06, 12, and 18 UTC (mandatory

and sigma level data) and daily at 00, 03,

06, 09, 12, 15, 18, and 21 UTC (surface

data only)

Key Geophysical Parameters:

geopotential heights

Processing Level:

4

Product Type:

standard, post-launch

Science Team Contact:

R. Rood, L. Takacs, D. Lamich

Suggested Reading

Bloom, S.C., et al., 1995.

Pfaendtner, J., et al., 1995.

Schubert, S.D., et al., 1995.

Schubert, S.D., et al., 1993.

Takacs, L.L., et al., 1994.
Chapter Twelve

Surface Temperatures

of Land and Oceans,

Fire Occurrence,

and Volcanic Effects
Surface Temperatures of Land and Oceans,

Fire Occurrence and Volcanic Effects

188 • Chapter Twelve: Surface Temperatures of Land and Oceans, Data Products Handbook Vol. I

Fire Occurrence, and Volcanic Effects

Relationship to Global Change Issues

The surface temperature of the land and ocean is a

parameter which determines the vigor of

biogeochemical activity and the rate of heat flow and

water transport between the surface and the

atmosphere. These in turn influence global

environmental processes. On land, soil and canopy

temperature are among the main determinants of the

rate of growth of vegetation and they govern seasonal

starting and termination of growth. Hydrologic

processes such as evapotranspiration and snow and

ice melt are highly sensitive to surface temperature

fluctuation. In addition, surface temperature has been

shown to be an important discriminating factor in

classification of land surface types which supports

the global land cover type product. For the oceans,

the surface temperature influences the rate of growth

of phytoplankton, the rate of absorption of carbon

dioxide and other gases by sea water, and the rate of

flow of heat to and from the atmosphere. Thus,

temperature products are key inputs to many of the

EOS product algorithms as well as providing data for

global temperature mapping and change observation.

In the ocean, phytoplankton carries out the fixation of

carbon through photosynthesis, which takes in

dissolved carbon dioxide in sea water and produces

oxygen enabling phytoplankton growth. This process

captures about an equal amount of carbon as does

photosynthesis by land vegetation. Changes in the

amount of phytoplankton indicate the change in

productivity of the oceans and provide a key ocean

link for global climate change monitoring. Ocean and

land biological productivity, heat exchange, and

water exchange processes are tightly coupled; global

change models take all interactions into account in

global climate change prediction. In addition, these

annual productivity products can be used for global

and regional scale studies of ocean productivity, for

comparisons with annual summations of daily

analytic algorithms, and for comparison of global

biogeochemical models to continually improve the

technology for global change analysis.

The need also exists for information on the

occurrence of fires on the land surface, since fire

changes the surface cover type and releases gases and

particulate matter into the atmosphere, affecting

ecosystems and atmospheric chemistry on a rapid and

intense scale. The burning of oil slicks on water is

also a significant but infrequent intense input into the

land and atmosphere systems which will be

monitored by the fire product.

Testing of volcanoes is important because of the

disastrous geophysical consequences they have on

the Earth’s atmosphere, including increased

precipitation, ozone destruction, and the lowering of

the global temperature. Moreover, cooling of the

middle and upper stratosphere—from the increase in

reflecting caused by aerosols and the reduced

absorption of ultraviolet light because of reduced

ozone from volcanoes and other sources—plays a

significant role in climate change.

What Must be Measured?

Surface temperature algorithms require calibrated

radiances as a basic input. Since the radiances

observed have passed through the atmosphere,

corrections must be made for atmospheric attenuation

and scattering, hence meteorological data on

temperature and water vapor profiles, as well as

chemical constituents, are needed. Also, the radiance

emitted by the surface is determined by surface

emissivity as well as temperature. The land surface

pixel can contain a mixture of materials having

different emissivity, geometric, and topographic

characteristics which must be known before

temperature can be computed. Land material

emissivity also can have significant variability as a

function of wavelength, and for the mid-wave IR

bands significant reflectance can exist such that solar

reflected energy must be predicted and removed from

the observed radiance. The emissivity of the sea

surface is assumed to be unity and the sea-surfacetemperature algorithm in this case produces what is

called the brightness temperature. The ancillary

information needed is supplied by external sources

and precomputed databases keyed on surface material

provided by the land cover type product and the

global ocean masks.

Fire detection and measurement must be

implemented with sensor bands which do not saturate

at fire temperatures. This requirement is met with
AM-1

Surface Temperatures of Land and Oceans,

Fire Occurrence, and Volcanic Effects

Data Products Handbook Vol. I Chapter Twelve: Surface Temperatures of Land and Oceans, • 189

Fire Occurrence, and Volcanic Effects

MODIS by a special fire channel that saturates at

500K, which provides sufficient range to segment

fire intensity. ASTER temperature data are used in

combination with the MODIS global fire data for

high resolution mapping of fires and in the

monitoring and analysis of volcanic processes.

Product Overview

The EOS land and ocean surface temperature and fire

products are provided by MODIS and ASTER

instruments. The MODIS land surface temperature

product (MOD 11) provides the needed composite

temperature of each pixel and the estimated

emissivity from database information plus

adjustments made possible by the multiwindow

algorithm. The accuracy is better than 1K for

materials with known emissivities. The ASTER

surface temperature product (AST 05, AST 08) has

high spatial resolution and will be used for

comparison and validation of the MODIS

temperature products and for the monitoring and

analysis of volcanic processes. The sea surface

temperature (MOD 28) derived from radiance

measurements is an estimate of the skin temperature

(top millimeter) of the 1 km pixel viewed by MODIS.

The algorithm is similar to the land surface

temperature algorithm in that it uses multiple

atmospheric window techniques to estimate the

atmospheric parameters which are required to

compensate for absorption and scattering. The bulk

temperature of the near-surface ocean is the

temperature of the upper 10-20 cm to meter as

measured by conventional thermometers on buoys

and ships. Extensive analysis has been done of

satellite and in situ data to enable the algorithm to

estimate bulk temperature as well as skin

temperature.

Assimilated ground temperature from the GEOS

DAS (DAS 35) will provide 3-hour estimates

combining MODIS ground temperature information

with pressure, temperature, wind, and moisture

observations. The thermal anomaly product

(MOD 14, MOD 40) contains information unique to

understanding the characteristics of fires, including

energy emitted, size, temperature, and the ratio of

smoldering to flaming area and is available for both

day and night periods. The composites produced

include a monthly day and night fire occurrence

aggregation and a summary of the number of fires in

classes related to the strength of the fire. The ASTER

land surface temperature product will provide high

resolution mapping of fires as a complement to

MODIS global fire data.

Product Interdependency and Continuity

with Heritage Data

The land surface temperature product requires

MODIS calibrated radiances and the cloud mask

product as inputs. Also, atmospheric temperature and

water vapor profiles are needed along with a database

of emissivities, the use of which is driven by the land

cover classification products which enable surface

material to be specified for each pixel. The land

surface temperature product from ASTER is needed

for comparison and spatial variation analysis of the

MODIS land-surface temperature product over test

sites. The land-surface temperature product is an

input to other land products including vegetation

indices, evapotranspiration, and net primary

production and as a background temperature estimate

for the fire product.

Similarly, the sea-surface temperature product uses

MODIS calibrated radiances and the cloud mask

product, but does not require the complex emissivity

estimation databases and algorithms that the landsurface temperature product needs. Sun glint is a

significant source of error in the mid-wave IR bands

which used so the mask product is critical to

identifying glint pixels as is sea surface wind

ancillary data which is used to estimate the

magnitude of the glint area radiance

In addition to the calibrated MODIS radiances and

cloud mask, the fire product uses a number of

products including vegetation indices, land surface

temperature, and cover type.

Existing meteorological stations on land and ship and

buoy-based instruments at sea measure surface

temperatures at large numbers of worldwide

locations; however, these measurements are not

adequate in terms of the radiative phenomenon or
Surface Temperatures of Land and Oceans,

Fire Occurrence and Volcanic Effects

190 • Chapter Twelve: Surface Temperatures of Land and Oceans, Data Products Handbook Vol. I

Fire Occurrence, and Volcanic Effects

spatial coverage. Land- and sea-surface temperature

data taken by TRMM and EOS AM-1 will add to the

valuable input already provided by AVHRR, HIRS,

Landsat, and CZCS, providing a large, long-term

global land and sea surface temperature data set in

order that we may more fully understand the effects

of the various Earth system cycles on temperature.

Suggested Reading

Asrar, G. and J. Dozier, 1994.

Becker, F., 1987.

Chaine, M.T., 1980.

Kaufman, Y.J., et al., 1991.

Price, J.C., 1983.

Robinson, J.M., 1991.

Running, S.W., et al., 1994.

Walton, C.C., et al., 1990.
AM-1 MOD11

MODIS Land Surface Temperature (LST)

& Emissivity

Data Products Handbook Vol. I Chapter Twelve: Surface Temperatures of Land and Oceans, • 191

Fire Occurrence, and Volcanic Effects

Product Description

This Level 2 and 3 product contains land surface

temperature and emissivity retrieved at spatial

resolutions of 1 km and 5 km over global land

surface. Both daytime and nighttime land surface

temperatures are included in its daily product. Daily,

8-day, and monthly Level 3 products will be also

generated at equal-angle grids of half degree latitude

and longitude. For land covers (including dense

evergreen canopies, lake surface, snow, ice, and most

soils) with high and stable emissivities in the splitwindow range, a view-angle dependent generalized

split-window LST algorithm will be used to retrieve

the surface temperature. For these land covers, the

accuracy of the generalized split-window LST

algorithm is better than 1K in most cases. This LST

algorithm is optimized by separating the ranges of

atmospheric column water vapor, lower boundary

temperature, and the surface temperature into

tractable subranges. For other land covers with

variable emissivities, a physics-based day/night LST

algorithm will be used to simultaneously retrieve

surface band emissivities and temperatures from a

pair of daytime and nighttime observations in seven

MODIS TIR bands through a statistical regression

approach and/or a least-square fit approach. Input

data for the MODIS LST algorithms include the

following MODIS products: Level 1A geolocation

and 1B TIR radiance; Level 2 cloud mask,

atmospheric temperature and water vapor profiles,

snow, sea ice, and NDVI; and the previous quarter’s

Level 3 land-cover product.

Research & Applications

Land surface temperature is a good indicator of both

the energy balance at the Earth’s surface and the

greenhouse effect because it is one of the key

parameters in the physics of the land-surface

processes. It is required for a wide variety of climate,

hydrological, ecological, and biogeochemical studies.

This product will be used in generating other MODIS

land products and in a variety of EOS

interdisciplinary studies.

MOD 11

PRODUCT SUMMARY

Coverage:

global land surface

Spatial/Temporal Characteristics:

1 km, daily Level-2 LST

1 km and 5 km, daily Level-3 LST

1-degree latitude and longitude, daily,

 8-day, and monthly Level-3 LST

Key Science Applications:

inputs to climate, hydrological,

ecological modeling

Key Geophysical Parameters:

land-surface temperature (MODIS

parameter 2484); land-surface emissivity

(MODIS parameter 3323)

Processing Level:

2, 3

Product Type:

standard, at-launch

Science Team Contact:

Z. Wan

Data Set Evolution

During the past decade significant progress has been

made in estimating land-surface emissivity and

temperature from airborne thermal IR data, including

multichannel, thermal log residual, and alpha residual

methods. Three types of methods have been

developed to retrieve LST from space: the single IR

channel method, the split-window method which is

also used in various multichannel sea-surface

temperature algorithms, and a new day/night MODIS

LST method which is designed to take advantage of

the unique capability of the MODIS instrument. The

strategy for development of the MODIS LST

algorithms incorporates experience from the SST

algorithms, surface emissivity knowledge base from

laboratory and field measurements, accurate radiate

transfer simulations, and efficient look-up table

anderpolation scheme for operational production.
AM-1 MOD11

MODIS Land Surface Temperature (LST)

& Emissivity

192 • Chapter Twelve: Surface Temperatures of Land and Oceans, Data Products Handbook Vol. I

Fire Occurrence, and Volcanic Effects

Suggested Reading

Running, S.W., et al., 1994.

Salisbury, J.W. and D.M. D’Aria, 1992.

Snyder, W. and Z. Wan, 1996.

Wan, Z. and J. Dozier, 1989.

Wan, Z. and J. Dozier, 1996.
AM-1 MOD14, MOD40

Thermal Anomalies –

Fires & Biomass Burning

Data Products Handbook Vol. I Chapter Twelve: Surface Temperatures of Land and Oceans, • 193

Fire Occurrence, and Volcanic Effects

Product Description

The product includes fire occurrence (day/night), fire

location the logical criteria used for the fire selection

and an energy calculation for each fire. The product

also includes composite 16 day and night fire

occurrence (full resolution), composite monthly day

and night fire occurrence (full resolution), gridded

10-km summary per fire class (daily/ten

day/monthly), gridded 0.5° summary of fire counts

per class (daily/ten-day/monthly). The Level 2

product includes various fire-related parameters

including the occurrence of day and nighttime

thermal anomalies which will be flagged and grouped

into different temperature classes with emitted energy

from the fire. These parameters are retrieved daily at

1-km resolution. The fire product uses the special fire

channel at 3.9 μm that saturates at 500K and the

high-saturation level of the 11-μm channel. During

the night, the fire product will also use the 1.65- and

2.15-μm channels. In the early post-launch period the

product will include the smoldering/flaming ratio per

fire class and an estimate of the area burned.

Research & Applications

Fire is an important process within a significant

number of terrestrial biomes, and the release of gases

and particulate matter during biomass burning is an

important contributor to the chemical reactions and

physical processes taking place in the atmosphere.

Fire is a significant and continuous factor in the

ecology of savannas, boreal forests, and tundra, and

plays a central role in deforestation. Fire information

will be used to drive regional emissions models, trace

gas transport models, and mesoscale models of

atmospheric chemistry. Important impacts of fires

include

• changes of physical state of vegetation and

release of greenhouse gases;

• release of chemically-reactive gases during

biomass burning;

• release of soot and other particulate matter

during fires;

• changes in the exchange of energy and water

between land surfaces and the atmosphere; and

MOD 14, MOD 40

PRODUCT SUMMARY

Coverage:

global, daytime/nighttime

Spatial/Temporal Characteristics:

1 km, 10 km, 0.5°

Key Geophysical Parameters:

fire occurrence and class, fire selection

criteria, fire location, smoldering and

flaming ratio, burned area

Processing Level:

2, 3

Product Type:

standard, at-launch and post-launch

Science Team Contact:

Y. Kaufman, C. Justice

• changes in plant community development

and soil nutrient, temperature, and moisture.

• cloud development and reflectivity

Data Set Evolution

The MODIS fire products build and improve upon

the experience of fire assessment primarily using the

NOAA AVHRR and GOES systems. Currently, no

one sensing system provides the instrument

characteristics needed for an effective global fire

monitoring program. The MODIS sensor has been

designed to include characteristics specifically for

fire detection and will provide a unique capability

over existing sensors in terms of fire monitoring. The

location accuracy and improved instrument

characterization and calibration will enable

unprecedented fire monitoring data sets. Attention

should be given to the overpass time (10:30 a.m. for

the AM-1 platform) for fire detection with respect to

the diurnal cycle of fire activity. MODIS will also

offer unique spatial and radiometric capabilities for

burn scar detection; automatic procedures for burn
AM-1 MOD14, MOD40

Thermal Anomalies –

Fires & Biomass Burning

194 • Chapter Twelve: Surface Temperatures of Land and Oceans, Data Products Handbook Vol. I

Fire Occurrence, and Volcanic Effects

Figure 30. NOAA/AVHRR Detected High Temperature Sources: June 25, 1992. Derived from the IGBP-DIS Global

1-km set provided by USGS Eros Data Center.

scar detection are under development as a postlaunch product.

Suggested Reading

Andreae, M.O., et al., 1994.

Global Biomass Burning. J.S. Levine, Ed., 1991.

Justice C.O., et al., 1993.

Kaufman, Y.J., et al., 1990b.

Penner, J.E., et al., 1992.
AM-1 MOD 28

Sea Surface Temperature (SST)

Data Products Handbook Vol. I Chapter Twelve: Surface Temperatures of Land and Oceans, • 195

Fire Occurrence, and Volcanic Effects

Product Description

This Level 2 and 3 product provides sea surface

temperature at 1-km resolution over the global

oceans. In addition, a quality assessment parameter is

included for each pixel. The Level 2 product is

produced daily and consists of global day and night

coverage every 24 hours. It is used to generate the

gridded Level 3 products daily and weekly for day

and night conditions. A quality parameter is provided

for each dataset.

Research & Applications

The global distribution and variability of sea-surface

temperature are key inputs to Earth energy and

hydrological balance studies and long-term climate

change studies. In addition, sea-surface temperature

is required by a number of MODIS algorithms

including generation of perceptible water, lifted

index, water-leaving radiance, productivity, oceanic

aerosol properties, and temperature and water vapor

profiles. MODIS sea-surface temperature retrievals

will be incorporated into a match-up database with

radiance and buoy sea-surface temperature

observations (see MOD 32).

Data Set Evolution

Sea-surface temperature determination is based on

MODIS calibrated mid- and far-IR radiances, (Bands

20, 22, 23, 31 and 32 from MOD 02) using an

algorithm which exploits the differences in

atmospheric transmissivity in the different IR bands

to enable highly accurate estimation of the

atmospheric effects which enables accurate SST

generation. The cloud screening product (MOD 04) is

an ancillary input to the algorithm along with a land

mask which is used to mark non-water pixels while

an ice-extent mask limits polar sea coverage. A

sequence of spatial and temporal homogeneity tests

are applied to validate the quality of the cloud-free

observations. The AIRS SST estimate (Parameter

2523) will be used as a near-real time quality

assessment of skin temperature. Visible and near-IR

radiances (Bands 3, 4, 5, 6) will be used as a

secondary cloud flag in the event that the cloud

screening product is not available.

MOD 28

PRODUCT SUMMARY

Coverage:

global ocean surface, clear-sky only

Spatial/Temporal Characteristics:

1 km/daily, weekly/day and night

Key Science Applications:

energy and hydrological balance, climate

change models

Key Geophysical Parameters:

sea-surface temperature (MODIS

parameter 2527) and quality assessment

(MODIS parameter 5359)

Processing Level:

2, 3

Product Type:

standard, at-launch

Science Team Contact:

O. Brown

Suggested Reading

Abbott, M.R. and D.B. Chelton, 1991.

Barton, I.J., et al., 1989.

Brown, O.B. and R.E. Chaney, 1983.

Edwards, T., et al., 1990.

Llewellyn-Jones, D.T., et al., 1984.

McClain, E.P., et al., 1985.

Minnett, P.J., 1991.

Minnett, P.J., 1995.

Schluessel, P., et al., 1990.

Smith, A.H., et al., 1994.

Smith, W.L., et al., 1996.

Strong, A.E. and E.P. McClain, 1984.
AM-1 MOD 28

Sea Surface Temperature (SST)

196 • Chapter Twelve: Surface Temperatures of Land and Oceans, Data Products Handbook Vol. I

Fire Occurrence, and Volcanic Effects

Figure 31. Global Infrared Sea Surface Temperature. This daily pre-MODIS Sea Surface Temperature (SST) image

for February 15, 1988, was produced using objective analysis techniques applied to NOAA AVHRR data. MODIS is

expected to reduce the uncertainties in IR SST by a factor of 2 because of improvements in sensor calibration and

algorithms. O. Brown, R. Evans, RSMAS

Figure 32. Changes in Pacific SST Due to El Niño. Sea Surface Temperature (SST) from the NOAA AVHRR, showing

patterns before (left) and during (right) the major 1992 El Niño. The improved accuracy of IR SST products expected

from MODIS will enable scientists to investigate the relatively small changes in SST hypothesized to be responsible

for triggering El Niño cycles in the Pacific and their ramifications globally. (Note: temperatures range from 20-30°C.)

O. Brown and G. Feldman, “Reports to the Nation: El Niño and Climate Prediction,” Spring 1994, No. 3, University

Corporation for Atmospheric Research., Boulder, CO.
AM-1 AST 05, AST 08

ASTER Surface Emissivity &

Surface Kinetic Temperature

Data Products Handbook Vol. I Chapter Twelve: Surface Temperatures of Land and Oceans, • 197

Fire Occurrence, and Volcanic Effects

Product Description

This Level-2 product (AST 05) contains surface

emissivity at 90-m resolution generated only over the

land from ASTER’s five thermal-IR channels.

Surface emissivity is required to derive land-surface

temperature (AST 08) data, also at a resolution of

90-m. Land surface temperatures are determined

from Planck’s Law, using the emissivities to scale the

measured radiances after correction for atmospheric

effects. Pixels classified as “cloud” will have no

atmospheric correction due to a lack of knowledge of

cloud height, and the cloud temperature will be given

as the brightness temperature at sensor.

Research & Applications

The emissivity product is critical for deriving land

surface temperatures. It is therefore important in

studies of surface energy and water balance. The

emissivity product is also useful for mapping

geologic and land-cover features.

The derived land surface temperature has

applications in studies of surface energy and water

balance. Temperature data will be used in the

monitoring and analysis of volcanic processes, day

and night temperature data will be used to estimate

thermal inertia, and thermal data will be used for

high-resolution mapping of fires as a complement to

MODIS global fire data.

Data Set Evolution

Current sensors provided only limited information

useful for deriving surface emissivity, and

researchers currently are required to use emissivity

surrogates such as land-cover type or vegetation

index in making rough estimates of emissivity and

hence land surface temperatures. The five thermal-IR

channels of the ASTER instrument enable direct

surface emissivity estimates.

Mapping of thermal features from optical sensors

such as Landsat and AVHRR has been used for many

developmental studies. These instruments, however,

lack the spectral coverage, resolution, and

AST 05, AST 08

PRODUCT SUMMARY

Coverage:

regional, land surface

Spatial/Temporal Characteristics:

90 m

Key Science Applications:

local heat balance, volcanology, biomass

burning, thermal inertia

Key Geophysical Parameters:

surface emissivity, surface temperature

Processing Level:

2

Product Type:

standard, on request

Science Team Contact:

A. Gillespie

radiometric accuracy that will be provided by the

ASTER instrument.

Suggested Reading

Dozier, J., and Z. Wan, 1994.

Hook, S.J., et al., 1992.

Kahle, A.B., 1986.
DAS 35

Assimilated Ground Temperature

198 • Chapter Twelve: Surface Temperatures of Land and Oceans, Data Products Handbook Vol. I

Fire Occurrence, and Volcanic Effects

Product Description

This Level 4 product consists of 3-hourly estimates

of ground temperature produced by the GEOS DAS.

It is based on the assimilation of MODIS-observed

ground temperatures along with pressure,

temperature, wind, and moisture observations from

conventional meteorological sources, both in situ and

remotely sensed, along with other TRMM and AM-1

data. Over the oceans, this product is the sea surface

temperature extracted from sources outside the

GEOS DAS. Three fields are provided, ground

temperature (GTMP), 3-hour average ground

temperature (GTAVE), and ground virtual

temperature (TVSFC).

Research & Application

Ground temperature determines how the lower

atmosphere is heated and cooled. Heating rates near

the Earth’s surface are needed to determine

atmospheric stability which drives local convection,

and to determine horizontal temperature gradients

which modify the atmosphere’s general circulation.

Ground temperature also regulates the rate of surface

evaporation.

Data Set Evolution

Most assimilation systems derive ground

temperatures strictly from air temperature

measurements near the ground. The new GEOS DAS

will also use ground temperature measurement from

MODIS and thus should provide much more accurate

estimates of this important field.

Suggested Reading

Bloom, S.C., et al., 1995.

Pfaendtner, J., et al., 1995.

Schubert, S.D., et al., 1995.

Schubert, S.D., et al., 1993.

Takacs, L.L., et al., 1994.

DAS 35

PRODUCT SUMMARY

Coverage:

global

Spatial/Temporal Characteristics:

2° × 2.5° lat-lon grid, daily at 00, 03, 06,

09, 12, 15, 18, and 21 UTC

Key Geophysical Parameters:

ground temperature

Processing Level:

4

Product Type:

standard, post-launch

Science Team Contact:

R. Rood, A. Molod, J. Joiner
Chapter Thirteen

Vegetation Dynamics,

Land Cover, and

Land Cover Change
Vegetation Dynamics, Land Cover,

and Land Cover Change

200 • Chapter Thirteen: Vegetation Dynamics, Land Cover, & Land Cover Change Data Products Handbook Vol. I

Relationship to Global Change Issues

About 30 percent of the Earth’s surface is covered

by land and much of this is vegetated, making land

surface processes important components of the

terrestrial climate system. Vegetation, land cover

and land use change provide the basis for

understanding land and water resource management

as well as a range of research and global monitoring

objectives, including global carbon modeling,

greenhouse gas emission inventories, regional

environment and development assessments, and

forest resource management. Ecologists and

climatologists have developed models which

describe the matter and energy exchanges, but lack

of long term global data limits their use in Earth

system models. Additionally, we lack good global

data on surface albedoes over land, on the

distribution of soils and important properties of

vegetation morphology and physiology, and on

water storage in soils and snow cover and their

relationship to runoff. Incorporation of this

information in the land-surface component of

climate models will improve estimates of exchanges

of water and energy with the atmosphere and lead to

more realistic climate simulations

Product Description

VEGETATION

The Land Vegetation products provide information

on terrestrial systems which have important links to

climate and atmospheric composition that are

needed for development and application of models

of the Earth’s system. These links involve

exchanges of energy and moisture, radiatively active

trace gases, e.g., carbon dioxide and methane, and

photochemically active species such as methane,

nitric oxide, and non-methane hydrocarbons that

soils, plants, and biomass burning release.

The EOS-AM Vegetation products provide the basic

measurements of the photosynthetic engines which

drive carbon transport on land. For land areas,

vegetation density and vegetation canopy factors are

produced which are used to generate the land

primary productivity, the amount of carbon fixed by

vegetation growth per day or longer unit of times

such as per year. The MODIS vegetation

measurements are provided by the Vegetation Index

(MOD 13), Leaf Area Index and Fraction of

Photosynthetically Active Radiation (FPAR)

(MOD 15), and Vegetation Cover Conversion

(MOD 44) products. Vegetation indices are

radiometric measures of the amount of vegetation

present on the ground in a particular pixel. The

indices are obtained from measurements of reflected

visible and near infrared energy from vegetation and

are called normalized difference vegetation indices

(NDVI). These indices have values between -1 and 1

and increase with increasing amounts of vegetation.

Several new indices have been developed which

compensate for soil and atmospheric effects and

these are combined into the modified vegetation

index (MVI), also be to produced by MODIS. The

indices will be produced at different resolutions and

time averaging intervals to provide measures of

vegetation density and productivity which are

required by global change models.

The Leaf Area Index product provides information

on the structural property of a plant canopy such as

the number of equivalent layers of leaves the

vegetation displays relative to a unit ground area. It

is derived from the vegetation index. This parameter

is an input to the Photosynthesis and Net Primary

Production algorithms which produce the global

model inputs for land. The FPAR product provides

another key input to the production algorithm. It

measures the proportion of available radiation in the

specific photosynthetically active wavelengths of the

spectrum that a canopy absorbs. It is also derived

from the vegetation index. These two products

provide the vegetation measurement inputs which

are combined with surface temperature and radiation

input information in the vegetation production

algorithm.

LAND COVER AND LAND COVER CHANGE

Land cover (MOD 12) maps global terrestrial land

cover at a 1-km resolution. The land cover change

product will detect areas of change and identify

types of change processes as they occur. Land cover

as well as both human and natural alteration of land

cover plays a major role in global-scale patterns of

climate and biogeochemistry.
Vegetation Dynamics,

Land Cover, and Land Cover Change

Data Products Handbook Vol. I Chapter Thirteen: Vegetation Dynamics, Land Cover, & Land Cover Change • 201

The land cover product will recognize 18 basic

classes of land cover types, independent of climate.

Vegetation classes will separate needleleaf,

broadleaf, and graminoid leaf structures; annually

deciduous and evergreen canopy habits; and sparse

to dense cover fractions following the International

Geosphere-Biosphere Program (IGBP) scheme.

Also, major classes of agricultural activities will be

recognized as well as several categories of land

surfaces with little or no plant cover—such as bare

sand, soil, or rock.

The land cover product will detect and categorize

changes in terrestrial features and processes on a

global scale. Independent of the land cover product,

the land-cover change algorithm will compare,

pixel-by-pixel, the temporal development curve of a

set of biophysical and spatial indicators derived

from MODIS data.

MISR LAND SURFACE DATA

The MISR instrument provides a variety of

information on the Earth’s surface. Product

descriptions for MISR Aerosol and Surface Data

(MIS 05, MIS 09) are available on page 115 of this

document.

Product Interdependency and Continuity

with Heritage Data

The land products flow in a chain from the Level 2

reflectance and land cover type parameters to the

biophysical and biochemical derived products which

are inputs to the Earth system models.

Specific product dependencies for the land

vegetation indices are MODIS radiance and imagery

products, including Level 1-B calibrated radiance,

surface leaving radiance and aerosol optical depth.

The vegetation index is the key input to Leaf Area

Index and FPAR products with are the key inputs

along with land cover type into the biomass product

algorithm.

Each instrument product set has its own science

requirements and objectives and support the

generation and validation of other instrument

products. Vegetation Index products have been

produced for many years from AVHRR and other

instrument data and MODIS will produce a

“continuity” NDVI to maintain the NDVI time

series using an algorithm which closely

approximates the heritage index. A worldwide

database has been created with the data and EOS

will continue this series.

References

Asrar, G. and J. Dozier, 1994.

Li, X., et al., 1995.

Moody, A. and C. E. Woodcock, 1995.

Running., S., et al., 1994.

Vorosmarty, C., et al., 1993.
AM-1 MOD 12

Land Cover/Land Cover Change

202 • Chapter Thirteen: Vegetation Dynamics, Land Cover, & Land Cover Change Data Products Handbook Vol. I

Product Description

This Level 3 product contains land cover type

(Param. 2669) and land cover change (2761)

parameters which will be produced at 1-km

resolution on a quarterly basis beginning 18 months

following launch of the AM-1 platform. The land

cover parameter identifies 17 categories of land cover

following the IGBP global vegetation database,

which defines nine classes of natural vegetation,

three classes of developed lands, two classes of

mosaic lands and three classes of nonvegetated lands

(snow/ice, bare soil/rocks, water.) The land cover

change parameter quantifies subtle and progressive

land-surface transformations as well as major rapid

changes. As such, it is not a conventional change

product that only compares changes in land cover

type at two times but combines analyses of change in

multispectral-multitemporal data vectors with models

of vegetation change mechanisms to recognize both

the type of change and its intensity. In addition to the

basic 1-km product, summary products containing

proportions of land covers and change characteristics

will be available at one-quarter, one-half, and 1°

resolutions.

Research & Applications

This product is used for biophysical and

biogeochemical parameterization of land cover for

input to global and regional scale models of climate,

hydrologic processes, and biogeochemical cycling.

Examples of biogeophysical parameters keyed to

land cover include leaf area index, vegetation

density, and FPAR. Other parameters are biomass

permanence and energy transfer characteristics of the

land surface.

Data Set Evolution

Recent attempts to produce regional scale land cover

datasets use coarse spatial resolution, high temporal

frequency data from the AVHRR instrument aboard

the NOAA series of meteorological satellites. Most

of these efforts have used the Normalized Difference

Vegetation Index (NDVI) parameter derived from

this data. The NDVI generally quantifies the

biophysical activity of the land surface and as such

does not provide land cover directly. The MODIS

MOD 12

PRODUCT SUMMARY

Coverage:

global, land only

Spatial/Temporal Characteristics:

1 km, 1/4°, seasonal

Key Science Applications:

biogeochemical cycles, land cover

change

Key Geophysical Parameters:

land cover type, land cover change

Processing Level:

3

Product Type:

standard, at launch

Science Team Contact:

A. Strahler, A. Huete,

S.W. Running, C. Justice

land cover algorithm draws from information

domains well beyond those used in these efforts

including directional surface reflectance, texture,

vegetation index, acquisition geometry, land surface

temperature, and snow/ice cover. The algorithm will

rely on a neural network approach to classification as

this method has been shown to outperform

conventional supervised classifiers such as maximum

likelihood. Product validation will be based on a

network of test sites chosen to represent major global

biomes and cover types. High resolution imagery

(e.g., Landsat) will be used to establish truth for the

sites which will be used post-launch to train the land

cover classifier and validate the products. The

validation procedure will characterize the accuracy of

the product as well as provide information that can be

used in spatial aggregation to provide land cover and

land cover change data at coarser resolutions.
AM-1 MOD 12

Land Cover/Land Cover Change

Data Products Handbook Vol. I Chapter 13: Vegetation Dynamics, Land Cover, & Land Cover Change • 203

Figure 33. Global Distribution of the Six Canopy Structure Based Vegetation Classes Required for

Carbon, Water and Climate Modeling, Derived from AVHRR Pathfinder Data.

Suggested Reading

Lambin, E.F. and A.H. Strahler, 1994.

Lambin, E.F. and A.H. Strahler, 1994.

Moody, A., et al., 1994.

Nemani, R.R. and S.W. Running, 1995.

Running, S.W., et al., 1994.

Running, S.W., et al., 1995.

Strahler, A., et al., 1995.

Townshend, J.R.G., et al., 1991.
MOD 13

MODIS Vegetation Indices: The Normalized

Difference Vegetation Index (NDVI) and an

Enhanced, Modified Vegetation Index (MVI)

204 • Chapter Thirteen: Vegetation Dynamics, Land Cover, & Land Cover Change Data Products Handbook Vol. I

Product Description

The MODIS vegetation indices (VIs) will provide

consistent spatial and temporal comparisons of global

vegetation conditions which will be used to monitor

the Earth’s photosynthetic vegetation activity for

phenologic, change detection, and biophysical

interpretations. The VIs are determined daily and

globally for land from MODIS blue, red and nearinfrared reflectances (centered at 470 nm, 648 nm,

and 858 nm, respectively). Two indices are planned;

the NDVI will be a continuity index with the existing

NOAA-AVHRR derived NDVI. A modified

vegetation index (MVI) uses the blue band to remove

residual atmospheric contamination due to smoke and

sub-pixel/thin clouds, and uses a feedback adjustment

to minimize canopy background variations and

enhance vegetation sensitivity from sparse to dense

vegetation conditions. The VIs use atmosphericallycorrected (at ~ 50 km resolution) bi-directional

surface reflectances masked for water, cloud, and

cloud shadow. The NDVI and MVI products are

archived at a 250-m pixel resolution, along with the

Sun and view angles of each grid cell.

Research & Applications

Vegetation Indices are used for global monitoring of

vegetation conditions. The VIs are used as input in

the land cover and land cover change products. They

also play an important role in the derivation of the

FPAR, LAI, and thermal products. The at-launch

version will be fully operational.

Data Set Evolution & Applications

Although a global validation scheme has been

implemented for the VIs, a thorough evaluation and

calibration of these indices will be made at launch.

MOD 13

PRODUCT SUMMARY

Coverage:

global land surface (Level 2)

Spatial/Temporal Characteristics:

(Level 2) daily at 250 m

Key Science Applications:

global vegetation monitoring, global

biogeochemical and hydrologic

modeling, global and regional climate

modeling, land cover characterization

Key Geophysical Parameters:

vegetation index

Processing Level:

2

Product Type:

standard, at-launch

Science Team Contact:

A. Huete

Suggested Reading

Huete, A., et al., 1994a.

Huete, A., et al., 1994b.

Liu, H.Q. and A.R. Huete, 1995.
AM-1 MOD 15

Leaf Area Index (LAI) & Fraction of

Photosynthetically Active Radiation (FPAR) –

Moderate Resolution

Data Products Handbook Vol. I Chapter 13: Vegetation Dynamics, Land Cover, & Land Cover Change • 205

Product Description

This Level 4 product includes two parameters, Leaf

Area Index (LAI, Parameter 2680) and Fraction

Photosynthetically Active Radiation (FPAR,

Parameter 5367) which will be retrieved at 500 m

spatial resolution and composited to 1 km on the

8-day compositing period. LAI defines an important

structural property of a plant canopy which is the

number of equivalent layers of leaves relative to a

unit ground area. FPAR measures the proportion of

available radiation in the photosynthetically active

wavelengths (0.4 to 0.7 μm) that a canopy absorbs.

The LAI product will be an LAI value between zero

and 12 of the global gridded database at the

corresponding MVI compositing interval. The FPAR

product will be an FPAR value between 0.0 and 1.0

assigned to each 1-km cell of the global gridded

database at the corresponding MVI compositing

interval.

Research & Applications

LAI and FPAR are biophysical variables which

describe canopy structure and are related to

functional process rates of energy and mass

exchange. Both have been related directly to the

NDVI from AVHRR by theoretical canopy modeling

and field studies. Both LAI and FPAR have been

used extensively as satellite derived parameters for

calculation of surface photosynthesis,

evapotranspiration, and NPP. These products are

essential in calculating terrestrial energy, carbon,

water cycle processes, and biogeochemistry of

vegetation. The LAI product is an input to BiomeBGC (Biogeochemical) models to produce

conversion efficiency coefficients which are

combined with the FPAR product to produce daily

terrestrial PSN (photosynthesis) and annual NPP.

Data Set Evolution

This product is derived directly from the modified

vegetation index (MVI) and ancillary information on

surface characteristics such as land cover type and

background. The algorithm combines these

MOD 15

PRODUCT SUMMARY

Coverage:

global

Spatial/Temporal Characteristics:

1-km/8-day composite

Key Science Applications:

biogeochemical cycle modeling, NPP

estimation

Key Geophysical Parameters:

leaf area index, fraction

photosynthetically absorbed radiation

Processing Level:

4

Product Type:

standard, at-launch

Science Team Contact:

S.W. Running

parameters using a 3-D radiative transfer model to

produce LAI and FPAR values which are stored in a

look-up table. This approach places the

computationally intensive load in a preprocessing

phase and minimizes the routine load on the PGS.

Suggested Reading

Asrar, G., et al., 1992.

Nemani, R.R., et al., 1993a.

Running, S.W., et al., 1994.

Sellers, P.J., 1987.
AM-1 MOD 15

Leaf Area Index (LAI) and Fraction of

Photosynthetically Active Radiation (FPAR) –

Moderate Resolution

206 • Chapter Thirteen: Vegetation Dynamics, Land Cover, & Land Cover Change Data Products Handbook Vol. I

Figure 34. Global Leaf Area Index Estimated with the NDVI-LAI Relationships Derived from the Radiative

Transfer Model and Applied to the Advanced Very High Resolution Radiometer (AVHRR) Pathfinder

NDVI Data Set. Biome-specific relations were applied to the 10-day composite NDVI data and the resulting

LAI values were averaged to obtain monthly LAI. The top image is the color-coded LAI in January obtained by

further averaging over the 9-year period of record. Similarly, the bottom image shows the global LAI distribution

during the month of July. Areas colored white denote either missing data (terminator effect) or where the

algorithm failed.
AM-1 MOD 16

Evapotranspiration &

Surface Resistance

Data Products Handbook Vol. I Chapter 13: Vegetation Dynamics, Land Cover, & Land Cover Change • 207

Product Description

This Level 3 product consists of Evapotranspiration

(ET, Param. 3722) and Surface Resistance(Param.

4335) parameters and have temporal resolution of

8 days at a spatial resolution of 1 km over the land

surface only. The Land Surface Resistance is a postlaunch product to calculate a remotely-sensed epsilon

for NPP and PSN. The spatial resolution of these

products is 1 km.

Research & Applications

These two parameters are essential to global

modeling of climate, water balance and gas traces. In

addition, they are required in estimating

photosynthesis, respiration, and net primary

production. The surface resistance product is a PM-1

(post-launch) research data product intended for realtime implementations. Practical applications of this

product are for wildfire danger monitoring and

crop/range drought monitoring.

Data Set Evolution

The ET product is derived from the product of

surface resistance, vegetation index (MVI),

photosynthetic active radiation (PAR) and the latent

heat of vaporization.

Suggested Reading

Dickinson, R.E., 1987.

Goward, S.N. and A.S. Hope., 1989.

Nemani, R. and S. W. Running, 1989.

Nemani, et al., 1993b.

Running, S., et al., 1994.

Running, S.W., et al., 1989.

MOD 16

PRODUCT SUMMARY

Coverage:

Global

Spatial/Temporal Characteristics:

1 km/8 days

Key Science Applications:

global water balance, net primary

production

Key Geophysical Parameters:

surface resistance, evapotranspiration

Processing Level:

4

Product Type:

PM-1, post-launch

Science Team Contact:

S. Running
AM-1 MOD 17

Vegetation Production &

Net Primary Productivity

208 • Chapter Thirteen: Vegetation Dynamics, Land Cover, & Land Cover Change Data Products Handbook Vol. I

MOD 17

PRODUCT SUMMARY

Coverage:

global

Spatial/Temporal Characteristics:

0.5, 1,10 km/every 8 days and yearly

Key Science Applications:

interannual variability of vegetation

Key Geophysical Parameters:

NPP, photosynthesis, respiration

Processing Level:

4

Product Type:

standard, at-launch

Science Team Contact:

S.W. Running

Product Description

MOD 17 is a Level 4 product consisting of 8-day Net

Photosynthesis (PSN, Parameter 3716) and Net

Primary Production (NPP, Parameter 2703). Annual

NPP is the time integral of the PSN product over a

year.

Research & Applications

This product provides an accurate measure of

terrestrial vegetation growth and production activity.

The theoretical use is to define the seasonally

dynamic flux of terrestrial surface carbon dioxide for

climate modeling. Fluxes will be computed specific

to each vegetation type. The practical utility is to

measure crop yield and forest production and any

other socially significant products of vegetation

growth. As this global NPP product becomes

regularly available, a wide variety of derived

products are expected to be developed making

regionally specific estimates of crop production. The

value of an unbiased, regular source of crop and

forest production estimates for global political and

economic decision making is immense.

Data Set Evolution

The NPP parameter is the yearly integral of the PSN

which is obtained from the product of PAR

(Photosynthetically Active Radiation), FPAR

(Fraction Photosynthetically Active Radiation) and

conversion efficiency coefficients obtained from

other MODIS products and other sensors and

ancillary data. The algorithms for these products are

based on the original logic of Monteith (1972) which

relates PSN and NPP to the amount of Absorbed

Photosynthetically Active Radiation (APAR). The

vegetation indices (MVI) along with climate and the

land cover product are used to estimate APAR.

Suggested Reading

Field, C.B., et al., 1995.

Monteith, J.L., 1972.

Prince, S.D. and S.N. Goward, 1995.

Ruimy, A., et al., 1994.

Running, S.W., 1990.

Running, S.W., et al., 1994.
AM-1 MOD 17

Vegetation Production &

Net Primary Productivity

Data Products Handbook Vol. I Chapter 13: Vegetation Dynamics, Land Cover, & Land Cover Change • 209

Figure 35. Global Net Primary Production During 1987. This dataset is produced using 1 × 1 degree gridded climate data

from 7000 weather stations distributed globally, Mathews land cover classes collapsed into our six classes, biome specific

NDVI-LAI relations used to estimate LAI from NOAA/NASA Pathfinder data and ecosystem simulation model, BIOMEBGC.
AM-1 MOD 34

MODIS Gridded Vegetation Indices:

The Maximum Value Composited NDVI (#2749a)

and the BRDF-Adjusted MVI (#4334a)

210 • Chapter Thirteen: Vegetation Dynamics, Land Cover, & Land Cover Change Data Products Handbook Vol. I

Product Description

The MODIS gridded VIs will provide consistent

spatial and temporal comparisons of global

vegetation conditions which will be used to monitor

the Earth’s photosynthetic vegetation activity for

phenologic, change detection, and biophysical

interpretations. The gridded VIs are 10- and 30-day

temporal and spatial, re-sampled products designed

to provide cloud-free vegetation maps at nominal

resolutions from 250 m to 0.5°. The NDVI

composites consist of cloud-free and atmospherically

corrected pixels at 250 m resolution and 10 day

intervals and are based on the maximum value of

NDVI. The MVI composites are calculated from

cloud-free and atmospherically corrected gridded

surface reflectances standardized to nadir views with

the help of BRDF models. The VIs are also

composited to monthly (30-day) intervals. The 10-

day NDVI composites contain 16 bytes for each grid

cell, which include maximum NDVI value, red and

NIR surface reflectances, solar and sensor zenith

angles, relative azimuth, and quality control

parameters. The 10-day MVI composites contain 12

bytes per pixel and include nadir-adjusted MVI

value, nadir-adjusted red, NIR, and blue surface

reflectances, median solar zenith and azimuth angles,

and quality control parameters.

Research & Applications

VIs are used for global monitoring of vegetation

conditions. The VIs are used as input in the land

cover and land cover change products. They also play

an important role in the derivation of the FPAR, LAI,

and thermal products. The at-launch version will be

fully operational.

Data Set Evolution & Applications

Although a global validation scheme has been

implemented for the VIs, a thorough evaluation and

calibration of these indices will be made at launch.

MOD 34

PRODUCT SUMMARY

Coverage:

global land surface (Level 3)

Spatial/Temporal Characteristics:

(Level 3) 10 day and monthly at 250 m

Key Science Applications:

global vegetation monitoring, global

biogeochemical and hydrologic

modeling, global and regional climate

modeling, land cover characterization

Key Geophysical Parameters:

vegetation index

Processing Level:

3

Product Type:

standard, at-launch

Science Team Contact:

A. Huete, C. Justice

Suggested Reading

Huete, A., et al., 1994a.

Huete, A., et al., 1994b.

Los, S.O., et al., 1994.
AM-1 MOD 34

MODIS Gridded Vegetation Indices:

The Maximum Value Composited NDVI (#2749a)

and the BRDF-Adjusted MVI (#4334a)

Data Products Handbook Vol. I Chapter 13: Vegetation Dynamics, Land Cover, & Land Cover Change • 211

Figure 36. Growing Season Average NDVI of Asia Derived from the Advanced Very High Resolution (AVHRR)

Pathfinder Data Set. Monthly NDVI was calculated as the average of three 10-day composites. The monthly values

were further averaged over the 9-year period of record before Mount Pinotubo eruption (1982-1990) to obtain longterm average monthly NDVI values.
AM-1 MOD 44

Vegetation Cover Conversion

212 • Chapter Thirteen: Vegetation Dynamics, Land Cover, & Land Cover Change Data Products Handbook Vol. I

Product Description

Using MODIS data from the two 250-m bands the

product will show the global distribution of the

occurrence of vegetation cover change. Where there

is sufficient evidence, the type of change will be

labeled (e.g. forest conversion to grassland). The

distribution of these changes will be represented at a

resolution of 250 m and as gridded 10-km

summaries. These will be generated at 3 monthly

intervals. An interannual product is also being

produced which displays the global distribution of

change during the previous year.

Research & Applications

Vegetation cover change is an important driver of

many important biogeochemical, hydrological and

climate processes. It also represents the integrated

response to several biophysical and anthropogenic

impacts. Among the important influences of

vegetation cover change are the following:

• Strongly affects changes in many biophysical

factors such as surface roughness and albedo.

• Has a major effect on changes in sensible heat

flux, since it affects global albedo and surface

roughness which affects atmospheric drag.

• Is of crucial importance for determining the

biogeochemical cycling of carbon, nitrogen,

and other elements at regional to global scales.

• Has a major impact on the runoff

characteristics of catchments through its

effects on evapotranspiration and partitioning

of precipitation into overland flow, interflow

and groundwater accretion.

• Gives a direct insight into ecosystem response

related to climate change and anthropogenic

influences

• Affects biodiversity through direct impacts on

habitat

• Forms an increasingly important set of

information for natural resource managers.

This product will be combined with data obtained

from finer spatial resolution data from sensors

such as Landsat and ASTER to assist the

identification of the types of conversion

occurring. The product also provides information

MOD 44

PRODUCT SUMMARY

Coverage:

global, daytime

Spatial/Temporal Characteristics:

250m, 10km, three monthly, yearly

Key Geophysical Parameters:

vegetation cover change occurrence and

type

Processing Level:

3

Product Type:

at-launch and post-launch

Science Team Contact:

J. Townshend

to assist the acquisition strategy of finer resolution

systems since they help flag areas where

significant changes are likely.

Data Set Evolution

Previous work has shown that data with a resolution

of 1 km and coarser are sufficient for the mapping of

the distribution of vegetation cover and for the

monitoring of those changes in vegetation cover

caused by seasonal to inter-annual climate change.

However such relatively coarse resolution data are

often inadequate to detect changes caused by

anthropogenic factors. Analyses of many types of

vegetation cover change indicates that they are

relatively small in size largely due to the inherently

local nature of anthropogenic vegetation cover

conversions. Consequently a very high proportion of

changes are only detectable at fine spatial resolutions.

For this reason it was decided to use the two 250-m

bands for the identification and mapping of this type

of conversion.

Suggested Reading

Townshend, J.R.G. and C.O. Justice, 1988.

Townshend, J.R.G., et al., 1991.
Chapter Fourteen

Phytoplankton and

Dissolved Organic

Matter
Phytoplankton and Dissolved Organic Matter

214 • Chapter Fourteen: Phytoplankton and Dissolved Organic Matter Data Products Handbook Vol. I

Relationship to Global Change Issues

The ocean stores huge amounts of chemical species,

primarily carbon, which influence the climate

system. The organic carbon dissolved in the oceans

equals the vegetative carbon reservoir on land; the

phytoplankton fixes as much carbon through

photosynthesis as terrestrial vegetation. However,

there are large differences in the turnover rates and

reservoirs of carbon and land and in the ocean. Land

ecosystems have large amounts of structural plant

material with life cycles ranging from one year to

centuries. The oceans, on the other hand, are

dominated by unicellular plants—phytoplankton that

have life cycles of days. Land soil is a large

reservoir of carbon and has a turnover time of many

years to centuries, whereas dissolved organic

material in the ocean is thought to be similar in

amount and turnover time to soil carbon; the carbon

cycle of marine bottom sediments has a turnover

time of millions of years.

Scientists have observed that over the last century,

with the dramatic increase of biomass burning and

fossil fuel burning, there has been a corresponding

increase in carbon dioxide released into the

atmosphere. Much of this carbon dioxide eventually

enters the oceans and is used by phytoplankton for

photosynthesis. The amount of carbon dioxide

consumed by the phytoplankton depends heavily on

ocean circulation which supplies nutrients to the

upper layers of the ocean where sunlight is

abundant. Some fraction of carbon produced by the

phytoplankton sinks to the ocean floor—a long-term

sink for atmospheric carbon dioxide. The fraction is

not well known and is a source of a large uncertainty

in the global carbon budget. Unraveling this

complex system—called the “biological pump”—is

difficult. Scientists wish to understand the coupling

of physical and biological aspects of the carbon

system in the ocean in order to estimate how

changes in the oceans will affect the global carbon

dioxide system as well as the population of

phytoplankton, which is an essential link in the

Earth’s marine biosphere. Hopefully, these products

will help scientists answer important questions, such

as: Is productivity in the ocean changing, and how

do changes relate to our understanding of the global

carbon cycle? How will the marine biosphere

respond to global changes, both natural and

anthropogenic?

Product Overview

The ocean phytoplankton products support the

estimation of the global ocean primary productivity

product as well as provide inputs needed for ocean

biophysical studies that will enable improvements in

the ocean process algorithms. The primary

productive provides the phytoplankton biomass

which governs the fixing of carbon and which

influences CO2 in the atmosphere and ultimately

global climate.

The Pigment Concentration product (MOD 19) and

three associated products, Suspended Solids

Concentration (MOD 23), Organic Matter

Concentration (MOD 24) and Ocean Water

Attenuation (MOD 26), are used by the ocean

scientific community to support development and

validation of the algorithms for primary

productivity. Similarly, the Coccolith Concentration

(MOD 25) product provides information on

phytoplankton products that deposit carbon on the

ocean floor in the form of calcium carbonate,

accounting for about 75 percent of the deposition of

carbon on the sea floor. In the same way, the

Phycoerythrin Concentration product (MOD 31)

provides information about a key phytoplankton

pigment group to enable study of the global

phytoplankton species variability. Extensive

experimental evidence has been obtained indicating

that water-leaving radiance includes backscattering

and absorption effects of phycoerythrin pigments,

and that these phenomena need to be measured and

studied to enable more accurate estimation of the

parameters used in the primary production

algorithm.

The primary phytoplankton indicator product used

as input to the primary ocean productivity algorithm

is Chlorophyll a Concentration (MOD 21). This

product uses the Water-leaving Radiances and

Match-up Databases to calibrate semi-analytical

models using the remote-sensing reflectances in the

MODIS oceans bands. The second phytoplankton

indicator used as input to the productivity algorithm

is the Chlorophyll Fluorescence product (MOD 20),

which provides another measure of chlorophyll
Phytoplankton and Dissolved Organic Matter

Data Products Handbook Vol. I Chapter Fourteen: Phytoplankton and Dissolved Organic Matter • 215

concentration and hence phytoplankton

concentration.

The MISR instrument also provides data on

phytoplankton pigment concentration. Product

information is described in MISR Aerosol and

Surface Data (MIS 05, MIS 09) on pages 104-105 of

this document.

The ocean production product provides the

analogous measure of the growth of

photosynthetically active material in the ocean as

NPP does for land. In the case of the ocean,

dissolved inorganic carbon is fixed into organic

matter through phytoplankton photosynthesis. The

algorithm estimates phytoplankton production from

chlorophyll data by linear regression using databases

obtained from ship-based sea water measurements

of phytoplankton, chlorophyll, and other

constituents from numerous sites and satellite data

from the Coastal Zone Color Scanner (CZCS) ocean

color sensor. These data, plus water-leaving

radiance and sea-surface temperature from MODIS,

are used by the chlorophyll and production

algorithms. A daily estimate of production for each

1-km square ocean pixel, the amount of carbon

generated in a column of sea water in milligrams per

square meter per day is generated. An annual total

production value is aggregated from the daily values

and is given in gigatons of carbon per year for the

global ocean. This is a running total which is

updated each day, and for the Level 3 product each

week.

Product Interdependency and Continuity

with Heritage Data

Specific product dependencies for the ocean

products include the water-leaving radiance and

matchup table products, which provide remote

sensing and in situ sea surface radiance

measurements. Some products require additional

parameters such as Chlorophyll a Case I for

Fluorescence and downwelling sea surface radiance

for Chlorophyll a. The correlated radiance and

directional reflectance products from MISR and

ASTER are used in the baseline radiance and image

products such as MOD 09 surface reflectance and

are not inputs to the product discussed here. The

MODIS pigment algorithms were developed from

existing CZCS pigment algorithms to provide a

direct link to the series of measurements made by

CZCS over the past 19 years.

Although there is already a long-term data set from

CZCS, it is important that other simultaneous data

sets be taken to provide further global context.

Extensive testing of the algorithms will be done with

SeaWiFS ocean color data, expected to be available

in 1996.

Suggested Reading

Asrar, G. and J. Dozier, 1994.

Goyet, C. and P.G. Brewer, 1993.

Running, S.W., et al., 1994.

Vorosmarty, C., et al., 1991.

Yoder, J.A., et al., 1993.
AM-1 MOD 19, MOD 23, MOD 24, MOD 26

Pigment Concentration

216 • Chapter Fourteen: Phytoplankton and Dissolved Organic Matter Data Products Handbook Vol. I

Product Description

This set of products provides particle concentrations

in Case 1 sea water. Product MOD 19 is total

Pigment Concentration (Parameter 2591); Product

MOD 23 is Suspended-Solids (Parameter 3085);

Product MOD 24 is Organic Matter Concentration in

two parameters, Particulate (2608) and Dissolved

(2580); and Product 26 gives Ocean Water

Attenuation Coefficient at two wavelengths, 490 nm

(Parameter 3199) and 530 nm (Parameter 3206). The

products are available at Level 2 daily and at Level 3

daily and weekly. Both levels are produced at 1 km

and 20 km.

Research & Applications

This set of ocean substance concentrations is needed

for input to the ocean productivity algorithm which is

a key element in global biogeochemical models and

ultimately global climate models. The pigment

parameter is the sum of the chlorophyll a and

phaeopigment concentration in Case 1 waters. Case 1

waters have optical properties which are dominated

by chlorophyll and associated covarying detrital

pigments. (Case 2 waters contain substances which

affect optical properties which may not covary with

chlorophyll such as gelbstoff, suspended sediments,

cocolithophores, detritus, and bacteria.) The

suspended-solids parameter is a measure of ocean

suspended sediments which is used in the analysis of

complex bio-optical properties of coastal and

estuarine regions/environments and helps to map the

extent of terrestrial changes. The organic matter

concentration relates to the composite of carbon and

nitrogen substances. The ocean water attenuation

coefficient is derived using MODIS bands 10 and 11

and describes penetration of sunlight in the sea.

Data Set Evolution

The algorithm is based primarily on methods and

algorithms developed for the CZCS program

described by Gordon and Clark (1980) and refined

and adapted to the MODIS bands. The recasting of

the CZCS forms of the phytoplankton pigment

algorithms in terms which are more representative for

MODIS has resulted in minor changes. Particularly

significant is that the multiple band ratios will

MOD 19, MOD 23, MOD 24, MOD 26

PRODUCT SUMMARY

Coverage:

global ocean surface, clear-sky only

Spatial/Temporal Characteristics:

1 km/daily, weekly

Key Science Applications:

ocean productivity, biogeochemical

models

Key Geophysical Parameters:

total ocean pigment, suspended solids,

organic matter concentration, attenuation

coefficient

Processing Level:

2, 3

Product Type:

standard, at-launch

Science Team Contact:

D. Clark

provide a robustness not possible with the CZCS’s

limited spectral coverage.

Suggested Reading

Gordon, H.R. and D.K. Clark, 1980.

Gordon, H.R., et al., 1980.

Gordon, H.R., and A.Y. Morel, 1983.

Lorenzen, C.J., and S.W. Jeffrey, 1980.

Smith, R.C. and K.S. Baker, 1977.
AM-1 MOD 19, MOD 23, MOD 24, MOD 26

Pigment Concentration

Data Products Handbook Vol. I Chapter Fourteen: Phytoplankton and Dissolved Organic Matter • 217

Figure 37. East Coast Ocean Color Image. A daily Coastal Zone Color Scanner product for the northeastern

coast of the United States reveals the high pigment concentrations along the coast and the influence of the

Gulf Stream. Phytoplankton concentrations, and additional ocean carbon system parameters from MODIS,

will be much more accurate than was possible with the CZCS because of improvements in spectral bands,

calibration, and algorithms. NASA, GSFC
AM-1 MOD 19, MOD 23, MOD 24, MOD 26

Pigment Concentration

218 • Chapter Fourteen: Phytoplankton and Dissolved Organic Matter Data Products Handbook Vol. I

Product Description

This Level 2 product contains several parameters

describing ocean chlorophyll fluorescence properties.

Fluorescence line height (Parameter 2575) is a

relative measure of the amount of radiance leaving

the sea surface at the fluorescence wavelength of

683 nm. Parameter 3211 is Fluorescence Efficiency

which provides a relative measure of the absorption

of PAR and its emission as chlorophyll fluorescence.

The third is Fluorescence Line Curvature (Parameter

2573). The spatial resolution will be 1 km for

chlorophyll levels greater than 1.5 mg/m3 and

5 × 5 km for values less than 1.5. The Level 2

product is produced daily and Level 3 is gridded and

produced daily and weekly.

Research & Applications

Solar stimulated chlorophyll fluorescence is a

measure of the current photophysology of

phytoplankton, in contrast to the biomass estimate

provided by chlorophyll. The product quantifies the

level of photosynthesis by phytoplankton in the

ocean. Historically, the coupling between

fluorescence and chlorophyll has been studied

extensively and recent research has focused on the

use of Sun-stimulated fluorescence to estimate

primary productivity (Kiefer and Reynolds, 1992).

Basic fluorometric measurements are made using an

instrument described by Holm-Hansen et al. (1965)

which uses blue light stimulation and this method has

been used unchanged for 30 years. Gower (1990) was

among the first to attempt using Sun-stimulated

radiance at 683 nm to estimate chlorophyll

concentrations from aircraft and satellites.

Data Set Evolution

Inputs to the algorithm are Chlorophyll

Concentration (MOD 19), Absorbed Radiation by

Phytoplankton (MOD 22), and Water-Leaving

Radiance (MOD 18). Water-leaving radiance for

MODIS bands 13 (667 nm), 14 (678 nm) and 15

(748 nm) are used in the algorithm. The algorithm is

applied to the daily input standard product datasets

and is remapped into standard Level 3 grids. The

validation approach will be to compare the

fluorescence line height result with other MODIS

MOD 20

PRODUCT SUMMARY

Coverage:

global ocean surface, clear-sky only

Spatial/Temporal Characteristics:

1 km for chlorophyll levels greater than

2.0 mg/m3/daily, weekly

Key Science Applications:

ocean chlorophyll, ocean productivity

Key Geophysical Parameters:

chlorophyll fluorescence

Processing Level:

2

Product Type:

standard, at-launch

Science Team Contact:

M. Abbott

data products (e.g. Chlorophyll a, comparison with

surface measurements and comparison of MODIS

fluorescence products with other satellite-based

estimates of the same products). The products are

produced only for non-cloud, glint-free ocean pixels

during daylight hours.

Suggested Reading

Abbott, M.R., et al., 1982.

Chamberlin, W.S. and J. Marra, 1992.

Gower, F.J.R. and G.A. Borstad, 1990.

Holm-Hansen, O., et al., 1965.

Kiefer, D.A. and R.A. Reynolds, 1992.

Topliss, B.J., and T. Platt, 1986.
AM-1 MOD 21, MOD 36

Chlorophyll a Pigment Concentration

Data Products Handbook Vol. I Chapter Fourteen: Phytoplankton and Dissolved Organic Matter • 219

MOD 21, MOD 36

PRODUCT SUMMARY

Coverage:

global ocean surface, clear-sky only

Spatial/Temporal Characteristics:

1 km/daily, weekly

Key Science Applications:

ocean productivity, bio-optical properties

Key Geophysical Parameters:

Case 1 and 2 chlorophyll a concentration,

absorption coefficients

Processing Level:

2, 3

Product Type:

standard, at-launch

Science Team Contact:

K. Carder

Figure 38. Satellite ocean color imagery, in addition to allowing the

concentration of single-celled plants to be remotely measured, permits the

detection and identification of certain algal blooms. The above images,

taken by the Coastal Zone Color Scanner sensors, are of the Gulf of Maine

and Gulf of St. Lawrence. The white to light grey waters in both regions are

blooms of a single type of algae (called coccolithophores) which affect

regional climate and fisheries.

Product Description

This is a Level 2 and 3 product which contains ocean

chlorophyll a pigment concentration for Case 1

waters (Parameter 2571) and Case 2 waters

(Parameter 2569) at 1-km resolution. It is produced

daily at Level 2 and 3 and weekly at Level 3.

Absorption parameters generated by the chlorophyll

algorithm are also provided as interim product

MOD 36 Total Absorption (Parameter 3206) which

includes absorption due to water, phytoplankton,

detritus and gelbstoff, and gelbstoff absorption only

(Parameter 5362). Valid data exists only for ocean

cloud-free pixels and the weekly composite will be

an average of cloud-free acquisitions for each ocean

pixel.

Research & Applications

Chlorophyll a concentration is a key input to the

primary ocean production product (MOD 27) and is

used to trace oceanographic currents, jets, and

plumes. The product provides the concentration of

Case 1 chlorophyll in sea water (water that has

optical properties which are dominated by

chlorophyll and associated covarying detrital

pigments) and in Case 2 waters (which are waters that contain substances which affect optical

properties but do not covary with

chlorophyll, such as gelbstoff

(marine chromomorphic dissolved

organic matter [CDOM] substance

which absorbs at 400 nm),

suspended sediments,

cocolithophores, detritus and

bacteria). The algorithm derives

from extensive research using

CZCS data in which good

performance was obtained for

Case 1 waters and has evolved to

perform successfully for the Case 2

waters for the MODIS algorithm.

The 1-km resolution and nearly

daily coverage will allow the

observation of mesoscale

oceanographic features in coastal

and estuarine environments which

are of increasing importance in

marine science studies.
AM-1 MOD 21, MOD 36

Chlorophyll a Pigment Concentration

220 • Chapter Fourteen: Phytoplankton and Dissolved Organic Matter Data Products Handbook Vol. I

Data Set Evolution

The product algorithm is based on a semi-analytical,

bio-optical model of remote sensing reflectance

which uses Water Leaving Radiance (MOD 18), PAR

(MOD 22), and Absorption Coefficient (MOD 36).

The model is inverted to obtain the absorption

coefficient due to phytoplankton at 675 nm and

chlorophyll a concentration is derived from this

coefficient. The algorithm will be thoroughly tested

during the SeaWiFS project and post launch

validation will be conducted using data from

instrumented collection cruises through ocean test

sites including the nine used for algorithm

development. Also, hyperspectral data from will be

used to simulate the 10 nm bands and produce

comparison results.

Suggested Reading

Austin, R.W., 1974.

Carder, K.L., et al., 1986.

Carder, K.L., et al., 1991.

Holm-Hansen, O. and B. Riemann, 1978.

Lee, Z.P., et al., 1995.

Smith, R.C. and K.S. Baker, 1982.
AM-1 MOD 25

Coccolith Concentration

Data Products Handbook Vol. I Chapter Fourteen: Phytoplankton and Dissolved Organic Matter • 221

Product Description

This Level 2 and 3 product provides five parameters

describing the concentration of coccoliths in sea

water. Parameter 2577 is the detached coccolith

concentration in number/m3 and Parameter 5355 is

the estimated calcite concentration due to the

coccoliths in mg-CaCO3/m3 . The pigment

concentration in the coccolithophore biomass is

Parameter 5356; Parameter 5357 is a descriptor for

the particular look-up table used, and Parameter 5358

is a quality measure. The product is produced a 1 km

and 20-km spatial resolution daily for Level 2 and 3

and weekly at Level 3.

Research & Applications

Coccolithophores are small marine phytoplankton

which form external calcium carbonate (CaCO3)

scales having diameters of a few μm and a thickness

of 250 to 750 nm called coccoliths. Coccolithophores

are the largest source of calcium carbonate on Earth.

Thus, coccolith production is an important part of the

biogenic carbon cycle. The observed characteristics

of coccolithophores, including their ubiquitous

nature, possible role in climate, and intense scattering

property, make a global-scale study of their

distribution an important application for MODIS

imagery. Specifically, it is important to estimate the

rate at which CaCO3 is formed by phytoplankton and

to look for long-term changes in that rate.

Data Set Evolution

The algorithm for extracting the detached coccolith

concentration from surface waters is based on the

semianalytic model of ocean color of Gordon (1988).

The model relates the normalized water-leaving

radiance to the absorption and scattering properties of

the constituents of the water using radiative transfer

theory. The absorption and scattering properties are

then related to the constituent concentrations through

statistical analysis of direct measurements. The

model is validated by comparison with a set of waterleaving radiances independent of the measurements

used to establish the statistical relationships between

constituents and optical properties.

MOD 25

PRODUCT SUMMARY

Coverage:

global ocean surface, clear-sky only

Spatial/Temporal Characteristics:

1 km, 20 km/daily, weekly

Key Science Applications:

input to global biogeochemical cycle

models

Key Geophysical Parameters:

coccolith and calcite concentration,

pigment concentration in coccolithophore

blooms

Processing Level:

2, 3

Product Type:

standard, at-launch

Science Team Contact:

H.R. Gordon, W. Balch

Suggested Reading

Balch, W.M., et al., 1991.

Gordon, H.R., et al., 1988.

Groom, S.B. and P.M. Holligan, 1987.

Holligan, P.M., et al., 1983.

Sarmiento, J.L., et al., 1988.

Sikes, S. and V. Fabry, 1994.
AM-1 MOD 31

Phycoerythrin Concentration

222 • Chapter Fourteen: Phytoplankton and Dissolved Organic Matter Data Products Handbook Vol. I

Product Description

This product consists of three parameters which give

the concentration of one of the major algal pigment

groups in ocean water: Phycoerythrobilin

Concentration (Parameter 3320), Phycourobilin

Concentration (Parameter 5360), and Constituent

Inherent Optical Properties (CDOM absorption,

chlorophyllous absorption, and particulate

backscatter, Parameter 5361). These quantities are

provided at Level 2 at 1 km spatial resolution daily

and at Level 3 daily and weekly at 1 km. The product

is valid only for clear-sky ocean pixels.

Phycoerythrin is one of three major algal pigment

groups found in marine phytoplankton and bacteria

(Bidigare, 1990). The phycoerythrins are further

subdivided into phycourobilin-rich (PUB) and

phycoerythrobilin-rich (PEB) phycoerythrins. This

algorithm retrieves both PUB and PEB rich cases.

Phycoerythrin is a chlorophyll accessory pigment and

serves to receive photosynthetically usable light in

the 480 - 505 nm and 540 - 560 nm ranges. It is used

to infer the global extent of phycoerythrin-bearing

phytoplankton such as cyanobacteria which are

nitrogen-fixing and thus provide information on the

nitrogen cycle. Used in conjunction with

phytoplankton chlorophyllous pigment, the apparent

species diversity of the oceans can be inferred.

Research & Applications

One of the intended uses of the phycoerythrin data

product is to allow scientific investigators to study

the global distribution(s) of the phycoerythrin

pigment, and in so doing allow definition of the

diversity of phycoerythrin-bearing species such as

cyanobacteria. When used in conjunction with

chlorophyll distribution, phycoerythrin allows global

phytoplankton species variability studies.

Data Set Evolution

The phycoerythrin retrieval algorithm requires waterleaving radiances which are generated from the

incident solar irradiance, the total backscatter and the

total absorption of sea water. The PUB and PEB

parameters are retrieved by a sequential-convergentiteration method (Gordon, 1988) which uses 5

MOD 31

PRODUCT SUMMARY

Coverage:

global ocean surface, clear-sky only

Spatial/Temporal Characteristics:

1 km/daily, weekly

Key Science Applications:

global phytoplankton species studies,

ocean productivity models

Key Geophysical Parameters:

phycoerythrobilin-rich (PEB) and

phycouroblin-rich (PUB) phycoerythrins

Processing Level:

2, 3

Product Type:

research, at-launch

Science Team Contact:

F. Hoge

independent bands. MODIS Band 10 (488 nm) and

Band 12 (551 nm) correspond to the peaks of the

PUB and PEB phycoerythrins. The major assumption

for the algorithm is that the pigment specific

absorption coefficient spectral model used is

applicable for the oceanic province where the

satellite image was acquired. The algorithm will be

validated by ship and airborne laser-induced and

water Raman normalized fluorescence

measurements.

Suggested Reading

Bidigare, R.R., et al., 1990.

Culver, M.E. and M.J. Perry, 1994.

Hoge, F.E. and R.N. Swift, 1986.

Hoge, F.E. and R.N. Swift, 1990.
AM-1 MOD 27

Ocean Primary Productivity

Data Products Handbook Vol. I Chapter Fourteen: Phytoplankton and Dissolved Organic Matter • 223

MOD 27

PRODUCT SUMMARY

Coverage:

global ocean surface, clear-sky only

Spatial/Temporal Characteristics:

1 km/daily, weekly

Key Science Applications:

ocean productivity, biogeochemical

models

Key Geophysical Parameters:

annual and daily ocean productivity

Processing Level:

2, 3

Product Type:

standard, at-launch

Science Team Contact:

W. Esaias, M. Abbott

Product Description

This product provides an estimate of the Ocean

Primary Productivity daily (Parameter 2602) and on

an annual basis (Parameter 2606). The Level 2

product is produced daily at 1 km for local and

regional areas and at 20 km for the annual

productivity as a running, annual average. The Level

3 product will be produced daily at 1 km and weekly

at 1 km and at 20 km for the global annual

productivity.

Research & Applications

The objective of the product is to quantify the

magnitude and inter-annual variability (for decadal

trends) in the oceanic primary productivity and

phytoplankton carbon fixation. Primary productivity

is the time rate of change of phytoplankton biomass,

and with allowance for excreted soluble carbon

compounds, reflects the daily integrated

photosynthesis within the water column. The

integral of the daily values over the year is the

annual primary productivity (Iverson and Esaias,

1994).The annual productivity product will be used

for global and regional scale studies of interannual

variability of ocean productivity, for comparisons

with annual summations of daily analytic

algorithms, and for comparison with global

biogeochemical models.

Data Set Evolution

Ocean primary productivity algorithms fall into two

general classes, termed empirical and analytic

algorithms. The empirical approach is based on

simple correlation between time averaged in situ

estimates of productivity and satellite derived

estimates of surface chlorophyll concentration. The

analytic approach is based on models of the general

photosynthetic response of the algal biomass as a

function of major environmental variables such as

light, temperature, and nutrient concentration. The

overall methodologies differ significantly in the way

various parameters are estimated and in the way they

are assigned spatially and temporally across ocean

basins. The approach taken for the MODIS

algorithm is to begin implementation of an annual,

global, empirical algorithm for at-launch product

generation, while pursuing a vigorous research

program within the SeaWiFS Science Team, to

develop a consensus analytic algorithm for daily

global productivity. Cloudiness prevents deriving

chlorophyll a concentrations over about 60 percent

of the ocean on a daily basis excluding that already

lost due to high Sun glint. Chlorophyll a

concentrations derived from other sensors such as

EOS Color and MODIS PM will be used to increase

sampling frequency since these plankton processes

vary rapidly over time and space.

Suggested Reading

Eppley, R.W., et al., 1985.

Fitzwater, S.E., et al., 1982.

Iverson, R.L., and W.E. Esaias, 1994.

Morel, A., and J.M. Andre, 1991.

Platt, T.C., et al., 1991.
AM-1 MOD27

Ocean Primary Productivity

224 • Chapter Fourteen: Phytoplankton and Dissolved Organic Matter Data Products Handbook Vol. I

Figure 39. Global Ocean Phytoplankton Biomass. A pre-MODIS, global ocean color product from the Coastal Zone

Color Scanner shows the algal pigment concentrations within the upper layers of the ocean and indicates the biomass

and productivity distribution within the oceans. While this is an annual product, MODIS will provide comparable

global coverage on a weekly to monthly basis to enable scientists to study large-scale changes in marine ecosystems

over weeks to decades. NASA, GSFC
Chapter Fifteen

Snow and Sea

Ice Cover
Snow and Sea Ice Cover

226 • Chapter Fifteen: Snow and Sea Ice Cover Data Products Handbook Vol. I

Relationship to Global Change Issues

Observing and predicting changes in elements of

the cryosphere (glaciers, frozen ground

[permafrost], snow cover, and floating ice [sea,

lake, and river]) are important to understanding

global change because the cryosphere is a sensitive

component of the geosphere to global climate

warming. Snow and ice cover dramatically

influence surface albedo, hydrologic properties, and

regulation of ecosystem biological activity.

MODIS data will be used to monitor the dynamics

of large-area (greater than 10 square kilometers)

snow and ice cover and, on a daily and weekly

basis, report the maximum area covered by each.

Information about snow and ice cover is important

for a number of agricultural, industrial, social, and

environmental reasons. For example, snow cover

data are needed to predict water supply, to plan for

agricultural and industrial developments, to

anticipate flooding, to forecast crop yield, and to

estimate freeze damage.

The amount and depth of snow cover affect climate

on a regional and global scale. When snow is

deposited on a land surface, it typically increases

the degree to which the surface reflects incoming

solar energy. In so doing, snow affects the Earth’s

radiation balance (the balance between energy

entering and leaving the Earth’s climate system)

and, in turn, its climate. By gaining more accurate

information about the properties and effects of

snow cover, scientists hope to develop more

accurate models to help them understand and

predict the role of snow in the Earth’s climate.

Product Overview

The Snow Cover products (MOD 10, MOD 33) are

based on the SNOMAP algorithm which will

identify snow by its unique reflectance, absorption,

and emittance properties. SNOMAP will also map

ice on large inland lakes. The algorithm consists of

a series of criterion tests that identifies snow in each

pixel of a MODIS image. Using SNOMAP and

ICEMAP, MODLAND will map the spatial and

temporal variability in reflected and emitted

radiances observed by MODIS over snow- and icecovered areas. These maps will help us to

understand how the spatial and temporal variability

in snow cover, lake ice cover, and sea ice cover

relate to surface water balance components over

continental-scale drainage basins.

The Sea Ice Cover product (MOD 29, MOD 42)

maps daily and weekly sea ice cover. The algorithm

ICEMAP is similar in structure to SNOMAP for

mapping sea ice.

The Assimilated Snow Depth product (DAS 10) is a

Level 4 product consisting of 3-hourly estimates of

snow depth reported in liquid-hourly equivalent.

The GEOS DAS calculates snow depth using its

estimates of temperature and precipitation along

with melting rates based on land surface

temperatures.

Product Interdependency and Continuity

with Heritage Data

NASA has had missions that collected ice data for

many years, and snow and ice is among the many

variables included in NASA’s Pathfinder Program,

which is providing research-quality data sets on

global change from past and current satellite

missions.

The Electrically Scanning Microwave Radiometer

(ESMR) on the Nimbus-5 satellite provided the first

detailed data sets of sea-ice distributions for cloud

and cloud-free conditions. The data from ESMR

and its successor, the Scanning Multichannel

Microwave Radiometer (SMMR), launched on

Nimbus 7, have resulted in three major atlases,

giving the history of the Arctic and Antarctic seaice covers from the years 1973-76 and 1978-87.

The Defense Meteorological Satellite Program

(DMSP) has flown a Special Sensor Microwave/

Imager (SSM/I) since 1987. This instrument is

similar to SMMR, and its data are being analyzed to

determine snow cover and sea-ice concentrations.

Other satellite data used in the study of snow and

ice include high-resolution images from the Landsat

series of satellites, and radar altimetry data from

NASA’s Seasat and the Department of Defense

Geosat satellites that have been used to determine

and map the elevation contours of the southern half

of the Greenland ice sheet.
Snow and Sea Ice Cover

Data Products Handbook Vol. I Chapter Fifteen: Snow and Sea Ice Cover • 227

Snow Clouds Other

Figure 40. Result of applying the SNOMAP algorithm (Hall, et al., 1995) to a Landsat TM scene of

northern Montana including Glacier National Park. The TM scene was acquired on March 14, 1991

(scene i.d. #5256917454). (Note: Snow and non-snow features were classified automatically by the

SNOMAP algorithm, but cloud features were classified by supervised classification.)

Suggested Reading

Chang, A.T.C., et al., 1987.

Foster, J.L. and A.T.C. Chang, 1993.

Hall, D.K., et al., 1995.
AM-1 MOD 10, MOD 33

Snow Cover

228 • Chapter Fifteen: Snow and Sea Ice Cover Data Products Handbook Vol. I

Product Description

Global snow cover (including ice on large, inland

water bodies) will be mapped daily and weekly over

the Earth’s land surfaces at 500-m resolution using

the SNOMAP algorithm (Hall, et al., 1995). A

global, daily snow cover map will be an at-launch

MODIS product. Snow cover, with its high albedo, is

a key parameter of the global energy balance,

reflecting much of the incident solar radiation back to

space. Snow cover of the Northern Hemisphere is

currently mapped by NOAA on a weekly basis, but

the accuracy of the maps has been difficult to

determine, in part, because a variety of techniques

has been used to map snow cover over the almost 30-

year period during which the maps have been

produced.

Additionally, snow/cloud discrimination is difficult

and often impossible using the current NOAA

sensors because of the available bands. MODIS

bands will allow automatic snow/cloud

discrimination, and, in conjunction with the cloud

mask to be produced by another MODIS investigator,

will allow automated snow mapping. The MODIS

weekly snow-cover product is designed to provide

snow-cover persistence statistics for each pixel so

that users can determine how long the snow has been

on the ground during a compositing period selected

by the user. This is especially important during the

transition seasons.

Research & Applications

Large, inland water bodies such as the Great Lakes,

are often ice-covered during the winter months, and

navigation during part of the winter is a significant

problem. NOAA data are currently used to map ice

cover on the Great Lakes, but snow/ice and cloud

discrimination is a problem. Additionally, ice cover

on lakes can be an important climate indicator, as the

dates of freeze-up and break-up are influenced by

meteorological conditions. A trend toward earlier

break-up, for example, could signify a warming as

has been observed in some areas (e.g., Comb, 1990).

Thus, it is important to measure ice conditions on

large lakes over an extended period of time in order

to detect trends as well as for operational uses over

the short term.

MOD 10, MOD 33

PRODUCT SUMMARY

Coverage:

global, daytime

Spatial/Temporal Characteristics:

500 m × 500 m daily (Level 2 MOD 10)

and 500 m × 500 m weekly (Level 3

mapped-MOD 33).

Key Geophysical Parameters:

snow cover, lake ice cover

Processing Level:

2, 3

Product Type:

MOD 10 - standard, at-launch

MOD 33 - standard, at launch

Science Team Contact:

D. Hall

Data Set Evolution

SNOMAP has a considerable heritage. It is based on

the normalized difference of a visible and a

shortwave-infrared band. This technique has been

used, since at least 1978, to map snow from aircraft

(Kyle, et al., 1978). Since the mid-1980s, it has been

used to map snow using Landsat data on the

drainage-basin scale (Dozier, 1989).

Global snow cover has also been mapped using

passive-microwave data at a resolution of about

50 km2 (Foster and Chang, 1993). While these data

allow snow mapping through cloud cover, passivemicrowave data do not provide a resolution that is

suitable for detailed snow mapping. Basin-scale

mapping is required for hydrological modeling, and,

in particular for snowmelt-runoff calculations which

are essential for hydroelectric-power generation and

for water-supply forecasts in the western U.S. and in
AM-1 MOD10, MOD33

Snow Cover

Data Products Handbook Vol. I Chapter Fifteen: Snow and Sea Ice Cover • 229

many other parts of the world. The expected use of

passive-microwave and MODIS snow-cover data

together should yield information on snow extent and

snow-water equivalent (Salomonson, et al., 1995).

Snow-cover data are also needed for general

circulation modeling.

Suggested Reading

Comb, D.G., 1990.

Dozier, J., 1989.

Foster, J.L. and A.T.C. Chang, 1993.

Hall. D.K., et al., 1995.

Kyle, H.L., et al., 1978.

Salomonson, V.V., et al., 1995.
AM-1 MOD 29, MOD 42

Sea-Ice Cover

230 • Chapter Fifteen: Snow and Sea Ice Cover Data Products Handbook Vol. I

Product Description

Global sea-ice cover will be mapped daily and every

10 days at 1-km resolution using an algorithm called

ICEMAP. Global sea-ice cover will be an at-launch

MODIS product. Sea ice is present over

approximately 13 percent of the Earth’s ocean

surface (Weeks, 1981). Snow-covered sea ice, with

its high albedo, is a key parameter of the global

energy balance, reflecting much of the incident solar

radiation back to space. Additionally, the sea-ice

cover is an insulating layer between the ocean and

atmosphere; heat loss through open water is up to

100 times greater than heat loss through thick ice. As

a consequence, leads and polynyas (linear and

nonlinear openings in the sea ice, respectively) are

significant to the energy budget of the ice-covered

ocean and to local and regional climatology. Such

open water areas and areas of reduced ice

concentration are also important for shipping in icecovered seas.

Research & Applications

Sea ice cover is currently mapped by NOAA visible

and near-infrared sensors, and by microwave sensors,

both passive and active. Using the NOAA sensors,

snow/cloud discrimination is a major hindrance in

identifying sea ice. The passive-microwave sensors

map sea ice through cloud cover, but at a resolution

of only about 30 km2. Active microwave sensors

have good spatial resolution, up to about 25 m2, but

currently do not map sea ice cover globally on a daily

basis. MODIS will be able to map sea ice globally

but with the significant limitation that cloud cover

will obscure the view of the surface for much of the

time. Together, the MODIS and microwave sensors

will provide important information on the presence

and concentration of sea ice. MODIS data, when

available, will provide the higher resolution view of

the sea ice that is not obtainable using passivemicrowave data.

Data Set Evolution

ICEMAP has a considerable heritage. It is based on

the normalized difference of a visible and a

shortwave infrared band. This technique has been

used to map snow from aircraft and satellites (Kyle,

MOD 29, MOD 42

PRODUCT SUMMARY

Coverage:

global, daytime over nonequatorial ocean

Spatial/Temporal Characteristics:

1 km × 1 km daily (Level 2 MOD 29)

and 1 km × 1 km weekly (Level 3

mapped MOD 42).

Key Geophysical Parameters:

sea ice extent

Processing Level:

2, 3

Product Type:

standard, at-launch

Science Team Contact:

D. Hall

et al., 1978; Dozier, 1984 and Hall, et al., 1995) and

has been shown to be effective for mapping sea ice as

well. The 10-day composited sea ice cover product is

designed to provide sea ice-cover persistence

statistics for each pixel so that users can determine

how long sea ice has been present during the

previous 10 days in any given location. A cloud mask

will be provided by another MODIS investigator.

Suggested Reading

Dozier, J., 1984.

Hall. D.K., et al., 1995.

Kyle, H.L., et al., 1978.

Weeks, W.F., 1981.
DAS 10

Assimilated Snow Depth

Data Products Handbook Vol. I Chapter Fifteen: Snow and Sea Ice Cover • 231

Product Description

This Level 4 product consists of 3-hourly estimates

of snow depth (reported in liquid water equivalent)

produced by the GEOS DAS. It is based on the

assimilation of pressure, temperature, wind, and

moisture observations from conventional

meteorological sources, both in situ and remotely

sensed, along with other TRMM and AM-1 data

such as TMI and MODIS. The GEOS DAS

calculates snow depth using its estimates of

temperature and precipitation along with melting

rates based on land surface temperature.

Research & Application

Snow melt plays a major role in the Earth’s

hydrologic cycle. Accurate estimates of the water

equivalent snow depth are needed to assess global

change. Many regions of the world rely on snow

melt as their major source of water.

Data Set Evolution

Satellite instruments such as MODIS can measure

snow cover but not snow depth. Assimilated snow

depth will provide added information to complement

MODIS snow cover estimates.

Suggested Reading

Bloom, S.C., et al., 1995.

Pfaendtner, J., et al., 1995.

Schubert, S.D., et al., 1995.

Schubert, S.D., et al., 1993.

DAS 10

PRODUCT SUMMARY

Coverage:

global

Spatial/Temporal Characteristics:

2° × 2.5° lat-lon grid, daily at 00, 03, 06,

09, 12, 15, 18, and 21 UTC

Key Geophysical Parameters:

snow depth

Processing Level:

4

Product Type:

standard, post-launch

Science Team Contact:

R. Rood, A. Molod
Appendix A –

Points of Contact
Points of Contact

234 • Appendix A: Points of Contact Data Products Handbook Vol. I

Abbott, Mark

College of Oceanography & Atmospheric Sciences

Oceanography Administration Bldg. 104

Oregon State University

Corvallis, OR 97331-5503

Phone: 503-737-4045

Email: mabbott@oce.orst.edu

Fax: 503-737-2064

Adler, Robert

NASA/Goddard Space Flight Center

Code 912

Greenbelt, MD 20771

Phone: 301-286-9086

Email: adler@agnes.gsfc.nasa.gov

Fax: 301-286-1762

Alley, Ronald

NASA/Jet Propulsion Laboratory

M/S 183-501

4800 Oak Grove Drive

Pasadena, CA 91109-8099

Phone: 818-354-0751

Email: ron@lithos.jpl.nasa.gov

Fax: 818-354-09066

Asrar, Ghassem

NASA Headquarters

Process Studies Program Office

Code Y

Washington, DC 20546-0001

Phone: 202-358-1510

Email: gasrar@mtpe.hq.nasa.gov

Fax: 202-358-2770

Bailey, Paul

National Center for Atmospheric Research

Atmospheric Chemistry Division

1850 Table Mesa Drive

Boulder, CO 80307

Phone: 303-497-2918

Email: bailey@ncar.ucar.edu

Fax: 303-497-8080

Balch, William

University of Miami

Department of Marine Biology and Fisheries

4600 Rickenbacher Cswy.

Miami, FL 33149-1098

Phone: 305-361-4653

Email: balch@rsmas.miami.edu

Fax: 305-361-4600

Barkstrom, Bruce

NASA/Langley Research Center

Mail Stop 423

Hampton, VA 23681-0001

Phone: 804-864-5676

Email: brb@ceres.larc.nasa.gov

Fax: 804-864-7996

Barnes, William L.

NASA/Goddard Space Flight Center

Code 970

Greenbelt, MD 20771

Phone: 301-286-8670

Email: wbarnes@neptune.gsfc.nasa.gov

Fax: 301-286-1761

Baum, Bryan

NASA/Langley Research Center

Mail Stop 420

Hampton, VA 23681-0001

Phone: 804-864-5670

Email: baum@cloud.larc.nasa.gov

Fax: 804-864-7996

Blakeslee, Richard

NASA/Marshall Space Flight Center

Code ES-43

Huntsville, AL 35812

Phone: 205-544-1652

Email: blakeslee@ssl.msfc.nasa.gov

Fax:205-544-5760

Brown, Otis

University of Miami

RSMAS/MPO

4600 Rickenbacker Causeway

Miami, FL 33149-1098

Phone: 305-361-4018

Email: obrown@rsmas.miami.edu

Fax: 305-361-4622

Bruegge, Carol

NASA/Jet Propulsion Laboratory

Mail Stop 169-237

4800 Oak Grove Drive

Pasadena, CA 91109-8099

Phone: 818-354-4956

Email: cjb@jord.jpl.nasa.gov

Fax: 818-393-4619

Carder, Kendall

University of South Florida

Dept. of Marine Science

140 Seventh Ave. South

St. Petersburg, FL 33710-5016

Phone: 813-893-9148

Email: kcarder@monty.marine.usf.edu

Fax: 813-893-9189
Points of Contact

Data Products Handbook Vol. I Appendix A: Points of Contact • 235

Chang, Alfred

NASA/Goddard Space Flight Center

Code 974

Greenbelt, MD 20771

Phone: 301-286-8997

Email: achang@rainfall.gsfc.nasa.gov

Fax: 301-286-1758

Charlock, Thomas P.

NASA/Langley Research Center

Mail Stop 420

21 Langley Boulevard, Room 175

Hampton, VA 23681-0001

Phone: 804-864-5687

Email: t.p.charlock@larc.nasa.gov

Fax: 804-864-7996

Christian, Hugh

NASA/Marshall Space Flight Center

Code ES41

977 Explorer Blvd.

Huntsville, AL 35806

Phone: 205-922-5828

Email: hugh.christian@msfc.nasa.gov

Fax: 205-922-5723

Clark, Dennis

NOAA/NSDS

Code E/RA28

5200 Auth Rd, Rm 105

Camp Springs, MD 20746-4304

Phone: 301-763-8102

Email: dennis@ardbeg.gsfc.nasa.gov

Fax: 301-763-8108

Davies, Roger

University of Arizona

Institute of Atmospheric Physics

PAS Building 81

Tucson, AZ 85721

Phone: 520-621-6844

Email: davies@air.atmo.arizona.edu

Fax: 520-621-6833

Diner, David

NASA/Jet Propulsion Laboratory

Mail Stop 169-237

4800 Oak Grove Drive

Pasadena, CA 91109-8099

Phone: 818-354-6319

Email: djd@jord.jpl.nasa.gov

Fax: 818-393-4619

Drummond, James

University of Toronto

Department of Physics

60 St. George Street

Toronto, Ontario M5S 1A7

Canada

Phone: 416-978-4723

Email: jim@atmosp.physics.utoronto.ca

Fax: 416-978-8905

Esaias, Wayne

NASA/Goddard Space Flight Center

Code 971

Greenbelt, MD 20771

Phone: 301-286-5465

Email: esaias@gsfc.nasa.gov

Fax: 301-286-0240

Evans, Robert

University of Miami

RSMAS/MPO

Meteorology & Physical Oceanography

4600 Rickenbacker Causeway

Miami, FL 33149-1098

Phone: 305-361-4799

Email: bob@miami.miami.edu

Fax: 305-361-4622

Fujisada, Hiroyuki

Electrotechnical Laboratory

1-1-4 Umezono

Tsukuba, Ibaraki 305

Japan

Phone: 81-298-58-5463

Email: fujisada@etlrips.etl.go.jp

Fax: 81-298-58-5523

Gillespie, Alan

University of Washington

Department of Geological Sciences

Box 351310

Seattle, WA 98195-1310

Phone: 206-685-2637

Email: alan@rad.geology.washington.edu

Fax: 206-685-2379

Goodman, H. Michael

NASA/Marshall Space Flight Center

Code ES44

977 Explorer Blvd.

Huntsville, AL 35806

Phone: 205-922-5890

Email: michael.goodman@msfc.nasa.gov

Fax: 205-922-5723
Points of Contact

236 • Appendix A: Points of Contact Data Products Handbook Vol. I

Goodman, Steven

NASA/Marshall Space Flight Center

Code ES44

977 Explorer Blvd.

Huntsville, AL 35806

Phone: 205-922-5891

Email: steven.goodman@msfc.nasa.gov

Fax: 205-922-5723

Gordon, H.R.

University of Miami

Department of Physics

1320 Campo Sano Drive

Coral Cables, FL 33146

Phone: 305-284-2323

Email: gordon@phyvax.ir.miami.edu

Fax: 305-284-4222

Green, Richard

NASA/Langley Research Center

Mail Stop 420

Hampton, VA 23681-0001

Phone: 804-864-5684

Email: green@green.larc.nasa.gov

Fax: 804-864-7996

Hall, Dorothy

NASA/Goddard Space Flight Center

Code 974

Greenbelt, MD 20771

Phone: 301-286-6892

Email: dhall@glacier.gsfc.nasa.gov

Fax: 301-286-1758

Hansen, Elaine

University of Colorado

Colorado Space Grant College

Campus Box 520

Boulder, CO 80309-0520

Phone: 303-492-3141

Email:ehansen@rembrandt.colorado.edu

Fax: 303-492-5456

Hoge, Frank

NASA/Wallops Flight Facility

Buildings N-159 (West)

Wallops Island, VA 23337

Phone: 804-824-1567

Email: hoge@osb1.wff.nasa.gov

Fax: 804-824-1036

Hook, Simon

NASA/Jet Propulsion Laboratory

Mail Stop 180-501

4800 Oak Grove Drive

Pasadena, CA 91109-8099

Phone: 818-354-0974

Email: simon@lithos.jpl.nasa.gov

Fax: 818-354-0966

Hou, Arthur

NASA/Goddard Space Flight Center

Code 910.3

Greenbelt, MD 20771

Phone: 301-286-3594

Email: hou@dao.gsfc.nasa.gov

Fax: 301-286-1754

Houze, Robert

University of Washington

Department of Atmospheric Sciences

ATG Bldg., Rm. 608, Code AK-40

Seattle, WA 98195

Phone: 206-543-6922

Email: houze@atmos.washington.edu

Fax: 206-543-0308

Huete, Alfredo

University of Arizona

Department of Soil and Water Science

429 Shantz Building, Room 38

Tuscon, AZ 85721

Phone: 602-621-3228

Email: swshuet@ssit.arizona.edu

Fax: 602-621-1647

Joiner, Joanna

NASA/Goddard Space Flight Center

Code 910.3

Greenbelt, MD 20771

Phone: 301-805-8442

Email: joiner@dao.gsfc.nasa.gov

Fax: 301-805-7960

Justice, Christopher

NASA/Goddard Space Flight Center

Code 923

Greenbelt, MD 20771

Phone: 301-286-7372

Email: justice@ltpsun.gsfc.nasa.gov

Fax: 301-286-3221
Points of Contact

Data Products Handbook Vol. I Appendix A: Points of Contact • 237

Kahn, Ralph

NASA/Jet Propulsion Laboratory

Mail Stop 169-237

4800 Oak Grove Drive

Pasadena, CA 91109-8099

Phone: 818-354-9024

Email: kahn@jord.jpl.nasa.gov

Fax: 818-393-4619

Kaufman, Yoram

NASA/Goddard Space Flight Center

Code 913

Greenbelt, MD 20771

Phone: 301-286-4866

Email: yoram@ltpmail.gsfc.nasa.gov

Fax: 301-286-1759

King, Michael

NASA/Goddard Space Flight Center

Code 900

Greenbelt, MD 20771

Phone: 301-286-8228

Email: king@climate.gsfc.nasa.gov

Fax: 301-286-1738

Kummerow, Christian

NASA/Goddard Space Flight Center

Code 912

Greenbelt, MD 20771

Phone: 301-286-6299

Email: kummerow@audry.gsfc.nasa.gov

Fax: 301-286-1762

Lamich, David

NASA/Goddard Space Flight Center

Code 910.3

Greenbelt, MD 20771

Phone: 301-805-7954

Email: lamich@dao.gsfc.nasa.gov

Fax: 301-805-7960

Lang, Harold

NASA/Jet Propulsion Laboratory

M/S 183-501

4800 Oak Grove Drive

Pasadena, CA 91109-8099

Phone: 818-354-8885

Email: gandpmail@nasamail.nasa.gov

Fax: 818-354-9066

Ledvina, David

NASA/Goddard Space Flight Center

Code 910.3

Greenbelt, MD 20771

Phone: 301-805-7955

Email: ledvina@dao.gsfc.nasa.gov

Fax: 301-805-7960

Lewicki, Scott

NASA/Jet Propulsion Laboratory

Mail Stop 169-315

4800 Oak Grove Drive

Pasadena, CA 91109-8099

Phone: 818-354-3534

Email: Scott.Lewicki@jpl.nasa.gov

Fax: 818-354-8862

Martonchik, John

NASA/Jet Propulsion Laboratory

Mail Stop 169-237

4800 Oak Grove Drive

Pasadena, CA 91109-8099

Phone: 818-354-2207

Email: john.v.martonchik@jpl.nasa.gov

Fax: 818-393-4619

Masuoka, Edward

NASA/Goddard Space Flight Center

Code 920.2

Greenbelt, MD 20771

Phone: 301-286-7608

Emial: emasuoka@ltpmail.gsfc.nasa.gov

Fax: 301-286-1757

Menzel, W. Paul

NOAA/NESDIS

University of Wisconsin Madison

1225 W. Dayton Street

Madison, WI 53706

Phone: 608-264-5325

Email: paulm@ssec.wisc.edu

Fax: 608-262-5974

Minnis, Patrick

NASA/Langley Research Center

Mail Stop 420

Hampton, VA 23681-0001

Phone: 804-864-5671

Email: p.minnis@larc.nasa.gov

Fax: 804-864-7996

Molod, Andrea

NASA/Goddard Space Flight Center

Code 910.3

Greenbelt, MD 20771

Phone: 301-286-3908

Email: molod@dao.gsfc.nasa.gov

Fax: 301-286-1754

Montgomery, Harry

NASA/Goddard Space Flight Center

Code 925

Greenbelt, MD 20771

Phone: 301-286-7087

Email: hmontgom@ltpmail.gsfc.nasa.gov

Fax: 301-286-1757
Points of Contact

238 • Appendix A: Points of Contact Data Products Handbook Vol. I

Muller, Jan-Peter

University College London

Dept. of Photogrammetry & Surveying

Gower Street

London, England WC1E 6BT

United Kingdom

Phone: 44-713807227

Email: jpmuller@ps.ucl.ac.uk

Fax: 44-713800453

Okamoto, Ken’ichi

Director, Global Environment Division

Communications Research Laboratory

4-2-1 Nukui-Kita

Konegai, Tokyo 184

Japan

Phone: 0423-27-7541

Email: okamoto@crl.go.jp

Fax: 0423-27-6665

Palluconi, Frank

NASA/Jet Propulsion Laboratory

M/S 183-501

4800 Oak Grove Drive

Pasadena, CA 91109-8099

Phone: 818-354-8362

Email: pallucof@jplpost.jpl.nasa.gov

Fax: 818-354-8862

Paradise, Susan

NASA/Jet Propulsion Laboratory

Mail Stop 169-237

4800 Oak Grove Drive

Pasadena, CA 91109-8099

Phone: 818-354-2112

Email: Susan.r.paradise@jpl.nasa.gov

Fax: 818-354-8862

Rood, Richard

NASA/Goddard Space Flight Center

Code 910.3

Greenbelt, MD 20771

Phone: 301-286-8203

Email: rood@dao.gsfc.nasa.gov

Fax: 301-286-1754

Running, Steven

University of Montana

School of Forestry

Missoula, MT 59812

Phone 406-243-6311

Email: swr@ntsg.umt.edu

Fax: 406-243-4510

Salomonson, Vincent

NASA/Goddard Space Flight Center

Code 900

Greenbelt, MD 20771

Phone: 301-286-8601

Email: vsalomon@pop900.gsfc.nasa.gov

Fax: 301-286-3884

Shiue, James

NASA/Goddard Space Flight Center

Code 975

Greenbelt, MD 20771

Phone: 301-286-6716

Email: jcshiue@meneg.gsfc.nasa.gov

Fax: 301-286-1762

Smith, Eric

Florida State University

Department of Meteorology

Tallahassee, FL 32306-3034

Phone: 904-644-4253

Email: esmith@metsat.net.fsu.edu

Fax: 904-644-9639

Smith, Louis

NASA/Langley Research Center

Mail Stop 420

Hampton, VA 23681-5678

Phone: 804-864-5678

Email: g.l.smith@larc.nasa.gov

Fax: 804-864-7996

Strahler, Alan

Boston University

Department of Geography

Center for Remote Sensing

675 Commonwealth Avenue

Boston, MA 02215

Phone: 617-353-5984

Email: alan@bu.edu

Fax: 617-353-3200

Takacs, Lawrence

NASA/Goddard Space Flight Center

Code 910.3

Greenbelt, MD 20771

Phone: 301-286-2510

Email: w3llt@dao.gsfc.nasa.gov

Fax: 301-286-1754
Points of Contact

Data Products Handbook Vol. I Appendix A: Points of Contact • 239

Tanré, Didier

Bat P5

U.S.T. de Lille

Laboratoire d'Optique Atmospherique

Villeneuve d'Ascq, 59655

France

Phone: 33-20434532

Email: tanre@loa.citilille.fr

Fax: 33-20434342

Thome, Kurt

Remote Sensing Group

Suite 100

1600 North Country Club Road

Tucson, AZ 8 5716 -3119

Phone: 602-621-4535

Email: kurt@charon.opt-sci.arizona.edu

Fax: 602-621-8292

Townshend, John R.G.

Department of Geography

University of Maryland at College Park

LeFrak Hall, Room 1113A

College Park, MD 20742

Phone: 301-405-4558

Email: jt59@umail.umd.edu

Fax: 301-314-9299

Wan, Zhengming

University of California, Santa Barbara

Computer Systems Lab/CRSEO

#1140, Grivetz Building

Santa Barbara, CA 93106-3060

Phone: 805-893-4541

Email: wan@crseolucsb.edu

Fax: 805-893-2578

Watanabe, Hiroshi

ERSDAC

Forefront Tower

3-12-1, Kachidoki, Chuo-ku

Tokyo, 104 Japan

Phone: 81-3-3533-9380

Email: watanabe@ersdac.or.jp

Fax: 81-3-3533-9383

Welch, Ronald

South Dakota School of Mines and Technology

Institue of Atmospheric Science

Rapid City, SD 57701

Phone: 605-394-2291

Email: welch@cloud.ias.sdsmt.edu

Fax: 605-394-6061

Wharton, Stephen

NASA/Goddard Space Flight Center

Code 902.2

Greenbelt, MD 20771

Phone: 301-286-9394

Email: wharton@eosdata.gsfc.nasa.gov

Fax: 301-286-0268

Wong, Takmeng

NASA/Langley Research Cennter

Mail Stop 420

Hampton, VA 23681-0001

Phone: 804-864-5607

Email: takmeng.wong@larc.nasa.gov

Fax: 804-864-7996
Appendix B –

Acronyms
Acronyms

242 • Appendix B: Acronyms Data Products Handbook Vol. I

ACRIM Active Cavity Radiometer Irradiance

Monitor

ADADS ASTER Data Archive and Distribution

System

ADC Affiliated Data Center

ADEOS Advanced Earth Observing Satellite (Japan)

ADM angular distribution model

AGP Ancillary Geographic Product (MISR)

AHWGP Ad Hoc Working Group on Processing

AIRS Atmospheric Infrared Sounder

ALT altimeter

AM EOS morning crossing mission

AMI Active Microwave Instrument

AMRIR Advanced Medium Resolution Imaging

Radiometer

AMSU Advanced Microwave Sounding Unit

APAR Absorbed Photosynthetically Active

Radiation

APOPP Aerosol Physical And Optical Properties

Product

ARP Ancillary Radiometric Product

ASF Alaskan SAR Facility (DAAC)

ASTER Advanced Spaceborne Thermal Emission

and Reflection Radiometer

ATBD Algorithm Theoretical Basis Document

AVHRR Advanced Very High-Resolution

Radiometer

AVIRIS Airborne Visible and Infrared Imaging

Spectrometer

BCC biogeochemical models

BDRF bi-directional reflectance factor

BDS Bi-Directional Scan Product (CERES)

BOREAS Boreal Ecosystem – Atmosphere Study

BRDF bi-directional reflectance distribution

function

BRF bi-directional reflectance factor

CADM CERES Angular Directional Model

CAGEX CERES/ARM/GEWEX Experiment

CAL/VAL calibration/validation

CCD Charge-Coupled Device

CDDIS Crustal Dynamics Data Information System

CDROM chromomorphic dissolved organic matter

CEES Committee on Earth and Environmental

Sciences

CEOS Committee on Earth Observation Satellites

CERES Clouds and the Earth’s Radiant Energy

System

CFC chlorofluorocarbon

CH3Cl methyl chloride

CH4 methane

CIESIN Consortium for International Earth Science

Information Networks

CNES Centre National d’Etudes Spatiales (France)

CO carbon monoxide

COARE Coupled Ocean-Atmosphere Response

Experiment (of TOGA)

Co-I Co-Investigator

CRS cloud radiative swath

CSA Canadian Space Agency

CZCS Coastal Zone Color Scanner

DAAC Distributed Active Archive Center

DAO Goddard EOS Data Assimilation Office

DEM digital elevation model

DLPO DoD Landsat Program Office

DMSP Defense Meteorological Satellite Program

DPH Data Products Handbook

ECMWF European Centre for Medium-Range

Weather Forecasts (UK)

ECS EOSDIS Core System

EDC EROS Data Center

ENSO El Niño Southern Oscillation

EOS Earth Observing System

EOSAT Earth Observation Satellite Company

EOSDIS Earth Observing System Data and

Information System

EOSDIS Earth Observing System Data and

Information System

EPOP European Polar-Orbiting Platform

ERBE Earth Radiation Budget Experiment

ERBS Earth Radiation Budget Satellite

EROS Earth Resources Observation System

ERS-1 European Remote-Sensing Satellite-1

ERTS-1 Earth Resources Technology Satellite-1

ESA European Space Agency
Acronyms

Data Products Handbook Vol. I Appendix B: Acronyms • 243

ESDIS Earth Science Data and Information System

(GSFC)

ESMR electrically scanning microwave radiometer

ESSC Earth System Sciences Committee

ETM Enhanced Thematic Mapper (Landsat)

EUMETSAT European Organization for the

Exploitation of Meteorological Satellites

EUV Extreme ultraviolet

FACE Florida Area Cumulus Experiment

FG first guess

FIFE First ISLSCP Field Experiment

FIRE First ISCCP Regional Experiment

FOV field of view

FPAR Fraction of Photosynthetically Active

Radiation

FSW (CERES)

GAC Global Area Coverage

GARP Global Atmospheric Research Program

GATE GARP Atlantic Tropical Experiment

GCDIS Global Change Data and Information

System

GCM General Circulation Model

GCMD Global Change Master Directory

GCRP Global Change Research Program

GEDEX Greenhouse Effect Detection Experiment

GEMS Global Environmental Monitoring System

GEWEX Global Energy and Water Cycle Experiment

GHz gigahertz

GIMMS Global Inventory Modeling and Monitoring

Study

GLAS Geoscience Laser Altimeter System

(formerly GLRS-A)

GLI Global Imager

GMS Geostationary Meteorological Satellite

GMT Greenwich Mean Time

GOES Geostationary Operational Environmental

Satellite

GOME Global Ozone Monitoring Experiment

GPCP Global Precipitation Climatology Project

GRD Geo-rectified Radiance Product (MISR)

GSFC Goddard Space Flight Center

GTE Global Tropospheric Experiment

HIRIS High Resolution Imaging Spectrometer

HIRS High Resolution Infrared Radiation Sounder

IAV incremental analysis update

ICWG International Coordination Working Group

IDS Interdisciplinary Science

IEOS International Earth Observing System

IFOV instantaneous field of view

IGBP International Geosphere – Biosphere

Programme

IMG Interferometric Monitor for Greenhouse

Gases (Japan)

IPAR Instantaneous Photosynthetically Active

Radiation

IR infrared

IR-RAD infrared radiometer

IRTS Infrared Temperature Sounder

ISAMS Improved Stratospheric and Mesopheric

Sounder

ISCCP International Satellite Cloud Climatology

Project

ISLSCP International Satellite Land Surface

Climatology Project

ISTP International Solar Terrestrial Physics

ITIR Infrared Thermal Imaging Radiometer

(renamed ASTER)

ITTP International TOVS Processing Package

IWC ice water content

JEOS Japanese Earth Observing Satellite

JERS Japanese Earth Remote-sensing Satellite-1

JPL Jet Propulsion Laboratory

LAC Local Area Coverage

LAI Leaf Area Index

Landsat Land Remote Sensing Satellite

LaRC Langley Research Center (DAAC)

LAWS Laser Atmospheric Wind Sounder

LIMS Limb Infrared Monitor of the Stratosphere

LIS Lightning Imaging Sensor

LMC length modulation cell

LST land surface temperature

LW long wave

LWC liquid water content

LWIR Long-Wavelength (Thermal) Infrared

LZP level zero processing
Acronyms

244 • Appendix B: Acronyms Data Products Handbook Vol. I

MAPS Measurement of Atmospheric Pollution

from Satellites

MAS MODIS Airborne Simulator

Mbps megabits per second

MESSR Multispectral Electronic Self-Scanning

Radiometer

METEOSAT Geosynchronous Meteorology Satellite

(ESA)

MHS Microwave Humidity Sounder

MHz megahertz

MISR Multi-Angle Imaging Spectroradiometer

MITI Ministry of International Trade and Industry

(Japan)

MODIS Moderate-Resolution Imaging

Spectroradiometer

MOPITT Measurements of Pollution in the

Troposphere

MRIR Medium Resolution Infrared Radiometer

(Nimbus)

MSFC Marshall Space Flight Center

MTPE Mission to Plant Earth

MVI Modified Vegetation Index

MWIR Medium-Wavelength Infrared

MWP microwave water path

NAS National Academy of Sciences

NASA National Aeronautics and Space

Administration

NASDA National Space Development Agency of

Japan

NCAR National Center for Atmospheric Research

NCDC National Climate Data Center (NOAA)

NCEP National Centers for Environmental

Prediction (formerly the NMC)

NDSC Network for the Detection of Stratospheric

Change

NDVI Normalized Difference Vegetation Index

NESDIS National Environmental Satellite, Data, and

Information Service

NEXRAD NEXt generation weather RADar

Nimbus NASA Meteorological Satellites

(1 through 7)

NIR near infrared

NMC National Meteorological Center (NOAA)

NOAA National Oceanic and Atmospheric

Administration

NPP net primary production

NRC National Research Council

NRL Naval Research Laboratory

NSCAT NASA Scatterometer

NSF National Science Foundation

NSIDC National Snow and Ice Data Center (DAAC)

NWS National Weather Service

OBC On-Board Calibrator

OH hydroxyl radical

OI (1) Singly Ionized Oxygen Emission Line

OLS Optical Linescan System

OPS Optical Sensor

ORNL Oak Ridge National Laboratory (DAAC)

OTD Optical Transient Detector

PAR photosynthetically available radiation

PAR photosynthetically active radiation

PBL planetary boundary layer

PEB phycoerythrobilin-rich phycoerythrins

PI Principal investigator

PISP Polar Ice Sheet Program

PM EOS afternoon crossing mission

(post meridian)

PMC Pressure-Modulator Cell

PMIR Pressure Modulator Infrared Radiometer

PoDAG Polar DAAC Advisory Group

POEM Polar-Orbiting Earth Mission (ESA)

POES Polar-Orbiting Environmental Satellite

ppb parts per billion

ppm parts per million

PR Precipitation Radar (TRMM)

PSN Net Photosynthesis

PSO Project Science Office

PUB phycourobilin-rich phycoerythrins

QA quality assurance

RLRA reflectivity level reference altitude

SAGE III Stratospheric Aerosol and

Gas Experiment III

SAMS Stratospheric and Mesospheric Sounder

SBUV Solar Backscatter Ultraviolet

SCF Science Computing Facility
Acronyms

Data Products Handbook Vol. I Appendix B: Acronyms • 245

SeaWiFS Sea-viewing Wide Field-of-view Sensors

second

SEDAC Socioeconomic Data and Applications

Center

SMMR Scanning Multichannel Microwave

Radiometer

SPOT Systeme pour l’Observation de la Terre

(France)

SPSO Science Processing Support Office

SRB Surface Radiation Budget

SSBUV Shuttle Solar Backscatter Ultraviolet

radiometer

SSF Single Satellite Footprint (CERES)

SSM/I Special Sensor Microwave/Imager (DMSP)

SST sea-surface temperature

SWIR shortwave infrared

TDRSS Tracking and Data Relay Satellite System

TES Tropospheric Emission Spectrometer

TIR Thermal Infrared

TIROS Television Infrared Observing Satellite

TMI TRMM Microwave Imager

TMR TOPEX Microwave Radiometer

TOA Top of the Atmosphere

TOGA Tropical Ocean Global Atmosphere

TOGA/ TOGA/Coupled Ocean-Atmosphere

COARE Response Experiment

TOMS Total Ozone Mapping Spectrometer

TOPEX/ Ocean Topography Experiment

Poseidon (joint US-France)

TRMM Tropical Rainfall Measuring Mission

(U.S.-Japan)

TSDIS TRMM Science Data and Information

System

UARS Upper Atmosphere Research Satellite

USGCRP U.S. Global Change Research Program

UTC Universal Time, Coordinated

UV Ultraviolet

VI Vegetation Index

VIR Visible and Infrared Radiometer

VIRS Visible Infrared Scanner (TRMM)

VISSR Visible/Infrared Spin-Scan Radiometer

VNIR Visible Near Infrared

WMO World Meteorological Organization

WN window channel radiometric data

WWW World Wide Web
Appendix C –

References
References

248 • Appendix C: References Data Products Handbook Vol. I

Abbott, M.R. and D.B. Chelton, 1991: Advances in

passive remote sensing of the ocean, U.S.

National Report to IUGG. Reviews of

Geophysics, Supplement, 571-583.

Abbott, M.R., P.J. Richerson, and T.M. Powell,

1982: In situ response of phytoplankton

fluorescence to rapid variations in light. Limn.

Oceanog., 27, 218-225.

Adler, R.F., G.J. Huffman, P.R. Keehn, and A.J.

Negri, 1994: Global tropical rain estimates

from microwave-adjusted geosynchronous IR

data. Remote Sens. Rev., 11, 125-152.

American Society of Photogrammetry, 1952:

Manual of Photogrammetry , 2nd edition,

Washington, DC, 876 pp.

Andreae, M.O., J. Fishman, M. Garstang, J.G.

Goldammer, C.O. Justice, et al., 1994: Biomass

burning in the global environment. Global

Atmospheric Chemistry, Environmental

Science Research, 48, Prinn, R.G., Ed., Plenum

Press, New York.

Asrar, G. and J. Dozier, 1994: EOS Science

Strategy for the Earth Observing System.

American Institute of Physics Press, Woodbury,

NY.

Asrar, G. and R. Greenstone, Eds., 1995:

MTPE/EOS Reference Handbook, National

Aeronautics and Space Administration,

Washington, D.C., NP-215, 276 pp.

Asrar, G., R.B. Myneni, and B.J. Choudhury, 1992:

Spatial heterogeneity in vegetation canopies

and remote sensing of Absorbed

Photosynthetically Active Radiation: a

modeling study. Rem. Sens. Environ, 41, 85-

101.

Atlas, D., D. Rosenfeld, and D.A. Short, 1990: The

estimation of convective rainfall by area

integrals, Part 1 the theoretical and empirical

basis. J. Geophys. Res., 95, 2153-2160

Austin, R.W., 1974: Inherent spectral radiance

signals of the ocean surface, in Ocean Color

Analysis, SIO ref. 74-10 , Scripps Inst. of

Oceanog., La Jolla, CA, 2.1-2.20.

Avis, L.M., R.N. Green, J.T. Suttles, and S.K.

Gupta, 1984: A robust pseudoinverse spectral

filter applied to the Earth Radiation Budget

Experiment (ERBE) scanning channels. NASA

Tech. Memo. TM-85781, 33 pp.

Balch, W.B., R. Evans, G. Feldman, C. McClain,

and W. Esaias, 1992: The remote sensing of

ocean primary productivity: Use of new data

compilation to test satellite algorithms. J.

Geophys. Res., 97(2), 279-293.

Balch, W.M., P.M. Holligan, S.G. Ackleson and

K.J. Voss, 1991: Biological and optical

properties of mesoscale coccolithophore

blooms in the Gulf of Maine. Limn. Oceanog. ,

34, 629-643.

Barkstrom, B.R., 1984: The Earth Radiation Budget

Experiment (ERBE), Bull. Amer. Meteor. Soc.,

65, 1170-1185.

Barkstrom, B.R. and G.L. Smith, 1986: The Earth

radiation budget experiment: Science and

implementation. Rev. Geophysics., 24, 379-390.

Barnsley, M.J. and J.-P. Muller, 1991:

Measurement, simulation and analysis of

directional reflectance properties of Earth

surface materials. ESA SP-319, 375-382.

Barry, R.G. and A.L. Varani, Eds., 1995: Earth

Observing Science: Highlights 1994, NSIDC

Distributed Active Archive Center, Boulder,

CO.

Barton, I.J., M. Zavody, D.M. O 'Brien,

D.R. Cutten, R.W. Saunders and

D.T. Llewellyn-Jones, 1989. Theoretical

algorithms for satellite-derived sea surface

temperatures. J. Geophys. Res., 94, 3365.
References

Data Products Handbook Vol. I Appendix C: References • 249

Basist, A., D. Garrett, R. Ferraro, N.C. Grody, and

K. Mitchell, 1996: A comparison between

visible and microwave snow cover products

derived from satellite observations, J. Appl.

Meteor., 35, 163-177.

Baum, B.A., R.M. Welch, P. Minnis, L.L. Stowe,

J.A. Coakley, Jr., et al., 1995a: Imager clearsky determination and cloud detection

(Subsystem 4.1). Clouds and the Earth’s

Radiant Energy System (CERES) Algorithm

Theoretical Basis Document, Volume III:

Cloud Analyses and Radiance Inversions

(Subsystem 4), NASA RP 1376, 3, CERES

Science Team, Eds., 43-82.

Baum, B.A., P. Minnis, J.A. Coakley, Jr., B.A.

Wielicki, et al., 1995b: Imager cloud height

determination (Subsystem 4.2). Clouds and the

Earth’s Radiant Energy System (CERES)

Algorithm Theoretical Basis Document,

Volume III: Cloud Analyses and Radiance

Inversions (Subsystem 4), NASA RP 1376, 3,

CERES Science Team, Eds., 83-134.

Baum, B.A., B.A. Wielicki, P. Minnis, and L.

Parker, 1992: Cloud property retrieval using

merged HIRS and AVHRR data. J. Appl.

Meteor., 31 (4), 351-369.

Becker, F., 1987: The impact of spectral emissivity

on the measurement of land surface

temperature from a satellite. Internat. J. Remote

Sensing, 8 (10), 1509-1522.

Berger, W.H., K. Fischer, C. Lai, and G. Wu, 1987:

Ocean productivity and organic carbon flux. 67,

SIO Reference Series No. 87-30, Scripps

Institution of Oceanography.

Bidigare, R.R., M.E. Ondrusek, J.H. Morrow, D.A.

Kiefer, 1990: In vivo absorption properties of

algal pigments. SPIE Vol. 1302, Ocean Optics

X, 290-302.

Birchfield, E.G., H. Wang, and J.J. Rich, 1994:

Century/millennium internal climate

oscillations in an ocean-atmosphere-continental

ice sheet model, J. Geophys. Res., 99, 12,459.

Bloom, S.C., L.L. Takacs, A.M. da Silva, and

D.Ledvina, 1995: Data assimilation using

incremental analysis updates. Mon Wea Rev.,

submitted.

Bohme, R., 1993: Inventory of world topographic

mapping . Elsevier, Permagon Press Inc., New

York. The ICA International Cartographic

Association, 1- 3, 1070 pp.

Brasseur, G.P. and R.G. Prinn, 1992: Biogenic and

anthropogenic trace gases in the atmosphere.

The Use of EOS for Studies of Atmospheric

Physics, J.C. Gille and G. Visconti, Eds., North

Holland, Amsterdam, 45-64.

Breigleb and Ramanathan, 1982: Spectral and

diurnal variations in clear-sky planetary albedo.

J. Climate Appl. Meteor., 21, 1168-1171.

Brenninkmeijer, C.A.M., M.R. Manning, D.C.

Lowe, G. Wallace, R.J. Sparks, and A. VolzThomas, 1992: Interhemispheric asymmetry in

OH abundance inferred from measurements of

atmospheric CO. Nature, 356, 50-52.

Brest, C.L., and S.N. Goward, 1987: Deriving

surface albedo measurements from narrow band

satellite data. Internat. J. Remote Sens., 8, 351-

367.

Brooks, D.R., E.F. Harrison, P. Minnis, J.T. Suttles,

and R.S. Kandel, 1986: Development of

algorithms for understanding the temporal and

spatial variability of the Earth’s radiation

balance. Rev. Geophys., 24, 422-438.

Brown, O.B. and R.E. Chaney, 1983: Advances in

satellite oceanography. Rev. Geophys. and

Space Phys., 21(5), 1216-1230.

Carder, K.L., R.G. Steward, J.H. Paul, and G.A.

Vargo, 1986: Relationship between chlorophyll

and ocean color constituents as they affect

remote-sensing reflectance models. Limn.

Oceanog., 31, 403-413.

Carder, K.L., S.K. Hawes, K.A. Baker, R.C. Smith,

R.G. Steward, and B.G. Mitchell, 1991:
References

250 • Appendix C: References Data Products Handbook Vol. I

Reflectance model for quantifying Chlorophyll

a in the presence of productivity degradation

products. J. Geophys. Res., 96 (C11), 599-611.

Carder, K.L., W.W. Greg, D.K. Costello, K.

Haddad, and J.M. Prospero, 1991:

Determination of Saharan dust radiance and

chlorophyll from CZCS imagery. J. Geophys.

Res., 96 (D3), 5369-5378.

Cess, R., E. Dutton, J. DeLuisi, and F. Jiang, 1991:

Determining surface solar absorption from

broadband satellite measurements for clear

skies: Comparisons with surface measurements.

J. Climatology, 4, 236-247.

Chaine, M.T., 1980: Infrared remote sensing of sea

surface temperature. Remote Sensing of

Atmospheres and Oceans, A. Deepak, Ed.,

Academic Press, New York, 411-434.

Chamberlin, W.S. and J. Marra, 1992: Estimation

of photosynthetic rate from measurements of

natural fluorescence: Analysis of the effects of

light and temperature. Deep Sea Research, 39,

1695-1706.

Chang, A.T.C., J.L. Foster, and D.K. Hall, 1987:

Nimbus-7 derived global snow cover

parameters. Journal of Glaciology, 9.

Chang, A.T.C., J.L. Foster, D.K. Hall,

B.E. Goodison, A.E. Walker, J.R. Metcalfe, and

A. Harby, in press: Snow parameters derived

from MW measurements during the BOREAS

winter field campaign, JGR (submitted).

Charlock, T.P., F. Rose, S.-K Yang, T. Alberta, and

G. Smith, 1993: An observation study of the

interaction of clouds, radiation, and the general

circulation. Proceedings of the IRS ’92: Current

Problems in Atmospheric Radiation. Tallinn

(3-8 August 1992), A. Deepak Publishing,

151-154.

Charlock, T.P. and T.L. Alberta, 1996: The

CERES/ARM/GEWEX Experiment (CAGEX)

for the retrieval of radiative fluxes with satellite

data. Bull. Amer. Meteor. Soc., submitted.

Chicano, R.J. and R.S. Oremland, 1988:

Biogeochemical aspects of atmospheric

methane. Global Biogeochemical Cycles, 2,

299-327.

Chiu, L.S., 1988: Rain estimation from satellites:

Areal rainfall-rain area relation. Third Conf. on

Satellite Meteorology and Oceanography, Feb.

1-5, Bull. Amer. Meteor. Soc., Anaheim, CA,

3663-368.

Christian, H.J., R.J. Blakeslee, and S.J. Goodman,

1989: The detection of lightning from

geostationary orbit. J. Geophys. Res., 94 (13),

329-337.

Christian, H.J., R.J. Blakeslee, and S.J. Goodman,

1992: Lightning Imaging Sensor (LIS) for the

Earth Observing System, NASA Technical

memorandum 4350, MSFC, Huntsville, AL.

Churchill, D.D. and R.A. Houze, Jr., 1984:

Development and structure of winter monsoon

cloud clusters on 10 December 1978. J. Atmos.

Sci., 41, 933-960.

Cicerone, R.J. and R.S. Oremland, 1988:

Biogeochemical aspects of atmospheric

methane. Global Biogeochemical Cycles, 2,

299-327.

Coakley, J., R.D. Cess and F.B. Yurevich, 1983:

The effect of tropospheric aerosol on the

Earth’s radiation budget: A parameterization

for climate models. J. Atmos. Sci. , 40, 116-138.

Comb, D.G., 1990: Ice-out in Maine. Nature, 347,

510.

Crutzen, P.J., 1991: Methane’s Sinks and Sources.

Nature, 350, 380-381.

Culver, M.E. and M.J. Perry, 1994: Detection of

phycoerythrin fluorescence in upwelling

irradiance spectra. EOS Trans. AGU, 75, 233.

Daley, R., 1991: Atmospheric Data Analysis.

Cambridge University Press, 457 pp.
References

Data Products Handbook Vol. I Appendix C: References • 251

Deschamps, P. and T. Phulpin, 1980: Atmospheric

correction of infrared measurements of sea

surface temperature using channels at 3.7, 11,

and 12μm. Boundary Layer Met. , 18, 131-143.

Deschamps, P.Y., M. Herman and D. Tanré, 1983:

Modeling of the atmospheric effects and its

application to the remote sensing of ocean

color. Appl. Opt., 33, 7096-7116.

Di Girolamo, L. and R. Davies, 1994: A BandDifferenced Angular Signature technique for

cirrus cloud detection. IEEE Trans. Geosci.

Rem. Sens. 32, 890-896.

Dickerson, R.R., G.J. Huffman, W.T. Luke, L.J.

Nunnermacker, K.E. Pickering, A.C.D. Leslie,

C.G. Lindsey, W.G.N. Slinn, T.J. Kelly, P.H.

Daum, A.C. Delany. J.P. Greenberg, P.R.

Zimmerman, J.F. Boatmen, J.D. Ray and D.H.

Stedman, 1987:Thunderstorms: an important

mechanism in the transport of air pollutants.

Science, 235, 460.

Dickinson, R.E., 1987: Evapotranspiration in global

climate models, Advances in Space Research,

7, 17-26.

Dickinson, R.E., B. Pinty, and M.M. Verstraete,

1990: Relating surface albedos in GCMs to

remotely sensed data. Agric. and Forest

Meteor. 52, 109.

Diner, D.J., C.J. Bruegge, J.V. Martonchik,

G.W. Bothwell, E.D. Danielson, V.G. Ford,

L.E. Hovland, K.L. Jones and M.L. White,

1991: A Multi-angle Imaging

Spectroradiometer for terrestrial remote sensing

from the Earth Observing System. Internatl. J.

Imaging Sys. and Tech., 3, 92-107.

Dlugokencky, E.J., L.P. Steele, P.M. Lang., and

K.A. Masarie, 1994: The growth rate and

distribution of atmospheric methane. J.

Geophys. Res., 99, 17021-17043.

Dorman, J. L., and P.J. Sellers, 1989: A global

climatology of albedo, roughness length and

stomatal resistance for atmospheric general

circulation models as represented by the Simple

Biosphere model (SiB). J. Appl. Meteor., 28,

833-855.

Dozier, J., 1984: Snow reflectance from Landsat-4

thematic mapper. IEEE Trans. Geosci. Rem.

Sens., 22, 323-328.

Dozier, J., and Z. Wan, 1994: Development of

practical multiband algorithms for estimating

land-surface temperature from EOS-MODIS

data. Adv. Space Res., 13 (3), 81-90

Drummond, J.R., 1992: Measurements of pollution

in the troposphere (MOPITT). The use of EOS

for Studies of Atmospheric Physics, J.C. Gille

and G. Visconti, Eds., North Holland,

Amsterdam, pp. 77-101.

Earth Observing System: Science and Mission

Requirements Working Group Report. NASA

Technical Memorandum 86129.

Ebert, E., 1987: A pattern recognition technique for

distinguishing surface and cloud types in the

polar regions. J. Clim. Appl. Meteor., 26, 1412-

1427.

Edwards, T., R. Browining, J. Delderfield, D.J. Lee,

K.A. Lidiard, R.S. Milborrow, P.H.

McPherson, S.C. Peskett, G.M. Toplis, H.S.

Taylor, I. Mason, G. Mason, A. Smith and S.

Stringer, 1990. The Along Track Scanning

Radiometer - Measurement of sea-surface

temperature from ERS-1. Journal of the British

Interplanetary Society, 43, 160.

Eppley, R.W., E. Stewart, M.R. Abbott, and U.

Heyman, 1985: Estimating ocean primary

production from satellite chlorophyll:

Introduction to regional differences and

statistics for the Southern California Bight. J.

Plank. Res., 7, 57-70.

Evans, R.H. and H.R. Gordon, 1994: CZCS

‘System Calibration:’ A retrospective

examination. J. Geophys. Res. , 99C, 7293-

7307.
References

252 • Appendix C: References Data Products Handbook Vol. I

Ferraro, R.R., N.C. Grody, and G.F. Marks, 1994:

Effects of surface conditions on rain

identification using the SSM/I. Remote Sens.

Rev., 11, 195-209.

Field, C.B., J.T. Randerson, and C.M. Malstrom,

1995: Global net primary production:

Combining ecology and remote sensing.

Remote Sensing of Environment, 51: 74-88.

Fitzwater, S.E., G.A. Knauer, and J.H. Martin,

1982: Metal contamination and its effect on

primary production measurements. Limn.

Oceanog., 27, 544-551.

Foster, J.L., D.K. Hall, A.T.C. Chang, and

A. Rango, 1984: An overview of passive MW

snow research and results. Reviews of

Geophysics, 22.

Foster, J.L. and A.T.C. Chang, 1993: Snow cover.

Atlas of Satellite Observations Related to

Global Change . R.J. Gurney, C.L. Parkinson

and J.L. Foster, Eds., Cambridge University

Press, Cambridge, 361-370.

Frey, R.A., S.A. Ackerman and B.J. Soden, 1995:

Climate parameters from satellite spectral

measurements part I: collocated AVHRR and

HIRS/2 observations of spectral greenhouse

parameter. J. Geophys. Res.

Fu, Q., and K. Liou, 1993: Parameterization of the

radiative properties of currus clouds. J. Atmos.

Sci, 50, 2008-2025.

Fujita, M., K. Okamoto, S. Yoshikado, and K.

Nakamura, 1985: Inference of rain rate profile

and path-integrated rain rate by an airborne

microwave rain scatterometer. Radio Science,

20, 631-642.

Furuhama, Y., Ihara, T., Fujita, M. Shinozuka, T.,

Nakamura, K. and J. Awaka, 1982: Propagation

characteristics of millimeter and centimeter

waves of ETS-II classified by rainfall types.

Annales des Telecom., 36, 24-32.

Gao, B.-C., A.F.H. Goetz, and W.J. Wiscombe,

1993: Cirrus cloud detection from airborne

imaging spectrometer data using the 1.38

micron water vapor band. Geophys. Res. Letter,

4, 301-304.

Gao, B.-C. and A.F.H. Goetz, 1990: Column

atmospheric water vapor and vegetation liquid

water retrievals from airbourne imaging

spectrometer data. J. Geophys. Res., 95, 3549-

3564.

Gao, B.-C., K.B. Heidebrecht, and A.F.H. Goetz,

1993: Derivation of scaled surface reflectances

from AVIRIS data. Rem. Sens. Environ., 44,

165-178.

Gillaspie, A.R., A.B. Kahle, and R.E. Walker,

1986: Color enhancement of highly correlated

images. I. Decorrelation and HSI contrast

stretches. Rem. Sens. Environ., 20, 209-235.

Global Biomass Burning . J.S. Levine, Ed., 1991

Cambridge, MA, The MIT Press.

Goodman, A.H. and A. Henderson-Sellers, 1988:

Cloud detection analysis: A review of recent

progress. Atm. Res. 21: 203.

Gordon, H.R. and A.Y. Morel, 1983: Remote

Assessment of Ocean Color for Interpretation

of Satellite Visible Imagery: A Review,

Springer-Verlag, New York, 114 pp.

Gordon, H.R. and M. Wang, 1994: Retrieval of

water-leaving radiance and aerosol optical

thickness over the oceans with SeaWiFS:

A preliminary algorithm. Appl. Opt., 33 (3):

473-483.

Gordon, H.R., 1978: Removal of atmospheric

affects from satellite imagery of the oceans.

Appl. Opt., 17, 1631-1636.

Gordon, H.R., 1987: Calibration requirements and

methodology for remote sensors viewing the

oceans in the visible. Rem. Sens. Environ., 22,

103-126.
References

Data Products Handbook Vol. I Appendix C: References • 253

Gordon, H.R. and A.Y. Morel, 1983: Remote

assessment of ocean color for interpretation of

satellite visible imagery: A review. Springer .

Gordon, H.R. and D.K. Clark, 1981: Clear water

radiances for atmospheric correction of coastal

zone color scanner imagery. Appl. Opt., 26,

2111-2112.

Gordon, H.R., and D.K. Clark, 1981: Clear water

radiances for atmospheric correction of coastal

zone color scanner imagery. Appl. Opt., 20,

4175-4180.

Gordon, H.R. and D.K. Clark, 1980: Atmospheric

effects in the remote sensing of phytoplankton

pigments. Boundary Layer Meteor. , 18, 300-

313.

Gordon, H.R., D.K. Clark, J.L. Mueller, and W.A.

Hovis, 1980: Phytoplankton pigments derived

from the Nimbus-7 CZCS: Initial comparisons

with surface measurements. Science, 210, 63-

66.

Gordon, H.R., and A.Y. Morel, 1983: Remote

assessment of ocean color for satellite visible

imagery. Springer-Verlag, New York, 114 pp.

Gordon, H.R. and M. Wang, 1994: Retrieval of

water-leaving radiance and aerosol optical

thickness over the oceans with SeaWiFS: A

preliminary algorithm. Appl. Opt., 33, 443-452.

Gordon, H.R., O.B. Brown, R.H. Evans, J.W.

Brown, R.C. Smith, K.S. Baker, and D.K.

Clark, 1988: A Semi-Analytic Radiance Model

of Ocean Color. Geophys. Res. 93D, 10909-

10924.

Goward, S.N. and A.S. Hope., 1989:

Evapotranspiration from combined reflected

solar and emitted terrestrial radiation:

Preliminary FIFE results from AVHRR data,

Adv. Space Res., 9 (7), 239-249.

Gower, F.J.R. and G.A. Borstad, 1990: Mapping of

phytoplankton by solar-stimulated fluorescence

using an imaging spectrometer. Internat.

Remote Sensing, 11, 313-320.

Goyet, C. and P.G. Brewer, 1993: Biochemical

properties of the oceanic cycle. Modeling

Oceanic Climate Interactions, J. Willebrand,

Ed., NATO Advanced Study Institute, I11,

271-297.

Granier, C, J.F. Muller, S. Madronich, and G.P.

Brasseur, 1995: Possible Causes for the 1990-

1993 decrease in the global tropospheric CO

abundance: a three-dimensional study.

Atmospheric Environment, submitted.

Green, R.N. and B.A. Wielicki, 1995: Convolution

of imager cloud properties with CERES

footprint point spread function (Subsystem 4.4).

Clouds and the Earth’s Radiant Energy System

(CERES) Algorithm Theoretical Basis

Document, Volume III: Cloud Analyses and

Radiance Inversions (Subsystem 4), NASA RP

1376, 3, edited by CERES Science Team, 177-

194.

Green, R.N., B.A. Wielicki, J.A. Coakley, L.L.

Stowe, and P.O’R. Hinton, 1995: CERES

inversion to instantaneous TOA fluxes

(Subsystem 4.5). “Clouds and the Earth’s

Radiant Energy System (CERES) Algorithm

Theoretical Basis Document, Volume III:

Cloud Analyses and Radiance Inversions

(Subsystem 4),” NASA RP 1376 Vol. 3, edited

by CERES Science Team, 195-206.

Green, R.O., and J.E. Conel, Movement of water

vapor in the atmosphere measured by an

imaging spectrometer at Rogers Dry Lake, CA,

Proc. Summaries of the Fifth Annual JPL

Airborne Earth Science Workshop, JPL Pub.

95-1, 1, 79–83, 1995.

Gregg, W.W. and K.L. Carder, 1990: A simple

solar irradiance model for cloudless maritime

atmospheres. Limn. Oceanog., 35 (8),1657-

1675.

Grody, N.C., 1991: Classification of snow cover

and precipitation using the Special Sensor
References

254 • Appendix C: References Data Products Handbook Vol. I

Microwave/Imager (SSM/I), J. Geophys. Res.,

96, 7423-7435.

Grody, N.C. and A. Basist, 1996: Global

identification of snow cover using SSM/I

measurements, IEEE Trans. Geosci. and Rem.

Sens., 34, 237-249.

Groom, S.B. and P.M. Holligan, 1987: Remote

sensing of coccolithophore blooms.

Proceedings of XXXVI COSPAR Meeting,

Toulouse, France.

Guo, C. and E.P. Krider, 1982: The optical and

radiation field signatures produced by lightning

return strokes. J. Geophys. Res., 87, 8913.

Gupta, S.K., W.L. Darnell, and A.C. Wilber, 1992:

A parameterization for longwave surface

radiation from satellite data: Recent

improvements. J. Appl. Meteor. 31, 1361-1367.

Gustafson, G.B., R.G. Isaacs, R.P. d’Entremont,

J.M. Sparrow, T.M. Hamill, C. Grassotti, D.W.

Johnson, C.P. Sarkisian, D.C. Peduzzi, B.T.

Pearson, V.D. Jakabhazy, J.S. Belfiore and A.S.

Lisa, 1994: Support of Environmental

Requirements for Cloud Analysis and Archive

(SERCAA): Algorithm descriptions. Phillips

Laboratory Scientific Report No. 2, Hanscom

Air Force Base, MA, 100 pp.

Hall, D.K., G.A. Riggs, and V.V. Salomonson,

1995: Development of methods for mapping

global snow cover using Moderate Resolution

Imaging Spectroradiometer (MODIS) data.

Rem. Sens. Environ..

Hansen, J., A. Lacis, R. Ruedy, M. Sato, and H.

Wilson, 1993: How sensitive is the world’s

climate? Research and Exploration , 9, 143-158.

Harrison, E.F., D.R. Brooks, P. Minnis, B.A.

Weilicki, W.F. Staylor, G.G. Gibson, D.F.

Young, F.M. Denn, and the ERBE Science

Team, 1988: First estimates of the diurnal

variation of longwave radiation from the

multiple-satellite Earth Radiation Budget

Experiment (ERBE). Bull. Amer. Meteor. Soc.

69, 1144-1151.

Harrison, E.F., P. Minnis, B.R. Barkstrom, B.A.,

Wielicki, G.G. Gibson, F.M. Denn, and D.F.

Young, 1990: Seasonal variation of the diurnal

cycles of Earth’s radiation budget determined

from ERBE. AMS 7th Conf. on Atmos.

Radiation, San Francisco, CA, July 23-27, pp.

87-91.

Harrison, E.F., P. Minnis, B.R. Barkstrom, V.

Ramanathan, R.D. Cess, and G.G. Gibson,

1990: Seasonal variation of cloud radiative

forcing derived from the Earth Radiation

Budget Experiment. J. Geophys. Res. , 95,

18687-18703.

Hayden, C.M., 1988: GOES-VAS simultaneous

temperature-moisture retrieval algorithm. J.

Appl. Meteor., 27, 705-733.

Hilland, J.E., et al., 1985: Production of global sea

surface temperature fields for the Jet Propulsion

Laboratory Workshop Comparisons. Geophys.

Res., 90 (C6): 11,642-11,650.

Hobish, M.K., Ardanuy, P.E, and V.V.

Salomonson, 1994: Surface imaging

technologies for NASA’s earth observing

system. J. Imaging Science and Technology,

38, 301-310.

Hoffman, L.H., W.L. Weaver, and J.F. Kibler,

1987: Calculation and accuracy of ERBE

scanner measurement locations. NASA

Technical Paper, 2670.

Hoge, F.E. and R.N. Swift, 1986: Chlorophyll

pigment concentration using spectral curvature

algorithms: An evaluation of present and

proposed satellite ocean color sensor bands.

Appl. Opt. , 25, 3677-3682.

Hoge, F.E. and R.N. Swift, 1990: Photosynthetic

Accessory Pigments: Evidence for the

Influence of Phycoerythrin on the Submarine

Light Field. Rem. Sens. Environ., 34, 19-35.
References

Data Products Handbook Vol. I Appendix C: References • 255

Holben, B.N., E. Vermote, Y.J. Kaufman, D. Tanré,

and V. Kalb, 1992: Aerosol Retrieval over

Land from AVHRR Data-Application for

Atmospheric Correction. IEEE Trans. Geosci.

Rem. Sens., 30, 212-222.

Holligan, P.M., M.Viollier, D.S. Harbour, P.

Camus and M. Champagne-Philippe, 1983:

Satellite and ship studies of coccolithophore

production along the continental shelf edge.

Nature, 304, 339-342.

Holm-Hansen, O. and B. Riemann, 1978:

Chlorophyll A determination: improvements in

methodology. Oikos, 30, 438-477.

Holm-Hansen, O., C.J. Lorenzen, R.W. Holmes,

and J.D.H. Strickland, 1965: Fluorometric

determination of chlorophyll. J. Cons. Int.

Explor. Mer. , 30, 3-15.

Hook, S.J., A.R. Gabell, A.A. Green, and P.S.

Kealy, 1992: A comparison of techniques for

extracting emissivity information from thermal

infrared data for geological studies. Remote

Sens. Environ., 42, 123-135.

Houghton, J.T., Taylor, F.W., and C.D. Rodgers,

1984: Remote Sounding of Atmospheres.

Cambridge University Press, Cambridge, UK,

343 pages.

Houze, R.A. Jr., 1989: Observed structure of

mesoscale convective systems and implications

for large-scale heating. Quart. J. Roy. Meteor.

Soc., 115, 425-461.

Huete, A., C. Justice, and H. Liu, 1994:

Development of vegetation and soil indices for

MODIS-EOS. Rem. Sens. Environ., 49, 224-

234.

Huete, A.R., C.O. Justice, and H.Q. Liu, MODIS

Vegetation Index. Algorithm Theoretical Basis

Document (ATBD), 1994.

Huffman, G.J., R.F. Adler, B. Rudolf. U.

Schneider, P.R. Keehn, 1995: A technique for

combining satellite data, raingauge analysis and

model precipitation information into global

precipitation estimates. J. Climate, 11 (5),

1284-1295.

Iguchi, T. and R. Meneghini, 1994:

Intercomparison of single frequency methods

for retrieving a vertical rain profile from

airborne or spaceborne data. J. Atmos Oceanic

Technol. 11, 1507-1516.

Inamdar, A.K. and V. Ramanathan, 1994: Physics

of greenhouse effect and convection in warm

oceans. J. Climate, 7, 715-731

Inoue, T. 1987: An instantaneous delineation of

convective rainfall areas using split window

data of NOAA-7 AVHRR. J. Meteor. Soc. of

Japan, 65, 469-481.

Intergovernmental Panel on Climate Change, 1992:

Scientific Assessment of Climate Change: 1992

IPCC Supplement, Cambridge University Press,

24 pp.

Iqbal, M., 1983: An Introduction to Solar

Radiation. Academic Press.

Irons, J.R., K.J. Ranson, D.L. Williams, R.R. Irish,

and F.G. Huegel, 1991: An off-nadir pointing

imaging spectroradiometer for terrestrial

ecosystem studies. IEEE Trans. Geosci. Rem.

Sens. 29, 66-.

Iverson, R.L., and W.E. Esaias, 1994: Global ocean

phytoplankton production components

determined using chlorophyll a data derived

from CZCS imagery. Submitted to J. Geophys.

Res., March 9, 1994, 63 pp.

Jarecke, P.J., M.A. Folkman, and L.A. Darnton,

1991: Radiometric calibration plan for the

clouds and the Earth’s radiant energy system

instruments. Proc. of SPIE, 1493, 244-254.

Jedlovec, G.J., 1987: Determination of atmospheric

moisture structure from high resolution MAMS

radiance data. Ph.D. Thesis, University of

Wisconsin - Madison.
References

256 • Appendix C: References Data Products Handbook Vol. I

Justice C.O., J.P.Malingreau and A.Setzer 1993:

Satellite remote sensing of fires: potential and

limitation. Fire in the environment; its

ecological, climatic and atmospheric chemical

importance, P. Crutzen and J. Goldammer,

Eds., John Wiley and Sons, Chichester.

Kahle, A.B., 1986: Surface emittance, temperature,

and thermal inertia derived from Thermal

Infrared Multispectral Scanner (TIMS) data for

Death Valley, California. Geophysicas, 52(7),

858-874.

Kalnay, E., R. Balgovind, W. Chao, D. Edelmann,

J. Pfaendtner, L. Takacs, and K. Takano, 1983:

Documentation of the GLAS fourth order

general circulation model - volume 1: Model

Documentation . NASA Tech Memo 86064,

Goddard Space Flight Center, 100 pp.

Kaufman, Y.J., R.S. Fraser and R.L. Mahoney,

1991: Fossil fuel and biomass burning effect on

climate-heating or cooling? J. Climate, 4, 578-

588.

Kaufman, Y.J. and C. Sendra, 1988: Algorithm for

automatic atmospheric corrections to visible

and near-IR satellite imagery. Internat. J. Rem.

Sens., 9, 1357-1381.

Kaufman, Y.J. and L.A. Remer, 1994: Detection of

forests using mid-IR Reflectance: An

application for aerosol studies. IEEE Trans.

Geosci. Rem. Sens., 32, 672-683.

Kaufman, Y.J. and B.-C. Gao, 1992: Remote

sensing of water vapor in the near IR from

EOS/MODIS. IEEE Trans. Geosci. Rem. Sens.,

30, 871-884.

Kaufman, Y.J., D. Tanré, L. Remer, E. Vermote

and B. N. Holben, Remote sensing of

tropospheric aerosol from EOS-MODIS over

the land using dark targets and dynamic aerosol

models, submitted to J. Geophys. Res., 1996.

Kaufman, Y.M. and B.N. Holben, 1996:

Hemispherical backscattering by biomass

burning and sulfate particles derived from sky

measurements. J. Geophys. Res., in press.

Kaufman, Y.J., C.J. Tucker, and I. Fung, 1990b:

Remote sensing of biomass burning in the

tropics. J. Geophys. Res., 95 (D7):,9927-9939.

Kaufmann Y.J., 1993: Measurements of the aerosol

optical thickness and the path radiance -

implications on aerosol remote sensing and

atmospheric corrections. J. Geophys. Res., 98,

2677-2692.

Keenan, T.D., G.J. Holland, M.J. Manton, and J.

Simpson, 1988: TRMM ground truth in a

monsoon environment: Darwin, Australia. Aust.

Meteor. Mag., 36, 81-90

Kiefer, D.A. and R.A. Reynolds, 1992: Advances in

understanding phytoplankton fluorescence and

photosynthesis. Primary Productivity and

Biogeochemical Cycles in the Sea, P.G.

Falkowski and A.D. Woodhead, Eds., Plenum,

New York, 155-174.

King, M.D., 1987: Determination of the scaled

optical thickness of clouds from reflected solar

radiation measurements. J. Atmos. Sci. , 44,

1734-1751.

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. Rem. Sens., 30, 1-27.

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, Airborne

scanning spectrometer for remote sensing of

cloud, aerosol, water vapor and surface

properties, J. Atmos. Oceanic Technol., 13,

777–794, 1996.

Kleepsies, T.J. and L.M. McMillan, 1984: Physical

retrieval of precipitable water using the split

window technique. Preprints Conf. on Satellite
References

Data Products Handbook Vol. I Appendix C: References • 257

Meteorology/Remote Sensing and

Applications, AMS, Boston, 55-57.

Kozu, T., K. Nakamura, R. Meneghini, and W.C.

Boncyk, 1991: Dual-parameter radar rainfall

measurement from space: A test result from an

aircraft experiment. IEEE Trans. Geosci. Rem.

Sens., 29, 690-703.

Krajewski, W.F., 1987: Co-kriging of radar-rainfall

and rain gage data. J. Geophys. Res., 92 (D8),

9571-9580.

Kummerow, C., and L. Giglio, 1994: A passive

microwave technique for estimating rainfall and

vertical structure information from space, Part

I: Algorithm description. J. Appl. Meteor., 33,

3-18.

Kyle, H.L., R.J. Curran, W.L. Barnes, and D.

Escoe, 1978: A cloud physics radiometer. Third

Conference on Atmospheric Radiation, Davis,

CA, 107-109.

Lambin, E.F. and A.H. Strahler, 1994: Changevector analysis in multitemporal space: A tool

to detect and categorize land-cover change

processes using high temporal-resolution

satellite data. Rem. Sens. Environ., 48, 231-244.

Lambin, E.F. and A.H. Strahler, 1994: Indicators of

land-cover change for change-vector analysis in

multitemporal space at coarse spatial scales.

Internat. J. Remote Sens., 15, 2099-2119.

Lee III, R.B., B.R. Barkstrom, S.L. Carmen, J.E.

Cooper, M.A. Folkman, P.J. Jarecke, L.P.

Kopia, and B.R. Wielicki, 1993: The Clouds

and Earth’s radiant energy system (CERES)

experiment, Earth Observing System (EOS)

instrument and calibrations. Proc. of SPIE,

1939, 67-71.

Lee III, R.B., B.R. Barkstrom, G.L. Smith,

J.E. Cooper, L.P. Kopia, and R.W. Lawrence,

1996: The Clouds and Earth’s Radiant Energy

System (CERES) sensors and preflight

calibration plans. J. Atmos. Oceanic

Technology, 12, 300-313.

Leith, H. and R.H. Wittaker, 1975: Primary

productivity of the biosphere. Springer-Verlag,

New York.

Lewis, P. and M.J. Barnsley, 1994: Influence of the

sky radiance distribution on various

formulations of the Earth surface albedo, Proc.

Sixth Intl. Symp. on Physical Measurements

and Signatures in Remote Sensing, Val d’Isere,

France, 17-21 January 1994.

Li, X. and A.H. Strahler, 1986: Geometric-optical

bidirectional reflectance modeling of a conifer

forest canopy. IEEE Trans. Geosci. Rem. Sens.,

24, 906-919.

Li, X., A. H. Strahler and C. E. Woodcock, 1995:

“A hybrid geometric optical-radiative transfer

approach for modeling albedo and directional

reflectance of discontinuous canopies,” IEEE

Trans. Geosci. Rem. Sens., in press.

Li, Z. and H. Leighton, 1993: Global climatologies

of solar radiation budgets at the surface and in

the atmosphere from 5 years of ERBE data. J.

Geophys. Res., 98, 4919-4930.

Li, Z., H.G. Leighton, K. Masuda, and T.

Takashima, 1993: Estimation of SW flux

absorbed at the surface from TOA reflected

flux. J. Climate, 6, 317-330.

Liang, S. and A.H. Strahler, 1993: Retrieval of

surface BRDF from multiangle remotely sensed

data. Rem. Sens. Environ., submitted.

Liou, K.-N., 1992: Radiation and Cloud Processes

in the Atmosphere, Oxford University Press,

487 pp.

Liu, H.Q. and A.R. Huete, 1995: A feedback based

modification of the NDVI to minimize soil and

atmospheric noise. IEEE Trans. Geosci. Rem.

Sens., 33, 457-465.

Llewellyn-Jones, D.T., P.J. Minnett, R.W. Saunders

and A.M. Zavody, 1984: Satellite multichannel

infrared measurements of sea surface

temperature of the N.E. Atlantic Ocean using
References

258 • Appendix C: References Data Products Handbook Vol. I

AVHRR/2. Quart. J. R. Meteor. Soc., 110, 613-

631.

Loeve, M., 1955: Probability Theory, D. van

Nostrand Co., Princeton, N.J.

Lorenzen, C.J., and S.W. Jeffrey, 1980:

Determination of chlorophyll in sea water,

UNESCO Tech. Paper. Marine Science , No. 35,

20 pp.

Los, S.O., C.O. Justice, and C.J. Tucker, 1994: A

global 1° by 1° NDVI data set for climate

studies derived from the GIMMS continental

NDVI data. Internat. J. Remote Sensing, 15,

3493-3518.

Ma, X.L., W.L. Smith, and H.M. Woolf, 1984:

Total Ozone from NOAA satellites - a physical

model for obtaining observations with high

spatial resolution. J. Climate Appl. Meteor., 23,

1309-1314.

Martonchik, J.V. and D.J. Diner, 1992: Retrieval of

aerosol optical properties from multi-angle

satellite imagery. IEEE Trans. Geosci. Rem.

Sens., 30, 223-230.

McClain, E.P., W.G. Pichel, C.C. Walton, 1985:

Comparative performance of AVHRR-based

multichannel sea surface temperatures. J.

Geophys. Res., 90 (C6), 11587-11601.

McMillin, L.M., 1975: Estimation of sea surface

temperature from two infrared window

measurements with different absorption. J.

Geophys. Res., 90, 11,587-11,600.

Meneghini, R. and J.A. Jones, 1993: An approach

to estimate areal rain-rate distribution from

spaceborne radar by the use of multiple

thresholds. J. Appl. Meteor. 32, 386-398.

Meneghini. R., and T. Kozu, 1990: Spaceborne

Weather Radar, Artech House, Boston,

Massachusetts, 197 pp.

Minnett, P.J., 1991. Consequences of sea surface

temperature variability on the validation and

applications of satellite measurements.

J. Geophys. Res., 96, 18,475-18,489.

Minnett, P.J., 1995. Sea surface temperature

measurements from the Along-Track Scanning

Radiometer on ERS-1. In Oceanographic

Applications of Remote Sensing, M. Ikeda and

F. Dobson (eds.). CRC Press Inc., 131-143.

Minnis, P. and E.F. Harrison, 1984: Diurnal

variability of regional cloud and clear-sky

radiative parameters derived from GOES data,

Part III: November 1978 radiative parameters.

J. Climate Appl. Meteor., 23, 1033-1051.

Minnis, P., K.-N. Liou, and Y. Takano, 1993:

Inference of cirrus cloud properties using

satellite-observed visible and infrared

radiances, Part I: Parameterization of radiance

fields. J. Atmos. Sci. , 50, 1279-1304.

Minnis, P., D.P. Kratz, J.A. Coakley, Jr.,

M.D. King, R. Arduini, D.P. Garber,

P.W. Heck, S. Mayor, W.L. Smith, Jr., and

D.F. Young, 1995: Cloud optical property

retrieval (Subsystem 4.3). Clouds and the

Earth’s Radiant Energy System (CERES)

Algorithm Theoretical Basis Document,

Volume III: Cloud Analyses and Radiance

Inversions (Subsystem 4), NASA RP 1376, 3,

CERES Science Team, Eds., 135-176.

Monteith, J.L., 1972: Solar radiation and

productivity in tropical ecosystems. J. Appl.

Ecology, 9, 747-766.

Moody, A., S. Gopal, A.H. Strahler, J. Borak, P.

Fisher, 1994: A combination of temporal

thresholding and neural network methods for

classifying multiscale remotely-sensed image

data. Proc. 14th Int. Geosci. and Remote

Sensing Symp., Pasadena, CA. Aug. 8-12,

1994: 4, 1877-1879.

Moody, A. and C. E. Woodcock, 1995: The

influence of scale and the spatial characteristics

of landscapes on land-cover mapping using

remote sensing, Landscape Ecology, in press.
References

Data Products Handbook Vol. I Appendix C: References • 259

MOPITT Algorithm Theoretical Basis Document:

Conversion of MOPITT Digital Counts into

Calibrated Radiances in Carbon Monoxide and

Methane Absorption Bands (Level 0 to

Level 1), MOPITT Science Team, 1995.

MOPITT Algorithm Theoretical Basis Document:

Retrieval of Carbon Monoxide Profiles and

Column Amounts of Carbon Monoxide and

Methane from MOPITT Radiances (Level 1 to

Level 2), MOPITT Science Team, 1995.

Morel, A., and J.M. Andre, 1991: Pigment

distribution and primary production in the

Western Mediterranean as derived and modeled

from Coastal Zone Color Scanner observations.

J. Geophys. Res., 96, 12,685-12,698.

Mugnai, A., E.A. Smith, and G.J. Tripoli, 1993:

Foundations for physical-statistical

precipitation retrieval from passive microwave

satellite measurements. Part II: Emission source

and generalized weighting function properties

of a time dependent cloud-radiation model. J.

Appl. Meteor., 32, 17-39.

Muller, J.F., and G.P. Brasseur, IMAGES: A threedimensional chemical transport model of the

global troposphere. to appear in J. Geophys.

Res., 1995.

Nakajima, T., and M.D. King, 1990: Determination

of optical thickness and effective particle radius

of clouds from reflected solar radiation

measurements- Part I: Theory. J. Atmos. Sci.,

47, 1879-1892.

Nakajima, T.Y. and T. Nakajima, 1994: Wide-area

determination of cloud microphysical

properties from NOAA AVHRR measurements

for FIRE and ASTEX regions, J. Atmos. Sci., in

press.

National Science and Technology Council, 1995:

Our Changing Planet: The FY 95 U.S. Global

Change Research Program, Supplement to the

President’s FY 95 Budget, Coordination Office

of the U.S. Global Change Research Program,

Washington, DC.

Nemani, R. and S. W. Running, 1989: Estimation

of regional surface resistance to

evapotranspiration from NDVI and thermal-IR

AVHRR data, J. Appl. Meter., 28, 276-284.

Nemani, R.R., L.L. Pierce, S.W. Running, and

L. Band, 1993: Forest ecosystem processes at

the watershed scale: Sensitivity to remotely

sensed leaf area index estimates. Internat. J.

Remote Sensing, 14, 2519-2534.

Nemani, R.R., L.L. Pierce, S.W. Running, and

S. Goward, 1993: Developing Satellite-derived

Estimates of Surface Moisture Status. J. Appl.

Meter., 32 (3).

Nemani, R.R. and S.W. Running, 1995: Satellite

monitoring of global landcover changes and

their impact on climate. Climatic Change, 31:

395-413.

Novelli, P.C., K.A. Masarie, P.P. Tans, P.M. Lang,

1994: Recent Changes in Atmospheric Carbon

Monoxide. Science, 263, 1587-1589.

O’Neill, M.A. and I.J. Dowman, 1993: A

simulation study of the ASTER sensor using

versatile general purpose rigid sensor modeling

systems. Internat. J. Remote Sensing, 14, 565-

585.

Paltridge, G.W., and C.M.R. Platt, 1976: Radiative

Processes in Meteorology and Climatology,

Elsevier.

Penner, J.E., R.E. Dickenson, C.A. O’Neill, 1992:

Effects of aerosol from biomass burning on the

global radiation budget. Science, 256, 1432-

1434.

Pfaendtner, J., S. Bloom, D. Lamich, M. Seablom,

M. Sienkiewicz, J. Stobie, and A. da Silva,

1995: Documentation of the Goddard Earth

Observing System (GEOS) Data Assimilation

System - Version 1. NASA Tech Memo.

104606, Vol. 4, NASA, Goddard Space Flight

Center, Greenbelt, MD, 44 pp.
References

260 • Appendix C: References Data Products Handbook Vol. I

Platnick, S., P.A. Durkee, K. Nielson, J.P. Taylor,

S.C. Tsay, M.D. King, R.J. Ferek and

P.V. Hobbs, The role of background cloud

microphysics in ship track formation, submitted

to J. Atmos. Sci., 1996.

Platt, T.C., C. Caverhill, and S. Sathyendranath,

1991: Basin scale estimates of oceanic primary

production by remote sensing: The North

Atlantic. J. Geophys. Res., 96 (15), 147-15,

149.

Platt, T., S. Sathyendranath, 1988: Ocean primary

production: Estimation by remote sensing at

local and regional scales. Science, 241, 1613-

1620.

Prabhakara, C., Conrath, B.J. and R.A. Hanel,

1970: Remote sensing of atmospheric ozone

using the 9.6 micron band. J. Atmos. Sci., 26:

689-697.

Prabhakara, C., et al., 1975: Estimation of sea

surface temperature from remote sensing in the

11 and 13μm window region. J. Geophys. Res.,

79, 5039-5044.

Price, J.C., 1983: Estimating surface temperature

from satellite thermal infrared data- a simple

formulation for the atmospheric effect. Rem.

Sens. Environ., 13, 353-361.

Price, J.C., 1984: Land surface temperature

measurements from the split window channels

of the NOAA 7 Advanced Very High

Resolution Radiometer. J. Geophys. Res., 89,

7231-7237.

Prince, S.D., 1991: A model of regional primary

production for use with coarse resolution

satellite data, Internat. J. Remote Sensing, 12,

1313-1330.

Prince, S.D. and S.N. Goward, 1995: Global

primary production: A remote sensing

approach. Journal of Biogeography, 22: 815-

835.

Ramanathan V., R.D. Cess, E.F. Harrison,

P. Minnis, B.R. Barkstrom, E. Ahmad, and

D. Hartmann, 1989b: Cloud-radiative forcing

and climate: Results for the Earth Radiation

Budget Experiment. Science, 243, 57-63.

Ramanathan, V., 1986: Scientific use of surface

radiation budget for climate studies, In Surface

Radiation Budget for Climate Applications,

Edited by J.T. Suttles and G. Ohring, NASA

RP-1169, Washington, DC, 58-86.

Ramanathan, V., 1987: The role of Earth radiation

budget studies in climate and general

circulation research. J. Geophys. Res., 92,

4075-4095.

Rao, C.R.N., E.P. McClain, and L.L. Stowe, 1989:

Remote sensing of aerosols over the oceans

using AVHRR data theory, practice and

applications. Internat. J. Remote Sens.: 10, 743-

749.

Rasmusson, E.M., 1985: El Nino and variations in

climate. American Scientist, 73, 168-178.

Reichle, Jr., H.G., H.A. Wallio and B.E. Gormsen,

1989: Feasibility of determining the vertical

profile of carbon monoxide from a space

platform. Appl. Opt., 28, 2104-2110.

Remer, L.A. and Y.J. Kaufman, 1996: The size

distribution of ambient aerosol particles: smoke

vs. urban industrial aerosol., AGU Chapman

Conference on Biomass Burning, submitted.

Remer, L.A., Y.J. Kaufman, and B.N. Holben, The

size distribution of ambient aerosol particles:

Smoke vs. urban/industrial aerosol, Global

Biomass Burning, in press, The MIT press,

Cambridge, MA, 1996.

Robinson, J.M., 1991: Fire from space: Global fire

evaluation using infrared remote sensing.

Internat. J. Remote Sensing, 12 (1), 3-24.

Rosenfeld, D., E. Amitai, and D.B. Wolff, 1995:

Classification of rain regimes by the 3-
References

Data Products Handbook Vol. I Appendix C: References • 261

dimensional properties of reflectivity fields. J.

Appl. Meteor., 34, 198-211.

Rosenfeld, D., D.B. Wolff, and E. Amitai, 1994:

The window probability matching method for

rainfall measurements with radar. J. Appl.

Meteor., 33, 682-693.

Rossow, W.B. and L.C. Garder, 1993: Cloud

detection using satellite measurements of

infrared and visible radiances for ISCCP. J.

Climate, 6, 2341-2369.

Rothery, D.A., 1987: Decorrelation stretching as an

aid to image interpretation. Internat. J. Remote

Sensing, 8, 1253-1254.

Ruimy, A., B. Saugier, and G. Dedieu, 1994:

Methodology for the estimation of terrestrial

net primary production from remotely sensed

data. Journal of Geophysical Research, 99(D3):

5263-5283

Running, S.W., R.R. Nemani, D.L. Peterson, L.E.

Band, D.F. Potts, L.L. Pierce, M.A. Spanner,

1989: Mapping regional forest

evaportanspiration and photosynthesis by

coupling satellite data with ecosystem

simulation. Ecology, 70,1090-1101.

Running, S.W., 1990: Estimating terrestrial primary

productivity by combining remote sensing and

ecosystem simulation. Remote Sensing of

Biosphere Functioning, R. Hobbs and H.

Mooney, Eds., New York: Springer-Verlag, 65-

86.

Running, S.W., C. Justice, V. Salmonson, D. Hall,

J. Barker, Y. Kaufmann, A. Strahler, A. Huete,

J-P. Muller, V. Vanderbilt, Z. Wan, P. Teillet,

D. Carneggie, 1994: Terrestrial remote sensing

science and algorithms planned for

EOS/MODIS. Internat. J. Remote Sensing, 15,

3587-3620.

Running,S.W., T.R. Loveland, L.L. Pierce,

R.R. Nemani, and E.R. Hunt, 1995: A remote

sensing based vegetation classification logic for

global landcover analysis. Remote Sensing of

Environment, 51: 39-48

Salisbury, J.W. and D.M. D’Aria, 1992: Emissivity

of terrestrial materials in the 8-14 μm

atmospheric window. Rem. Sens. Environ. , 42,

83-106.

Salomonson, V.V., D.K. Hall, and J.Y.L. Chien,

1995: Use of passive microwave and optical

data for large-scale snow-cover mapping,

Proceedings of the Second Topical Symposium

on Combined Optical-Microwave Earth and

Atmosphere Sensing, 3-6 April 1995, Atlanta,

CA, 35-37.

Sarmiento, J.L., J.R. Toggweiler and R. Najjar,

1988: Ocean carbon-cycle dynamics and

atmospheric CO 2. Phil. Trans. R, Soc. Lond. A ,

325, 3-21.

Saunders, R.W. and K.T. Kriebel, 1988: An

improved method for detecting clear-sky and

cloud radiances for AVHRR data. Internat. J.

Remote Sensing, 9, 123-150.

Schluessel, P., W.J. Emery, H. Grassl and

T. Mammen, 1990. On the bulk-skin

temperature difference and its impact on

satellite remote sensing of sea surface

temperatures. J. Geophys. Res., 95, 13,341-

13,356.

Schols, J.L. and J.A. Weinman, 1994: Retrieval of

hydrometeor distributions over the ocean from

airborne single-frequency radar and multifrequency radiometric measurements.

Atmospheric Research, 34, 329-346.

Schubert, S.D., C.-K. Park, C.-Y Wu, W. Higgins,

Y. Kondratyeva, A. Molod, L. Takacs, M.

Seablom, and R. Rood, 1995: A multiyear

assimilation with the GEOS-1 system:

Overview and results. NASA Tech. Memo.

104606, Vol. 6, NASA, Goddard Space Flight

Center, Greenbelt, MD, 207 pp.

Schubert, S.D. and R.B. Rood, 1995: Proceedings

of the workshop on the GEOS-1 Five yearassimilation,. NASA Tech Memo 104606, Vol.

7, Goddard Space Flight Center, 201 pp.
References

262 • Appendix C: References Data Products Handbook Vol. I

Schubert, S.D., R.B. Rood, and J. Pfaendtner, 1993:

An assimilated dataset for earth science

applications. Bull. Amer. Meteor. Soc., 74,

2331-2342.

Sellers, P.J., 1987: Canopy reflectance,

photosynthesis and transpiration. Internat. J.

Remote Sensing, 6, 1335-1372.

Sellers, P.J., F.G. Hall, H. Margolis, R. Kelly, D.

Baldocchi, J. denHartog, J. Cihlar, M. Ryan, B.

Goodison, P. Crill, J. Ranson, D. Letternmaier

and D. Wickland, 1995 (in press): The Boreal

Ecosystem-Atmosphere Study (BOREAS): an

overview and early results from the 1994 field

year. Bull. Amer. Meteor. Soc.

Shapiro, M.A., Krueger, A.J., and P.J. Kennedy,

1982: Nowcasting the position and intensity

of jet streams using a satellite borne total

ozone mapping spectrometer. Nowcasting,

K.A. Browning, Ed., Academic Press, Inc.

London, 137-145.

Sikes, S. and V. Fabry, 1994: Photosynthesis,

CaCO 3 deposition, coccolithophorids and the

global carbon cycle. in: Photosynthetic Carbon

Metabolism and Regulation of Atmospheric

CO2 and O2, N.E. Tolbert and J. Preiss, Eds.,

Oxford University Press, London.

Simpson, J., Ed., 1988: TRMM: A Satellite Mission

to Measure Tropical Rainfall, Report of the

Science Steering Group, NASA Report.

Simpson, J., C. Kummerow, W.-K. Tao, and

R.F. Adler, 1996: On the Tropical Rainfall

Measuring Mission (TRMM). Meteorol. Atmos.

Phys. 60, 19-36.

Slater, P.N., S.F. Biggar, R.G. Holm, R.D. Jackson,

Y. Mao, M.S. Moran, J.M. Palmer and B.

Yuan, 1987: Reflectance- and radiance-based

methods for the in-flight absolute calibration of

multispectral sensors. Rem. Sens. Environ. , 22,

11-37.

Slater, P.N., S.F. Biggar, R.G. Holm, R.D. Jackson,

Y. Mao, M.S. Moran, J.M. Palmer, and B.

Yuan, 1987: Reflectance-based and radiancebased methods for the in-flight absolute

calibration of multi-spectral sensors. Rem. Sens.

Environ., 22, 11-37.

Smith, A.H., R.W. Saunders and A.M. Zavody,

1994. The validation of ATSR using aircraft

radiometer data over the Tropical Atlantic.

J. Atmos. and Oceanogr. Techn., 789-800.

Smith, E.A., C Kummerow, and A. Mugnai: 1994:

The emergence of inversion-type profile

algorithms for estimation of precipitation from

satellite passive microwave measurements.

Rem. Sens. Rev., 11, 211-242.

Smith, E.A., X. Xiang, A. Mugnai, and G.J. Tripoli,

1994: Design of an inversion-based

precipitation profile retrieval algorithm using

an explicit cloud model for initial guess

microphysics. Meteor. Atmos. Phys., 54.

Smith, G.L. and Bess, T.D., 1983: Sampling Errors

in Regional Radiation Results Based on

Satellite Radiation Measurements. Proceedings

of the Ninth Conference on Aerospace and

Aeronautical Meteorology, Omaha, NE, June

1983.

Smith, G.L., R, N, Green, E. Raschke, L.M. Avis,

J.T. Suttles, B.A. Wielicki, and R. Daview,

1986: Inversion methods for satellite studies of

the Earth’s radiation budget: Development of

algorithms for the ERBE mission. Rev.

Geophys., 24, 407-421.

Smith, R.C. and K.S. Baker, 1982: Oceanic

chlorophyll concentrations as determined by

satellite (Nimbus-7 Coastal Zone Color

Scanner). Marine Biology, 66: 269-279.

Smith, R.C. and K.S. Baker, 1977: The bio-optical

state of ocean waters and remote sensing.

Scripps Institution of Oceanography Ref. 77-2,

36 pp.
References

Data Products Handbook Vol. I Appendix C: References • 263

Smith, R.C. and W.H. Wilson, 1981: Ship and

satellite bio-optical research in the California

Bight, in Oceanography from Space, J.F.R.

Gower, Ed., Plenum, New York, pp. 281-294.

Smith, W.L. and F.X. Zhou, 1982: Rapid extraction

of layer relative humidity, geopotential

thickness and atmospheric stability from

satellite sounding radiometer data. Appl. Opt.,

21, 924-928.

Smith, W.L., Woolf, H.M., and A.J. Schreiner,

1985: Simultaneous retrieval of surface and

atmospheric parameters: A physical and

analytically direct approach. Advances in

Remote Sensing, A. Deepak, H.E. Fleming and

M.T. Chahine, Eds.

Smith, W.L., R.O. Knuteson, H.E. Revercombe,

W. Feltz, H.B. Howell, W.P. Menzel,

N.R. Nalli, O.B. Brown, J. Brown, P.J. Minnett

and W. McKeown, 1996. Observations of the

infrared radiative properties of the ocean–

implications for the measurement of sea-surface

temperature via satellite remote sensing. Bull.

Am. Met. Soc., 77(1), 1996, 41-51.

Snyder, W. and Z. Wan, 1996: “Surface

temperature correction for active infrared

reflectance measurements of natural materials,”

Appli. Optics, 35(13), 2216-2220.

Sohn B.-J. and F.R. Robertson, 1993:

Intercomparison of observed cloud radiative

forcing: A zonal and global perspective. Bull.

Amer. Meteor. Soc., 74, 997-1006.

Stamnes, K., and P. Conklin, 1984: A new multilayer discrete ordinate approach to radiative

transfer in vertically inhomogeneous

atmospheres. J. Quant. Spectrosc. Radiat.

Transfer, 31, 273-282.

Steele, L.P., E.J. Dlugokencky, P.M. Lang, P.P.

Tans, R.C. Martin, and K.A. Masarie, 1992:

Slowing down of the global accumulation of

atmospheric methane during the 1980s. Nature,

358, 313-316.

Steiner, M., and R.A. Houze, Jr., 1993: Threedimensional validation at TRMM ground truth

sites: Some early results from Darwin

Australia. Preprints, 26th International

Conference on Radar Meteorology, Norman,

Oklahoma, Amer. Meteor. Soc, 417-420.

Steiner, M., R.A. Houze, Jr., and S.E. Yuter, 1995:

Climatological characterization of threedimensional storm structure from operational

radar and raingauge data. J. Appl. Meteor., 34,

(in press).

Stowe, L.L., E.P. McClain, R. Carey, P. Pellegrino,

G. Gutman, P. Davis, C. Long, and S. Hart,

1991: Global distribution of cloud cover

derived from NOAA/AVHRR operational

satellite data. Adv. Space Res., 11, 51-54.

Stowe, L., P. Ardanuy, R. Hucek, P. Abel, and H.

Jacobowitz, 1993: Evaluating the design of an

earth radiation budget instrument with system

simulations. Part I: Instantaneous Estimates.” J.

of Atmos. Oceanic Tech., Vol. 10, No. 6, Dec.

1993, Pp. 809-826.

Strabala, K.I., S.A. Ackerman and W.P. Menzel,

1994: Cloud properties inferred from 8-12

micron data. J. Appl. Meteor., 33, 212-229.

Strahler, A., A. Moody, and E. Lambin, 1995: Land

cover and land-cover change from MODIS.

Proc. 15th Int. Geosci. and Remote Sensing

Symp. , Florence, Italy, July 10-14, 1995, vol. 2,

pp. 1535-1537.

Strong, A.E. and E.P. McClain, 1984: Improved

ocean surface temperature from spacecomparisons with drifting buoys. Bull. Am.

Meteor. Soc., 65 (2), 138-142.

Sullivan, J. Gandin L., Gruber A., and W. Baker,

1993: Observation Error Statistics for NOAA10 Temperature and Height Retrievals. Mon.

Wea. Rev., 121, 2578-2587.

Suttles, J.T., and G. Ohring, 1986: Surface radiation

budget for climate applications. NASA RP1169, Washington, DC, 136 pp.
References

264 • Appendix C: References Data Products Handbook Vol. I

Suttles, J.T., B.A. Wielicki and S. Vemury, 1992:

Top-of-atmosphere radiative fluxes: Validation

of ERBE scanner inversion algorithm using

Nimbus-7 ERB data. J. Appl. Meteor., 31, 784-

796.

Suttles, J.T., R.N. Green, P. Minnis, G.L. Smith,

W.F. Staylor, B.A. Wielicki, I.J. Walker, D.F.

Young, V.R. Taylor, and L.L. Stowe, 1988:

Angular radiation models for Earth-atmosphere

system, Part I: Shortwave radiation. NASA

Reference Publication 1184,Vol. I.

Takacs, L.L., A. Molod, and T. Wang, 1994:

Documentation of the Goddard Earth

Observing System (GEOS) General Circulation

Model—Version 1, NASA Tech. Memo.

104606, Vol. 1, NASA, Goddard Space Flight

Center, Greenbelt, MD, 100 pp.

Takano, Y., and K.N. Liou, 1989: Radiative

transfer in cirrus clouds I: Single scattering and

optical properties of oriented hexagonal ice

crystals. J. Atmos. Sci. , 46, 3-20.

Tanré, D.,Y.J. Kaufman, M. Herman and S. Mattoo

Remote sensing of aerosol over oceans from

EOS-MODIS, J. Geophys. Res., in press, 1996.

Tanré D., M. Herman, Y.J. Kaufman, Information

on the aerosol size distribution contained in the

solar reflected spectral radiances, J. Geophys.

Res., 101, 19043-19060, in press, 1996.

Taylor, M.M., 1973: Principal components color

display of ERTS imagery. Third Earth

Resources Technology Satellite-1 Symposium,

10-14 December, NASA SP-351, Vol. 1,

Section B, 150-160.

Theon, J.S., Ed., 1988: Tropical Rainfall

Measurements, A. Deepack Publishing, 528 pp.

Theon, J.S., T. Matsuno, T. Sakata, and N. Fugono,

Eds., 1992: The Global Role of Tropical

Rainfall. A. Deepak Publishing, 280 pp.

Topliss, B.J., and T. Platt, 1986: Passive

fluorescence and photosynthesis in the ocean:

Implications for remote sensing. Deep Sea Res.,

33, 849-864.

Tovinkere, V.R., M. Penaloza, A. Logar, J. Lee,

R.C. Weger, T.A. Berendes, and R.M. Welch,

1993: An intercomparison of artificial

intelligence approaches for polar scene

identification. J. Geophys. Res., 98, 5001-5106

Townshend, J.R.G. and C.O. Justice, 1988:

“Selecting the spatial resolution of satellite

sensors required for global monitoring of land

transformations.” International Journal of

Remote Sensing 9: 187-236.

Townshend, J.R.G., C.O. Justice, W. Li, C. Gurney

and J. McManus, 1991: “Global land cover

classification by remote sensing: present

capabilities and future possibilities.” Remote

Sensing of Environment 35: 243-256.

Vermote, E., L.A. Remer, C.O. Justice, Y.J.

Kaufman, and D. Tanré, 1995: Atmospheric

Correction Algorithm: Spectral Reflectance/

Surface Leaving Radiance. Algorithm

Theoretical Basis Document (ATBD), EOS ID

#2015, MODIS Science Team.

Vermote, E.F., N.Z. El Saleous, C.O. Justice,

J.L. Kaufman, Y.J. Privette, L. Remer,

J.C. Roger, and D. Tanré, 1996: Atmospheric

correction of visible to middle infrared EOSMODIS data over land surfaces: background,

operational algorithm and validation. Journ. of

Geophys. Res. accepted.

Vorosmarty, C., A. Grace, B. Moore III, B.

Choudhury, and C.J. Willmott, 1991: A

strategy to study regional hydrology and

terrestrial ecosystem processes using satellite

remote sensing, ground-based data, and

computer modeling. Acta Astronautica, 25,

785-792.

Walton, C.C., E.P. McClain and J.F. Sapper, 1990:

Recent changes in satellite-based multi-channel
References

Data Products Handbook Vol. I Appendix C: References • 265

sea surface temperature algorithms. Preprint,

Marine Technology Society Meeting, MTS ’90,

Washington, DC, Sept. 1990.

Wan, D. Teillet, and D. Carneggie, 1994:

Terrestrial remote sensing science and

algorithms planned for EOS/MODIS. Internat.

J. Remote Sensing, 15, 3587-3620.

Wan, Z. and J. Dozier, 1989: Land-surface

temperature measurement from space physical

principles and inverse modeling. IEEE Trans.

Geosci. Rem. Sens., 27 (3), 268-278.

Wan, Z. and J. Dozier, 1996: “A generalized splitwindow algorithm for retrieving land-surface

temperature from space,” IEEE Trans. Geosci.

Remote Sens., 34(4), 892-905.

Wang, M. and H.R. Gordon, 1994: Estimating

aerosol optical properties over the oceans with

MISR: Some preliminary studies. Appl. Opt .

33, 4042-4057.

Wanner, W., X. Li, and A.H. Strahler, 1995: On the

derivation of kernels for kernel-driven models

of bidirectional reflectance. J. Geophys. Res.,

vol., 21077-21090.

Wanner, W., A. Strahler, J.-P. Muller, M. Barnsley,

P. Lewis, X. Li, and C. Barker Schaaf, 1995:

Global mapping of bidirectional reflectance and

albedo for the EOS MODIS project: The

algorithm and the product. Proc. 15th Int.

Geosci. and Remote Sensing Symp. , Florence,

Italy, July 10-14, 1995, vol. 1, pp. 525-529.

Weeks, W.F., 1981: Sea ice: the potential of remote

sensing. Oceanus, 24, 39-48.

Welch, R. and H. Lang, 1994: ASTER as a source

of global topographic data in the late 1990s.

Internat. Arch. Photogram. Rem. Sens., 30,

222-224.

Welch, R.M., S.K. Sengupta, and D.W. Chen,

1990: Cloud field classification based upon

high spatial resolution textural features. 1. Gray

level co-occurrence matrix approach. J.

Geophys Res., 93, 12663-12681.

Welch, R.M., K.S. Kuo and S.L. Sengupta, 1990:

Cloud and surface textural features in polar

regions. IEEE Trans. Geosci. Rem. Sens. , 28,

520-528.

Whitlock, C.H., T.P. Charlock, W.F. Staylor, R.T.

Pinker, I. Laszlo, A. Ohmura, H. Gilgen,

T. Konzelman, R.C. DiPasquale, C.D. Moats,

S.R. LeCroy, and N.A. Ritchey, 1995: First

global WCRP shortwave surface radiation

budget dataset. Bull. Amer. Meteor. Soc., 76, 6,

905-922.

Wielicki, B.A. and L. Parker, 1992: On the

determination of cloud cover from satellite

sensors: The effect of sensor spatial resolution.

J. Geophys. Res. 97, 12799-12823.

Wielicki, B.A., R.D. Cess, M.D. King, D.A.

Randall, and E.F. Harrison, 1995: Mission to

Planet Earth: Role of Clouds and Radiation in

Climate. Bull. Amer. Meteor. Soc., 76, 2125-

2153.

Wielicki, B.A., B.R. Barkstrom, E.F. Harrison,

R.B. Lee III, G.L. Smith, and J.E. Cooper,

1996: Clouds and the Earth’s Radiant Energy

System (CERES): An Earth Observing System

Experiment. Bull. Amer. Meteor. Soc., 77.

Wilheit, T.T., A.T.C. Chang and L.S. Chiu, 1991:

Retrieval of monthly rainfall indices from

microwave radiometric measurements using

probability distribution functions. J. Atmos. and

Oceanic Tech., 8, 118-136.

Wofsy, S.C., J.C. McConnell, and M.B. McElroy,

1972: Atmospheric CH4, CO and CO 2 . J.

Geophys. Res., 77, 4477-4493.

Wu, M., and L.-P. Chang, 1992: Longwave

radiation budget parameters computed from

ISCCP and HIRS2/MSU products. J. Geophy.

Res., 97 (10), 1083-10101.

Wu, R.J.A. Weinman, and R.T. Chin, 1985:

Determination of rainfall rates from GOES

satellite images by a pattern recognition

technique. J. Atmos. Ocean Tech., 2, 314-330.
References

266 • Appendix C: References Data Products Handbook Vol. I

Wylie, D.P., W.P. Menzel, H.M. Woolf and K.I.

Strabala, 1994: Four Years of Global Cirrus

Cloud Statistics Using HIRS. J. Climate, 7,

1972-1986.

Yoder, J.A., C.R. McClain, G.C. Feldman, and

W.E. Esaias, 1993: Annual cycles of

phytoplankton chlorophyll concentrations in the

global ocean, A satellite view. Global

Biogeochemical Cycles, 7, 181-193.

Yuter, S.E., and R.A. Houze, Jr. 1995: Threedimensional kinematic and microphysical

evolution of Florida cumulonimbus. Part II:

Frequency distributions of vertical velocity,

reflectivity and differential reflectivity. Mon.

Wea. Rev.

Zander, R.P., Demoulin, D.H. Ehhalt, U. Schmidt,

and C.P. Rinsland, 1989: Secular increase of

the total vertical column abundance of carbon

monoxide above central Europe since 1950. J.

Geophy. Res., 94, 11021.