STAR LOOKS AT THE EARTH

Satellite Measurements of the Atmosphere, Oceans and Land

By The Scientists of the Center for Satellite Applications and Research (STAR) and their Collaborators

Edited By:

Alfred M. Powell, Jr

George Ohring Mike Kalb

Mike Goldberg

National Environmental Satellite, Data, and Information Service

National Oceanic and Atmospheric Administration

U.S. Department of Commerce
STAR LOOKS AT THE EARTH

Satellite Measurements of the Atmosphere, Oceans and Land

By The Scientists of the Center for Satellite Applications and Research (STAR) and their Collaborators

Edited By:

Alfred M. Powell, Jr

George Ohring Mike Kalb

Mike Goldberg

National Environmental Satellite, Data, and Information Service

National Oceanic and Atmospheric Administration

U.S. Department of Commerce

2012
pic of new ‘star’ 

bldg.?
This book is dedicated to our federal and private sector support staff, and to our university partners, 

whose contributions have facilitated STAR’s advances in remote sensing research and applications.
Section 1: Prelude

1

From the Assistant Administrator3

From the Director4

Introduction5

Section 2: The Satellite System7

Satellite Remote Sensing 101 (Part 1)8

Satellite Remote Sensing 101 (Part 2) 10

Evolution of the Global Satellite Observing System 12

Research to Operations 14

Calibration 16

Checking the Measurements 18

The NOAA Products Validation System 20

Web-Based Monitoring 22

Section 3: The Atmosphere 25

The Atmosphere 26

Atmospheric Temperature Sounding 28

Atmospheric Water Vapor Sounding 30

Winds 32

Cloud Properties 34

Amazon Rivers in the Sky 36

Precipitation and Flash Floods 38 

Atmospheric Instability: Signaling Extreme Weather 40

Thunderstorms and Tornadoes 42

Bridging the Gap: Nearcasting 44

Tropical Cyclones 46

Snowfall 48

Satellite Data Assimilation 50

The Community Radiative Transfer Model 52

Cloud Resolving Models 54

Air Quality 56

Nitrogen Dioxide 58

Microburst Prediction 60

Aviation Hazards: Volcanic Ash 62

Section 4: Oceans 65

Planet Ocean 66

Sea Surface Temperature from Polar Satellites 68

Ocean Temperature: The Continuous View 70

GEO-LEO Sea Surface Temperatures 72

Ocean Surface Winds 74

Satellite Sea Surface Salinity 76

Sea Ice 78

Ocean Color Remote Sensing 80

Global Distribution of Coccolithophore Blooms 82

Coastal Water Quality 84

Contents
The Ocean Surface Topography Satellite Constellation 86

Aliasing of Altimeter Observations 88

Mapping the Global Ocean Floor from Space 90

International Efforts to Improve Global Ocean Depth Map 92

Oceanic and Atmospheric Features from Synthetic Aperture Radar 94

High-Resolution Coastal Winds 96

Ocean Hazards Response: Oil Spills 98

CoastWatch 100

Oceanic Heat Content 102

Section 5: Land 105

The Continents 106

Drought and Vegetation Health 108

Global Vegetation and Society 110

Snow Cover 112

Soil Moisture 114

Land Surface Temperature 116

Urbanization, Heat Islands, and Climate 118

Vegetation Fires 120

Sunlight 122

Section 6: Climate 125

Our Changing Planet 126

Earth Radiation Budget 128

Greenhouse Gases 130

Atmospheric Temperature Trends 132

Trends in Temperature Profiles from a Single Instrument 134

Sea Surface Temperature Trends 136

Cloudiness Trends 138

Global Precipitation Monitoring 140

Water Vapor Trends 142

Sea Level Rise 144

Monitoring the World’s Ozone 146

Arctic Sea Ice Cover Trends 148

Arctic Sea Ice Thickness 150

Coral Bleaching 152

Vegetation and Surface Temperature Trends 154

Satellite Vegetation Phenology 156

Aerosols, Volcanoes and Climate 158

Arctic Climate Interactions 160

Climate Reconstructions 162

Checking Climate Reanalyses 164

Abrupt Climate Change 166

Acknowledgements 168

List of Acronyms and Abbreviations 169

STAR Looks at the Earth | Satellite Measurements of the Atmosphere, Oceans and Land 1

Section 1 

Prelude 1
In 2012 the Center for Satellite Research and 

Applications moved to the new NOAA Center 

for Weather and Climate Prediction building 

on the campus of the University of Maryland in 

College Park. 
From the Assistant Administrator

The National Environmental Satellite, Data, and Information Service (NESDIS), known as the NOAA Satellite and 

Information Service, acquires and operates the Nation’s operational environmental satellites and maintains long-term 

archives of global environmental data through its World Data Centers. Our mission is to provide timely access to global 

environmental data from satellites and other sources to promote, protect, and enhance the Nation’s economy, security, 

environment, and quality of life. 

For over 50 years, we have been the world’s best at ensuring a continuous uninterrupted flow of environmental data from 

satellites to weather forecasters and other users. This function is recognized as a Primary Mission Essential Function, or 

one that is essential to support the continuity of Government. Satellite observations are the foundation on which the 

Nation’s forecasts, warnings, and environmental observational systems are based. Our environmental satellites are key 

national infrastructure that helps protect lives and property and add immense value to the national economy.

As we continue our current operations, we also plan for the future – our next generation polar-orbiting and geostationary satellite programs, currently 

in the development stage, are poised to deliver enhanced observations and improve forecasts and warnings. NOAA is investing today to ensure that 

the Nation can continue to rely on these critical observations for years to come. This key national infrastructure is essential to our Nation’s ability to 

prepare for and deal with severe weather and other environmental phenomena.

The Center for Satellite Applications and Research (STAR) is NESDIS’ science office and provides the foundational science information for 

selecting the measurement bands for monitoring the Earth from satellites, improving our understanding of the Earth and its processes, and 

transitioning new products and services into operations. This book is dedicated to the efforts of NESDIS’ scientific capabilities and the knowledge 

retained in its people. 

NOAA’s mission is to understand and predict changes in climate, weather, oceans, and coasts, to share that knowledge and information with others, 

and to conserve and manage coastal and marine ecosystems and resources. This mission is central to many of today’s greatest environmental challenges: 

climate change, severe weather, natural and human-induced disasters, declining biodiversity, ocean acidification, threatened or degraded ocean and 

coastal resources. Successfully addressing these pressing issues requires timely and usable information to aid decision-making, and the science that 

underpins our knowledge of these systems. NESDIS and STAR are key inputs into NOAA’s success in addressing these important challenges. 

Mary E. Kicza 

Assistant Administrator for Satellite and Information Services

National Oceanic and Atmospheric Administration (NOAA)

U.S. Department of Commerce

STAR Looks at the Earth | Satellite Measurements of the Atmosphere, Oceans and Land 3
4 Section 1 | PRElude

From the Director

The Center for Satellite Applications and Research (STAR) is the National Oceanic and Atmospheric Administration’s 

(NOAA) premier research office for developing the satellite algorithms needed to transform remote satellite observations to 

environmental measurements. We strive to improve our monitoring, predicting, and understanding of the environment, while 

helping to meet the requirements of environmental decision and policy makers. Our team primarily uses the observations 

of NOAA’s operational satellites, but also capitalizes on the measurements of Earth observing research satellites and 

the satellite data of our international partners. Supported by our university affiliates and contract scientific staff, STAR 

researches, develops, applies and transitions to routine operations the best methods for monitoring the Earth and its climate.

Science, service and stewardship are the foundational cornerstones of NOAA research. Our select scientific workforce 

makes STAR research a key contributor to achieving NOAA’s goals by providing the highest quality science. The 

discovery and application of new science and an improved understanding of the planet motivates our staff to develop 

better products. We are committed to advancing the frontiers of satellite capabilities so that our service can permit cost 

effective and prudent environmental decisions. Most importantly, our Center takes to heart the need to be good stewards for our country and the 

planet on which we all live. Understanding how the climate is changing and how it will impact future generations is a fundamental concern. Working 

to ensure a sustainable environment for our sons and daughters and the generations that will follow them is a primary principle that drives us to be 

good stewards of our home – Earth.

From the vantage point of space, we can see the changing Earth in its entirety. Since the environmental satellite’s inception over 50 years ago, STAR has 

continued to develop the capability to monitor the ozone hole over Antarctica, track changes in the Earth’s vegetation, and measure the recent decline in 

the North polar ice cap. Satellite observations now provide over 95% of the data used in NOAA’s numerical weather prediction models and have been a 

key factor in improving the reliability of long-range weather forecasts–today’s 6-day forecasts are about as accurate as 4-day forecasts were two decades 

ago. This book highlights our ongoing research and spans the set of atmosphere, ocean and land variables for monitoring the planet and its changes.

This book embodies the heart and soul of the dedicated team of NOAA researchers interested in understanding and improving our stewardship of the 

planet. It is both dedicated to this group of devoted and passionate scientists and a tribute to their success. May you enjoy the short explanations and 

images as you learn about the environment. For those who find this work interesting, I hope you will follow our lead and join us in studying the Earth 

from space!

Alfred M. Powell, Jr

Director, Center for Satellite Applications and Research

Satellite and Information Services

National Oceanic and Atmospheric Administration (NOAA)

U.S. Department of Commerce
STAR Looks at the Earth | Satellite Measurements of the Atmosphere, Oceans and Land 5

Introduction

The first weather satellite was launched into space over 50 years ago. Since that time, 

advances in space-based Earth observing systems have revolutionized the way we predict the 

weather, monitor the oceans, assess climate change, and detect environmental hazards. This 

monograph is a snapshot of the current capabilities of satellites to observe the ever changing 

global environment, as seen through the research of the scientists of the Center for Satellite 

Applications and Research (STAR) and their collaborators. The goal is to provide the reader 

with a book that is interesting, informative, aesthetically pleasing, and easy to digest. It 

consists of a compilation of 76 short articles, each devoted to a particular research topic 

and accompanied by relevant illustrations and illuminating sidebars. Every STAR scientist 

participated in writing this monograph, either as an author or co-author of at least one article. 

For a number of the articles, they were joined by collaborators from STAR’s Cooperative 

Institutes, and scientists from academia, other government agencies, and the private sector.

The book is divided into five sections. The first contains articles on the basic concepts of 

satellite observations, the evolution of the international array of Earth observing satellites, 

the methods used to check on the accuracy of these remote measurements, and the transition 

of advances from research to operations. The remaining chapters are on the atmosphere, 

the oceans, the land, and the climate. Each chapter describes applications of satellite 

observations for monitoring, predicting, and understanding the different aspects of the 

Earth’s environment. Each chapter contains an introductory overview. Most of the articles 

are on individual environmental variables - e.g., sea surface temperature, atmospheric ozone, 

etc. Each article places the research topic in context, provides background, explains in simple 

terms the physical basis for the measurement, captures applications and results, and indicates 

the contribution of STAR scientists to advancing the research.

NOAA is the primary user of the satellite measurements, but a host of other national and 

international bodies, both public and private, apply the data and STAR’s research results to 

environmental problems.

We hope you learn something about the “magic” of remote sensing from this monograph, 

and that you share the excitement of STAR scientists as they expand the application of 

space-based measurements of the Earth. 

Image of the Earth from the Suomi NPP Visible Infrared Imager Radiometer Suite (VIIRS) observations on 

January 24, 2012. (Image credit: NASA/NOAA/GSFC/

Suomi NPP/VIIRS/Norman Kuring)

Launch of NOAA’s latest satellite – the joint NOAA/NASA 

Suomi National Polar-orbiting Partnership (Suomi NPP) – 

on October 29, 2011. This next generation polar-orbiting 

satellite has unprecedented capabilities for observing the 

Earth’s weather, oceans, land and climate. (Photo credit: 

NASA/Bill Ingalls)

STAR Looks at the Earth | Satellite Measurements of the Atmosphere, Oceans and Land 7

Section 2 

The Satellite System 2
8 Section 2 | The satellite system

Satellite Remote Sensing 101 (Part 1)

Don Hillger

The first two articles present some of the basic concepts needed to understand how 

satellites measure the Earth’s properties from space.

Satellite instruments are either passive or active. Passive instruments – think of 

a digital camera – measure electromagnetic radiation reflected or emitted by the 

Earth and its atmosphere. Active instruments – think of radar – send a man-made 

signal that is bounced off the Earth’s surface or its atmosphere before returning to 

the satellite. In both cases, the measured radiation is sensitive to the surface and/or 

atmospheric conditions. The signal also depends on light measured from a specific 

part of the electromagnetic spectrum. For example, clouds are seen in the visible 

part, temperatures are detected in the infrared.

Satellite instruments have steadily improved, with accurate measurements now 

available globally at fine spatial resolution, with rapid updates – as frequently as 5 

minutes – and at thousands of wavelengths of the spectrum.

NOAA’s primary sources of remote sensing data are its own operational satellite systems – the Geostationary Orbiting Environmental Satellites (GOES) series 

and the Polar Orbiting Environmental Satellites (POES) series (see figure opposite page) – and the operational satellites of other international satellite agencies. Additional observations are available from the research satellites flown by the 

National Aeronautics and Space Administration (NASA) and other international 

agencies.

The satellite observations are processed into a wide variety of environmental information products for use in numerous real-time and research applications. These 

include: weather warnings and prediction; ocean monitoring and prediction; climate monitoring and prediction; tracking snow and ice cover; measuring global sea 

level; monitoring air pollution; detecting environmental hazards such as fires, volcanic ash, algal blooms, coral bleaching, and microbursts; tracking variations of the 

Earth’s vegetation and drought areas; and checking up on the Earth’s ozone layer.

How many of Earth’s artificial 

satellites are remote sensing satellites?

About 8000 artificial satellites have been launched by 

countries of the world since the first Sputnik in October 

1957. The early satellites were mostly experimental, 

but they quickly progressed into physics experiments, 

communications applications, and then remote sensing 

platforms from satellite orbit.

Although some satellites have multiple purposes, 

approximately one fifth of the satellite launches can be 

considered to be Earth, solar, or space science satellites. 

These satellites help us to understand the Earth and its 

place in the cosmos and record the Earth’s constantly 

changing atmosphere, land and oceans.

These space observations improve weather and ocean 

forecast accuracy, enhance environmental monitoring, 

provide more trustworthy climate observations, and lead 

to increased knowledge of the Earth system.

A rendering of a GOES Satellite. 
STAR Looks at the Earth | Satellite Measurements of the Atmosphere, Oceans and Land 9

Two Main Types of Weather Satellites

POES

GOES

Weather satellites are generally either polar orbiting or geostationary. Polar satellites orbit (circle) the Earth as it rotates beneath 

them. Each orbit takes about 100 minutes and typically crosses over the poles. Geostationary satellites orbit the Earth near the 

equator at the same rate the Earth rotates. Thus, they remain stationary with respect to the Earth and constantly monitor the same 

geographic region. These satellites are the mainstays of NOAA’s National Environmental Satellite, Data, and Information Service. 

While a single geostationary satellite can continuously view a large portion of the Earth, it can only see what is below it. A polarorbiting satellite can monitor the entire Earth on a daily basis since it sees a different part of the Earth with each orbit. Together, 

and with more than one of each, these two types of satellites provide the data needed for the analysis and prediction of the Earth’s 

environment. 
10 Section 2 | The satellite system

Satellite Remote Sensing 101 (Part 2)

Don Hillger

Remote sensing of the Earth from satellites can be divided into two main types: 

measurements of solar radiation (sunlight) reflected by the Earth and measurements 

of thermal radiation emitted by the Earth. Solar radiation is concentrated at short 

wavelengths – ultraviolet and visible – and thermal radiation at longer wavelengths 

– infrared (IR) and microwave. The spectrum of reflected solar radiation provides 

not only images of clouds but also information on snow and ice cover, vegetation, 

ocean phytoplankton, atmospheric pollutants and ozone. The thermal radiation 

spectrum is sensitive to surface, cloud, and atmospheric temperatures, water vapor, 

and other gaseous components of the atmosphere. It’s at thermal wavelengths that 

the Earth can be seen both day and night, making tracking of cloud systems 24 

hours a day possible.

While clouds are generally opaque to IR radiation, they are transparent or partially 

transparent to the longer wavelength microwave radiation, depending on the cloud 

thickness and microwave observing wavelength. This characteristic of microwave 

radiation makes it particularly useful for measuring the vertical temperature 

structure of a cloudy atmosphere as well as the internal structure of clouds and the 

amount of falling rain.

More recently active satellite instruments – radars and lidars (radio and light 

detection and ranging) – have come into use. Their main advantage is the ability to 

determine, with very high accuracy, the heights from which the radiation that they 

pulse toward the Earth is being reflected, allowing, for example, measurements 

of sea surface height with an accuracy of centimeters. Another type of active 

measurement is based on the radio signals emitted by the high altitude GPS 

satellites – the same system used by our GPS devices. By measuring, from low 

Earth-orbiting satellites, the GPS radio signals that have traversed the atmosphere, 

very accurate information on atmospheric temperature can be derived.

How are digital satellite images made?

Any image of the Earth consists of pixels (picture 

elements) of data that are collected successively by 

either the movement of the sensor or a combination 

of the movement of the sensor and the satellite. After 

the data are transmitted down to a ground station, 

images of the Earth are reconstructed by putting the 

pixels back into the correct order. Because of finer 

spatial and temporal resolution, as well increases in 

the number of spectral bands (channels) that are being 

measured, the volume of data sent down by satellites 

has vastly expanded. For example, the next generation 

of Geostationary Operational Environmental Satellites 

(GOES) will be sending down about 50 times as much 

data as current GOES. This huge data stream requires 

high-speed computers and rapid communications to 

disseminate real-time remote sensing data for weather 

analysis and forecasting.

Each day, the next generation GOES satellite will transmit a data stream to Earth, equivalent to all the books in 

the Library of Congress (above)! (Photo credit: Library of 

Congress)
STAR Looks at the Earth | Satellite Measurements of the Atmosphere, Oceans and Land 11

Although remote sensing is most common at visible/reflective and infrared/emissive wavelengths, much of the electromagnetic 

spectrum can be measured. These measurements reveal characteristics of the land and ocean surfaces, the many types of 

clouds in the atmosphere, as well as the atmosphere itself. Satellite images can be enhanced by the addition of color (for 

example, the thermal radiation image above, with high, thick clouds in color), or images can be combined to create various 

products and services that are useful for detecting or discriminating features of interest. 

Remote Sensing at the Visible and Infrared Wavelengths
12 Section 2 | The satellite system

Evolution of the Global Satellite 

Observing System

Laurie Rokke and Mitch Goldberg

The following banner headline ran across all six columns of the first page of the 

New York Times on April 2, 1960.

U.S. ORBITS WEATHER SATELLITE; 

IT TELEVISES Earth AND CLOUDS

The satellite produced pictures of clouds and storms around the globe that might 

have otherwise gone undetected for days using conventional observation methods. 

The first geostationary satellite – the Applications Technology Satellite - launched 

in 1966-67, took the first color images of the Earth. Advances in instrumentation 

expanded the observational spectrum from the visible and infrared to the microwave 

region, which enabled measurements through clouds, and to the ultraviolet for 

ozone measurements. Finer spectral and spatial resolution and more frequent 

measurements from the increasing number of polar and geostationary satellites in 

the space-based global observing system (see figure opposite page) now provide 

continuous three-dimensional monitoring of the Earth’s surface and atmosphere.

STAR scientists are active participants in the Coordination Group for 

Meteorological Satellites (CGMS) and the Committee on Earth Observation 

Satellites (CEOS), international bodies that coordinate the development of spacebased observations. STAR was a founding member of the Global Space-based 

Inter-Calibration System (GSICS), an international collaboration to monitor, 

improve and harmonize data quality from operational environmental satellites.

STAR is contributing to the development of the next generation of NOAA 

geostationary – GOES-R – and polar – the Joint Polar Satellite System ( JPSS) – 

satellites, which will have advanced capabilities to meet the nation’s ever expanding 

needs for accurate and timely environmental data.

Then and now!

The Television and Infrared Observation Satellite (TIROS 

1) was launched on April Fool’s Day (1 April) 1960. The 

figure below shows the first photograph ever taken by 

a weather satellite. On-board was a modified television camera (Vidicon). The satellite lasted 78 days and 

produced 23,000 pictures. It revealed a tropical cyclone 

off the coast of Australia.

Image of Hurricane Katrina from the current GOES 

satellite. Since the mid-1960s no tropical cyclone has 

gone undetected. (Photo credit: NASA (top))
STAR Looks at the Earth | Satellite Measurements of the Atmosphere, Oceans and Land 13

Schematic diagram of the global satellite observing system. Low Earth orbiting satellites (LEOs, inner rings) circle the Earth at 

about 800 km in about 100 minutes. Polar orbiting LEOs can provide daily global coverage as the Earth spins beneath them. 

Geostationary satellites (GEOs, outer ring) orbit the Earth at the same rate as the Earth rotates. As a result, geostationary GEOs 

observe the same locations day and night. Recently launched LEO satellites not appearing in this diagram include the U.S. Suomi 

National Polar Partnership (NPP) in late 20011 and the Japanese Global Change Observation Mission 1 (GCOM-1) in 2012. 

(Photo credit: WMO)

The Global Satellite Observing System
14 Section 2 | The satellite system

Research to Operations

Michael Chalfant and Laurie Rokke

STAR provides the science that turns satellite data into products that support 

NOAA operational missions 24 hours a day, 7 days a week. Challenges to the 

successful transition from research to operations (R2O) include satisfying many 

different customers, extracting the maximum information from instruments that 

sense remotely, and developing data processing systems that transform raw satellite 

observations into geophysical products in near-real time (minutes to hours). A 

lifecycle of satellite products from requirements to retirement/replacement is 

shown in the chart on the opposite page.

New satellite data product requests come in from NOAA’s weather, ocean, and 

climate operations. With the assistance of STAR scientists, instruments are designed 

to meet these needs and launched into space. Once the instrument specifications are 

finalized, STAR scientists set to work to create the needed products. They develop 

algorithms - a series of mathematical steps - for processing the observations. Teams 

of programmers, computer scientists and engineers develop and transition the 

often complicated science code into streamlined and standardized programming 

languages that can meet the demands of the operational environment. The teams 

also track and document changes to the algorithms, take into account instrument 

anomalies, and monitor and update the science and operational code as the 

instruments age. A NESDIS board reviews and approves all new operational 

products, enhancements, and updates to existing products.

The next generation of satellites to be launched into polar and geostationary 

orbits will fulfill a host of additional NOAA requirements and provide new 

challenges for the R2O transition teams to develop, validate and maintain 

these products in operations.

Mass producing a catapult!

The transition of new ideas into successful products has 

been a problem since at least the time the Romans failed 

to transition their recently designed catapult into mass 

production. Despite the best efforts of satellite research 

to operations transition teams, unforeseen instrument 

problems may occur. The transition teams are constantly 

on high alert to detect and correct malfunctions.

The figure below shows data from the NOAA-15 

Advanced Microwave Sounding Unit (AMSU-A). The 

R2O team immediately detected and resolved a serious 

problem seen in the rightmost orbital swath. Channel 7 

(upper panel), which observes at a microwave frequency 

sensitive to the atmosphere, had been swapped onboard 

the instrument with the surface observing channel 

15 (lower panel) – the channels had been incorrectly 

wired. The simulated solution to this problem - shown 

on the next two orbits – led to the corrective action by 

STAR scientists, instrument engineers, and operational 

personnel that overcame this major flaw for the 

remainder of its multi-year mission. 
STAR Looks at the Earth | Satellite Measurements of the Atmosphere, Oceans and Land 15

The life cycle of a satellite product. 

From Concept to Mastery
16 Section 2 | The satellite system

Calibration

Changyong Cao, Xiangqian Wu, Robert Iacovazzi, and Fangfang Yu

Measuring the Earth’s reflected and/or emitted radiation from satellites is similar 

to taking photographs with a digital camera. A major difference, however, is 

that the digital data in Earth observations have to be calibrated and converted 

from unitless digital numbers to a measure of radiant energy – radiances – with a 

physical unit (see sidebar). The calibration must be traceable to reference standards 

so that the instrument’s observations can be compared with other measurements. 

STAR scientists are responsible for ensuring that NOAA’s satellite instruments 

provide reliable data.

Climate change detection provides an especially demanding challenge for 

calibration accuracy and consistency since the signal can be very small, e.g., about 

0.1 to 0.2 degrees Centigrade (C) per decade for temperature changes. Also, since 

satellite instruments last only about five years, observations from instruments on 

a series of satellites are used to establish climate trends. But even the same series 

of radiometers do not automatically produce consistent measurements. Our team 

then steps in and maintains consistency.

To attack this problem, the World Meteorological Organization (WMO) 

organized the Global Space-based Inter-Calibration System (GSICS) to ensure 

the comparability of measurements from all satellites. Since its inception in 2006, 

15 national and international agencies, including all weather satellite operators 

and other space agencies around the world, have joined the program. GSICS 

helps to provide consistent measurements from all satellites, from all countries, 

to support the environmental monitoring and prediction missions of NOAA 

and other agencies. STAR played a key role in organizing GSICS, co- chairs its 

Executive Team, and houses the GSICS Coordination Center to ensure that the 

many satellite instruments are accurately calibrated.

Ice, boiling water, and calibration

Most satellite instruments use two relatively well-known 

points for calibration – similar to using the freezing and 

boiling point temperatures of water for calibrating thermometers. One is typically the space-view, or SP0

 in the 

figure below (left), and the other is an onboard source 

of known amount of radiation, or calibration point CP0. 

This device is typically a blackbody for the infrared and 

microwave instruments, and a solar diffuser (reflector) 

or lamp for the solar or visible bands. Assuming that the 

instrument has a linear response to incoming radiation, 

a perfect two-point calibration system would be sufficient 

to calibrate all satellite observations (delta counts in the 

figure) to radiances. If pre-launch laboratory measurements indicate that the calibration is non-linear, a curve 

such as NL would be used. 
STAR Looks at the Earth | Satellite Measurements of the Atmosphere, Oceans and Land 17

In support of the Global Space-based Inter-Calibration System, STAR scientists have designed, implemented, and routinely 

operated a system to inter-calibrate the infrared channels of all the geostationary weather satellites with the well calibrated 

hyperspectral (thousands of observing channels) instruments: NASA’s Atmospheric Infrared Sounder (AIRS) and Europe’s Infrared 

Atmospheric Sounding Interferometer (IASI). In this example, a European geostationary satellite, METEOSAT-9, is compared with 

AIRS (blue) and IASI (red) for one of its many channels over a period of two and a half years. The degradation of the Meteosat IR 

channel with respect to AIRS and IASI is clearly shown by the sloping red and blue lines of dots. These data also demonstrate the 

high consistency of AIRS and IASI observations (black dots). 

Correcting for Instrument Drift
18 Section 2 | The satellite system

Checking the Measurements

R. Bradley Pierce, Andrew Heidinger, Sebastian Schmidt, and Sara Lance

When an instrument is placed directly in or on an object to measure its properties – 

for example, thermometers measuring air temperature – the measurement is called 

a direct measurement. Satellite observations of geophysical variables are indirect 

measurements: they depend on a conversion – through the atmospheric radiative 

transfer equation – of the remotely sensed radiance to the variable of interest, a 

process referred to as “retrieval”. The quality of the retrieval is not only dependent 

on the accuracy of the radiance measurements – see discussion in article on 

calibration – but also on uncertainties in the radiative transfer equation. In many 

cases, other factors besides the variable of interest affect the observed radiation 

signal, and these must be properly accounted for in the retrieval procedure.

Validation is the process of checking the quality of the satellite retrievals using 

independent and more accurate measurements, that is, by comparing them with 

observations from another source. Independent measurements include in-situ 

networks – weather stations, radiosondes, ozone and aerosol networks, ocean buoys 

and ships – and special aircraft and research campaign measurements.

The sidebar and the figure on the opposite page illustrate the use of field campaign 

measurements to verify satellite-based measurements of cloud properties. The 

field measurements – in this case aircraft and ship observations – are carefully 

coordinated with the satellite passes.

Understanding the quality of the satellite retrievals is crucial to monitoring, 

predicting, and understanding the Earth/atmosphere system. Validation provides 

the traceable estimates of the reliability of satellite retrievals that are necessary to 

make informed use of the data. Validation of satellite retrievals is a major part of 

STAR’s program.

Validation of GOES cloud retrievals

STAR scientists collaborated with researchers from the 

University of Colorado and the NOAA Earth System 

Research Laboratory to use data collected during the 

NOAA CalNex field mission to validate experimental 

GOES cloud retrievals.

On May 16th, 2010, the Woods Hole Oceanographic 

Institution research vessel “Atlantis” and the NOAA P3 

aircraft conducted joint measurements within a marine 

cloud layer west of Santa Catalina Island off the coast of 

Southern California. 

The GOES cloud retrievals are compared to the ship 

and aircraft measurements. Satellite observations are 

considered validated when they agree with the other 

measurements. 
STAR Looks at the Earth | Satellite Measurements of the Atmosphere, Oceans and Land 19

Time series of the GOES (red) and Atlantis (Ship) Solar Spectral Flux Radiometer (SSFR) (black) retrievals, and P3 (Aircraft) 

in-situ (blue) measurements of Cloud Liquid Water during the NOAA P3 rendezvous with the Woods Hole Research Vessel 

Atlantis on May 16, 2010. Airborne in-situ measurements from cloud probes onboard the NOAA P3 research aircraft provide direct 

measurements of the microphysical properties of the clouds, which can be used to understand differences between the GOES 

and SSFR retrievals. 

Validating GOES Cloud Retrievals with Aircraft and Ship Observations

Observations
20 Section 2 | The satellite system

The NOAA Products Validation System

Anthony Reale and Bomin Sun

The previous article describes how specially conducted field observations are used to 

check satellite retrievals. Here we show how STAR leverages existing observational 

networks to verify the satellite measurements.

STAR has developed the NOAA Products Validation System (NPROVS). 

NPROVS relies on global “ground truth” observations, typically from weather 

balloons (radiosondes and dropsondes) and numerical weather prediction (NWP) 

models to estimate the prevailing atmospheric conditions at a given location and 

time. These are then compared to atmospheric measurements from polar (NOAA, 

MetOp, DMSP and NASA), geostationary (GOES) and occulting satellite 

platforms at the same times and locations. An example of an occulting satellite 

is the Constellation Observing System for Meteorology, Ionosphere and Climate 

(COSMIC) where signals traverse the limb of the Earth from one satellite to 

another, versus being reflected from the Earth’s surface. A comparative analysis of 

these measurements yields information on the quality and integrity of the satellite 

data.

NPROVS is the first system to combine satellite observations from polar, 

geostationary and occulting sensor technologies into a single dataset collocated 

to common ground truth measurements. NPROVS will play a key role in the 

validation and deployment of advanced next generation satellite sensors and 

products. Goals include the compilation of long-term records linking the 

performance of past, present and future environmental observations and the 

construction of homogeneous observations to monitor climate.

A stamp of approval

NOAA spends billions of dollars annually on programs 

to design, build and operate multiple Earth orbiting 

satellites and a variety of onboard environmental 

sensors. The primary objective is to provide critical 

information in support of NOAA’s mission goals in 

weather, ocean, climate and other environmental 

applications. However, it is not enough to simply 

collect data – how do we know that the measurements 

are correct? Validation – the process of checking the 

satellite measurements against reliable, independent 

observations – allows NOAA to provide a “stamp of 

approval” on the space-based measurements.

Collocated satellite and weather balloon observations 

(radiosondes) processed by NOAA’s Products Validation 

System are a key contribution to validation of multiple 

satellite sensors and derived weather products on a 

global scale. (Photo credit: NOAA/NWS/Robin Ingram, 

Sara Presnall, and Art Wildman)
STAR Looks at the Earth | Satellite Measurements of the Atmosphere, Oceans and Land 21

NOAA’s Products Validation System (NPROVS) routinely compares global atmospheric weather data derived from sixteen 

environmental sensors (in italics) onboard eight different satellites (green) against collocated radiosonde, dropsonde and 

numerical weather prediction (NWP) forecast and analysis data (red). Next generation satellites to be added to the system 

include NOAA’s Joint Polar Satellite System (JPSS), the NASA-NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) 

satellite (launched in 2011 as the precursor to JPSS), and MetOp-B (yellow). NPROVS collocation datasets are compiled daily 

and long term records are archived at STAR. 

Routine Global Monitoring of Satellite Weather Products
22 Section 2 | The satellite system

Web-Based Monitoring

Alexander Ignatov, Xingming Liang, Prasanjit Dash, Feng Xu, and John Sapper

With the expanding operational applications of satellite products and the ever 

increasing number of users, an easily accessible, comprehensive, and consistent 

monitoring and verification system is needed to:

● Provide fully automated, online, timely, and global monitoring

● Monitor in-situ data used for checking the satellite product

● Monitor satellite radiances associated with the satellite product

A key feature of a prototype system recently developed by STAR for monitoring 

sea surface temperatures (SST) is its timeliness – it is done in near real-time, that 

is, immediately after the satellite observations have been made.

The web-based system consists of three components:

● The Satellite SST Quality Monitor tracks various SST products for 

stability, analyzes them for self- and cross-consistency, and verifies them 

against quality controlled in-situ data. 

● The in-situ SST Quality Monitor performs quality control of in-situ 

SST data, monitors them on-line, and serves them to outside users.

● The system for Monitoring of IR Clear-sky Radiances over Oceans 

for SST keeps tabs on the clear-sky radiances (brightness temperatures) 

of the ocean – from which the SSTs are derived – using a radiative 

transfer model in conjunction with atmospheric information from an 

NWP model to simulate the satellite clear-sky radiances.

This near real–time, web-based system makes the SST verification data easily and 

quickly accessible to STAR scientists and the user community.

Why near real-time online monitoring?

STAR develops improved products. Their performance 

should be evaluated against heritage products, and their 

improvements should be demonstrated.

Similar products are generated by STAR partners. STAR 

wants to know how its products are performing relative 

to these counterparts.

Near-real time monitoring is needed to objectively 

evaluate the quality of STAR products and quickly fix 

anomalies in operational data. 

Online monitoring is needed to effectively communicate 

status and quality of STAR products to data users, 

developers, and managers. 

Quality monitoring is also the key element in the 

development and verification of Climate Data Records 

(CDR).

STAR scientist Alex Ignatov checks the quality of 

satellite sea surface temperature measurements using 

the web-based monitoring system. 
STAR Looks at the Earth | Satellite Measurements of the Atmosphere, Oceans and Land 23

Systematic differences – biases in nighttime SST between the four NOAA Advanced Very High Resolution Radiometer (AVHRR) 

instruments on NOAA-16, -17, -18, -19 and a reference satellite, Metop-A – as seen in STAR’s prototype online system. Generally, the 

systematic differences are small – less than 0.2 °C – and stable, except for the NOAA-16 AVHRR (green curve), whose suboptimal 

calibration affects the quality of the derived SSTs – biases are not stable and are as large as -1 °C. Work is underway to improve the 

calibration of the NOAA-16 AVHRR, and test the effect on SST consistency using the on-line system. 

Web-based Monitoring of Sea Surface Temperature

STAR Looks at the Earth | Satellite Measurements of the Atmosphere, Oceans and Land 25

Section 3 

The Atmosphere 3
26 Section 3 | The atmosphere

The Atmosphere

Mark DeMaria and Debra Molenar

The Earth’s atmosphere is primarily comprised of oxygen (21%) and nitrogen 

(78%). The remaining 1% is still very important to the Earth-atmosphere system 

because of its role in biological processes and interaction with the incoming solar 

and outgoing infrared radiation. Two scarce but important molecules are ozone, 

which absorbs ultraviolet light from the sun, and carbon dioxide, which absorbs 

infrared radiation and helps to regulate atmospheric temperature. By absorbing 

the Sun’s ultraviolet radiation, ozone blocks these life destroying rays from reaching 

the Earth’s surface. Without the trace gas ozone, the Earth might be uninhabitable.

Under normal temperatures, all of the atmospheric constituents are gases except 

water molecules which can be in solid (ice), liquid (cloud and rain droplets), and 

gaseous (water vapor) form. When water molecules change phase, heat is released 

to or absorbed from the atmosphere. This plays an important role in the formation 

of severe storms and tropical cyclones, as well as the general circulation – prevailing 

wind patterns – of the atmosphere. Water vapor is also the most important 

greenhouse gas – without it the Earth would be about 20 degrees Centigrade 

colder than it is now.

Most of the incoming solar radiation passes directly to the surface, heating the 

atmosphere from below. About 90% of the atmosphere is in the troposphere (see 

figure opposite page) where most of the weather with human impact occurs. The 

temperature increases with height in the stratosphere due to ultraviolet absorption 

by ozone and in the thermosphere because a small amount of energy absorption 

results in large temperature increases due to the very low density.

The atmosphere is heated more at the equator than at the poles because of the sunEarth geometry, which provides the energy for the large-scale circulation patterns 

of the atmosphere and establishes the Earth’s major climatic zones.

Ice crystal habits

Water is one of the few atmospheric constituents that 

can exist in all three phases (solid, liquid, gas) under 

the normal range of atmospheric temperatures. The ice 

phase is extremely complex, where a multitude of crystal 

habits (shapes) exist. Small variations in temperature 

and moisture content lead to very different structures, 

although most have a hexagon appearance. Below are 

several examples of atmospheric ice crystals many of 

which are very aesthetically pleasing.

(Photo credit: www.Snowcrystals.com)
STAR Looks at the Earth | Satellite Measurements of the Atmosphere, Oceans and Land 27

Temperature Change with Altitude

The average temperature of the atmosphere versus height. The right vertical axis is pressure measured in millibars. The atmosphere is 

divided into 4 layers (troposphere, stratosphere, mesosphere and thermosphere). The mass in an atmospheric layer is proportional to the 

pressure difference of the layer, which indicates that most of the atmospheric mass is in the troposphere. 
28 Section 3 | The atmosphere

Atmospheric Temperature Sounding

Lihang Zhou, Mitch Goldberg, Fuzhong Weng, and Tong Zhu

Imagine looking through the eye of a hurricane and knowing the storm structures 

from the inside out – that is something that today’s satellite technology can 

do! Accurate observations of the three dimensional distribution of atmospheric 

temperature over the globe are critical for numerical weather prediction and climate 

studies. Satellite infrared or microwave instruments measure the radiation emitted 

by the atmosphere at different wavelengths (so called channels). Different channels 

are sensitive to the radiation emitted from the different altitudes of the atmosphere. 

The amount of emitted radiation depends on the temperatures at these altitudes. 

From a careful analysis of the radiance data observed by different channels and 

solving the radiative transfer equation, one can deduce the atmospheric temperature 

at different altitudes.

The atmospheric sounding channels are located in parts of the infrared (IR) and 

microwave spectrum for which the atmosphere is the main contributor to the 

measured radiance. Microwave sounders have the ability to see through cloud and 

hence offer nearly all-weather capability; but their spatial resolution is generally 

lower than that of the IR instruments.

The new generation of hyper-spectral sounders has advanced capabilities for 

atmospheric sounding. NASA’s Atmospheric InfraRed Sounder (AIRS) is the 

first hyperspectral – thousands of spectral channels – instrument to be used 

operationally for weather forecasting. With 2378 spectral channels, AIRS has a 

spectral resolution more than 100 times greater than previous IR sounders, which 

enables retrievals with much improved vertical resolution. From 2002 to the 

present, STAR has been actively participating in the development of the AIRS 

products and has developed a retrieval system to process and distribute the high 

quality AIRS temperature, moisture and trace gas sounding products in near real 

time to National Weather Prediction centers worldwide.

Seeing Into the heart of a hurricane

A cross-section of Tropical Cyclone Giovanna (below) off 

the coast of Madagascar at 0630Z, February 13, 2012, 

reveals its internal temperature anomaly from the average temperature at each level as a function of altitude 

(pressure, vertical axis) and east longitude (horizontal 

axis). Note the high temperatures toward the upper central part of the storm. This warm region is caused by the 

release of latent heat as the water vapor drawn upward 

from surface condenses into cloud droplets. The colder 

areas in the lower part are due to radiational cooling 

caused by heavy rain. The temperatures are from the 

Suomi NPP Advanced Technology Microwave Sounder 

(ATMS). 
STAR Looks at the Earth | Satellite Measurements of the Atmosphere, Oceans and Land 29

Extending the Range of Accurate Weather Forecasts

The first tests of the impact of a hyperspectral temperature sounder – NASA’s Atmospheric InfraRed Sounder (AIRS) – on weather forecasts revealed 

significant increases in forecast skill in the Southern Hemisphere and improvements in the Northern Hemisphere. The figure shows the 500 mb 

anomaly correlation – a standard measure of forecast skill – for the Southern Hemisphere as it falls off with forecast duration. OPS represent the 

forecasts without the AIRS data, and OPS+AIRS includes assimilation of AIRS radiance observations. The increase in forecast skill at 5 days is 

equivalent to gaining a 5 hour extension of prediction capability. While this may seem small, it is quite significant when compared to the rate of general 

forecast improvement over the last decades: a 5 hour increase in forecast range normally takes several years of NWP model development to achieve. 

(Photo credit: NASA-NOAA-DOD Joint Center for Satellite Data Assimilation)
30 Section 3 | The atmosphere

Atmospheric Water Vapor Sounding

Lihang Zhou, Mitch Goldberg, Laurie Rokke, Walter Wolf, and Xingpin Liu

Water vapor plays a critical role in the transfer of energy and water in the 

atmosphere/ocean/land system. Satellite instruments measure the radiation 

emitted at wavelengths where water vapor is weakly and strongly absorbing in 

both the microwave and infrared spectral regions. The different wavelengths are 

sensitive to different layers of the atmosphere and the observed radiation depends 

on the temperature and water vapor content in these layers. Since the temperatures 

are known from the satellite temperature soundings, the water vapor profile can be 

retrieved.

STAR has developed several systems to generate and distribute operational water 

vapor products. The Advanced TIROS Operational Vertical Sounder (ATOVS) 

data processing system provides near real-time water vapor mixing 

ratios (the ratio of the amount of water compared to a dry gas) at 19 

levels (from surface to 200 mb) with a 40 km horizontal resolution 

derived from the AMSU-A and HIRS measurements on board the 

NOAA polar orbiting and European Metop satellites series. The 

operational Microwave Integrated Retrieval System (MIRS) is an 

upgrade to the NESDIS operational microwave retrieval system 

known as MSPPS (Microwave Surface and Precipitation Products 

System) with the extended capability of providing atmospheric 

temperature and moisture profiles in all-weather conditions and 

over all-surface types.

The three dimensional global distribution of water vapor from 

the surface to the top of atmosphere has been obtained from the 

hyperspectral IR observations of AIRS since 2002 and the European 

Infrared Atmospheric Sounding Interferometer (IASI) since 

2006. The narrow spectral channels and huge number of observing 

wavelengths provide increased vertical resolution, and minimize the 

sensitivity to interference signals. The more accurate water vapor 

retrievals with higher vertical resolution provide enhanced water 

vapor information for assimilation in NWP models and capture the 

long term variability and trend of the vertical structure of water vapor.

Water vapor retrieval from AIRS

The water vapor cross section below shows the vertical 

distribution of water vapor for different latitudes, from the 

equator to the poles, as observed by the Atmospheric 

Infrared Sounder. The Y-axis is the altitude in pressure 

units; X-axis is the latitude in degrees. The water vapor 

is the column precipitable water (vertically integrated 

water vapor in a column of atmosphere) in units of g/cm2.

The high water vapor amounts in the tropics and very 

low values in polar regions are quite evident, as is the 

decrease of water vapor with altitude, which is especially 

rapid in the tropics. 
STAR Looks at the Earth | Satellite Measurements of the Atmosphere, Oceans and Land 31

Global Map of Water Vapor in the Middle of the Atmosphere

Average water vapor amount (grams of water) in a 1-km layer at the 5-km level of the atmosphere for the month of July 2006. While most

of the atmosphere is relatively dry at this level with only about 0.1 g of water vapor, the India monsoon area has about six times as much

moisture. With its fine spectral resolution and large number of observing channels, the Atmospheric Infrared Sounder instrument (AIRS)

enables measurement of water vapor at many levels in the atmosphere.
32 Section 3 | The atmosphere

Winds

Jaime Daniels

Atmospheric winds derived from passive instruments aboard geostationary and 

polar satellites provide tropospheric wind information over the vast regions of 

the Earth without weather stations, including the oceans, tropics, Southern 

Hemisphere land masses, and Polar Regions. The satellite winds are assimilated 

into numerical weather prediction (NWP) models, where their use improves the 

skill of the weather forecasts and hurricane warnings. In addition, the satellite 

winds help meteorologists at weather stations around the country to make better 

local weather analyses and forecasts.

Winds were originally derived from satellite images by tracking drifting cloud 

systems on successive images. Techniques were then developed to track features 

in water vapor images (images constructed from measurements at a wavelength 

at which water vapor emits radiation). Developed for geostationary satellites with 

their frequent measurements, the techniques have recently been implemented for 

polar satellites. Polar orbiters pass over the poles about every 90 minutes, allowing 

winds to be derived from sequential images.

STAR scientists have developed the algorithms that enable the generation of 

atmospheric winds from a number of existing NOAA, NASA, and international 

satellites that include NOAA’s GOES, NASA’s polar orbiting Terra and Aqua, 

and the Japanese geostationary Multi-Functional Transport Satellite (MTSAT). 

They are now developing new and improved algorithms for the next generation of 

NOAA geostationary and polar satellites that include GOES-R, Suomi NPP, and 

JPSS.

Cloud and moisture drifts reveal winds

Atmospheric winds at different levels in the atmosphere 

are derived by tracking the motion of clouds or moisture 

features over time. This is done using measurements 

from geostationary and polar orbiting satellites. The 

image shows winds in the Eastern Pacific derived 

from NOAA’s GOES-11 satellite. Counter-clockwise 

circulation of low-level winds (shown by yellow wind 

barbs) clearly shows the position of a low pressure 

system located south-east of the Aleutian Islands and its 

associated cold front where upper-level winds (shown 

by red wind barbs) indicate the location and strength of 

the jet stream. Parts of the Western United States are 

being impacted by an upper and mid-level low pressure 

system as indicated by the counter-clockwise circulation 

upper (shown by red wind bards) and mid-level (shown 

by cyan wind barbs) winds. White areas are regions of 

cloudiness. 
STAR Looks at the Earth | Satellite Measurements of the Atmosphere, Oceans and Land 33

Winds around Hurricane Isabel

GOES-12 cloud-drift winds derived over and in the vicinity of Hurricane Isabel on September 15, 2003. The wind barbs indicate the direction 

and speed of the derived wind as determined by measuring the motion of clouds in and around the hurricane. The wind barbs are color 

coded to indicate the height of the derived winds. Blue, cyan, and green wind barbs indicate that the winds are 7-14 km, 3-7 km, and 1-3 km 

high in the atmosphere, respectively. Note how the low-level counterclockwise inflow into the hurricane changes to a clockwise outflow at 

upper levels (compare the direction of the green arrows versus the blue arrows around the hurricane). 
34 Section 3 | The atmosphere

Cloud Properties

Andrew Heidinger and Andi Walther

When the Earth is viewed from space, the most dominant feature is its cloud 

cover – typically about 70% of the Earth’s surface is covered by cloud at any one 

time. Clouds are important modulators of radiation, play significant roles in the 

hydrological cycle, and provide important insights into the dynamical structure of 

the atmosphere.

STAR scientists are preparing a suite of products to provide advanced capabilities 

for cloud detection and retrieval of cloud properties. The Team has developed five 

algorithms to generate ten unique cloud products for the Advanced Baseline Imager 

instrument that will fly on the next generation NOAA geostationary satellite – 

GOES-R. Versions of these algorithms have been developed for all of the current 

and future NOAA satellite imagers including the Visible and Infra-red Imager 

on the next generation NOAA polar orbiting satellite – the Joint Polar Satellite 

System. The enhanced resolution and additional spectral channels provided by the 

future sensors will offer new opportunities for remote sensing, providing more 

accurate measurements of cloud location, cloud type and phase, and cloud top 

properties. Recent advancements in radiative transfer and ancillary data have also 

led to significant improvements of the cloud properties derived from the current 

instrument suite.

STAR has access to measurements from NASA’s advanced active remote sensing 

cloud instruments. The lidar and radar (light and radio detection and ranging) 

observations of cloud vertical structure from the NASA Cloud-Aerosol Lidar and 

Infrared Pathfinder Satellite (CALIPSO) and CloudSat missions have greatly 

improved STAR’s efforts to validate and characterize its cloud products from the 

passive NOAA satellite instrument observations.

Examining clouds from space

Is there a cloud? 12 different tests that exploit the 

spectral, spatial and temporal characteristics that make 

clouds appear distinct from clear conditions are applied. 

For example, clouds are brighter, colder, and more 

patchy than the clear background.

Water or ice cloud? IR observations at several 

wavelengths are used to separate ice and water clouds, 

since they have different radiative emission properties.

At what altitude is the cloud? IR observations at 

three wavelengths are used to estimate the height 

and microphysics of the highest clouds. Basically, 

the observed IR radiation depends on the cloud 

temperature, which can be converted to cloud height if 

one knows how the temperature varies with altitude. 

How much water is in the cloud? Cloud Optical 

Properties, which can be converted to cloud water 

amount, are determined by analyzing the spectral 

variation of cloud brightness and emitted IR radiation.

Swirls of clouds resulting from the interaction of the 

atmospheric flow and the topographical features of the 

Cape Verde Islands, as seen from the Space Shuttle. 

(Photo credit: Astronaut Don Pettit/NASA)
STAR Looks at the Earth | Satellite Measurements of the Atmosphere, Oceans and Land 35

Cloud Water Amount

Map of cloud water path – the amount of water in a vertical column of cloud – as determined by the Daytime STAR’S Cloud Optical 

and Microphysical Properties algorithm. The observations are from the GOES-12 imager on day 90 of 2010 at 17:45 UTC. Note the 

particularly high water amounts (yellow and red areas) in the cloud systems of the equatorial oceans, the strong convective regions of 

Brazil and Bolivia, and the mid-latitudinal storm system in the Atlantic Ocean. Cloud water path is a critical variable because it provides 

information on how clouds impact the hydrological cycle and the Earth’s radiation budget. 
36 Section 3 | The atmosphere

Amazon Rivers in the Sky

Ralph Ferraro, Mark DeMaria, Stan Kidder, and John Forsythe

Flooding affects more people worldwide than any other naturally occurring highimpact phenomenon. Our ability to predict and monitor the occurrence, location 

and duration of precipitation events cannot be accomplished without weather 

satellites; they provide forecasters with a global view of evolving atmospheric 

conditions related to flood producing storms.

The Earth’s atmosphere is very efficient in transporting water in gaseous form 

(referred to as “water vapor”, “atmospheric moisture”, and “precipitable water”) 

that contributes to rain and snow. Most of this moisture is concentrated in the 

atmospheric layer from the surface to around 10,000 feet. When prolonged and 

heavy rainfall events occur, there is usually an “atmospheric river” associated with 

them – it is a long (hundreds to a thousand miles), relatively narrow (a hundred 

miles or so) plume of moist air that feeds into a weather system to sustain and 

enhance the precipitation associated with it. The river will typically focus itself 

on a particular region, prolonging the duration of the event. The rivers generally 

originate in tropical zones and extend to the mid-latitude areas of North America 

and Europe. It has been calculated that the amount of water vapor transported by 

an atmospheric river is on the same order as the discharge from the Amazon River, 

which is over 4000 miles long!

Satellite instruments, primarily passive microwave sensors that are flown on NOAA, 

NASA and Department of Defense satellites, can accurately measure this water 

vapor so that the atmospheric rivers can be mapped and tracked around the globe. 

Scientists from STAR and its Cooperative Institute partner at Colorado State 

University have recently developed a “blended” product, which incorporates all 

available satellite and, over the U.S., ground-based GPS water vapor measurements 

into a single composited image of global moisture. This product vastly improves a 

weather forecaster’s ability to monitor and predict heavy, long duration precipitation 

events (see figure).

Water vapor, clouds, precipitation, and 

atmospheric rivers

Where does our water vapor come from?

The primary source of water vapor is evaporation from 

the ocean and other large water bodies. Evaporation 

also occurs over land, but to a much lesser extent. 

The water vapor evaporated into the atmosphere is 

transported around the globe by wind.

How is water vapor related to precipitation?

The warmer the atmosphere, the more water it can 

hold before it will become saturated and condensation 

begins (i.e., water goes from gas to a liquid form). 

Meteorological conditions that lead to rising air ultimately 

yield clouds – collections of water and ice particles – 

which, with sufficient water vapor, grow and produce 

rain and snow when the particles become too large 

to stay suspended in the atmosphere. Because the 

atmosphere is warmer in summer than in winter, most 

intense precipitation events occur in the summer. The 

atmospheric river (a long, relatively narrow plume of 

moist air) is an efficient process that helps sustain 

the development of clouds and precipitation; when a 

river is captured by a slow-moving storm system, the 

precipitation falls in a long duration event that leads to 

flooding episodes.

Where and when are atmospheric rivers most prevalent 

in the U.S?

Along the west coast of the U.S., atmospheric rivers 

occur mostly in the fall and winter seasons. In the 

eastern U.S., they can occur any time of the year; 

however, the most extreme instances are in the summer 

and fall when rivers emanate from Atlantic Hurricanes.
STAR Looks at the Earth | Satellite Measurements of the Atmosphere, Oceans and Land 37

Blended Total Precipitable Water and Atmospheric Rivers

An example of NOAA’s Blended Total Precipitable Water product for the time period 0206 – 1147 UTC on 20 March 2011. It consists of 

measurements from several U.S. and European satellites, integrated into a single product for easier use by NOAA weather forecasters. 

The colors represent different levels of moisture as indicated by the color bar below (units are in mm). Over the U.S., ground 

measurements are integrated with the satellite product. At this time, a long duration precipitation event was occurring over California; note 

the “river” of moisture (green) extending across the Pacific Ocean and targeting the California region. 
38 Section 3 | The atmosphere

Precipitation and Flash Floods

Robert J. Kuligowski

Floods are one of the most costly natural disasters in the world – according to the 

United Nations International Flood Initiative, they affect an average of 520 million 

people each year, with 25,000 deaths and $50-60 billon in damages. Floods have 

a variety of causes (dam breaks, melting snow, tidal surges), but many are the result 

of excessively heavy rain during a very short period of time.

Weather forecasters often use rain gauges and radar to detect flood-producing 

heavy rain, but both have limitations: rain gauges are usually spaced too far apart 

to detect smaller storms, and weather radars are not available in many parts of the 

world.

Data from satellites, however, cover the entire globe, and weather forecasters have 

used space observations to monitor rainfall since the 1970s. Manual techniques 

have been replaced by automated programs that can compute rainfall estimates for 

the entire globe in a matter of minutes. Speed is essential to warn people who are 

in the path of a rapidly-evolving flash flood.

The earliest methods for estimating rainfall relied on estimates of cloud-top 

temperature (thick clouds, which produce rain, extend high into the atmosphere and 

have low cloud-top temperatures) from infrared sensors on geostationary satellites. 

More accurate estimates can be obtained using microwave observations (at certain 

wavelengths, they are sensitive to the radiation scattered by rain drops), but these 

measurements are available only from polar orbiting satellites that provide updated 

information only a few times per day. STAR scientists are researching how best 

to combine these two sources of data to provide the best possible rainfall estimates 

for forecasters.

Worldwide use of satellite rainfall data

U.S. National Weather Service forecasters use estimates 

of rainfall from satellite data to supplement radar and 

rain gauge information, especially in regions where radar 

coverage is lacking – for instance, in the Rio Grande River 

region where heavy rains in Mexico can cause significant 

flooding but go undetected by the U.S. radar network.

Forecasters in other parts of the world find even greater 

value in satellite-based estimates of rainfall since radar 

and rain gauge data are less readily available. Weather 

services in Mexico, Brazil, and South Africa are running 

their own versions of the operational U.S. rainfall 

algorithm. Also, a partnership among NOAA, the World 

Meteorological Organization, and a private nonprofit 

organization is working to implement a real-time flash 

flood forecast system that uses rainfall estimates from 

satellites. Forecasters in Central America, southern 

Africa, Southeast Asia, and parts of the Middle East are 

benefiting from this information, which will result in better 

warnings of flash floods and fewer fatalities.

STAR scientist Bob Kuligowski lectures at the Korean 

weather service on techniques for measuring rainfall 

from satellites. 
STAR Looks at the Earth | Satellite Measurements of the Atmosphere, Oceans and Land 39

Flash Floods from Space

An illustration of the process of estimating rainfall from infrared satellite data from a case of heavy rain and flash flooding over Delaware 

and southern New Jersey. Colder cloud tops are assumed to have stronger updrafts transporting moisture upward, and thus heavier rates 

of rainfall production. (Photo credits: NOAA/Tracy Felty (upper left); USGS (upper right))

Low (red) 

temperatures on 

infrared satellite 

imagery indicate 

deep, cold 

thunderstorm 

clouds with 

heavy rain.

These data are 

used to estimate 

rainfall and alert 

forecasters 

to potential 

flooding.

GOES-12 infrared image from 12:45 PM EDT 12 July 2004. Satellite-based total rainfall from 11 AM-7 PM EDT 12 July 2004
40 Section 3 | The atmosphere

Atmospheric Instability: 

Signaling Extreme Weather

Timothy J. Schmit and Gary S. Wade

Forecasting high impact weather events such as severe thunderstorms and 

tornadoes is extremely difficult. Compared to the large scale mid-latitude storms 

that develop and grow relatively slowly – and last for days as they move across the 

country – thunderstorms and tornadoes are small, develop quickly, and do their 

damage within a few hours. Current numerical weather prediction models are not 

up to the job of predicting these events very accurately.

Thunderstorms and tornadoes are likely to form in regions of atmospheric 

instability. In such regions, a parcel of rising air becomes less dense than its 

environment as a result of the release of latent heat as clouds form. Being lighter 

than the surrounding air, the parcel continues to rise. This unstable situation can 

produce convective cells that have the potential to develop into severe weather. The 

Lifted Index (LI) is an indicator of atmospheric instability. It can be derived from 

GOES Sounder observations and represents the temperature difference between 

the actual air at 500 hPa (about 5 km) and that of an idealized “parcel” lifted to that 

level. Negative values of LI indicate instability.

In addition to the Lifted Index, a suite of other products from the GOES Sounder – 

Total Precipitable Water (TPW) (the amount of moisture that would be produced 

if all the water vapor in an atmospheric column were converted to liquid), cloudtop pressure, and effective cloud amount – is generated operationally in the form of 

Derived Product Images (DPI). These hourly images are displayed and animated 

within the National Weather Service visualization system – AWIPS, the Advanced 

Weather Interactive Processing System – for use by NWS Weather Forecast 

Offices across the country. The LI and the other DPI products enable forecasters 

to maintain a critical eye on rapidly developing severe weather conditions and issue 

timely warnings.

What do forecasters say?

During a National Weather Service (NWS) Assessment 

of the operational value of the GOES Sounder products, 

forecasters said that the Sounder products provided 

valuable information, often indicating temporal and 

spatial gradients unavailable from any other source. 

Forecasters indicated that in 79% of the active weather 

situations considered, the GOES Sounder products led 

to improved forecasts.

Selected quotes from forecasters:

Tornado warning, Minneapolis, MN:

 “The Sounder Derived Product Imagery (DPI) helped a 

lot in anticipating convective development over southern 

MN this evening.”

Flash flood warning, Grand Junction, CO:

“Edge of cloud shield revealed significantly higher Total 

Precipitable Water (TPW) values over ... our forecast 

area. Based on the TPW and [other data] issued flash 

flood warning.” (Photo credit: NOAA)
STAR Looks at the Earth | Satellite Measurements of the Atmosphere, Oceans and Land 41

GOES Lifted Index Predicts Severe Weather

Left: Derived product imagery (DPI) of Lifted Index (LI) stability derived from the GOES-12 Sounder at 2046 UTC on 13 April 2006. The 

most unstable air (yellow to red areas) is across east central Iowa. Clouds are shown in shades of gray. Overlaid is the NWS model 

forecast (isopleths) of LI for the same time; the NWP maximum instability is over the western border of Missouri and Iowa. The image to the 

right shows that the most severe weather developed much closer to the LI maximum instability area than to the NWP forecast.

Right: GOES-12 IR image at 0045 UTC on 14 Apr 2006. A strong convective complex (gray area) with very cold cloud tops (-60 F or less) 

is evident over east Iowa and northwest Illinois. Severe weather reports from the NOAA Storm Prediction Center (SPC), for the evening of 

13-14 April 2006, are overlaid (red=tornado; white=hail; light blue=damaging wind). The convection is seen to have first developed in the 

area of the most unstable GOES Sounder LI DPI (left image). 
42 Section 3 | The atmosphere

Thunderstorms and Tornadoes

Daniel T. Lindsey

Thunderstorms occur almost everywhere on the Earth at virtually any time of year, 

but the strength and type of storms vary greatly. Stronger storms may produce 

intense lightning, flash flooding, large hail, damaging winds, and, sometimes, 

tornadoes. The necessary ingredients for thunderstorm formation – sufficient lowlevel water vapor, an unstable atmosphere, and a “trigger” mechanism – are most 

common in the spring and summer in the U.S. The Gulf of Mexico is the primary 

source for low-level water vapor, and as this high humidity air moves north across 

the Central Plains, it encounters dry, unstable air aloft, resulting in strong, and 

sometimes violent storms. Vertical wind shear, or the change of wind with height, 

is another important factor that helps determine how storms organize. In general, 

more wind shear results in stronger storms, and wind shear in the lowest 3 km 

above the Earth’s surface is particularly important for the formation of tornadoes. 

“Tornado alley,” or the region from Central Texas northward to central Nebraska, 

gets its name from the frequency of large, damaging tornadoes. These potentially 

catastrophic events most often spawn from supercell storms, or isolated storms that 

have a rotating updraft.

In the U.S., where the coverage of ground-based Doppler radar is good, forecasters 

most often use geostationary satellite image sequences to locate regions where 

thunderstorms are starting to form, and then radar to issue warnings. But in 

mountainous regions or especially other countries, satellite imagery is the only 

source of information on storm strength. Visible imagery is used during the day, 

and infrared imagery, which also provides information about the vertical extent of 

storms, is used day and night.

Tornadoes in 2011 

By all accounts, 2011 was a historic year for tornadoes 

in the U.S. By the end of September, there had been 

1784 reported tornadoes, placing the year among the 

most active tornado years on record. Associated with 

these storms, there were 546 fatalities, the third highest 

since collection of data began in 1875, and the most in 

one year since 1936.

The strength of tornadoes is measured by the Enhanced 

Fujita Scale, which ranges from 0 to 5, with an EF-5 

having wind gusts exceeding 200 mph. A single tornado 

(rated EF-5) devastated the town of Joplin, Missouri, on 

May 22, killing 159 people, making it the deadliest storm 

since 1947 and the 7th deadliest on record. The most 

active single day during 2011 was April 27, when a total 

of 292 tornadoes were reported in the southeast and 

eastern U.S. Eleven of these were classified EF-4 – 166 

to 200 mph – and four were rated EF-5. A total of 316 

people were killed on April 27.

Damage from F5 tornado that struck Joplin, MO on 

May 22, 2011. (Photo credit: NOAA/NWS)
STAR Looks at the Earth | Satellite Measurements of the Atmosphere, Oceans and Land 43

Supercell Thunderstorm

GOES-13 Visible (left) and color-enhanced Infrared (right) imagery from 22 May 2010 at 2345 UTC showing a supercell thunderstorm 

over northern South Dakota. Visible imagery is useful for locating key thunderstorm markers (overshooting tops, outflow boundaries, and 

flanking lines). The infrared imagery provides information on storm vertical extent and cloud top temperatures. This particular storm’s top 

had a temperature of -67 °C and probably extended into the lower stratosphere. The photo in the middle shows a large tornado associated 

with this supercell at the same time as the GOES imagery, and the approximate location of the tornado is shown with the white arrows. The 

tornado was rated EF-4 (wind gusts 166 to 200 mph) on the enhanced Fujita scale, but luckily remained mostly over open country. (Photo 

credit: Chris Collura (center image))
44 Section 3 | The atmosphere

Bridging the Gap: Nearcasting

Robert Aune and Ralph Petersen

A serious gap exists in the guidance, based on nowcasts or predictions, that 

forecasters rely on to assess the potential for severe weather such as strong 

thunderstorms and tornadoes. Operational nowcasts are based on real-time radar 

and GOES imager observations that provide a snapshot of current conditions, 

but do not necessarily indicate what will happen in the next three to six hours. 

Numerical weather prediction (NWP) models are apt to contain erroneous 

perturbations in the first 6 to 12 hours that may incorrectly activate severe weather 

processes within the model. NWP models typically need to process forecasts out 

to 18 hours before these perturbations are systematically damped. NWP models 

also tend to smooth out the sharp horizontal changes in temperature and moisture 

where thunderstorms often form. This leads to errors in the timing and location 

of severe weather.

To close this gap, STAR has developed an innovative nearcasting 

system. The 18-channel infrared sounder on GOES measures 

temperatures and dew points at multiple layers in the atmosphere at 

locations 10 to 14 kilometers apart on an hourly basis across most of 

the United States. These measurements are converted by NESDIS 

into atmospheric instability indicators that are useful to forecasters 

for delineating areas where thunderstorms are likely to form. But, in 

the 20 to 40 minutes needed to process and transmit the information 

to forecasters, conditions may have changed. STAR scientists have 

developed a method that projects these stability indicators at multiple 

levels forward in time on trajectories driven by the predicted winds. 

The projected sounder observations are then re-examined to identify 

areas of maximum de-stabilization in the atmosphere. This approach 

has shown skill at identifying areas of severe weather potential out to 6 

hours, thus filling the guidance gap.

Since the nearcasting model is relatively simple and very efficient, it 

can quickly provide hourly guidance to the field forecasters when they 

need it, leading to more accurate and timely severe storm warnings and 

forecasts. 

Thunderstorm formation

Thunderstorms form when warm moist low level air 

moves under dryer air aloft. Air parcels near the surface 

become buoyant and begin to rise. Cloud formation 

produces additional warming, causing the parcel to 

accelerate upward, eventually forming a thunderstorm. 

The GOES Sounder can measure the temperature and 

moisture – the thermal energy – in these layers prior to 

the formation of clouds. Predicted winds are used to 

move the thermal energy forward in time at each level, 

as shown below. Areas favorable for thunderstorm 

development can then be identified in advance, as 

illustrated on the opposite page. 
STAR Looks at the Earth | Satellite Measurements of the Atmosphere, Oceans and Land 45

Predicting a Strong Line of Thunderstorms

6-hour nearcast (left) of the thermal energy profile measured by the GOES-13 Sounder valid 21UTC, July 9, 2009. Red areas indicate 

that the thermal energy profile is favorable for thunderstorm development. The National Weather Service radar (right) indicates a strong 

line of thunderstorms has formed along the arc of thermal instability as predicted by the GOES Sounder Nearcaster. The area of storms in 

southern Iowa was dissipating at this time, verifying the Nearcaster prediction that these storms would not intensify. 
46 Section 3 | The atmosphere

Tropical Cyclones

John A. Knaff

The primary energy source for tropical cyclones is the vast heat energy stored 

in the oceans in late summer and early fall. Common regional names for these 

low pressure systems include hurricane (in the North Atlantic and eastern North 

Pacific), typhoon (in the western North Pacific) and tropical cyclone. What makes 

tropical cyclones exceptional is that their strongest winds – up to 200 miles per 

hour – occur near the surface rather than at higher altitudes as with other low 

pressure systems. They are also large: the areas of damaging winds and heavy 

rainfall sometimes exceed 250,000 km2

. Because wind-related damage and storm 

surge are related to the size and strength of the wind field, these ferocious systems 

are considered the most dangerous and destructive of all storms.

Tropical storms generally develop and intensify in remote 

oceanic regions, and satellite observations are universally used 

to estimate tropical cyclone location and motion, intensity 

and associated wind fields, cloud structures, rainfall rates 

and environmental conditions – information that is fed into 

models that provide storm track and intensity forecasts. 

STAR scientists have developed, and continue to improve, the 

Statistical Hurricane Intensity Prediction Scheme (SHIPS), 

an empirical model used by the National Hurricane Center as 

one of its tools for predicting hurricane intensity. A key input 

to SHIPS is satellite altimetry measurements, which provide 

information on ocean heat content. Use of the SHIPS model 

leads to significant improvements – up to 5 % – in forecasting 

the intensity, or strength, of these systems (see figure). Forecasts 

of hurricane intensity – as opposed to hurricane track forecasts 

– are extremely difficult, and improving intensity forecasts 

(by 20% in ten years) is a high priority of NOAA’s Hurricane 

Forecast Improvement Program. Such advances will result in 

longer forecast lead times and ultimately reduce evacuation costs 

and loss of life and property.

Over 100 deaths, $25 billion damage

Ike, the third most costly U.S. hurricane, tracked 

across the northern Caribbean in 2008, and slammed 

parts of the United States Gulf Coast. It was directly 

responsible for over a hundred deaths and widespread 

damage from strong winds, storm surge, and inland 

flooding across Hispaniola, Cuba, the Turks and Caicos 

Islands, the Bahamas and Florida, Louisiana, and 

Texas. After landfall, additional deaths and significant 

damage occurred across parts of the Ohio Valley and 

southeastern Canada as the remnants of the storm 

moved through those areas.

Before and after image of the Bolivar Peninsula, Texas. 

(Photo credit: U.S. Geological Survey)
STAR Looks at the Earth | Satellite Measurements of the Atmosphere, Oceans and Land 47

Improving Hurricane Intensity Forecasts

Improvement in hurricane intensity forecasts using the Statistical Hurricane Intensity Prediction Scheme, which incorporates Oceanic Heat 

Content derived from satellite altimetry, for six major hurricanes that occurred during the 2003, 2004 and 2005 Atlantic Hurricane Seasons 

(Isabel, Ivan, Emily, Katrina, Wilma and Rita). The actual improvement in terms of knots (kt; nautical miles per hour, 1 kt=0.514 m/s) is 

shown above each bar, and the number of cases (in parentheses) is listed below the forecast hour. 
48 Section 3 | The atmosphere

Snowfall

Huan Meng and Nai-Yu Wang

Snowfall is a major form of precipitation in mid-to high-latitude regions. It 

accounts for about 8 to 10 percent of total world-wide precipitation, and plays an 

important role in the global water cycle. Freshwater from snowmelt provides the 

main source of water for human, animal, and plant sustenance in many areas of 

the world. However, snowfall can also pose a threat to the normal functioning of 

societies. Even moderate snowstorms often cause problems ranging from serious 

economic disruptions to significant injuries and deaths. For these reasons, effective 

monitoring of snowfall has many intrinsic societal benefits well beyond water 

resources management and weather forecasting interests.

Ground-based snowfall observations are usually inadequate, and very often nonexistent, due to a variety of challenges. Observations from polar-orbiting satellites 

provide an effective way to monitor snowfall with global coverage. Microwave 

(MW) frequency measurements from satellites are used to detect snowfall and 

retrieve snowfall rates. Unlike infrared and visible sensors, which can only observe 

the top of clouds, MW signals can penetrate clouds and detect the environmental 

conditions within and below the clouds. This advantage gives MW sensors the 

ability to receive signals that contain the “signature” of snowfall.

Snowflakes are single ice crystals or their aggregates that can scatter electromagnetic 

signals. Some MW frequencies respond strongly to such scattering, and are 

significantly depressed in the presence of snowfall. By utilizing these properties, 

STAR investigators have constructed snowfall retrieval algorithms that use MW 

measurements from a combination of frequencies.

February 2010 blizzards

Two major snowstorms hit the mid-Atlantic region of 

the U.S. in early February 2010. The combined snow 

accumulation from these storms – over 50 inches in 

many regions – placed the 2009-2010 snow season 

well above the historical record in the Baltimore and 

Washington DC area. The blizzards caused significant 

social and economic disruptions in the region – power 

was cut to hundreds of thousands of people, many 

roads went unplowed for more than a week, the federal 

government was closed for four days, and numerous 

school districts were shut down for nearly two weeks.

A skier in front of the U.S. Capitol on Feb 6, 2010. 

(Photo credit: Getty Images)
STAR Looks at the Earth | Satellite Measurements of the Atmosphere, Oceans and Land 49

Snowfall Rate

A map of snowfall rate (water equivalent) retrieved using satellite measurements during the February 5-6, 2010 blizzard in the mid-Atlantic 

region. This storm produced record breaking snowfall in many areas across the region.
50 Section 3 | The atmosphere

Satellite Data Assimilation

Sid-Ahmed Boukabara

Data assimilation is a scientific technique for merging satellite and conventional 

observations with the short term (~6 hr) forecast of a numerical weather prediction 

(NWP) model to provide a “best estimate” of the atmospheric state. It requires 

knowledge of fundamental physics, radiative transfer, remote sensing, mathematics, 

several environmental sciences, and meteorology. Data assimilation helps to increase 

weather forecast accuracy at global and regional scales. This is achieved by offering 

global and frequent coverage of the Earth’s environment at high enough spatial 

and temporal resolutions. These frequent snapshots of the Earth’s status, called 

analyses (see figure), serve as initial conditions for sophisticated NWP models. 

Satellite observations provide over 95% of the observations used to generate these 

initial conditions.

Modern satellite data assimilation relies on a combination of several geostationary 

and polar-orbiting satellites to allow optimal coverage. Satellite data are assimilated 

in global models, with physics focused on synoptic scale (~1000 km) weather 

systems (for example, mid-latitude cyclones), and in regional models, where 

mesoscale (~10 to 100 km) weather events (for example, thunderstorm systems) 

are modeled. Satellite data assimilation is therefore crucial for the prediction of 

medium-range (out to a week or more) weather at global scales, as well as for the 

prediction of severe storms, hurricanes and typhoons at regional scales.

Satellite data usually have a greater positive impact on weather prediction accuracy 

in the Southern Hemisphere than in the Northern Hemisphere. The main reason 

for this is the prevalence of conventional data in the Northern Hemisphere. Other 

applications of satellite data assimilation include climate variability assessment 

(through reanalyses data sets), model development, and support of strategic 

decisions in observing system development.

A number of STAR scientists contribute to the management and research of the 

NASA-NOAA-DoD Joint Center for Satellite Data Assimilation.

Satellite data assimilation, golf, and the 

pre-satellite era

How is satellite data assimilation related to weather 

forecasting?

Satellite data, in combination with conventional 

observations and a short term Numerical Weather 

Prediction (NWP) forecast, provide a global snapshot 

of the Earth’s environment (atmosphere, land, oceans, 

cryosphere) several times a day. This snapshot, called 

an analysis, serves as the input to sophisticated weather 

forecasting models. For a reliable weather forecast, it 

is critical to have initial conditions that are as accurate 

as possible. One can compare the importance of the 

analysis for weather prediction to the importance of 

precisely striking a golf ball to reach a hole. And just as 

in golf, where the player usually strikes the ball several 

times before reaching the hole, satellite data assimilation 

is also performed several times a day by meteorological 

centers, each time readjusting more or less the state of 

the analysis to obtain a better forecast. 

Prior to meteorological satellite data?

In the past, data assimilation relied exclusively on 

conventional data (balloon-borne radiosondes, ground 

measurements, etc) to measure the pulse of our planet. 

These measurements were sparse and concentrated 

in specific areas. Although conventional observations 

are still a source of data for assimilation, measurements 

from Earth-Observing satellites have become the 

cornerstone of weather-oriented data assimilation. 

They offer global coverage, multiple times a day, and 

provide unprecedented insight into the state of the planet 

through their measurements of atmospheric clouds, ice, 

rain, temperature, moisture, and winds, and surface 

characteristics of snow, sea-ice, vegetation, etc.
STAR Looks at the Earth | Satellite Measurements of the Atmosphere, Oceans and Land 51

Generating a Global Humidity Field 

from Sparse and Irregular Observations

Humidity observations (in units of water vapor mixing ratio, g/kg) (left panel) from radiosondes are sparser than the model 

grid, and highly irregular in their spatial and temporal distribution. Both qualitatively and quantitatively, it is difficult to 

reconstruct the full humidity field from these data alone. In contrast to the observational dataset, the humidity analysis 

(right panel) resulting from the data assimilation system (including satellite data) is global. This analysis reveals the 

signature of dynamical and physical processes at a level of detail that cannot be extracted directly from the observations 

themselves. (Maps courtesy of Lars-Peter Riishojgaard).
52 Section 3 | The atmosphere

Community Radiative Transfer Model

Yong Han, Fuzhong Weng, Paul vanDelst, Quanhua Liu, Yong Chen, David Groff and 

Banghua Yan

Since remote satellite observations of the radiation reflected and emitted by the 

Earth are affected by atmospheric and surface conditions – surface and atmospheric 

temperatures, atmospheric composition, clouds, rain, and snow, and surface 

reflectivity – it is possible to retrieve quantitative information about the Earth if 

one can relate the observations to these characteristics. A radiative transfer model 

provides the glue that ties the satellite observations to the Earth’s properties.

For use in numerical weather prediction (NWP) models, the radiative transfer 

model has to be both fast – after all, the forecast has to be produced before the 

weather arrives – and accurate. The Community Radiative Transfer Model 

(CRTM), conceived and developed in large part by STAR scientists for the NASANOAA-DOD Joint Center for Satellite Data Assimilation, has been adopted by a 

number of NWP centers in the U.S. and abroad. The CRTM is a vital part of the 

NWP model and enables assimilation of the satellite observations to provide initial 

conditions for the forecasts. It is capable of calculating radiation streams through the 

atmosphere that are affected by gases, aerosols, clouds, and precipitation particles at 

wavelengths ranging from the ultraviolet to the microwave. The CRTM supports 

more than 100 currently in-orbit and historical radiometric instruments.

One current research focus is on improving the calculations for cloudy atmospheres. 

The illustration on the opposite page shows that the accuracy of cloudy sky 

calculations is approaching that of clear sky simulations.

Radiative transfer theory

Radiative transfer is the physical phenomenon of 

energy transfer in the form of electromagnetic radiation. 

Radiation is characterized by its intensity, wavelength, 

and polarization. The propagation of radiation in the 

atmosphere-surface system is affected by absorption, 

emission and scattering processes. Radiative 

transfer theory provides the physical foundation for 

understanding these processes. The equation of 

radiative transfer describes the radiative transfer 

theory mathematically. Equations of radiative transfer 

have application in a wide variety of subjects including 

optics, astrophysics, atmospheric science, and remote 

sensing. A radiative transfer model is a piece of 

computer software that solves the radiative transfer 

equation numerically. In remote sensing and NWP 

data assimilation, such models are used to simulate 

the amount of radiation received by an Earth-viewing 

radiometer.
STAR Looks at the Earth | Satellite Measurements of the Atmosphere, Oceans and Land 53

The Community Radiative Transfer Model: Cloudy Atmospheres

Root-Mean-Square (RMS) differences (degrees C) between observed and simulated brightness temperatures – a measure of the radiant 

energy – of the Advanced Microwave Sounding Unit-A for cloud-free (blue) and cloudy (red) cases for 10 different instrument channels 

(wavelengths). Calculation of radiative transfer through clouds is a complex problem, but the graph shows that the CRTM calculations for 

cloudy skies are almost as good as those for clear conditions.
54 Section 3 | The atmosphere

Cloud Resolving Models

Xiaofan Li

The range of skillful numerical weather prediction (NWP) forecasts has increased 

at the rate of about one day per decade over the past few decades. For example, 

today’s 4-day forecasts are about as accurate as 3-day forecasts were just 10 years 

ago. These accomplishments have been achieved without information on what 

we normally think of as weather – cloudiness and precipitation – in the initial 

conditions of the NWP models.

 A number of obstacles currently lie in the way of assimilating such observations, 

and state of the art NWP models are today initialized primarily with observations 

of clear skies. One of the obstacles is our inability to construct computer models of 

the complex processes involved in cloud and precipitation formation, a necessary 

first step to assimilate cloud/precipitation observations.

The horizontal and vertical resolutions of NWP models are too coarse – 

10 to 100 km in the horizontal – to model the micro-physical processes 

involved in the formation of individual clouds. These processes can 

only be parameterized (approximated). One approach to improving the 

numerical modeling of clouds in NWP models is to exploit the results of 

cloud resolving model research. Cloud resolving models have resolutions 

small enough – 1 km in the horizontal – to capture the essential physical 

processes, and the results of tests of these models can be used to obtain 

better approximations for the NWP models.

STAR scientists are evaluating cloud resolving models as one step in 

improving NWP cloud parameterizations with the ultimate goal of 

facilitating assimilation of satellite cloud and precipitation observations 

in NWP models.

A cloud-resolving model and associated 

precipitation

Favorable atmospheric temperature and water vapor 

conditions lead to the development of clouds through 

cloud microphysical processes. These processes include 

condensation of water vapor into cloud drops or ice 

particles and growth of these hydrometeors through 

collision, accretion, riming, and other processes. When 

the hydrometeors are large enough, they fall to the 

ground as precipitation – either rain or snow depending 

on the atmospheric temperatures. Cloud-resolving 

models with fine spatial resolutions (~1 km) can 

simulate the rain events resulting from the microphysical 

processes in weather producing clouds.
STAR Looks at the Earth | Satellite Measurements of the Atmosphere, Oceans and Land 55

Evaluation of Precipitation from a Cloud Resolving Model

Time and spatial-scale dependence of rain-rate error (%) in a tropical rainfall case simulated using a two-dimensional cloud-resolving 

model. Rain-rate error decreases as the time period and area for which the error is computed increase. Results from cloud resolving 

models can be used to improve numerical weather prediction modeling of clouds with an ultimate goal of assimilating satellite cloud and 

precipitation observations.
56 Section 3 | The atmosphere

Air Quality

Shobha Kondragunta

Aerosols (airborne dust-like particles) are a key component of urban/industrial 

photochemical smog. They are also the primary pollutant in natural environmental 

disasters such as volcanic eruptions, dust outbreaks, biomass burning associated with 

the clearing of forests for agricultural purposes, and forest fires. High concentrations 

of aerosols, when inhaled, lead to upper respiratory diseases including asthma. 

They decrease visibility and lead to unsafe conditions for transportation. The 

Environmental Protection Agency (EPA) estimates that more than 106 million 

people in the United States live in areas of poor air quality, costing about $143 

billion dollars per year in hospital expenditures.

The Aerosol Optical Depth (AOD) is a measure of the amount of aerosol in a vertical 

column from the Earth’s surface to the top of the atmosphere. By measuring AOD 

from satellites, one can obtain information on surface aerosol concentrations for 

air quality monitoring and forecasting applications. Visible imagery from NOAA’s 

GOES satellites is used to derive pollution information for the United States. 

STAR scientists are working to integrate the satellite measurements into air quality 

prediction models. The combination of numerical weather prediction guidance 

and satellite aerosol imagery in real time will improve air quality warnings and 

alerts. Analyses of the satellite data aid the EPA in its development of pollution 

mitigation strategies.

Emergency room visits for heart failure

During an intense smoke pollution event during the 

summer of 2008, hospitals in North Carolina experienced 

a 37% increase in emergency room visits for heart 

failure. Patients with prior poor health were most 

susceptible to the high smoke levels. Satellite monitoring 

provided scientists from the Environmental Protection 

Agency (EPA) with data to study the link between 

pollutant levels and resulting emergency room visits.

The color-coded map shows Daily Mean Aerosol 

Optical Depth (AOD) observed by GOES-12 on 

June 11, 2008 over rural North Carolina. The AOD 

range is from 0.0 (green) to 2.0 (brown). The high values 

are due to smoke from a fire whose location is shown by 

the red dot. The EPA used NOAA’s GOES AOD 

measurements as a proxy for pollution exposure during 

the wildfire.
STAR Looks at the Earth | Satellite Measurements of the Atmosphere, Oceans and Land 57

Do Satellite Air Quality Measurements Agree with Surface Monitors?

Aerosol Optical Depth (a measure of amount of aerosol in a vertical column of atmosphere) for July 18, 2008 from NOAA’s geostationary 

satellite is displayed as a color map. Circles with filled colors show Air Quality Index (AQI) as observed by the Environmental Protection 

Agency’s (EPA) ground monitors. The satellite measurements (red/yellow indicates high pollution and blue/green indicates low pollution) are 

in general agreement with the ground monitors (USG in the AQI scale denotes: Unhealthy for Sensitive Groups).
58 Section 3 | The atmosphere

Nitrogen Dioxide

Trevor Beck

Nitrogen dioxide (NO2

) is a direct precursor of ground level ozone, an air pollutant 

that causes harmful health effects in people, plants, animals, and livestock. NO2 

is also a damaging pollutant in its own right, leading to decreases in atmospheric 

visibility. It was a major contributor to the Brown Cloud episodes in Denver in 

the 1980’s. The primary sources of NO2 are smokestack emissions and automobile 

traffic (with conventional combustion engines). NO2

 is short lived, on the order of 

hours. It peaks in the morning in polluted regions and gradually decreases until 

sunset. NO2

 is converted to ground level ozone by strong sunlight in a photochemical process.

Ground level ozone is difficult to measure from space, but by measuring the 

tropospheric NO2

, the approximate amount of surface ozone may be inferred. 

Thus, the NO2 measurements serve as a proxy for ground level ozone and for poor 

air quality.

STAR has developed the methodology to measure the amount of tropospheric NO2 

from observations of the Global Ozone Monitoring Experiment–2 (GOME-2) 

instrument on the METOP-A polar orbiter. This methodology has recently been 

implemented operationally at NOAA, and near-real time daily global measurements 

are produced. Merging the satellite NO2 data with in-situ measurements in air 

quality models should lead to more accurate pollution forecasts.

Why is brown smog brown?

During the 1980s the city of Denver suffered through 

a number of severe photochemical smog episodes. 

Because of the color of the smog, they were called 

brown cloud, haze, or smog events. The images below 

show the difference between a clear (top) and smoggy 

(bottom, with brown smog in the distance) day in Denver. 

These events caused numerous deleterious effects on 

the environment, the economy, and human health and 

well being. The brown color of the smog is in large part 

due to the high levels of NO2. NO2 absorbs the blue 

rays of the solar spectrum, which together with the 

preferential scattering of red light by the smog particles, 

causes the brownish color.

(Photo credit: Copyright University Corporation for Atmospheric Research (bottom)).
STAR Looks at the Earth | Satellite Measurements of the Atmosphere, Oceans and Land 59

Tropospheric Nitrogen Dioxide, Summer 2010

Due largely to emissions from automobiles, NO2 is greatest in the vicinity of large cities. Note the high concentrations in the Washington, 

DC-Boston megalopolis and Los Angeles area.
60 Section 3 | The atmosphere

Microburst Prediction

Ken Pryor

A microburst is a strong downdraft that induces an outburst of damaging winds 

on or near the ground. The intense downdraft is driven by a combination of the 

weight of precipitation in a thunderstorm and the cooling that occurs due to 

the evaporation of rain when dry air is ingested into the core of the storm. The 

environment that is most favorable for the generation of a microburst is a dry air 

layer in the middle troposphere that overlies a warm and moist lower troposphere. 

This environment would cause the downdraft to accelerate as it approaches the 

ground and produce very strong winds upon impact, sometimes in excess of 

hurricane-force. Microbursts are typically very small with diameters 4 km or less 

and have lifetimes less than 5 minutes. The small size of the microburst is a major 

factor in generating the low-level wind shear that is a hazard to aviation and marine 

transportation.

STAR researchers have found that particular signatures or patterns in GOES 

imagery are useful in the short-term prediction of microburst potential. These 

features are associated with the generation of intense thunderstorm downdrafts. 

A “dry-air notch” in GOES infrared imagery is a V- or U-shaped region of 

relatively warm brightness temperature that typically appears on the rear flank 

of a microburst-producing thunderstorm. The dry-air notch can signify the 

development of a microburst as dry air is being channeled into the thunderstorm 

core. The dry-air notch has been associated with numerous significant microburst 

events over the past decade.

National Weather Service forecast offices integrate the satellite measurements with 

other data to predict these events and issue timely and effective warnings.

Microburst maritime disasters

Since 2000, microbursts have caused a number of 

marine transportation disasters resulting in fatalities or 

the total loss of a vessel.

In February 2010, the Canadian Sailing Vessel (SV) 

Concordia (below) capsized and sank in a microburst 

that occurred 290 nautical miles south-southeast of 

Rio de Janeiro. Amazingly, all 64 of the passengers on 

board, including crew, faculty, and students, survived the 

overturning and sinking of the ship.

In March 2004, the “Lady D”, a water taxi servicing 

the Baltimore Harbor, was flipped over by a strong 

microburst, causing five fatalities and injuring many 

more.

These events underscore the importance of detecting 

the hazardous winds produced by isolated and intense 

thunderstorms and warning vulnerable vessels.

(Photo credit: West Island College Class Afloat, Lunenburg, 

Nova Scotia, Canada, B0J 2C0)
STAR Looks at the Earth | Satellite Measurements of the Atmosphere, Oceans and Land 61

Sinking of the Concordia

Conceptual model of a microburst-producing convective storm (left) and GOES-12 imager microburst product at 1709 UTC 17 February 

2010 (right) that indicates a microburst in progress over the South Atlantic Ocean off the coast of Brazil. The white triangle marks the 

location of the sinking of the Sailing Vessel (SV) Concordia by a microburst at 1722 UTC. Magenta shading indicates very cold cloud tops 

associated with intense thunderstorms. Note that the dry-air notch was pointing toward the location of the Concordia, and, thus, the vessel 

was in the direct path of downburst winds. Also, it appears that the Concordia was in an optimal location to experience both the horizontal 

and vertical components of the downburst winds. (Photo credit: NASA (left); Planiglobe (right)) 
62 Section 3 | The atmosphere

Aviation Hazards: Volcanic Ash

Michael Pavolonis

Airborne volcanic ash is a major aviation, health, and infrastructure hazard. When 

ingested into aircraft engines, volcanic ash causes engine damage or failure. For 

example, in December 1989, a 747 jetliner carrying 231 passengers encountered an 

ash cloud during an eruption of the Mount Redoubt volcano, located southwest 

of Anchorage, AK. Within 60 seconds of encountering the heavy ash cloud, all 

four engines of the aircraft had stalled. Fortunately, the pilot was able to restart 

the engines, narrowly avoiding a crash. Volcanic ash is extremely abrasive, and even 

small concentrations can cause severe damage to the exterior of aircraft. In addition, 

ash falls pose significant health and infrastructure threats to those on the ground.

Satellite-based measurements of infrared radiation have been the primary means for 

tracking volcanic ash clouds since the 1980’s. While measurements at ultra-violet, 

visible, near infrared, and microwave wavelengths are also useful for monitoring 

volcanic ash clouds, infrared measurements are available day and night on many 

geostationary and polar satellites.

In the infrared part of the spectrum, the amount of radiation absorbed by volcanic 

ash generally decreases as the wavelength of the radiation increases. The opposite 

is true for weather clouds. Thus, volcanic ash clouds can often be differentiated 

from other types of clouds. Infrared measurements can also be used to determine 

the properties (e.g., height and concentration) of ash clouds. In recent years, STAR 

scientists have developed greatly improved methods to detect volcanic ash clouds 

and quantify their properties. These satellite measurements are critical for volcanic 

ash cloud warnings and forecasts.

High impact eruptions in 2009 and 2010

The economic impacts of airborne volcanic ash are 

significant. For instance, Alaska Airlines was forced to 

cancel 295 flights (stranding over 20,000 passengers) 

in March and April of 2009 due to ash clouds produced 

by Mount Redoubt. While the economic impact of the 

2009 Redoubt eruptions is still being tabulated, the total 

estimated cost of eruptions of Redoubt in 1989-1990 

was $160 million. In April and May 2010, volcanic ash 

from the eruption of Eyjafjallajökull in Iceland shut down 

commercial aviation in Europe for several days. The total 

economic impact of the Eyjafjallajökull eruption was over 

2 billion dollars.

Over 100,000 flights were cancelled during the eruption 

of Eyjafjallajökull in April and May 2010 (Photo credit: 

Getty Images).
STAR Looks at the Earth | Satellite Measurements of the Atmosphere, Oceans and Land 63

Kasatochi Erupts and Sends Volcanic Ash to 20 km

A map of ash cloud top height (km) after the eruption of Kasatochi, Alaska, on August 8, 2008. The ash cloud heights (color bar 

scale, with grey indicating the greatest heights) are derived from infrared satellite measurements and are critical for locating the ash 

and forecasting its spread.

STAR Looks at the Earth | Satellite Measurements of the Atmosphere, Oceans and Land 65

Section 4 

Oceans 4
66 Section 4 | oceans

Planet Ocean

Paul DiGiacomo and Marilyn Yuen Murphy

The world’s oceans span more than 70% of the surface of our planet and contain 

approximately 97% of its water. They significantly influence Earth’s weather. For 

example, currents like the Gulf Stream (see figure opposite page) can drive hurricane 

intensification. The oceans play a critical role relative to climate variability and 

change; topics such as carbon cycling, ocean warming, acidification and sea level 

rise are currently of great interest. The oceans are also extremely important from a 

socio-economic perspective by providing valuable goods and services (e.g., fisheries, 

energy, transportation, tourism, and recreation). 

From a global perspective, the ocean plays a significant role in the carbon cycle (e.g., 

solubility and biological “pumps”), the water cycle (e.g., evaporation and terrestrial 

runoff ), and the cycling of nutrients (e.g., nitrogen, phosphorus). A majority of the 

global population lives close to a coast. Ocean impacts on the coastal population 

can come from sea level rise, storm surges, tsunamis and other inundation events, 

harmful algal blooms, etc. In turn, anthropogenic impacts on the coastal zone and 

ocean waters include loss and modification of habitat, pressures on living marine 

resources, and water quality concerns such as oil spills, discharge of pollutants and 

pathogens, eutrophication (increase in plant biomass due to excessive nutrient 

enrichment) and hypoxia (oxygen depletion). 

Observing the ocean can be quite challenging due to its vastness, depth, remoteness 

from land, and complex dynamics. Observations from satellites are invaluable in 

advancing our scientific understanding of the ocean and improving our ability to 

efficiently monitor and manage the ocean and coastal marine ecosystems. 

STAR scientists develop and utilize satellite data and products for a number of key 

ocean parameters. They compare and integrate these data with in situ measurements 

(for example, from buoys), and support development of modeling approaches and 

applications to help assess, monitor and manage areas under environmental stress 

(e.g., coastal zones, coral reefs, and Polar Regions). 

At land’s end...

Coastal zones are dynamic, productive regions, 

occurring at the interface of the atmospheric, oceanic 

and terrestrial domains (e.g., Chesapeake Bay below). 

Significant impacts also result from the large human 

populations that live in close proximity to the oceans. 

Satellites provide routine global coverage and are 

crucial for understanding and monitoring oceanic 

conditions. However, ocean phenomena can be difficult 

to characterize given their complexity and variability, as 

well as due to cloud cover which can obscure satellite 

observations. In particular, small-scale, episodic and/or 

ephemeral features such as eddies, plumes and blooms, 

although common in coastal waters, can be difficult to 

monitor. Future satellite missions will provide increased 

spatial, temporal and spectral resolution, improving our 

ability to understand and monitor coastal waters. 

MODIS-Aqua image of chlorophyll-a concentrations (a 

proxy for phytoplankton biomass) in Chesapeake Bay.
STAR Looks at the Earth | Satellite Measurements of the Atmosphere, Oceans and Land 67

The Gulf Stream

Gulf Stream sea surface temperature (SST) derived from the Advanced Very High Resolution Radiometer (AVHRR) on NOAA-12. Currents 

like the Gulf Stream can be recognized in satellite SST imagery by their sharp temperature gradient between warm and cold water. In this 

image, red/yellow signifies warmer water, and blue/green depicts cooler water. The current flows along the darkest red ribbon in the image, 

from the southwest to the northeast, manifesting several rings and eddies.
68 Section 4 | oceans

Sea Surface Temperature from 

Polar Satellites

Alexander Ignatov, John Sapper, Xingming Liang, Boris Petrenko, Yury Kihai 

and John Stroup

In the early 1980s, NESDIS pioneered the first global operational sea surface 

temperature (SST) product from satellite observations. The SSTs are derived from 

IR measurements in “atmospheric windows” – spectral regions that permit the 

ocean’s emitted radiation to pass through the atmosphere with little absorption 

by atmospheric gases. The instrument – the Advanced Very High Resolution 

Radiometer – a workhorse of satellite observations and applications, about which 

an entire book has been written – is still flying on the NOAA and European Metop 

polar-orbiting satellites. The satellite SSTs have been widely used for research and 

applications in the fields of climatology, meteorology and oceanography.

Perhaps the most difficult problem in measuring SST is ensuring that one has 

a clear field of view – i.e., cloud cover, or even small patches of clouds are not 

blocking the ocean signal. In 2006, STAR initiated a fundamental redesign of the 

heritage SST processing system, to meet the new more stringent requirements for 

accurate SST measurements. A key attribute of the new system is a much better 

way to screen for clouds in the field of view. In May 2008, the new Advanced 

Clear-Sky Processor for Oceans (ACSPO) product became operational.

The unique feature of ACSPO is the use of the fast Community Radiative 

Transfer Model, in conjunction with estimates of SST and atmospheric moisture 

and temperature from numerical weather prediction models, to produce top-ofatmosphere first-guess infrared radiances for clear skies, at each retrieval point. In 

addition to more accurate cloud screening, the first-guess radiances are also critical 

for improved SST measurements.

Polar SST: From 1981 to tomorrow 

Accurate SST retrievals have been made from the 

Advanced Very High Resolution Radiometer (AVHRR) 

flown onboard NOAA satellites since 1981. NOAA 

AVHRR provides global coverage with 4 km resolution 

twice daily. The most recent U.S. polar satellite, NOAA19, was launched in February 2009.

The first European polar-orbiting satellite, Metop-A, was 

launched in 2006 with an AVHRR capable of measuring 

global SST with a resolution as fine as 1km. Metop-B and 

-C will follow and extend the AVHRR record into 2020s.

The Visible/Infrared Imager/Radiometer Suite (VIIRS) 

is an advanced sensor that builds upon the heritage of 

the MOderate resolution Imaging Spectroradiometer 

(MODIS), which was flown on NASA’s Terra and Aqua 

satellites. 

With sub-km spatial resolution, more spectral channels, 

and improved accuracy, it was launched into space 

in 2011 on the NASA-NOAA Suomi NPP satellite, the 

precursor to NOAA’s next generation Joint Polar Satellite 

System. As the replacement for the AVHRR, it will 

provide even more reliable SST observations.

The Visible/Infrared Imager/Radiometer Suite (VIIRS) will 

measure global sea surface temperatures with unprecedented detail and accuracy. (Photo Credit: Ball Aerospace)
STAR Looks at the Earth | Satellite Measurements of the Atmosphere, Oceans and Land 69

New Versus Heritage Sea Surface Temperature Maps

Daytime 24-hour SST composite produced by the heritage NESDIS SST (left) and the new NESDIS (right) systems. Black areas have no 

measurements because of gaps in observational coverage or cloudiness. The new system provides ~30 times more SST measurements 

and significantly improves global coverage, with comparable accuracy statistics. In addition to SST, the system also delivers two other 

products over oceans: clear-sky infrared radiances and aerosols.

Resolution 8 km

6.6 x 104 SST observations

8.3% ocean covered in 1 day

Resolution 4 km

2.1 x 106 SST observations

32.7% ocean covered in 1 day
70 Section 4 | oceans

Ocean Temperature: 

The Continuous View

Eileen Maturi and Jonathan Mittaz

Sea surface temperature (SST) derived from infrared (IR) observations by 

geostationary satellites form a uniquely powerful data set that provides continuous 

— hourly – updates of global ocean conditions at horizontal resolutions as fine 

as 4 km. A significant advantage of geostationary satellites over polar satellites is 

their ability to view the same location almost continuously, substantially increasing 

the opportunity for the cloud-free views needed to actually see and measure the 

ocean temperature. Not unexpectedly, such SST data sets have a broad spectrum 

of applications.

NOAA’s National Marine Fisheries Service uses the GOES SST to support 

commercial fisheries management and protection of endangered species, like 

monitoring turtle exclusion for fishing and mammal protection. NOAA’s Coral 

Reef Watch Program applies GOES SSTs to its coral bleaching warnings and 

assessments. NOAA’s National Weather Service incorporates GOES SST into its 

prediction models to improve forecasts of high seas, current and eddy formation, 

and hurricane tracks. The climate community uses the GOES SST observations to 

study the phases of the El Niño-Southern Oscillation phenomenon and climate 

variability.

STAR scientists have developed the algorithms for processing the GOES data 

and are contributing to the development of algorithms for the next generation of 

NOAA geostationary satellites – GOES-R.

Tracking the Gulf of Mexico oil spill with 

SST observations

Sea surface temperature (SST) fronts (strong horizontal 

temperature changes represented by wiggly colored 

lines in the figure) are identified and located in 24 hour 

composites of SSTs derived from geostationary satellites 

(GOES-East and GOES-West). Near these fronts, sea 

water converges, leading to accumulations of oil. As 

a result, satellites can indirectly help to pin-point the 

locations of oil plumes – information that can be fed to 

hazard response officials. The smoothed gradient color 

bar describes how fast the sea surface temperature is 

changing per kilometer. For example, the light green 

in the color bar corresponds to 0.2 degrees Kelvin per 

kilometer. This translates to a temperature change of 2 

degrees across the 10 km wide green frontal zone. The 

light grey areas are warm regions, the dark grey areas 

cold regions.
STAR Looks at the Earth | Satellite Measurements of the Atmosphere, Oceans and Land 71

Four Geostationary Satellites: One Global Sea Surface Temperature Map

A 24-hour merged sea surface temperature map derived from geostationary satellites: NOAA’s GOES-W and GOES-E, Europe’s Meteosat 

Second Generation (MSG-9), and Japan’s Multi-Functional Transport Satellite (MTSAT). Black areas indicate persistent cloudiness during 

the day, precluding views of the ocean, and creating gaps in the satellite coverage. STAR plans to fill the gap in coverage between MSG 

and MTSAT in the Indian Ocean with SSTs from the Meteosat Second Generation (MSG-8). The approximate orbital locations over the 

equator are shown by the satellite names at the bottom of the figure: GOES-WEST (W)-135°W/GOES EAST (E)-75°W, MSG -0°E, and 

MTSAT -140°

E
72 Section 4 | oceans

GEO-LEO Sea Surface Temperatures

Eileen Maturi and Andy Harris

While geostationary satellites (GEOs) provide continuous observations, low Earth 

orbiting satellites (LEOs) carry more accurate thermal infrared (IR) instruments 

as well as microwave instruments for SST measurements. The microwave sensors 

have coarser spatial resolution (50 km) than the IR instruments (5 km). However, 

unlike IR instruments, microwave instruments on LEO satellites can see through 

clouds. Originally, separate SST maps were created independently from the GEO 

and LEO measurements.

STAR scientists have developed a system for merging the LEO 

and GEO observations into a consistent SST analysis that exploits 

the advantages of each type of satellite. The multi-scale analysis 

methodology collects the SSTs produced by each satellite on a daily 

basis, quality controls them for errors, removes the errors and then 

blends them to produce the best estimate of SST with no gaps.

The daily global multi-platform blended SST maps benefit weather, 

ocean and climate scientists who need a single reliable, consistent 

standard. SST variability is important in understanding and 

predicting monsoons and the onset of El Niño and La Niña as well 

as improving daily and weekly forecasts.

NOAA’s Coral Reef Watch program is using these GEO-LEO 

blended analyses to generate new monitoring tools for detecting 

coral reef bleaching around the world.

Tracking small scale oceanic features

The blended sea surface temperature analysis from 

geostationary and polar-orbiting satellites allows small 

scale oceanic features to be continuously monitored and 

tracked. The image shows the Gulf Stream meanders 

(1), the loop current and its associated eddies in the 

Gulf of Mexico (2), and the meanders and eddies in the 

Gulf of Tehuantepec (3).
STAR Looks at the Earth | Satellite Measurements of the Atmosphere, Oceans and Land 73

Multi-Satellite Sea Surface Temperature Map

Sea surface temperatures (Deg C) on July 18, 2010 generated from merged polar and geostationary satellite data. 

Geostationary satellites: NOAA’s GOES-E/W, Europe’s Meteosat Second Generation (MSG), Japan’s Multi-functional Transport Satellite 

(MTSAT); Polar Satellites: NOAA-19, Europe’s MetOp-A. By merging polar and geostationary SST satellite retrievals a complete global map 

of SST is obtained each day without data gaps.
74 Section 4 | oceans

Ocean Surface Winds

Paul S. Chang and Zorana Jelenak

Ocean surface winds are a crucial piece of information in understanding and 

predicting the short-term and longer-term processes that drive our planet’s 

environment. As the largest source of momentum for the ocean surface, winds 

affect the full range of ocean movement, from individual surface waves to complete 

current systems. Winds along the ocean surface regulate interaction between the 

atmosphere and the ocean by modifying the air-sea exchanges of heat, moisture, 

gases, and particulates. These interactions influence global and regional climates.

Satellite-borne radars are used to measure both the speed and direction of the wind 

– the Ocean Surface Wind Vector. The power of the radar signal reflected back to the 

satellite by the ocean surface depends on the strength of the capillary-gravity waves 

that are generated by the prevailing winds. Wind direction 

is obtained by viewing the surface at different angles. The 

QuikSCAT scatterometer provided more than a decade of 

high quality global ocean vector wind observations with 

better sampling than any other scatterometer. Its reliability, 

high quality, relatively fine spatial resolution, and large 

daily geographical coverage resulted in the largest impact 

of any scatterometry mission used for operational marine 

weather forecasting and warnings. And, for the first time, 

a number of satellite-based wind vector climatologies has 

been constructed to help characterize winds over the ocean.

STAR scientists have developed and continue to improve 

the algorithms for processing the satellite observations and 

making them available in real-time to weather and ocean 

computer models and field forecasters.

Flying into storms

STAR scientists are usually not out in the field making 

their own measurements: they sit at their computers 

and analyze the observations made by satellite 

instruments. But to test new versions of scatterometer 

instruments and to help researchers better understand 

the ocean surface winds in powerful storms – including 

hurricanes – they sometimes leave their comfortable 

offices and fly into the hearts of storms. The aircraftborne scatterometers provide unique data not 

otherwise obtainable. This information will lead to 

improved satellite scatterometers and, with increased 

understanding of storm winds, better weather forecasts 

in the future.

STAR scientist Paul Chang (standing), STAR Visiting 

Scientist Zorana Jelenak, and University of Massachusetts graduate student Joe Sapp on NOAA’s P-3 aircraft. 

(Photo credit: NOAA)
STAR Looks at the Earth | Satellite Measurements of the Atmosphere, Oceans and Land 75

Daily Ocean Wind Coverage from QuikSCAT’s Radar

Left, an example of the daily ocean wind speeds (knots, with reddish colors representing highest wind speeds) from the QuikSCAT 

scatterometer. Left upper panel: nighttime observations; left lower panel: daytime observations. The broad 1800 km continuous orbital 

swaths covering 90% of the oceans each day coupled with QuikSCAT’s high wind speed sensitivity made it extremely valuable for weather 

forecasts and warnings. The graphic on the right is a close-up view of QuikSCAT wind vectors using the wind barb notation off the Pacific 

Northwest coast. Red/yellow represents the strongest winds and green/blue the lighter winds.
76 Section 4 | oceans

Sea Surface Salinity

Eric Bayler

Salinity, the local concentration of salts in ocean water, can now be observed from 

space by the European Space Agency’s Soil Moisture-Ocean Salinity (SMOS) 

mission and NASA’s Aquarius mission. These new satellites provide nearsimultaneous sea surface salinity (SSS) observations across the globe, yielding the 

first comprehensive assessment of the world’s SSS and its variability. The satellites 

passively sense the ocean’s emission of microwave energy, which is a function 

of salinity, temperature, and frequency, as well as surface roughness due to wind 

speed. The SMOS design, an entirely passive instrument, is a synthetic aperture 

interferometer. The Aquarius employs passive radiometers in conjunction with an 

active scatterometer.

Salinity, analogous to moisture in the atmosphere, is a primary characteristic of the 

ocean, combining with temperature to determine density. Density differences drive 

the ocean’s thermohaline circulation, which governs much of the flow between 

the surface and the deep ocean. The ocean’s thermohaline circulation responds to 

and drives climate-scale processes and variability. Near-term ocean modeling and 

forecasting will also benefit from improved SSS data, especially in the depiction 

of near-surface dynamics. Determination of upper-ocean heat content requires 

knowledge of altimetry, temperature, and salinity. In addition to climate-scale 

processes, the upper-ocean heat content governs, in part, the intensification of 

hurricanes.

The salinity information is also relevant to passive microwave remote sensing 

of other environmental characteristics because those measurements frequently 

depend on a representation of the energy emitted from the Earth’s surface. Thus, 

satellite SSS observations have the potential for improving satellite observations of 

the atmosphere and improving numerical weather prediction.

STAR is developing and will provide the satellite SSS data sets and products for 

transition into operational use, as well as data quality analysis.

Historical sea surface salinity (SSS)

NOAA’s World Ocean Atlas 1998 (top) depicts the total 

number of SSS observations accumulated over the 

historical time period. The Argo float system, deployed 

over the next decade in conjunction with international 

partners, substantially increased SSS observations, 

as noted in NOAA’s World Ocean Atlas 2009 (bottom). 

Note the significant areas of the world’s oceans still with 

no observations ever (black regions).
STAR Looks at the Earth | Satellite Measurements of the Atmosphere, Oceans and Land 77

Difference Between Satellite Salinity Observations and Climatology

The difference between the mean satellite sea surface salinity observations for April 2011 and climatological averages highlights regions 

of potential bias due to the spatial and temporal sparseness of the data comprising the climatology. The sea surface salinity data are from 

the European Space Agency’s Soil Moisture-Ocean Salinity (SMOS) mission, as processed by the SMOS Barcelona Expert Center. The 

climatology used for comparison is from NOAA’s World Ocean Atlas 2009. In general, regions with apparently larger differences appear 

to be associated with significant ocean surface currents. Due to newness, the satellite sea surface salinity data used for this figure are still 

developmental and not fully validated.
78 Section 4 | oceans

Sea Ice

Pablo Clemente-Colón

Sea ice, or frozen ocean water, forms in the Polar Regions as well as in sub-Arctic 

regions, such as the Baltic and Yellow seas, as a result of decreased sunlight at 

high latitudes and extreme heat loss from the ocean surface into the atmosphere. 

Satellites afford a vantage point from which to routinely monitor daily, seasonal, 

and longer term sea ice changes in both hemispheres. Satellite imagery from diverse 

sources, including passive and active microwave, and visible and infrared sensors, 

as well as in-situ and model observations, are routinely processed and analyzed to 

characterize and monitor the sea ice cover. A similar analysis is done for lake (fresh 

water) ice over the Great Lakes. This information is transmitted to a wide number 

of operational and research users for applications ranging from ship navigation to 

weather forecasting, seasonal outlooks and climate research.

Increasing maritime operations in both the Arctic and Antarctic regions require 

maintaining and improving our capabilities to detect and characterize the presence 

of thick first year ice, multi-year ice, and icebergs (ice of land origin) for both 

safe navigation and the protection of the environment. The National Ice Center 

(NIC) ensures that ice analyses and forecasts based on the satellite observations 

are available to its wide user base that includes the Navy submarine and surface 

fleets, the National Weather Service and international weather services, NOAA 

marine living resources research and hydrographic survey vessels, U.S. Coast Guard 

(USCG) icebreaker operations, and National Science Foundation Polar research 

and logistic operations. The shipping, oil and gas, and cruise ship industries, 

academia, and recreational users also use the ice forecasts and analysis. 

The National Ice Center is comprised of NOAA, Navy, and USCG components, 

and its Chief Scientist is a member of STAR.

Keeping ships away from trouble

Satellite observations of sea ice support the operations 

of NOAA, Navy, and U.S. Coast Guard vessels in, 

under, or near ice-infested waters, and are also used by 

the broader maritime community. (Photo credit: (top to 

bottom) NOAA, US Coast Guard, US Navy)
STAR Looks at the Earth | Satellite Measurements of the Atmosphere, Oceans and Land 79

Different Satellite Sensors are used to Monitor and Chart Sea Ice

Arctic Chart for 17-23 July 2011 (left); Antarctic Chart for 24-30 July 2011 (right)

Satellite sensors (upper panel) used by STAR for sea ice monitoring include synthetic aperture radars (SAR) aboard the Canadian 

RADARSAT and European Envisat satellites. Radar satellite observations can always see the ice since they can penetrate the clouds to the 

surface. Visible and infrared radiometers aboard the NOAA Polar-Orbiting Environmental Satellites also provide key observations in areas 

where there are no clouds. The combination of observation sources provides the best overall sea ice coverage.

Detailed charts (lower panel) depicting sea ice extent, type, and concentration (indicated in tenths by the colors in the charts, with red and 

grey representing the greatest sea ice concentrations) are produced from the analysis of the satellite observations.

RADARSAT-2 Envisat POES
80 Section 4 | oceans

Ocean Color Remote Sensing

Menghua Wang, Wei Shi and SeungHyun Son

The “color” of the ocean depends on the substances in the water, particularly 

phytoplankton – microscopic, free-floating photosynthetic organisms – and 

inorganic particulates. Satellite ocean color observations provide the only means to 

quantify changes in the global ocean biosphere, as well as on regional ocean scales, 

and over long time periods. Ocean color also helps scientists link biological changes 

to changes in the ocean environment. For this reason, chlorophyll concentration is 

a key parameter derived from the ocean color measurements.

NASA’s Coastal Zone Color Scanner (CZCS: 1978–1986) demonstrated that 

measurements of ocean color based on sunlight reflected from the water 

in the visible and near-infrared (NIR) spectral bands could be used to 

monitor ocean substances. Since the CZCS, a number of ocean color 

instruments have been flown on research missions. The first operational 

sensor – the Visible Infrared Imaging Radiometer Suite (VIIRS) – will 

be part of NOAA’s next-generation polar satellite system, the Joint Polar 

Satellite System.

Usually less than 10% of the visible radiance measured by an ocean color 

sensor comes from ocean waters - the rest is sunlight scattered back 

to space by the atmosphere and sea surface. Detecting the weak ocean 

signal places very stringent requirements on the sensor’s calibration and 

the algorithm that is used to correct for the atmospheric distortion. For 

open oceans, STAR has developed an atmospheric correction algorithm 

that uses the near-infrared (NIR) bands; for turbid coastal and inland 

waters, STAR’s algorithm depends on the shortwave infrared (SWIR) 

band measurements.

The ocean color measurements are used by the NOAA community 

for a wide variety of applications, including analyses of carbon cycling, 

detection and monitoring of harmful algal blooms, assessment of water 

quality, and the characterization of fisheries habitats.

Chlorophyll and sunlight penetration

STAR’s atmospheric correction algorithm has been 

used to derive improved ocean color products along the 

U.S. east coastal region. Chlorophyll-a concentration 

(panel a) is an index of phytoplankton biomass which 

is a key ecological property in the aquatic system. 

Another product measures the murkiness of the water 

(diffuse attenuation coefficient shown in panel b) which 

provides information on light penetration and light 

availability. Satellite ocean color observations are critical 

for our understanding of ocean optical, biological, and 

biogeochemical processes and phenomena.
STAR Looks at the Earth | Satellite Measurements of the Atmosphere, Oceans and Land 81

Biomass and Clarity of the Ocean

Global ocean optical and biological properties can be derived from satellite measurements. The figure shows color images for the global 

composite distribution of the MODIS-Aqua-derived normalized water-leaving radiance in panel a (at 443 nm wavelength (nLw(443)) and in 

panel b (at 645 nm wavelength (nLw(645)). Chlorophyll-a concentration is shown in panel c and diffuse attenuation coefficient (at 490 nm 

wavelength (Kd(490)) is shown in panel d. These results were derived using the near and shortwave infrared (NIR-SWIR) ocean color data 

processing. MODIS-Aqua measurements from January, April, July, and October of 2005 were used to make the images. Data scales are 

indicated in the color bar –linear for nLw(443) and nLw(645) and logarithmic for chlorophyll-a and Kd(490) – with data ranges indicated above 

and below color bar. Panel a shows inverse of chlorophyll and algae absorption, while panel b can be directly related to the water nearsurface total suspended sediment. Reds indicate high values and blues low values. The global maps clearly reveal highly productive regions 

along coastal and near-shore ocean areas in panel b, c, and d, while panel a indicates regions of strong chlorophyll from phytoplankton and 

algae.
82 Section 4 | oceans

Global Distribution of 

Coccolithophore Blooms

Christopher W. Brown

When oceanic conditions are favorable, high concentrations or “blooms” of 

the coccolithophores (see sidebar) can occur. These profoundly affect the 

biogeochemical and optical properties of the waters they occupy. Their presence 

modifies the chemistry of surface water through coccolith (calcium scale) and 

dimethylsulfide (DMS) formation. As the major calcifiers in the open ocean, 

coccolithophores alter the equilibrium of the inorganic carbon system and the 

alkalinity of seawater that can drive the outgassing of CO2

 to the atmosphere – 

which could modify the greenhouse effect – and the export of particulate inorganic 

carbon to depth. Coccolithophore blooms also impact higher levels of the food 

web, including fish, birds and marine mammals. For example, during the summers 

of 1997 to 2000, blooms of the coccolithophore Emiliania huxleyi blanketed the 

Bering Sea, resulting in high mortality rates of short tail shearwaters, and shifts 

in the distribution of many species, including the endangered right whales, and a 

reduction in local salmon runs.

Blooms of several coccolithophore species, particularly those of E. huxleyi, are 

detectable in visible satellite observations owing to the high reflectance of their 

coccoliths. These blooms are observed in satellite imagery at several locations, 

particularly in mid- and high latitude regions. Their high reflectivity reduces the 

solar heating of the oceans; any changes in the frequency or extent of these blooms 

could amplify or reduce greenhouse warming.

STAR scientists use satellites, such as the Sea-viewing Wide Field-of-view Sensor 

(SeaWiFS), to monitor the distribution pattern of the coccolithophore blooms on 

a regional and global scale in order to assess the magnitude of change in the marine 

ecosystem.

Phytoplankton, coccolithophores, and 

Emiliania huxleyi

Phytoplankton are microscopic, single-celled 

plants and form the base of the aquatic food web. 

Coccolithophores, an abundant and widely distributed 

component of marine phytoplankton, are golden-brown 

algae that possess external calcareous (calcium) 

scales called coccoliths. Scientists estimate that these 

organisms generate more than 1.5 million tons of calcite 

a year, making them the leading calcite producers in the 

ocean.The coccolithophore species Emiliania huxleyi is 

considered the most productive lime-secreting species 

on Earth.

Scanning electron photomicrograph of the coccolithophore Emiliania huxley.
STAR Looks at the Earth | Satellite Measurements of the Atmosphere, Oceans and Land 83

Interannual Variability of Coccolithophore Blooms

Annual composites of coccolithophore blooms during the first eight years of the SeaWiFS mission. Annual periods extend from October to 

September. The blooms are colored white, the ocean without blooms is blue, the land is green, and locations with no data are gray. Note 

the variability in the size of blooms located in the vicinity of Iceland from year to year, and the appearance of blooms in the Southern Ocean 

to the east of New Zealand in 2003-2004.
84 Section 4 | oceans

Coastal Water Quality

Michael Ondrusek and Eric Stengel

Using satellites to observe water quality in coastal regions has advantages over 

physical sampling and traditional monitoring techniques. Shipboard measurements 

are accurate and can provide observations at depth but are expensive and not 

continuous over space and time. Buoys provide good temporal coverage at one 

location but are isolated in space. Satellites expand the observations spatially 

for near-simultaneous measurements over a large area. An integrated sampling 

approach is best for monitoring water quality in coastal regions.

Satellite detection of water quality is limited to items that have an electromagnetic 

signature that can be observed by existing sensors. Particulate and dissolved 

colored material can be detected and quantified by ocean color sensors through 

algorithms that relate their concentrations to light emitted from the water. STAR 

uses these relationships to monitor runoff and its consequences over time and relay 

these findings to decision makers who regulate sources of these pollutants.

The Chesapeake Bay is the nation’s largest estuary and is located in a highly populated 

region. Industrial and urban pressures have degraded the bay ecosystem. Increased 

agricultural and human land use has resulted in high sediment and nutrient runoff 

with detrimental effects in the bay. Sediments reduce light available to submerged 

aquatic vegetation and reduce habitats for oysters and other organisms. Increased 

nutrient fluxes are generally associated with these runoff events. The increased 

nutrients cause increased phytoplankton growth; their decay deprives the water 

column of the oxygen that is essential for the survival of all economically important 

marine organisms.

STAR investigators have developed satellite remote sensing tools to monitor runoff 

of Total Suspended Material (TSM) and other water quality indicators. These 

tools are used to support Chesapeake Bay monitoring and restoration efforts.

Satellite true color image of high 

sediment runoff in the Northern 

Chesapeake Bay

Following heavy runoff in the Chesapeake Bay in 

January 2010, sediments (muddy water inside oval) can 

be seen cascading down the bay from the Susquehanna 

River basin to over 40 miles south. The true color 

imagery is produced from a red, green and blue 

composite of MODIS Aqua bands 1, 3, and 4. (Photo 

Credit: NASA)
STAR Looks at the Earth | Satellite Measurements of the Atmosphere, Oceans and Land 85

Satellite monitoring of total suspended matter (TSM) in the Chesapeake Bay for 2009. Each image is a monthly average demonstrating 

the ability to quantify seasonal variations of runoff into the bay. Concentrations are in milligrams per liter (mg/l), with red representing the 

muddiest water, and the spatial resolution is 250 m. In 2009, highest monthly averaged TSM concentrations were observed in the northern 

Chesapeake Bay in January. The explanation of seasonal variability in TSM is complicated but can be tied to increased river discharge, 

sediment resuspension and erosion. All three causes can be related to storm and precipitation activity.

Total Suspended Matter in the Chesapeake Bay

January February March April May June

July August September October November December
86 Section 4 | oceans

The Ocean Surface Topography Satellite 

Constellation

Laury Miller

Satellite radar altimeters map sea level features globally by precisely measuring 

the difference between a satellite’s altitude and the distance between the satellite 

and the sea surface. However, present radar technology only provides point “looks” 

at the ocean along a satellite’s ground track that typically has a repeat sampling 

interval of 10 to 30 days. The spacing of adjacent ground tracks between orbits is 

a couple of 100 kilometers. Clearly one satellite radar altimeter is not enough for 

these measurements.

To deal with this challenge, the Committee on Earth Observation Satellites 

(CEOS), a 40-nation organization including the U.S., France, Germany, India, 

and China, has established a program to coordinate on-going and future radar 

altimeter missions. The principal goals are threefold. The first and highest priority 

is to ensure continuity of the more than 20-year old, high-accuracy TOPEX/Jason 

reference mission that forms the basis for the sea level climate data record. The 

second goal is to maintain at least two additional medium-accuracy altimeters for 

sampling meso-scale (100 km horizontal scale) ocean variability. The third goal is 

to encourage innovation.

STAR personnel are currently serving as NOAA Program and Project Scientists 

for several satellite altimetry missions. Using the data from the satellite altimeters, 

the sea level rise of the Earth’s oceans can be monitored.

Sea level rise: Climate change or natural 

variation?

Observations from three overlapping TOPEX/Jason 

satellite radar altimeter missions, collectively the 

international Committee on Earth Observation Satellites 

(CEOS) Reference Mission, show an almost steady 

increase in sea level of 3.0 mm/yr, nearly twice the 

historical rate of 1.7 mm/yr estimated from coastal tide 

gauges over the past century. Whether the present 

higher rate reflects an acceleration related to global 

warming or natural, multi-decadal variability is yet to be 

determined.

The TOPEX/Jason series was initiated by NASA and 

the French National Center for Space Studies (CNES); 

however, beginning with Jason-3 the series will be jointly 

managed by NOAA and EUMETSAT, with NASA and 

CNES in supporting roles.
STAR Looks at the Earth | Satellite Measurements of the Atmosphere, Oceans and Land 87

Ocean Features Revealed by Constellation of Three Satellite Altimeters

The average ocean surface topography off the U.S. East Coast measured by the Jason-1, Jason-2 and ENVISAT satellite radar altimeters between 

23 March and 21 April 2011. Solid black lines are the tracks of the satellite observations. The sharp boundary between red and blue areas marks the 

location of the Gulf Stream running along the Southeast Coast and then eastward from Cape Hatteras. The presence of eddy-like features scattered 

throughout the Sargasso Sea (east of the Gulf Stream) and the narrow structure of the Loop Current in the Gulf of Mexico highlight the need for having 

a constellation of radar altimeters to adequately sample meso-scale (~ 100 km) variability in the ocean.
88 Section 4 | oceans

Aliasing of Altimeter Observations

C. K. Tai

Without a sufficient number of observations of a physical phenomenon (as is usually 

the case for ocean remote sensing), the resulting analysis of the measurements may 

suffer from artifacts. This effect is called aliasing in sampling theory (see sidebar). 

A simple example is provided by the silent-film-era movie. In those times, the 

movie camera was not fast enough to take many pictures per second. If a bicycle 

was filmed moving forward, the bicycle wheels often appeared to rotate backwards 

instead, especially for fast riders. This is a classic illustration of the phenomenon 

of aliasing.

When one measures the sea surface height using a satellite altimeter that observes 

each point on the ocean only once every 10 days, one clearly does not have enough 

measurements to avoid the aliasing phenomenon, especially for fast changing ocean 

features (see illustration opposite page). Moreover, if one were to measure the sea 

surface height with more than one satellite, one has to coordinate the satellites in a 

way to optimize the sampling characteristics, that is, to avoid the aliasing as much 

as possible, and thus avoid being fooled.

STAR scientists have studied these issues for many years and developed the best 

ways to process the data to reduce aliasing.

Scientists fooled by aliasing

In the illustration below, the signal is the red curve. 

Observations are made at fixed intervals of time marked 

by 0, 1, 2, etc. The blue curve is the result of connecting 

the observation points. Instead of the true signal 

represented by the red line, the observations appear 

to represent the blue line. This is the basic concept of 

aliasing. It is clearly caused by not observing frequently 

enough.

In the published peer-reviewed scientific literature, there 

are examples of aliased results being interpreted as real 

results. These are not only embarrassing, but can lead 

the scientists to pursue illusory goals, thus wasting talent 

and funding. It is important for all scientific observers to 

be cognizant of aliasing.

(Photo credit: Aliasing in Wikipedia. Retrieved November 

29, 2012 from http://en.wikipedia.org/wiki/Aliasing); Image 

author Moxfyre
STAR Looks at the Earth | Satellite Measurements of the Atmosphere, Oceans and Land 89

Tidal Aliasing in Theory and Observations

The zonal-wavenumber (x-axis) and frequency (y-axis) spectrum of alitimetric sea level over about two years in 1994 and 1995, at 28.3°N, 

from 144.6 to 186.3°E in the North Pacific. The tides have periods (about half a day to a day) shorter than the 20 days that the repeat 

altimeter observations at 10 days apart are able to resolve. They are aliased into periods of about 2 months with a wavelength about 

10° longitude as predicted by the aliasing theory (see the red rectangle with white outline at j=12 and k=4), and thus can be discounted 

as fictitious. The largest real signal in the spectrum is at j=5 and k=-5 (blue rectangle with white outline), representing an oceanic wave 

(called Rossby wave), which is instrumental in ocean dynamics. The remaining colored rectangles represent either aliasing of other 

tidal components or real signals. Note that a spectrum separates the signal into its spectral components at various frequencies and 

wavenumbers (i.e., wavelengths) and gives the power at each spectral component, just as the sunlight can be separated into various colors 

and even more frequencies (from ultraviolet to infrared, for example).
90 Section 4 | oceans

Mapping the Global Ocean Floor 

from Space

Walter H. F. Smith

Most of the Earth’s surface is ocean more than 200 miles from any coast, and 

beyond the mapping responsibilities of coastal nations. Only a small percentage of 

this vast area has been surveyed. The paths taken by bathymetric (depth measuring) 

survey ships cover the remote ocean about as sparsely as the Interstate Highway 

System covers the United States.

Tsunami forecasts, ocean circulation and climate models, fisheries and habitat 

management, and many other applications – including the laying of transoceanic 

telephone cables (in fact, this author’s first cruise at sea was funded mostly by 

AT&T for a trans-Pacific cable survey) – need a map of the depth of the global 

ocean floor. The shape of the bottom steers the flow of currents, while the roughness 

of the bottom controls mixing rates. Seamounts (undersea mountains, volcanic in 

origin) produce upwelling water and provide rich habitats. An estimated 100,000 

seamounts more than 1 km tall have yet to be discovered.

Satellite radar altimeters measure the elevation of the ocean surface. Although 

the radar cannot “see” through to the ocean floor, subtle slopes in the ocean surface 

height reveal the gravity anomalies induced by topography below. STAR scientists 

have led the development of the technique which translates ocean surface slope 

into bottom topography details, which has been validated by scientific and military 

surveys worldwide. The method has only moderate resolution – mountains on the 

seafloor need to be about 10 km wide and 2 km tall in order to generate enough 

gravity anomaly to be detected in presently available measurements – and so will not 

supplant traditional acoustic echosounding. Even so it furnishes the background 

view (e.g., in Google Earth) and the global framework into which more detailed 

surveys may be placed.

How sea level reveals the sea floor

Satellite altimeters measure sea level from space. 

The height of the sea level with respect to a reference 

ellipsoid varies from place to place and time to 

time. Time-varying height changes are due to tides, 

meandering currents and eddies, winds and atmospheric 

pressure, and climate changes. But sea level also has 

permanent bulges and hollows caused by variations 

in the pull of gravity due to undersea mountains and 

valleys. Where a mountain displaces water, its extra 

mass increases the pull of gravity nearby, pulling extra 

seawater around the mountain and bulging the sea 

outward. The height of this bulge can be 1 meter or 

more. After the time-varying sea level signals are 

removed, the remaining constant level shows these 

bulges. The tilt of the sea level can be used to calculate 

the gravity anomaly, which can then be verified by ships 

carrying gravimeters. Satellite altimeters have resolved 

these tilts to a few micro-radians, fine enough to reveal 

most of the plate tectonic features of the ocean floor.
STAR Looks at the Earth | Satellite Measurements of the Atmosphere, Oceans and Land 91

Global Sea Floor Topography from Satellite Altimetry

Satellite altimetry has revealed the tectonic fabric of the sea floor. The mid-ocean ridges (yellow-green) are the longest, and in places, most 

rugged, mountain ranges on Earth. Earth’s tallest mountains are the volcanic islands, like Hawaii, that rise from the sea bed four or more 

kilometers below sea level. Nearly all of Earth’s active and dormant volcanoes are on the ocean floor. Tides rubbing against the rugged 

ocean bottom dissipate energy at rates of a few terawatts (1 terawatt = 1012 watts), equivalent to the rate at which winds do work on the sea 

surface. The shape of the bottom is thus fundamental to modeling the mixing and circulation of the ocean.
92 Section 4 | oceans

International Efforts to Improve Global 

Ocean Depth Map

Karen Marks

General Bathymetric Charts of the Ocean (GEBCO), which emerged from a 

commission chaired by Prince Albert I of Monaco in 1903, aims to provide the most 

authoritative publicly-available global bathymetry data sets of the world’s oceans. It 

has grown into an international organization with more than 80 member states that 

operate under the auspices of the International Hydrographic Organization (IHO) 

and the Intergovernmental Oceanographic Commission (IOC) of UNESCO. The 

current GEBCO data grid is based on a NOAA altimetric bathymetry model that 

combines seafloor depths derived from satellite gravity data with in-situ data such 

as soundings and shorelines, the International Bathymetric Chart of the Arctic 

Ocean (IBCAO) grid, and other data. STAR works to continuously improve the 

NOAA altimetric bathymetry model.

STAR is playing a leading role in the GEBCO “Cookbook” Working Group, 

which is writing a user’s manual to guide nascent regional mapping projects. The 

Cookbook will be a technical reference manual on the development of bathymetric 

grids and will include step-by-step instructions that users can follow to produce 

high-quality grids from their own data sets and then submit them for incorporation 

into the GEBCO global grid. The Cookbook will also cover topics such as data 

cleaning, metadata (description of the data) documentation, various gridding 

methods, testing, and uncertainty assessments.

How deep is the ocean?

Depths shown on this User’s Guide for General 

Bathymetric Charts of the Ocean cover the range from 

shallow seamounts (orange) to more than 7000 m deep 

(purple) in the Mariana Trench (upper left).
STAR Looks at the Earth | Satellite Measurements of the Atmosphere, Oceans and Land 93

Western Pacific Ocean Depths from Combined Ship and 

Satellite Observations

3-D view of GEBCO global ocean depth map, centered on the Pacific Ocean (lighter blue colors, shallower; darker blue colors, deeper). 

GEBCO_08 is a global 30-arc second (about 1 km) grid based on quality-controlled ship depth soundings and estimated depths from the 

NOAA altimetric bathymetry model, plus other bathymetric datasets. The GEBCO User’s Guide provides a manual for regional mapping 

efforts to produce and test high-quality grids that may be incorporated into the GEBCO global bathymetry grid. (Photo credit: http://www.

gebco.net)
94 Section 4 | oceans

Oceanic and Atmospheric Features from 

Synthetic Aperture Radar

Xiaofeng Li and William Pichel

Wide-swath synthetic aperture radar (SAR) active microwave instruments observe 

the large-scale ocean surface in unique ways. Modern satellite SAR instruments 

can actively monitor phenomena in the coastal ocean and marine atmospheric 

boundary layer (MABL) at very high spatial resolution (tens of meters) in all 

weather conditions, day and night. SAR is particularly useful in coastal regions 

where clouds are usually present, causing observation problems for visible and 

infrared sensors. The ScanSAR/Wide-swath SAR can provide coverage of about 

450 km perpendicular to the satellite orbit, wide enough to discern oceanic and 

atmospheric meso-scale features. Any physical processes in the MABL and ocean 

column that change the surface wind and currents will change the sea surface 

roughness, leave an imprint on the ocean surface, and change the backscatter signal 

measured by SAR.

Studies have shown that SAR observations combined with theoretical and/or 

numerical models can lead to better weather forecasts. NOAA and other scientists 

have taken advantage of SAR observations to study hurricanes, atmospheric vortex 

streets (shown in sidebar), down-slope coastal mountain winds, atmospheric 

gravity waves (vertical atmospheric waves), atmospheric roll vortices (horizontal 

convective rolls), internal waves, and shallow and deep-water bathymetry, which 

are examples of phenomena important for navigation and safety of aviation and 

shipping, and coastal management.

With the increasing number of SAR sensors available today and near-future 

operational SAR satellites (Europe’s Sentinel-1 and Canada’s RADARSAT 

Constellation Mission), researchers and other users will have more repeat SAR 

observations of these ocean and MABL features with multiple radar frequencies 

and polarizations, resulting in more accurate environmental measurements. STAR 

scientists continue to explore and develop new applications using SAR data.

Atmospheric vortex streets

Sea surface roughness changes associated with ocean 

and atmospheric phenomena can be imaged by SAR 

with very high spatial resolution in all weather conditions, 

day or night. 

The figure shows the imprint on the sea surface (light 

and dark areas coming down the page) of an atmospheric vortex street – a double row of opposite rotating, mesoscale eddies – off the Aleutian Islands (top of 

figure) imaged by the RADARSAT-1 SAR (© CSA 1999).
STAR Looks at the Earth | Satellite Measurements of the Atmosphere, Oceans and Land 95

Synthetic Aperture Radar Captures Signatures of Internal Ocean Waves 

A wide swath mode image (415 km × 330 km) acquired from the European Space Agency’s ENVISAT ASAR (Advanced SAR) at 2:13 UTC 

12 August 2009 shows alternating bright-dark features, which are sea surface imprints of tide-generated internal wave packets propagating 

from the Luzon Strait (400 km east of the center of this image) westward in the South China Sea. These internal wave packets are 

separated by tens of kilometers with amplitudes over 100 m. (©ESA 2009)
96 Section 4 | oceans

High-Resolution Coastal Winds

William Pichel, Frank Monaldo, and Xiaofeng Li

Winds have been derived from satellite remote sensing data for many years, 

beginning with cloud-drift winds in the troposphere from visible and infrared 

imagery picture pairs, and progressing to sea surface winds from passive microwave 

sensors and active microwave scatterometers. The most recent sensor to be used for 

wind measurement is synthetic aperture radar (SAR). SAR instruments are active 

microwave sensors similar to scatterometers, and like scatterometer observations, 

increases in radar backscatter are related to increasing wind speeds. Scatterometers 

have the advantage of also measuring wind direction and providing quasi-global 

daily coverage, whereas SAR instruments have the advantage of very high spatial 

resolution (approximately 0.5 km vs. 10 to 25 km for scatterometers) and the ability 

to measure winds right up to the coast and in straits, bays, and larger lakes. Wind 

accuracy for SAR winds is 1.5-2.5 m/s, on the order of scatterometer accuracy.

 SAR measurements reveal the highly variable wind field along coasts with rugged 

topography, such as in the states of Alaska and Washington. Down-slope (i.e., 

katabatic) winds, barrier jets, and gap winds can result in dangerously high winds 

in one area, with benign wind conditions close by – a situation very difficult to 

forecast when employing only traditional satellite wind measurements. These 

phenomena are well depicted in SAR wind imagery, such as that shown in the 

accompanying figure. 

National Weather Service meteorologists of the Alaska Region have requested that 

SAR winds be transitioned from a research mode to a real-time operational activity. 

Although SAR wind measurements to this point have relied on commercial and 

research satellites, the first of a constellation of five operational SAR satellites is 

scheduled for launch in the near future. STAR is developing the algorithms to be 

used in the real-time processing of the data from these satellites.

SAR coastal wind climatology 

applications

In addition to the use of SAR-derived wind 

measurements in near-real-time weather forecasting, 

some important climatological applications have 

emerged

SAR coastal winds are proving to be of value to the 

offshore wind energy generation industry. Long-term 

averages of high-resolution winds can indicate the best 

sites for establishing wind farms and how wind turbines 

affect the wind field for current or future turbines located 

downwind.

In addition, coastal winds determined from SAR imagery 

have been correlated with weather conditions to derive 

a statistical relationship that can be used to predict what 

the coastal wind conditions are likely to be for a given 

weather situation.

Wind turbines off the coast of Barrow, UK.(Photo credit: 

NOAA Fisheries)
STAR Looks at the Earth | Satellite Measurements of the Atmosphere, Oceans and Land 97

Synthetic Aperture Radar Images of Coastal Winds near Alaska

RADARSAT-1 SAR wind image, March 14, 2007 at 03:29 UT. The region shown is the Alaska coast of the Gulf of Alaska beginning 

at Kodiak Island at the bottom left of the image, and extending to Yakutat Bay at center right. Winds are from the north and northwest 

(indicated by arrows) with high winds extending southward over the ocean from gaps in the coastal mountains and islands. Wind arrows are 

from the Navy Operational Global Atmospheric Prediction System (same color bar as for the image). High winds can also be seen in the 

straits of Prince William Sound (center right of the image).
98 Section 4 | oceans

Ocean Hazards Response: Oil spills

Christopher R. Jackson, William G. Pichel and Oscar Garcia-Pineda

Identifying and mapping the location and extent of oil on the ocean’s surface is 

important for understanding and assessing its impact on the ocean and the coastal 

environment. Oil pollution can be the result of either natural seeps or accidental/

intentional release during its extraction, use and transportation. Synthetic aperture 

radars measure the echo returns from many radar pulses as the satellite moves in its 

orbit, which are processed into a single radar image. It’s a way of achieving very fine 

horizontal resolution without having to fly enormous radar antennas. Imagery from 

satellite-based synthetic aperture radar (SAR) is currently one of the best sources 

for detecting oil on the ocean’s surface and can be used to effectively monitor large 

areas, with individual SAR images covering 100’s of square kilometers.

The radar energy that is returned from the ocean is primarily from the short, 

wind-generated capillary waves. These are damped by surface oil, making the 

oil stand out by appearing darker than the ocean’s surface under moderate wind 

conditions (3 - 12 meters/second). SAR also has the advantage of being able to 

make observations both day and night and to penetrate cloud cover. While SAR 

is sensitive to relatively small amounts of surface oil, care must be exercised so as 

not to confuse oil with other dark features such as low winds or upwelling water.

Currently, several countries use satellite SAR to support coastal monitoring for 

oil pollution from ship traffic. The satellite imagery is used to identify possible 

polluters, who can then be confirmed from aircraft or ship observations and 

subjected to heavy fines for illegal dumping. The imagery is also used to support 

hazard response during major oil spills, most recently the spill from the Deepwater 

Horizon event in the Gulf of Mexico. STAR scientists are currently working on 

advanced oil spill mapping algorithms, including multi-polarization methods.

Deepwater Horizon - 2010

On 20 April 2010, the Deepwater Horizon oil rig exploded 

and sank, resulting in the largest oil spill in U.S. waters. 

Over the course of 4 months, more than 600 SAR data 

collections from 9 international satellites were used 

by STAR to determine the location and extent of the 

Deepwater Horizon slick in support of clean-up efforts.

The automated oil analysis shown in the inset was derived from this ENVISAT Advanced SAR (ASAR) image 

acquired 2 May 2010 at 3:51 UTC. The automated oil 

mapping algorithm is being developed in a collaboration 

between STAR and Florida State University. It is estimated that at this time the oil spill covered 2274 sq km.
STAR Looks at the Earth | Satellite Measurements of the Atmosphere, Oceans and Land 99

Ocean Surface Oil Signatures in Synthetic Aperture Radar Images

Examples of ocean surface oil signatures. Left: Surface oil signatures resulting from natural oil seep leakage on the ocean floor are regular 

features in SAR imagery of the Gulf of Mexico. The slicks can extend for tens of kilometers and persist for several days. Right: Oil pollution 

from ships is a problem in coastal waters and is closely monitored in many parts of the world. Oil pumped from a ship’s bilge leaves a 

characteristic trail behind the ship that is easily identifiable in satellite SAR imagery. The ship can be seen as the bright ‘dot’ at the end of 

the narrowing dark oil trail toward the upper right of the image.
100 Section 4 | oceans

CoastWatch

Kent Hughes and Michael Soracco

A harmful algal bloom is a rapid growth of algae, typically from nutrient rich 

waters, that impacts marine life or people by eutrophication (depletion of the 

oxygen in the water) or the production of toxic substances. In the late 1980s, a 

harmful algal bloom blanketed a section of the North Carolina coast and reportedly 

cost the community $25 million in damage and clean-up costs. In response to this 

event, STAR led the development of a NOAA program to collect satellite data to 

monitor the conditions of such blooms and give coastal managers advance warning 

of similar circumstances. Since then, this CoastWatch system has evolved into a 

robust collection of products that make near real-time oceanographic satellite data 

available to everyone.

Fast modern networks move data sets from receiving stations, through processing, 

to six regional nodes, and, ultimately, to the user. These regional nodes – operated 

through NOAA lines located in the U.S. East Coast (Chesapeake Bay), West Coast 

(Pacific Coast, California), Great Lakes (Ann Arbor, Michigan), Caribbean/Gulf 

of Mexico (Stennis Space Center, Mississippi), Alaska Region (Anchorage), and 

Central Pacific (Honolulu, Hawaii) – organize the data into a number of products, 

including ocean color, ocean surface wind, true color, and sea surface temperature 

from a variety of platforms and sensors. Custom products, such as those used 

to support the Deepwater Horizon Oil Spill event, are also provided using the 

expertise of personnel located at these regional offices.

Modern technology has also made it much easier to adapt these data to a variety 

of systems. The CoastWatch Data Analysis Tool, for instance, is a graphic user 

interface program available from the CoastWatch website that enables users to 

handily manipulate satellite data for their unique applications. The CoastWatch 

team also continues to work on new and improved products, partnering with 

the European Space Agency and other international agencies operating satellite 

systems, to offer even more diverse and detailed data on ocean conditions across 

the globe.

Red, green and brown tides!

Harmful algal blooms (HABs) occur when certain 

species of algae grow quickly in the water, forming large 

patches. They come in a variety of colors – red, green, 

and brown. The blooms block the oxygen and sunlight 

that other organisms need to thrive, or emit toxins that 

are detrimental to humans and other life forms. It is 

estimated that HAB events cost the US coastal economy 

up to $82 million each year.

The NOAA CoastWatch program’s ability to detect 

impending HABs through monitoring ocean color data 

is still one of its most popular uses. Ocean color data 

derived from satellites is processed into daily, average, 

and anomaly products that help scientists identify and 

track HABs. This information is then communicated 

to coastal managers and other agencies in the Gulf 

of Mexico – during the bloom “season” a special HAB 

bulletin is issued – to help them prepare for impending 

blooms. (Photo credit: NOAA Ocean Service)
STAR Looks at the Earth | Satellite Measurements of the Atmosphere, Oceans and Land 101

The Deepwater Horizon Oil Spill

CoastWatch generates many products; this visible image is an example of the oil spill product used during Deep Water Horizon. The image, 

from the European Space Agency’s Envisat MERIS (Medium Resolution Imaging Spectrometer) instrument, was provided by the NOAA 

CoastWatch Gulf of Mexico Node to the NESDIS Satellite Analysis Branch in support of NOAA’s efforts to track the spill.
102 Section 4 | oceans

Oceanic Heat Content

Eileen Maturi, Nick Shay and Jodi Brewster

The passage of hurricanes over the ocean causes the sea surface temperature (SST) 

to drop (1 to 6 °C) in response to the strong winds that stir the upper layers of 

the ocean, bringing cooler waters from the lower depths to the ocean surface. The 

amount of cooling at the ocean surface depends on how quickly the hurricane 

moves over the ocean, the strength of the winds, and the depth of the mixed layer. 

The ocean mixed layer begins at the ocean surface and extends to depths of 25 - 

100 meters.

The Oceanic Heat Content (OHC) is a measure of the heat contained in the mixed 

layer. The cooling effect of a hurricane is greatest when the mixed layer is shallow 

(25 to 30 m). If the layer is deep (≥ 50 m), the cooling influence is reduced because 

it is diffused over a larger layer. In this case, the OHC remains high, the SST 

decreases more slowly during hurricane passage, and the warmer ocean promotes 

storm intensification.

Global studies show that regions with high OHC are a better indicator of the 

ocean’s effect on hurricane intensification than just the sea surface temperature 

alone. Satellite radar altimeters that measure the height of the ocean – sea level or 

the sea surface height – can be used to infer OHC variations, since as the ocean 

water warms it expands, and its level rises. OHC is being used at the National 

Hurricane and Joint Typhoon Warming Centers to forecast hurricane and typhoon 

intensities.

STAR scientists are generating an OHC product that combines satellite SST and 

altimeter measurements to obtain more accurate estimates of oceanic heat content 

(see figure, opposite page).

Katrina intensifies over warm 

Gulf waters

Hurricanes are fueled by warm ocean waters, which 

transfer a greater amount of moisture and heat energy 

into the atmosphere. As Hurricane Katrina moved 

across the Gulf of Mexico, it passed over pools of water 

with very high Oceanic Heat Content (red areas). As 

a result, It rapidly intensified to a Category-5 storm – 

winds greater than 155 mph – the highest level on the 

Saffir-Simpson Hurricane Scale. The illustration shows 

Katrina’s track (black curve) and intensity (colored 

circles, red and dark red are category 4 (131-155 mph) 

and 5, respectively) overlaying a map of Ocean Heat 

Content derived from satellite radar altimetry.
STAR Looks at the Earth | Satellite Measurements of the Atmosphere, Oceans and Land 103

Oceanic Heat Content of the Atlantic Ocean Basin

Oceanic Heat Content (OHC) of the North Atlantic Ocean Basin on 25 June 2010 based on sea surface height measurements from three 

radar altimeters and sea surface temperatures from the joint NASA- Japan Aerospace Exploration Agency Tropical Rainfall Measuring 

Mission (TRMM) Microwave Imager. This satellite product is corroborated by temperature observations from the NOAA research aircraft.
104 Section 4 | oceans
STAR Looks at the Earth | Satellite Measurements of the Atmosphere, Oceans and Land 105

Section 5 

The Land 5
106 Section 5 | The Land

The Continents

Ivan Csiszar

Land covers approximately one-third of the total surface of our planet. In its natural 

state, the vegetation, snow or ice cover of each location is determined by the climatic 

and geological conditions. Land also hosts a broad variety of animal species. Land, 

however, is also the primary habitat for mankind, and human activities have altered 

most of the habitable land area. Additionally, the land surface is an integral and 

critical component of the global climate system through its impact on radiation, 

water balance, and atmospheric and oceanic circulations.

Satellites are particularly suitable for the large-scale observation of land 

characteristics. Analysis of reflected solar radiation helps determine the amount 

and condition of vegetation. Sensors detecting infrared radiation are used to 

measure land surface temperature and detect fires. Microwaves penetrating deeper 

into the soil provide information on soil moisture content. Complex multispectral 

and spatial analysis is used to determine land cover type at an increasingly finer 

spatial scale. Disturbances, such as land clearing, insect infestation or fires can also 

be detected by satellites. In the chart on the next page, the Green Vegetation 

Fraction product shows areas where the amount of green vegetation is estimated 

from satellite measurements. This provides information about the state of the 

growing conditions in a region and also the surface background for use in NOAA’s 

forecast models.

NOAA’s environmental satellites have been extensively used for land surface 

monitoring. The Advanced Very High Resolution Radiometer (AVHRR) 

instrument on the NOAA polar satellites now provides a 30-year record or 

vegetation and fire activity. The GOES data are particularly useful for observing 

rapid changes, such as the diurnal cycle of land surface temperature. STAR scientists 

are developing a number of advanced land surface products for the next generation 

geostationary – GOES-R – and polar – Suomi NPP/JPSS – satellites.

Land-atmosphere interactions

The land surface is the lower boundary of the 

atmosphere. Along the land-atmosphere interface 

significant transfer of energy takes place by radiation, 

conduction, or convection. Evapotranspiration and 

precipitation are key processes in water exchange. 

Vegetation absorbs and emits various greenhouse gases 

through photosynthesis, respiration and decomposition. 

The land surface and any objects thereon also represent 

physical obstructions to the movement of air, resulting in 

friction or the diversion of airflow. 

The proper representation of land-atmosphere 

interactions in numerical computer models is critical for 

weather and climate analysis and prediction. Satellite 

information on the land surface, such as vegetation 

cover, soil moisture, surface albedo (reflection of 

sunlight), or snow cover is provided at global or 

continental scales and is assimilated into advanced 

numerical weather prediction models.

Depiction of a continental surface and its interaction with 

the atmosphere. (Image credit: UCAR/NOAA COMET 

Program)
STAR Looks at the Earth | Satellite Measurements of the Atmosphere, Oceans and Land 107

Global Map of Green Vegetation Fraction

Global map of green vegetation fraction – the fraction of the surface covered with green vegetation – in May 2011. In this May snapshot 

of the annual cycle of vegetation activity, most vegetated areas in the mid-latitudes of the Northern Hemisphere already show vigorous 

vegetation cover; the green-up, however, is not yet widespread at higher Northern latitudes. Since May is a late spring month, many areas 

(like in the western U.S.) show a low fraction (red) of green vegetation since the mountainous terrain and cooler conditions delay the onset 

of greenup. In the southeast U.S., the spring conditions, typically with greater moisture and warmer temperatures, result in a higher green 

vegetation fraction.
108 Section 5 | The Land

Drought and Vegetation Health

Felix Kogan

Drought is a typical phenomenon of the Earth’s climate. The losses from droughts 

are staggering. In the USA, a country with advanced technology, the average 

annual cost of drought is around $6 billion. Weather station data used for drought 

monitoring are not sufficient because they characterize individual points rather 

than areas, and the observation network is limited and not uniformly distributed. 

Satellite systems offer an opportunity to overcome these difficulties.

STAR scientists have developed a vegetation health index (VHI) to characterize 

plant health based on measurements of the sunlight (VIS) and near infrared 

radiation (NIR) reflected, and the infrared radiation (IR) emitted, by the vegetation 

canopy. The reflected solar radiation observations serve as a proxy for vegetation 

greenness and vigor (see sidebar). The IR radiation is a measure of the vegetation’s 

temperature. In dry years, the vegetation canopy overheats and loses vigor due to 

a lack of water. This intensifies the negative effects of the low moisture levels and 

slows vegetation development, reducing productivity. The combination of the solar 

reflectance and thermal emission measurements are a better indicator of vegetation 

health and drought intensity than either one alone.

The satellite-based VHI measurements are from the Advanced Very High 

Resolution Radiometer (AVHRR), which has flown on the NOAA polar-orbiting 

satellites since 1981. The reflected solar radiation observations are from the NIR 

and VIS channels, and the vegetation temperature measurements are from the 

10.3-11.3 μm IR channel. The VHI varies from 0 (extreme vegetation stress) to 

100 (favorable vegetation conditions), with 50 corresponding to average vegetation 

health conditions.

Monitoring vegetation from space

Vegetation is green because the chlorophyll it contains 

absorbs the blue and red parts of the solar spectrum 

and reflects the green. This is the basis for the 

photosynthesis process, which converts the sun’s energy 

to plant carbohydrate. With plenty of rain, sunlight, and 

warm temperatures, the forests, grasslands, and crops 

are green and abundant. But with prolonged adverse 

weather conditions, or with the reduced sunlight and 

colder temperatures in the fall and winter the amount of 

vegetation decreases and the vegetation color changes 

from green to yellow, brown, and red.

Being a strong absorber – or weak reflector – of the 

visible part (VIS, about 0.4 - 0.7 μm) of the solar 

spectrum, vegetation appears dark at these wavelengths 

(see diagram). But it strongly reflects near-infrared (NIR, 

0.75 – 1.4 μm) radiation. Thus, the difference between 

the NIR and VIS is high for healthy vegetation and low 

for poor vegetation. This difference presents a basis for 

determining vegetation greenness and vigor from space.
STAR Looks at the Earth | Satellite Measurements of the Atmosphere, Oceans and Land 109

A Disastrous Drought in the Ukraine and Russia

Monthly variations of vegetation health (a combination of moisture and thermal conditions) in Russia and Ukraine in 2010. Red indicates 

severe vegetation stress (<12). Vegetation stress began in the spring of 2010 and intensified through the summer and fall. The drought 

was exaggerated by thermal stress (not shown). The conditions were so severe that marsh areas of the north burst into flame and burned 

for two-three months until the fall’s rains. The last time such severe conditions occurred was in 1972. With products such as the vegetation 

health index, the formation of a drought can be identified before it becomes severe, and this information can help guide planning for 

mitigating the effects of the drought.
110 Section 5 | The Land

Global Vegetation and Society

Felix Kogan

Global vegetation health depends on weather, climate, and ecological processes, 

as well as human activities. Changes in vegetation health, in turn, feedback on, or 

affect, many aspects of the environment. STAR has constructed a global vegetation 

health (GVH) data set for operational monitoring, assessment, and prediction of 

a wide variety of environmental conditions. The data set is global, available since 

1981, and has a spatial resolution of 4 km. It contains measurements of sunlight 

reflected from, and infrared (IR) radiation emitted by, the vegetation; indices of 

vegetation condition, temperature condition, and vegetation health constructed 

from the reflectance/emission measurements; climatological values of the data; and 

other products.

The GVH data and products address the following issues: 

● Prediction of crop and pasture production 

● Estimation of fire risk area and intensity 

● Monitoring mosquito-borne diseases

● Early drought detection and monitoring its area, intensity, 

origination and impacts 

● Flooded and saturated soil

● Climate change and vegetation trends

● El Niño/La Niña impacts on land cover 

● Relationships between ocean temperatures and ecosystems 

● Global warming and regional climate change

● Environmental security (air and soil pollution)

● Vegetation coverage and ecosystem characterization for NWP models

● Monitoring invasive species 

● Land cover change 

● Food security

● Climate constraints, technology and grain production

Satellite indices for vegetation health 

and drought

Solar reflectance and infrared (IR) emission from 

vegetation canopies are converted to indices (proxies) 

characterizing vegetation greenness (Normalized 

Difference Vegetation Index, NDVI) and brightness 

temperature (BT), a measure of the vegetation 

temperature. NDVI and BT are converted to indices 

characterizing vegetation condition and health. These 

indices are Vegetation Condition (VCI, based on 

NDVI), Temperature Condition (TCI, based on BT) and 

Vegetation Health (VHI, based on a combination of VCI 

and TCI).

The image shows VCI, TCI and VHI dynamics during 

the 2007 severe drought in the Ukraine. The area of 

vegetation stress (intensifying towards zero) is yellow 

and favorable conditions are blue. The 2007 drought 

was triggered by extreme thermal stress (TCI), which 

aggravated the moisture condition (VCI) and vegetation 

health (VHI) in the summer.
STAR Looks at the Earth | Satellite Measurements of the Atmosphere, Oceans and Land 111

Predicting Agricultural Output

Comparison of agricultural output (crop yield, tons/hectare) predicted from Vegetation Health (VHI) data and observed output in Morocco. 

The prediction model is based on statistical relationships established between the VHI and measured future agricultural output. The 

predictions correctly forecast most of the variations in agricultural output.
112 Section 5 | The Land

Snow Cover

Peter Romanov

About one third of the Earth’s land surface is affected by seasonal or perennial snow 

cover. Accurate and timely information on the areas covered by snow, its depth, 

and its physical properties is important for weather prediction, transportation, 

electric power generation, water management, recreation, and a number of other 

practical applications. In many areas of the world, humans rely on seasonal snow 

and glaciers as the major source of fresh water for drinking and agriculture. Snow 

cover is also a sensitive climate change indicator: shorter snow cover duration in 

winter and earlier snow melt in spring are among the most obvious consequences 

of the general warming of the Earth’s climate.

Satellites have been used to monitor the extent of global snow since the late 1960s. 

The first snow mapping technique implemented at NOAA involved human analysts 

who visually examined satellite visible images and drew maps of snow cover by 

hand. This technique has proved to be robust, reliable and accurate and therefore, 

despite its high labor cost and lengthy production time, still remains operational 

at NOAA. Since the mid-1970s automated satellite image classification and 

snow mapping programs have been widely used. Automated techniques utilize 

observations made with satellite sensors in the visible, thermal and microwave 

spectral range.

STAR scientists are working on the development of advanced automated snow 

detection and mapping algorithms. These algorithms combine observations made 

in different spectral bands from both polar orbiting and geostationary satellites. 

With current satellite systems, information on the snow cover distribution over the 

entire globe can be updated one-two times a day at a spatial resolution of several 

kilometers.

Is the area of the Earth covered by snow 

changing?

Estimates of the yearly average snow extent derived 

from NOAA’s satellite-based snow cover maps show 

an overall decreasing trend both in Eurasia and North 

America (see the figure), and thus are generally 

consistent with other indications of global climate 

warming. But most of the decrease occurred in late 

1970s and 1980s, and there has been no clear change 

in snow extent in the 1990s and 2000s. A possible 

explanation is the increasing number of heavy snowfalls 

in mid-latitudes in the 1990s and 2000s, which may be 

related to circulation changes associated with global 

warming. Climate modeling studies provide some 

support for this hypothesis.

Yearly average snow-covered area
STAR Looks at the Earth | Satellite Measurements of the Atmosphere, Oceans and Land 113

Annual Cycle of Snow Cover

Global maps of snow cover frequency of occurrence for the months of April, July, and October 2009 and January 2010. The monthly maps 

are based on corresponding NESDIS daily snow maps derived with an automated algorithm from combined observations of polar orbiting 

and geostationary satellites. White color indicates locations with persistent snow during the entire month, dark gray shows areas where 

snow cover was intermittent or was observed only several days during the month.
114 Section 5 | The Land

Soil Moisture

Xiwu Zhan, Jicheng Liu, Christopher Hain and Martha Anderson (USDA)

Soil moisture is the amount of water in the soil – the top-most stratum of the 

Earth containing the roots of plants, crops, and trees. The ability of the soil to 

emit low-frequency microwave radiation depends on its dielectric constant, which 

in turn is controlled by its water content. Thus, microwave radiation emitted 

by the land surface and observed by a satellite sensor is related to soil moisture 

amount. Researchers at STAR are developing a global Soil Moisture Product 

System (SMOPS) using microwave satellite sensors. Currently the Advanced 

Microwave Scanning Radiometer on the NASA Earth Observing Satellite Aqua, 

the Advanced Scatterometer on the Metop satellite of the European Organization 

for the Exploitation of Meteorological Satellites, and the Soil Moisture Ocean 

Salinity satellite of the European Space Agency provide observations that help 

characterize the soil moisture. The Soil Moisture Active/Passive mission of NASA, 

to be launched in 2014, is specifically designed to monitor global soil moisture.

Land surface temperature – in particular the rate of change of temperature during 

the day – depends on soil moisture content, through its control of the soil’s 

heat capacity and evaporative cooling. STAR scientists and U.S. Department of 

Agriculture (USDA) collaborators are developing a new technique to generate soil 

moisture information from the surface temperature measurements of the thermal 

infrared sensors on the GOES satellites (see opposite page).

Satellite soil moisture data products are being used in the multi-agency U.S. 

Drought Monitor, in NOAA’s numerical weather and seasonal climate prediction 

models, and in world crop forecasts of the USDA’s Foreign Agricultural Service.

How soil moisture of the land surface 

may affect your daily life

Most people know that dry soil may kill crops in the fields 

and grass in their front yards. They may also be aware 

that very wet soils result in muddy roads, hampering 

land transportation and, for example, an army’s ability 

to move its mechanized forces. But they probably do 

not know that large scale soil moisture has significant 

impacts on weather and climate forecast accuracy, 

the world crop commodities markets, the likelihood of 

flash floods, the management of agricultural and city 

water, and the spread of vector borne diseases such as 

Dengue fever and malaria.

In Spring 2010, a severe drought devastated a vast 

region of southwest China. By March 2, 2010, more 

than 4 million hectares of crops were damaged, and 15 

million people and 9 million livestock had lost access 

to fresh water. If continuous soil moisture monitoring 

had been in place, early warnings could have been 

issued, and better drought mitigation plans prepared and 

implemented. 

A Chinese farmer walks across a dried-up reservoir in 

Shuitang village in southwest China during the drought 

of 2010. (Photo credit: AP)
STAR Looks at the Earth | Satellite Measurements of the Atmosphere, Oceans and Land 115

Monitoring Droughts with Satellite Soil Moisture Measurements

Maps of Evaporative Stress Index (ESI) – a measure of soil moisture from a model using GOES IR observations – compared with the 

Monthly US Drought Monitor (USDM) for the growing season of 2008. The GOES-based maps match the USDM maps well and have 

higher spatial resolution. The ESI is being tested for operational applications, for example, to supplement model estimates and microwave 

observations as input to the U.S. Drought Monitor.

USDM ESI USDM ESI

June May AprilSeptember August July
116 Section 5 | The Land

Land Surface Temperature

Yunyue Yu, Yuling Liu, and Xiaolong Wang

Most of the solar energy incident on the Earth is absorbed at the surface. This 

absorbed energy drives our weather and climate. The Land Surface Temperature 

(LST) is a key indicator of the fraction of the absorbed energy used to evaporate 

surface water or to heat the air near the surface. It is fundamental for estimating 

the net radiation budget at the Earth’s surface and for monitoring the state of crops 

and vegetation. Long term trends of LST are indicative of the rate of greenhouse 

warming. Satellite land surface temperatures have been routinely available from 

geostationary and polar-orbiting satellites. As of 2011, more than 30 years of global 

satellite LST had been accumulated.

Land surface temperature is determined from the measurements of infrared (IR) 

radiation in an atmospheric ‘window’ (a portion of the electromagnetic spectrum 

where atmospheric absorption is a minimum) under clear sky conditions (since 

clouds absorb the IR radiation). Corrections are made for the small amount of 

atmospheric absorption that does occur.

Satellite LST is assimilated into NWP models, climate models, and mesoscale 

atmospheric and land surface models to estimate the exchange of energy between 

the surface and atmosphere by radiation, evaporation, conduction, and convection. 

Commercial applications include the use of LST to evaluate water requirements 

for crops in summer and estimate where and when damaging frost may occur in 

winter. High LST is a warning sign for possible forest and grass fires, as well as an 

indicator of possible drought.

Land temperature: The view from space

A land surface temperature map for Aug 30, 2007, 

14:15 GMT is shown below. Observations are from the 

European Meteosat Second Generation (MSG) Spinning

Enhanced Visible and Infrared Imager (SEVIRI). Blue 

and white colors indicate ocean and cloud areas, 

respectively. The color bar scale shows the temperature 

in degrees Centigrade. The hot Saharan Desert areas – 

close to 60 °C or 140 °F – are clearly seen.
STAR Looks at the Earth | Satellite Measurements of the Atmosphere, Oceans and Land 117

Continental U.S. Land Surface Temperatures for the Month of July

A map of mean land surface temperatures for the month of July compiled from GOES satellite observations. The temperatures in July tend 

to be high, and in the southern U.S. range from 20 to 30 degrees Celsius, or approximately 68 to 86 degrees Fahrenheit. The temperatures 

are lower in the Rocky Mountains due to their higher elevation and are also less uniform there due to the complexity of the terrain.
118 Section 5 | The Land

Urbanization, Heat Islands, and Climate

Kevin Gallo

American cities are growing, and are projected to continue to expand as rural 

migration to urban areas continues. Urban population within the United States 

initially surpassed the rural population in 1920, and the portion of U.S. population 

within urban areas has increased steadily to 81% in 2010. The influence of 

urbanization on local and regional climate (often called the Urban Heat Island 

Effect) has been well documented. Urban areas tend to have higher nighttime 

temperatures and a smaller daily difference between maximum and minimum 

temperatures, compared to nearby rural areas. Urban development influences local 

climate through a wide variety of processes that result from the conversion of 

natural land cover (often some type of vegetation) to developed land (rooftops, 

asphalt, and concrete). In addition to differences in temperatures between urban 

and rural environments, indirect observations of the influence of the urbanization 

(e.g., length of growing season), have also been observed. Health-related concerns 

within urban environments during the summer months frequently result in weather 

warnings associated with elevated temperatures and decreased air quality.

STAR investigators are using satellite data to observe and monitor the differences 

in the physical features of urban and rural environments that contribute to the 

Heat Island Effect. Satellite observations of land surface temperature and green 

vegetation have been used to estimate differences in air temperatures that might be 

due to increased urbanization. These monitoring efforts are also critical to assure 

that changes in land use and urbanization do not interfere with measurements of 

global climate change.

From farms to cities

Urban population within the United States initially 

surpassed the rural population in 1920, and the portion 

of U.S. population within urban areas has increased 

steadily to greater than 80% in 2010.

Example of the expansion of urban areas into rural areas. 

(Photo credit: US Department of Agriculture)
STAR Looks at the Earth | Satellite Measurements of the Atmosphere, Oceans and Land 119

Growth of Dallas/Fort Worth over Three Decades

Landsat images from 1974 (left), 1989 (center) and 2003 (right) show vegetation in red, water in blue, and urban areas in white or grey 

tones. Similar images and data can be used to determine the influence of the city area on the local climate, and changes that may affect 

humans, plants, and insects within the urbanized regions. (Landsat images courtesy of US Geological Survey)
120 Section 5 | The Land

Vegetation Fires

Ivan Csiszar, Christopher Schmidt, and Wilfrid Schroeder

The burning of forest or other vegetation cover (also called biomass burning) is 

a process of paramount importance for our planet and for our society. Emissions 

of gases and particulate matter from fire affect atmospheric composition, weather 

and climate. Burning of vegetation cover can also permanently alter the landscape. 

Fire is one of the major natural disasters that can severely impact human life and 

property. Smoke emissions from fires represent a major health hazard and also 

impact transportation safety. Fires set by humans are used in agricultural land 

management, including slash-and-burn agriculture, which is a major agent of 

deforestation.

Fires are found across a wide range of ecosystems around the world, including 

remote and unpopulated areas. Accurate and consistent large-scale fire monitoring 

is possible only using Earth observing satellites. Sensors that include measurements 

in the near infrared (4 μm) are particularly useful; the radiative emission spectrum 

of high temperature fires peaks at this wavelength making it particularly sensitive 

to detecting fires. NOAA’s GOES satellite series provides observations at high 

temporal frequency, thus enabling the mapping of diurnal fire activity over the 

Western Hemisphere. Sensors on polar platforms, such as NOAA’s POES or 

NASA’s EOS Terra and Aqua satellites, with their finer spatial resolution (≤ 1 km) 

detect smaller fires, but only a few times a day.

Changes in global fire regimes, in particular the frequency and distribution of 

wildfires, have been linked to climate oscillations (e.g., El Niño) and to land 

management (e.g., fire suppression and forest conversion). The long-term archive 

of satellite data now enables reconstructing and analyzing fire history since the 

mid-1990s.

Deforestation and fires in the Amazon

Deforestation in the Amazon is occurring primarily 

to clear land for agricultural use. The live vegetation 

removed from the rain forest is piled up until it is dry 

enough to burn. During the dry season, thousands 

of such fires are set throughout the region. Satellites 

detect many of these fires and the information provided 

helps land management and other government agencies 

to monitor, regulate and enforce the controlled use of 

fires.

The Amazon is observed at favorable near-nadir view 

angles with NOAA’s geostationary (GOES East and 

West) satellites. An operational fire product, using 

the Wildfire Automated Biomass Burning Algorithm 

developed by STAR, has been generated since 2002. It 

is used extensively to study fire occurrence in the region.

Aerial photograph of burning agricultural wastes in the 

Amazon. Many such fires are detected by the GOES 

satellite. (Photo credit: NASA)
STAR Looks at the Earth | Satellite Measurements of the Atmosphere, Oceans and Land 121

Ten Years of Fires in South America

The fraction of observations with fires detected from the GOES-East satellite. Data show only detections in July, August and September, 

which is the peak dry season and the time of high fire activity in the tropical regions of South America south of the Equator. Most of the 

activity is concentrated along the Arc of Deforestation, where the Amazon rain forest has been cleared by slash-and-burn agriculture. Other 

fires can be attributed to annual agricultural maintenance. Note the variation between years due to climate conditions and varying human 

activity.

Photos courtesy of UMD and CIMSS
122 Section 5 | The Land

Sunlight

Istvan Laszlo

Sunlight is the term used to collectively describe the light from the Sun with 

different colors. 99% of all colors in sunlight are represented by the familiar rainbow 

colors of visible light and the colors of ultraviolet and near infrared light that are 

invisible to the human eye. Sunlight delivers energy from the Sun to the Earth, 

where it warms the surface and the atmosphere, drives weather and climate and 

determines processes that are vital for life. Part of this energy is captured in solar 

power plants and converted into electricity.

The amount of sunlight reaching the Earth’s surface (also called solar radiation 

or insolation) varies according to geographic location, season, time of day, and 

local weather. STAR scientists use sophisticated computer models to calculate the 

amount of sunlight available at the surface from measurements made by satellites. 

This is possible because the amount of sunlight striking the uppermost layers of 

the atmosphere is well known, and the fraction of energy that makes it through the 

atmosphere can be calculated from the reflected sunlight (from clouds and snow/ice 

mainly) observed by satellites. Polar orbiting satellites enable global observations of 

sunlight at the surface, including remote oceanic and land areas where conventional 

ground measurements are not readily available. 

Measurements from geostationary satellites 

allow the calculation of sunlight at different 

times of the day. This is very important for many 

applications (e.g. solar power, prediction of 

coral bleaching) since changing meteorological 

conditions (cloudiness) significantly alter the 

amount of sunlight available at the surface.

The sunlight derived from satellites is used in 

the verification of forecasts from numerical 

weather prediction models. It is also one of 

the key inputs to models that predict coral 

bleaching.

The journey of sunlight though the 

atmosphere

As sunlight enters the Earth’s atmosphere and travels 

to the surface it encounters clouds, gas molecules and 

aerosols. As a result of these encounters, about 30% of 

the sunlight, on average, is reflected back to space and 

is not available to heat the Earth. 20% is absorbed in 

the atmosphere and 50% is absorbed by the land and 

ocean surface. The absorbed energy is converted mostly 

to heat that warms the surface and the atmosphere.

Computer models – solutions of radiative transfer 

equations – that specifically account for the interactions 

between light, the surface and the atmosphere are used 

in estimating the sunlight available at the surface. The 

example below shows how the regular and symmetric 

pattern of light on an average July day over the 

continental U.S. before entering the atmosphere (left 

panel) is changed by these interactions by the time it 

reaches the surface (right panel.)
STAR Looks at the Earth | Satellite Measurements of the Atmosphere, Oceans and Land 123

From Satellite Image to Sunlight

The computer model developed at STAR converts the visible image taken by the satellite instrument (left) into an estimate of sunlight at the 

surface (right). White areas in the satellite image represent bright, thick clouds while dark gray areas are mostly free of clouds. The white 

cloudy areas in the satellite image are translated into low sunlight (shades of blue) while the dark cloud-free areas are translated into high 

sunlight (red to dark red colors). Note that the range of sunlight varies significantly (between zero and 1000 Watts/square meter) depending 

on cloudiness and the height of the sun above the local horizon. (The example shown uses the image taken by the imager instrument 

onboard the Geostationary Operational Environmental Satellite (GOES) 13 on April 11, 2011 at 17:45 UTC.)
124 Section 5 | The Land
STAR Looks at the Earth | Satellite Measurements of the Atmosphere, Oceans and Land 125

Section 6 

Climate 6
126 Section 6 | Climate

Our Changing Planet

Ingrid Guch

What is climate and why is it important? Climate reflects the most likely or 

expected weather based on long term observations of actual weather conditions 

accumulated for a given time period and location. Useful climate information 

includes averages for a date and time, as well as information about the likelihood 

and size of deviations from the average.

Climate changes over many time and space scales – from month to month, 

season to season, year to year, decade to decade, and century to century, globally 

and regionally. Monthly temperatures and precipitation statistics, generally based 

on 30-year spans of weather observations, describe the climate for cities around 

the world and are used every day by businesses and individuals in planning their 

activities – consider how climate information could influence your decision on 

what month to plan a Disneyworld vacation. Short term climate fluctuations, 

some caused by the occurrence of El Niño (see sidebar), can impact rainfall and 

temperatures over large regions of the Earth for months at a time. Droughts 

impact agriculture and can contribute to wide-spread famine in less developed 

countries. Climate fluctuations also have very local effects. For example, regions 

such as the Chesapeake Bay have climate indicators tailored to better understand 

fish mortality (see figure opposite page).

Long term changes in the Earth as a whole are described by several climate 

variables including globally-averaged temperature and sea level height (see 

figure opposite page). Changes in the Earth’s climate can have large impacts 

on humanity, global economies, and major ecosystems. Scientists agree that the 

Earth has been warming since the industrial age, with increasing emissions of 

CO2

 and other greenhouse gases very likely the primary cause.

Satellite data are increasingly well-suited for climate measurements. STAR 

utilizes NOAA, NASA, and international satellite data to describe, monitor, and 

understand the Earth’s climate.

Blame it on El Niño

In 1997 Halloween celebrants sported a record number 

of “El Niño” costumes, a sign of both the record high 

ocean temperatures off the coast of Peru and of the 

incredible media frenzy that resulted. Advanced satellite 

and in-situ observations at the time clearly showed the 

strong El Niño conditions (the temperature in the Pacific 

equatorial region was much warmer than average as 

shown in the figure below in the yellow to orange colors) 

and this meant winter was likely to be warmer than 

normal in the northwest United States, and wetter than 

usual in the southeast. However, to many in the general 

public the message became muddled, and nearly every 

bad weather event was blamed on El Niño, making it a 

great idea for a scary costume.

Warmer than normal Equatorial ocean temperatures 

indicative of El Niño. 
STAR Looks at the Earth | Satellite Measurements of the Atmosphere, Oceans and Land 127

Climate: Regional to Global

Top: Local climate information about the Chesapeake Bay from December 7, 2010 – January 7, 2011. The drop in temperatures from 

44F to 36F is thought to have stressed many of the juvenile spot fish in the area, leading to their mortality. Bottom: Annual averages of 

the global mean sea level (mm). The red curve shows reconstructed sea level fields since 1870 (updated from Church and White, 2006); 

the blue curve shows coastal tide gauge measurements since 1950 (from Holgate and Woodworth, 2004) and the black curve is based 

on satellite altimetry (Leuliette et al., 2004). The red and blue curves are deviations from their averages for 1961 to 1990, and the black 

curve is the deviation from the average of the red curve for the period 1993 to 2001. Error bars show 90% confidence intervals. (Image 

and caption from Figure 5-13, Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate 

Change).
128 Section 6 | Climate

Earth Radiation Budget

Istvan Laszlo and Hai-Tien Lee

The Earth receives energy in the form of radiation (sunlight) from the Sun. At the 

same time it also returns energy to space by reflecting back part of the incoming 

solar radiation and by emitting thermal – or infrared – radiation. The net difference 

between the energy received and that returned is the “Earth radiation budget”.

The radiation budget varies both geographically and seasonally. For any given 

region and time the budget is usually not balanced, meaning some areas receive 

more energy than they return to space – for example, tropical areas – leading 

to warming, while others lose more energy than they receive – Polar Regions - 

leading to cooling. This north-south variation in energy input to the Earth is also 

the driving force for the major atmospheric wind patterns: the easterly trade winds 

in the tropics and the prevailing westerlies at temperate latitudes. Whether the 

budget is balanced on a global scale and over a long period of time is of paramount 

importance for understanding climate change.

The thermal radiation emitted to space is often referred to as the outgoing 

longwave radiation (OLR). Low OLR values are usually associated with thick, 

precipitating clouds, making OLR measurements a useful proxy for estimating 

precipitation. NOAA’s Climate Prediction Center uses OLR data to monitor El 

Niños, and, more generally, OLR observations are used to diagnose, assess, and 

verify predictions of rainfall by climate models.

Earth radiation budget from satellites

In the past three decades, several dedicated NASA and 

European research instruments have measured the 

Earth’s radiation budget. Because research missions 

have a finite lifetime, without guarantee of replacement, 

they cannot be relied on to provide the continuous, 

sustained observations required for monitoring changes 

in the Earth’s radiation budget over the long term.

STAR scientists have developed techniques to measure 

the radiation budget from the observations of imager and 

sounder instruments onboard NOAA polar operational 

satellites, e.g., the Advanced Very High Resolution 

Radiometer and the High-resolution Infrared Radiation 

Sounder. The operational nature of NOAA satellites and 

measurements assures their long term continuity. Such 

a gap-free data record makes these measurements 

potentially valuable for monitoring long-term changes in 

the climate. 

However, constructing a dataset for climate studies 

from these operational satellites is not trivial. Climate 

variations are much smaller in magnitude than typical 

weather changes. These smaller signals are more 

difficult to measure and require particular attention to 

instrument calibration and the long-term stability of 

the instruments. In addition, these instruments do not 

measure the total (all-wavelengths) radiation; instead 

they make observations in a limited number of narrow 

spectral regions. Models are used to convert these 

narrowband measurements to the quantity needed for 

radiation budget.
STAR Looks at the Earth | Satellite Measurements of the Atmosphere, Oceans and Land 129

Changes in the Earth’s Radiation Budget

The variability of the radiation budget is directly related to changes in the state and composition of the Earth’s surface and atmosphere, 

especially changes in cloudiness. The top panel shows that during the period 1983-1995 the planet reflected more (blue color), while 

between 1996 and 2004 it reflected less (red color), solar radiation back to space than it did on average. The reflected amount was 

especially large during and following the eruptions of the El Chichon and Pinatubo volcanoes. The outgoing longwave radiation, shown 

in the plot in the bottom panel, also experienced sustained periods of excessive emitted radiation (blue color) during the strong El Niño 

episodes in 1998 and 2003-2007, and reduced emitted radiation (red color) during 1988-1997. The reflected solar radiation was derived 

from observations of operational geostationary and polar satellites as available in the International Satellite Cloud Climatology Project 

(ISCCP). The OLR data were derived from the High-resolution Infrared Radiation Sounder (HIRS) onboard NOAA’s operational polarorbiting satellite.
130 Section 6 | Climate

Greenhouse Gases

Chris Barnet

Natural variations of greenhouse gases are very large since the biosphere essentially 

breathes carbon dioxide in/out on daily, seasonal, and inter-annual time scales. 

Measuring the small long term change in carbon dioxide – a few parts per million/

year – relative to this natural background is very difficult. Observations began in 

the 1950’s by sampling the atmosphere with flasks. These are precise measurements, 

but they are labor intensive and, therefore, are only carried out at a handful of 

locations.

Satellites offer a global view of greenhouse gases (GHG), including the ability to 

identify and monitor sources of GHG emissions. Infrared instruments designed 

for weather applications have been used since the 1980’s to measure carbon 

dioxide with some success. The basis of the technique is that, as a greenhouse 

gas, CO2 reduces the Earth’s emission of infrared (IR) radiation to space. The 

most recent advanced weather sounding instruments – hyperspectral infrared 

instruments – are sensitive to carbon dioxide, methane, and nitrous oxide and have 

shown continental scale changes in these gases. The European Union and Japan 

have launched satellites to measure GHG. The USA built the Orbiting Carbon 

Observatory (OCO) satellite, which was designed to accurately measure carbon 

dioxide; however, it failed to achieve orbit and a second attempt to launch an OCO 

instrument is being considered.

STAR scientists pioneered the retrieval of GHG information from the hyperspectral 

observations. Measuring GHG is an active area of research that most likely will 

require merging the space-borne observations and precise in-situ measurements 

with biosphere models to extract the GHG signal.

The most famous scientific 

curve of our time

Greenhouse gases warm the planet by trapping the 

heat – infrared (IR) radiation - emitted by the Earth. 

Adding a small amount of a greenhouse gas such as 

carbon dioxide, nitrous oxide, or methane can reduce 

our planet’s ability to cool itself. All three of these gases 

are produced by the vital human activities of energy 

production, transportation, and agriculture. As a result, 

the amount of these gases has increased significantly 

in recent decades, with profound effects on the Earth’s 

climate. The figure below shows the increase in 

atmospheric carbon dioxide at NOAA’s observing station 

at Mauna Loa, Hawaii. Since the late 1950s carbon 

dioxide has increased by about 25%.

The most famous scientific curve of our time: the increase 

of atmospheric carbon dioxide (red: monthly values; black: 

seasonally adjusted data). (Photo credit: NOAA)
STAR Looks at the Earth | Satellite Measurements of the Atmosphere, Oceans and Land 131

Observing Networks for Greenhouse Gases

The globe in the upper left is an example of a modeled CO2 field (red, high values; blue, low values) during winter showing typical spatial 

patterns. The various instrument approaches are shown as a function of their precision (y-axis) and spatial scale (x-axis, km). In-situ 

measurements (flask sites, towers, and aircraft sampling) have the highest precision (lowest error) and tend to cover regional scales. 

Satellite instruments are less precise but allow continental scale measurements. Combining all the measurements into a network will likely 

result in high global precision.
132 Section 6 | Climate

Atmospheric Temperature Trends

Cheng-Zhi Zou

Increases in atmospheric greenhouse gases such as carbon dioxide cause global 

warming of the Earth’s surface and troposphere, and cooling of the stratosphere. 

The tropospheric temperature trend, which ranges from 0.1 to 0.2 °C per decade, is 

one of the most important indicators of the rate of global warming. Superimposed 

on the long term trend are shorter term (years) variations in global temperature due 

to phenomena such as El Niño/La Niña, intense volcanic eruptions, and natural 

variability. These shorter fluctuations, which can have amplitudes of a few tenths of 

a degree C, make temperature trend determinations very difficult. It may take over 

30 years for the global warming signal to emerge from the “noise” of short term 

variability and be detected with confidence.

Since 1979, the microwave sounding instruments on polar-orbiting satellites 

have measured the temperatures of different layers of both the troposphere and 

lower-stratosphere. Compared to other observation systems, such as radiosondes 

and satellite infrared instruments, the microwave soundings have the advantage of 

long-term continuity, global coverage, and insensitivity to cloud effects. Since the 

observations are made from instruments on different satellites, good inter-satellite 

calibration is crucial to obtaining reliable temperature change values.

STAR’s well calibrated data time series on the opposite page indicate that over the 

last three decades the middle-troposphere has warmed by 0.14±0.07 °C/decade and 

the upper troposphere by 0.04±0.08 °C/decade, while the lower-stratosphere has 

cooled at a rate of 0.33±0.31 °C/decade, where the plus-minus signs (±) indicate 

the uncertainties of the trends.

What are layer temperatures?

The satellite microwave temperature sounding 

instruments measure radiation emitted by the surface 

and absorbed and reemitted to space by atmospheric 

oxygen at individual frequencies (wavelengths). The 

radiance (intensity of the radiation) measured at 

each frequency comes from a different layer of the 

atmosphere, depending on the strength of the absorption 

at that frequency. The percentage contribution of 

individual levels within the layer to the measured 

radiance is represented by a vertical weighting function 

of the temperatures in the layer. 

The figure above shows the weighting functions for the temperatures of the mid-troposphere (TMT), upper troposphere 

(TUT), and lower-stratosphere (TLS) layers.
STAR Looks at the Earth | Satellite Measurements of the Atmosphere, Oceans and Land 133

Variability and Trends of Atmospheric Temperatures

Monthly, global-mean temperature trends in the mid-troposphere, upper-troposphere, and lower-stratosphere. Colored dots represent 

recalibrated observations from different NOAA and MetOp-A polar orbiting satellites. The pink dashed lines represent the long-term trends 

of the time series. The temperature trend changes from a warming of 0.14 °C/decade of the mid-troposphere to a cooling of 0.33 °C/

decade of the lower stratosphere. Signatures due to volcanic eruptions and El Niño are marked by arrows.
134 Section 6 | Climate

Trends in Temperature Profiles from a 

Single Instrument

Tsan Mo

The Advanced Microwave Sounding Unit-A (AMSU-A) was first flown on the 

NOAA-15 polar orbiting satellite. It observes the microwave (radio wavelengths) 

emission of the Earth at 15 different frequencies, which are absorbed by 

atmospheric oxygen and then re-emitted by the atmosphere to space. The measured 

radiance depends on surface and atmospheric temperatures, with each frequency 

sensitive to a different atmospheric layer. Having 11 more observing frequencies 

than the microwave sounder that it replaced, the AMSU presents an opportunity 

to monitor temperature trends in more atmospheric layers. The NOAA-15 

AMSU-A performed measurements over its very long lifetime from May 1998 

through December 2007. Although, for lack of absolute calibration standards in 

space, its absolute stability over this long time period is not possible to determine, 

STAR’s analyses indicate that the NOAA-15 AMSU had good long term stability 

and its measurements could be used to derive long term climate trends.

STAR scientists have constructed a time series of global daily mean brightness 

temperatures – conversions of the observed radiation to emitting temperatures, 

which correspond to temperatures of atmospheric layers – of the near-nadir 

(directly below the satellite) measurements for the period. After removal of the 

typical annual cycle of temperature from the data, one is left with a time series 

representing the inter-annual and longer term trends in the temperatures. Linear 

trends fitted to the time series of the measurements at individual frequencies 

reveal that tropospheric layers are warming and stratospheric layers are cooling 

(see figure opposite page), in agreement with climate model simulations and with 

more sophisticated multi-sensor analyses, which have to account for inter-satellite 

biases.

What does a microwave radiometer 

look like?

The AMSU-A is a cross-track scanning microwave 

radiometer consisting of two physically separate 

modules, each about 70 cm by 35 cm by 60 cm with a 

mass of about 50 kg. AMSU-A1 provides measurements 

at 13 different wavelengths and AMSU-A2 at an 

additional two frequencies. AMSU-A has a spatial 

resolution of 50 km and a scan width of 2300 km.

AMSUA1

AMSUA2
STAR Looks at the Earth | Satellite Measurements of the Atmosphere, Oceans and Land 135

Temperature Trends of Atmospheric Layers: 1998-2007

Temperature trends at multiple atmospheric layers derived from the NOAA-15 Advanced Microwave Sounding Unit data at different 

frequencies (channels) from May 1998 through Dec. 2007. Channel 15 measurements represent the layer closest to the Earth’s surface, 

channels 1 – 8 represent temperatures at increasingly higher layers of the troposphere, and channels 9 – 14 represent increasingly higher 

layers of the stratosphere. The data clearly show the transition from tropospheric warming to stratospheric cooling, as predicted by climate 

models. 
136 Section 6 | Climate

Sea Surface Temperature Trends

Thomas M. Smith and Richard W. Reynolds

The trend in global average surface temperature is the most widely used indicator 

of climate change. Since oceans cover about 70% of the Earth’s surface, ocean 

temperatures should dominate, and are necessary to ensure accuracy of any analyses 

of global temperature variations. However, in contrast to land areas which have 

large numbers of weather station observations, the oceans are poorly observed. 

Prior to 1981, the major sources of sea surface temperature (SST) observations 

were merchant ships plying the world’s trade routes. However, beginning with 

the launch of the Advanced Very High Resolution Radiometer on NOAA’s polar 

orbiting satellites in 1981, satellite data have provided more complete coverage of 

the ocean surface. In addition, many buoys, both moored and drifting, have been 

deployed to measure SST since the 1980s. But then, how confident can we really 

be about ocean-only and thus global surface temperature estimates prior to the 

1980s?

Analyses of the sparse and irregular early pre-satellite SST records are often 

referred to as reconstructions. Reconstructions analyze the data to a constant 

grid globally and over the analysis period. They are produced to evaluate climate 

variations over the historical period and as boundary conditions for climate 

models. Reconstructions require high-density modern data – such as those from 

satellite observations – to compute statistics needed to analyze the relatively sparse 

historical data.

The current version of STAR’s SST reconstruction uses a satellite analysis merging 

in-situ and infrared-based satellite SST estimates to form base statistics that enable 

relationships between a sparse field of observations and a global analysis to be 

established. These relationships are used to reconstruct a time series of global SST 

from the sparse historical observations, dating from the late 1800s, to the present, 

which is used for climate monitoring and studies by national and international 

climate-research centers.

Historical sea surface temperature 

(SST) variations

Historical SST variations reconstructed by several 

international centers consistently show increasing global 

temperatures since the 19th century. The SST time 

series in the figure indicates a warming of about 0.6°C 

during the 20th century. We estimate that the uncertainty 

of this temperature change is about ±0.2°C, mostly 

due to sparse data and data bias early in the historical 

record. The 20th century increase is consistent with 

increases in independently analyzed land temperatures.

The figure (opposite page) shows large increases at 

high latitudes, consistent with many climate models 

that indicate greater warming in these regions. There 

are also large changes in the east tropical Pacific 

and Atlantic, regions that are typically cooler than 

surrounding areas due to upwelling of cool subsurface 

waters. Time series show the averages of both the 

historical analysis and the satellite-based data. The 

spatially complete satellite-based data are used to 

compute statistics used in the historical analysis. 

Comparison of the two shows that the historical analysis 

is able to resolve realistic SST variations.
STAR Looks at the Earth | Satellite Measurements of the Atmosphere, Oceans and Land 137

20th Century SST Trend

Near-global sea surface temperature (SST) variations. The geographical distribution of SST trends for the 20th century reconstruction is 

shown in the upper panel. The annual 60°S-60°N average over ocean areas is shown in the lower panel for the reconstruction and for the 

satellite-based analysis (heavy dashed line).
138 Section 6 | Climate

Cloudiness Trends

Andrew Heidinger and Mike Foster

Clouds play a critical role in regulating Earth’s climate, heating the Earth by 

downward emission of infrared radiation while cooling the Earth through 

reflection of solar radiation. On average, the cooling effect of clouds is greater 

than their heating effect. Even small changes in this balance have implications for 

the Earth’s climate. How clouds and their radiative effects will change as a result 

of greenhouse warming is a major source of uncertainty in model projections of 

future climate. The ability to make and sustain long-term measurements of cloud 

properties will provide climate scientists with data for monitoring cloud trends, 

constructing improved models of cloud processes, and checking climate model 

predictions.

STAR scientists have constructed a cloud record from 1981 to present based on 

data from the Advanced Very High Resolution Radiometer (AVHRR) on NOAA’s 

polar orbiting satellites. The record includes the following cloud variables: amount, 

height, temperature, and particle size and density. The processing framework and 

algorithms used to generate the cloud record also incorporate data from the more 

recent GOES imager satellite series. The stationary positioning of the GOES 

satellites relative to the Earth’s surface allows for the continuous observation of 

how cloud patterns change throughout the day at a single location, contributing 

valuable information to the climate records.

The accompanying figure shows that global cloudiness has decreased since 1981 

at a rate of 0.51%/decade, with an uncertainty of +/- 0.42%/decade. Since clouds 

generally act to cool the Earth, a decrease in cloudiness would enhance the 

greenhouse effect due to human activity.

Challenges in detecting long-term 

changes in cloudiness

Identifying long-term signals, or trends, in a satellite 

cloud record can provide some interesting challenges. 

The longest satellite records span roughly three 

decades, which from a climate perspective is a relatively 

short period of time. What this means in practical 

terms is that short term phenomena that affect global 

cloudiness, such as the El Niño-Southern Oscillation and 

major volcanic eruptions, can mask potential long-term 

signals.

There are also challenges related to the satellites 

themselves. For example, as time passes, polar-orbiting 

satellites sometimes drift away from their original paths, 

causing a slow shift in the local time of day at which they 

observe the Earth. This can cause the phenomenon 

known as aliasing, which could lead to artificial trends in 

the data.

Aided by the lengthening cloud record and advanced 

measurements from more recent satellites, STAR 

researchers are overcoming these challenges and 

providing an accurate long-term cloud climate record.
STAR Looks at the Earth | Satellite Measurements of the Atmosphere, Oceans and Land 139

Cloudiness Record over the Satellite Era

Upper panel: a false-color global image using three channels (red=visible, green=near-IR, blue=infrared) used to detect clouds and their 

properties. Lower panel: time-series (red), including an uncertainty estimate (grey), of mean global total cloudiness anomaly (60°S-60°N) 

spanning the AVHRR satellite record. The data indicate that global cloudiness has decreased at rate of 0.51%/decade, with an uncertainty 

of +/- 0.42%/decade.
140 Section 6 | Climate

Global Precipitation Monitoring

Ralph Ferraro, Robert Adler, Phillip Arkin, and Arnold Gruber

Precipitation – rain, snow, drizzle, hail, sleet and other variants – is the result of water 

vapor in the atmosphere condensing and falling to the Earth’s surface. Extended 

periods of months or years of deficient rainfall may result in drought, often with 

significant social and economic consequences, regionally and globally. Seasonal and 

interannual fluctuations in rainfall and droughts are related to climatic variations 

such as El Niño (see sidebar). On longer time scales, the intensity and geographic 

distribution of rainfall will likely be significantly altered due to greenhouse warming.

 A wealth of high quality surface measurements of precipitation from rain gauges 

and weather radars exists. The problem is that these measurements are concentrated 

in populated land areas, while large regions of the world, in particular the oceans, 

are poorly observed. Satellite observations have been utilized to complement the 

surface reports and fill in these large observational gaps.

A project called the “Global Precipitation Climatology Project”, or “GPCP”, 

an international effort lead by scientists at NASA and NOAA, in which STAR 

scientists play a leading role, has developed a series of global precipitation data 

sets that combine geostationary and polar orbiting satellite measurements from 

infrared and microwave sensors, along with high quality rain gauges, to produce 

a 30-year (1979 to 2008) global climatology. This data set has become the most 

widely accepted precipitation data set by the scientific community and is used to 

monitor seasonal to interannual precipitation variability and longer term trends 

over the past few decades.

Is the Earth’s precipitation changing?

Arguably, precipitation is the most critical climate 

variable affecting humankind. Data sets such as the 

Global Precipitation Climatology Project (GPCP) are 

valuable for studying interannual climate variability due 

to phenomena such as El Niño and La Niña. The figure 

shows the monthly anomalies (departures from normal) 

in rainfall over the tropical Pacific Ocean – values 

greater than zero correspond to El Niño events while 

those less than zero correspond to La Niña.

Global trends, however, are difficult to determine, since 

the small changes averaged over the entire Earth are 

still within the uncertainty in the data set. The figure 

shows no clear increase or decrease in the rain over 

the 30 year period 1979 - 2008. The uncertainty in the 

time series arises from the varying sources of data 

used over the three decades, each with its own set of 

measurement errors.
STAR Looks at the Earth | Satellite Measurements of the Atmosphere, Oceans and Land 141

Average Rainfall

Seasonal rainfall climatologies (mm/month): the top panel is for the Northern Hemisphere winter season (December, January and 

February), while the bottom is for summer (June, July and August). Note the northward movement of the tropical rain belt from NH winter to 

summer, the development of the south Asian monsoon in NH summer, and the oceanic precipitation associated with mid-latitude storms in 

the winter seasons of both hemispheres.
142 Section 6 | Climate

Water Vapor Trends

Fuzhong Weng

In addition to its importance for cloud formation, precipitation, and the 

hydrological cycle, atmospheric water vapor plays a crucial role in anthropogenic 

warming. A warmer atmosphere due to increasing greenhouse gas emissions can 

hold more water vapor. As a strong absorber of infrared radiation, the increased 

water vapor significantly amplifies the initial anthropogenic heating, leading to 

increased temperatures.

Retrievals of column-integrated water vapor from Department of Defense’s Defense 

Meteorological Satellite Program (DMSP) Special Sensor Microwave Imager 

(SSM/I) series are generally regarded as providing the most reliable measurements 

of water vapor over the global oceans. The long-term multiple SSM/I measurements 

– since 1987 – cannot be simply stitched together to create a time series of water 

vapor variations. Inter-sensor systematic differences arise from multiple factors 

such as slight variations in instrument characteristics, satellite orbital drifts, and 

missing measurements during their observational periods. Using the DMSP F13 

satellite as a reference satellite with longevity, the mean differences between other 

satellites and F13 are derived. If there are no coincident measurements of the F13 

and another satellite, a third satellite that intersects the F13 orbit is used as a 

transfer radiometer. This cascaded approach has been applied to all the SSSM/I 

sensors on the DMSP satellites –F10, F11, F13, F14, and F-15 – from 1991-2006 

to derive the stable water vapor time series shown on the opposite page.

Water vapor and the greenhouse effect

Without infrared absorbing gases in the Earth’s 

atmosphere, the surface temperature would be some 33 

degrees Celsius cooler that it is now. Water vapor is the 

most important infrared absorbing gas, causing most of 

the Earth’s natural greenhouse effect. Carbon dioxide’s 

contribution to the natural greenhouse is only about 

30%, but it is the increasing industrial emissions of CO2

that are causing global warming. However, due to the 

water vapor feedback – more water vapor with higher 

temperatures – total global warming is actually double 

that produced by CO2 alone.

Consistent with the above reasoning, the satellite data 

(opposite page) indicate that atmospheric water vapor 

is increasing. The figure shows the variations in total 

precipitable water – the total water vapor in a vertical 

column of the atmosphere – between 1991 and 2006. 

Water vapor is increasing at a rate of about 0.3 mm/

decade – or about 1%/decade – over the global oceans 

and 0.6 mm/decade over the tropical oceans. These 

results are significant at the 99% level (only one chance 

in a hundred that they are due to chance).
STAR Looks at the Earth | Satellite Measurements of the Atmosphere, Oceans and Land 143

Water Vapor Trend

Monthly values (symbols) and trends (dashed lines) of the total precipitable water vapor (TPW, the total water vapor in a vertical column 

of atmosphere) over the global and tropical oceans from microwave observations of Defense Meteorological Satellite Program (DMSP) 

satellites (different colors). Note the positive trends and the amplitudes of the inter-annual fluctuations, particularly the positive anomaly 

associated with the El Niño of 1997-98 (vertical line).
144 Section 6 | Climate

Sea Level Rise

Eric Leuliette

About thirty percent of the United States population lives near the ocean, and 

worldwide roughly 200 million people inhabit coastal floodplains less than one 

meter above current sea level. The continued rise of the oceans will cause these 

low-lying regions to experience permanent inundation, repeated flooding, faster 

erosion of cliffs and beaches, increasingly saline estuaries, and salt contamination 

of groundwater.

As global temperatures increase, sea level rises for two major reasons. First, most 

of the Earth’s heat is stored in the ocean and additional heat causes sea water 

to expand. Second, as glaciers and ice sheets on land melt, the ocean gains the 

additional water. While each of these processes contributed about the same amount 

to late 20th century sea level rise, additional warming will accelerate sea level rise 

due to increased melting of continental ice.

Most estimates of 20th century sea-level rise have depended on averaging the 

rates of rise from the few, long, high-quality tide-gauge records that are available. 

Since 1992, radar altimeters carried on satellites have measured sea level with an 

unprecedented accuracy, providing nearly global coverage of the world’s oceans 

every ten days. Data from these satellite missions allow STAR scientists to monitor 

global and regional sea level rise more accurately than is possible from the sparse 

array of tide gauges. The map of sea level trends (opposite page) revealed by 18 years 

of altimetry shows a highly non-uniform spatial pattern, partly driven by natural 

decadal changes in circulation and ocean’s thermal structure. STAR scientists use 

these data in combination with other ocean observations to understand the causes 

of the sea level rise.

Why is sea level rising?

While satellite radar altimeters measure changes in sea 

level, they don’t explain why the ocean is rising or falling. 

An accounting of the major processes that cause sea 

level change is often called the sea level budget.

Two new observing systems have recently been put into 

operation that complement sea level data from altimeters 

and allow the sea level budget to be assessed. The 

first, Argo, is an array of 3000 robotic floats that monitor 

temperature and salinity in the upper part of the 

ocean. The second, the Gravity Recovery and Climate 

Experiment (GRACE) satellite mission, measures 

changes in the Earth’s gravity field and can “weigh” the 

ocean every month.

Together, measurements from the Jason series of 

altimeters, Argo, and GRACE allow sea level variations 

to be separated into sea-level change from changes in 

ocean-water density caused by heat and freshwater, 

and changes in ocean mass that reflect the exchange 

of water between the ocean and land (such as glaciers, 

ice caps, ice sheets, and ground water). When the 

observations from all three systems agree, the sea level 

budget is said to be “closed” or balanced”.
STAR Looks at the Earth | Satellite Measurements of the Atmosphere, Oceans and Land 145

Sea Level Rise

Trends in sea level from 1993–2010 as measured by the satellite radar altimeters TOPEX/Poseidon, Jason-1, and Jason-2. As a global 

average, sea level has risen at a rate of 3.2 ± 0.4 mm/year during this period. Regional variations are significant, with sea level falling in the 

east Pacific, while parts of the west Pacific have risen by more than 10 ± 0.4 mm/year.
146 Section 6 | Climate

Monitoring the World’s Ozone

Larry Flynn and Craig Long

NOAA instruments measuring the amount of ultraviolet sunlight scattered back 

to space are part of a global system to monitor the world’s atmospheric ozone 

levels. Seven Solar-Backscatter UltraViolet Spectrometers (SBUV/2 instruments) 

have flown on NOAA polar satellites since 1985. Their observations are used to 

generate total column ozone and ozone vertical profiles. These measurements are 

used in a number of applications: issuing Ultraviolet Index (a measure of sunburn 

risk) warnings; monitoring the ozone hole (see sidebar) from year to year, assimilation into weather and climate forecast models, and providing global assessments 

of the state of the ozone layer that are published every four years in the World 

Meteorological Organization Scientific Assessments of Ozone Depletion. The last 

instrument in this series was launched in February of 2009 and is performing well.

The ozone record from the SBUV/2 instruments will be continued by the nextgeneration Ozone Mapping and Profiler Suite (OMPS). The OMPS measurements 

will also provide a capability to make more detailed estimates of the vertical 

distribution of ozone. Researchers will make use of these improved values, over 

the next two decades, to investigate interactions between global climate changes 

and the expected recovery of ozone levels from the depletion caused by man-made 

chemical releases.

The figures on the opposite page show a time sequence of ozone measurements 

from the SBUV/2 instruments for the Southern Hemisphere documenting the 

evolution of Antarctic Ozone Holes since 1979. The colors represent different total 

amounts of ozone present from the bottom to the top of the atmosphere. Areas 

with the largest amounts of ozone are light blue and those with the lowest amounts 

are red.

The hole in the ozone layer

Ozone is an unstable gas consisting of three Oxygen 

atoms; O3. Although O3 is a very minor component of 

the atmosphere – its fraction of the air is measured in 

parts per million – it is critical in blocking most of the 

harmful Ultraviolet Solar radiation from reaching the 

Earth’s surface. Ozone is distributed throughout the 

atmosphere but it is mainly concentrated in the lower 

and middle stratosphere, often called the Ozone Layer.

During the 1980s and 1990s, increasing levels of 

chlorine and bromine – caused by release of man-made 

chlorofluorocarbons (CFCs) – led to chemical reactions 

that destroyed stratospheric ozone molecules, resulting 

in world-wide decreases in ozone amounts at a rate of 

approximately 4% per decade. These ozone destroying 

chemical reactions are enhanced by the presence 

of Polar Stratospheric Clouds in the Antarctic Spring 

leading to the formation of a dramatic Antarctic Ozone 

Hole each year (See the time series of ozone hole 

images on the opposite page).

Restrictions on the production and release of CFCs 

following the Montreal Protocol of 1987 and subsequent 

amendments have been effective in reducing the 

abundance of these chemicals so that their levels in the 

stratosphere peaked in the early 2000s. Even though 

the concentrations of ozone-depleting chemicals are 

decreasing, their long lifetimes in the stratosphere 

ensure that it will be decades before their levels are 

below that needed to create an Ozone Hole.
STAR Looks at the Earth | Satellite Measurements of the Atmosphere, Oceans and Land 147

The Antarctic Ozone Hole: 1979 to 2010

Average monthly total column ozone (1 Dobson unit = 1 matm-cm of O3) in October from 1979 (top left) to 2010 (bottom right). The redcolored regions represent ozone amounts below 220 Dobson Units. These low values define Ozone Hole conditions. The variations from 

one year to another are influenced by the size and temperature of the Antarctic Polar Vortex (weather) while the general increase in Ozone 

Hole sizes during the late 1980s and early 1990s is related to the buildup of atmospheric chlorine and bromine from man-made chemical 

releases. Data prior to 1989 are from the NASA Nimbus-7 SBUV instrument. 
148 Section 6 | Climate

Arctic Sea Ice Cover Trends

Pablo Clemente-Colón

Satellite observations indicate that the Arctic Ocean sea ice cover has been 

retreating rapidly during the last four decades – much faster than climate models 

have suggested. The synoptic capabilities of polar-orbiting environmental satellites 

have provided a unique view of changes over this region. The effects of global 

warming appear to be magnified in the Arctic with temperature increases more 

than twice as large as those in lower latitudes. Changes in the Arctic are not only 

an indicator of climate change but in turn have feedbacks on the global system.

The significant retreat of the Arctic sea ice pack in recent years has been documented 

using satellite data (see figure opposite page) with record low and second lowest 

minimum extents observed in summer 2007 and summer 2011, respectively. In 

fact, 4 of the last 5 summers have had sea ice extents falling even lower than the 

downward trend line. Furthermore, Arctic sea ice cover is experiencing a downward 

trend not only during the summer minimum but also during the winter maximum 

extent period according to the satellite observations.

While the rapid retreat of the overall Arctic sea ice cover is widely recognized, a 

similarly rapid loss of the multiyear ice (MYI), which is the oldest and thickest 

part of the ice cover, is not as well known by the public. The dramatic loss of MYI 

(see sidebar) in both extent and age (i.e., thickness) and the increased prevalence of 

seasonal ice in the pack contribute to a dramatic overall reduction of the Arctic sea 

ice cover mean thickness. In addition to changes in sea ice extent, summer melt 

appears to be occurring earlier in the season, with freeze-up occurring later in the 

fall.

A nose–dive in Arctic multiyear 

ice extent

The decrease in multiyear ice (MYI) extent – sometimes 

referred to as perennial sea ice cover – has accelerated 

in the last decade as captured by 1957-2008 buoy 

drift and age model, and scatterometer satellite data. 

Observations indicate that 2009-2011 perennial ice 

extent has remained low.
STAR Looks at the Earth | Satellite Measurements of the Atmosphere, Oceans and Land 149

Arctic Sea Ice Extent Summer Minima

National Ice Center ice charts derived from daily analysis of multi-satellite imagery showing that the Arctic sea ice extent minima at the 

end of the 2006-2011 summer melting seasons fell well below the 1972-2007 September climatology median extent (indicated by the 

translucent blue overlaid line). The red areas represent pack ice characterized by sea ice concentrations of 80 per cent and above, yellow 

areas represent the marginal ice zone characterized by sea ice concentrations below 80 per cent, and white areas represent ice-free 

waters. Dates of minimum ice extent and the areal ice coverage at those times are included in the charts.
150 Section 6 | Climate

Arctic Sea Ice Thickness

Dave McAdoo, Sinead Farrell, and Larry Connor

The floating sea-ice cover or “pack ice” is the defining feature of today’s Arctic 

Ocean. Sea ice volume and thickness are important climate parameters and widely 

accepted as an important component of climate change owing, in part, to an icealbedo (reflectivity) feedback mechanism: ice, which reflects most solar radiation 

is replaced by open ocean, which absorbs most solar radiation, thus amplifying any 

greenhouse warming.

While passive microwave satellites closely track the decline in sea ice area, they do 

not observe thickness changes. Evidence for associated thinning (or thickening) of 

the ice pack has been at best, equivocal, until quite recently. Upward-looking sonar 

(acoustical radars) observations from submarines since the late 1950’s indicate 

some thinning albeit with limited spatial and temporal coverage. Other recent, but 

quite sparse, surface observations show thinning in the past 5 years.

More significantly, satellite altimeter data from NASA’s ICESat and ESA’s Envisat, 

with comprehensive coverage of the Arctic Ocean to 86°N and 81°N respectively, 

analyzed by STAR scientists and their colleagues, show a robust, basin-scale 

thinning of sea ice since 2003. Climate modelers suggest that predicted Arctic 

amplification i.e., pronounced increases in lower atmosphere air temperatures over 

the Arctic, may already have begun in association with the decline in sea ice extent 

– and thickness — during the past decade.

Thinning ice

In the coming decade, satellite altimetry will track 

whether Arctic sea ice continues to thin and what role 

this ice will play in any future global warming.

Digital Image of sea ice in the Canada Basin (April 21, 

2009) from the NASA P3B aircraft. Gray area (upper left) is 

a refreezing stretch of open water. White area is a snowcovered, ridged area of older ice. (Photo credit: NASA)
STAR Looks at the Earth | Satellite Measurements of the Atmosphere, Oceans and Land 151

Change in Arctic Sea Ice above the Water Level

Sea ice freeboard (height of ice above the water level) in 2003 and 2008, where freeboard is a proxy for total ice thickness.

Note the increased amount of thinner ice (bluer areas) in 2008. Although the ICESat mission has come to an end, altimetric remote sensing 

of sea ice thickness has a bright future. NASA plans to launch a second laser altimeter satellite, ICESat-II, about five years from now.
152 Section 6 | Climate

Coral Bleaching

C. Mark Eakin, Jacqueline Rauenzahn, Gang Liu, Tim Burgess, Tyler Christensen, Scott 

Heron, Jianke Li, Ethan Lucas, Jessica Morgan, Britt Parker, William Skirving, and Alan 

Strong

Coral reefs are among the most diverse and valuable ecosystems on Earth. They 

provide humans with hundreds of billion dollars in economic and environmental 

services such as food, coastal protection, and tourism. Serious threats, including 

climate change, unsustainable fishing, and land-based pollution, impact coral 

health and contribute to coral bleaching, reduced growth, infectious diseases, and 

coral death. Approximately half of the coral reef ecosystem resources currently 

monitored is considered to be in ‘poor’ or ‘fair’ condition. The world has lost 19% of 

its original coral reef area; 15% is seriously threatened with loss within 10–20 years; 

and 20% is under threat of loss in 20–40 years.

NOAA Coral Reef Watch (CRW), initiated and implemented by STAR, has 

developed and delivers a suite of products based on satellite observations to provide 

information to managers and researchers on environmental stresses to coral reef 

ecosystems. CRW’s operational sea surface temperature (SST)-based products are 

the world benchmark for predicting coral bleaching (sidebar). This global system 

uses near-real-time data from NOAA satellites to monitor coral bleaching thermal 

stress and issue alerts. Developed by STAR, key products, which are currently 

delivered at a 50-km resolution and are updated twice-weekly, include SST, SST 

Anomaly, Coral Bleaching HotSpots, Degree Heating Weeks, Bleaching Alert 

Areas, Short-Term SST Trends, and a free, automated bleaching alert e-mail 

system that alerts subscribers when conditions can cause bleaching.

CRW is collaborating with numerous domestic and international partners to develop 

new products with higher spatial and temporal resolutions based on NOAA’s next 

generation satellite SST products and using additional environmental parameters. 

Coral bleaching in action

Mass coral bleaching is caused by unusually high water 

temperatures that stress corals. Coral polyps expel the 

symbiotic microalgae living in their tissues, exposing 

the white skeleton underneath. Corals typically recover 

from mild bleaching and gradually regain their color 

by repopulating algae. However, severe or prolonged 

bleaching can kill all or part of the coral. The photo below 

shows the substantial bleaching of both the coral colony 

and the clam living within it, indicating that many reef 

organisms are susceptible to bleaching. Also note the 

green macroalgae that have overgrown dead portions 

of the coral. Algal overgrowth hinders the coral colony’s 

recovery and can shift reef community structure from a 

dominance by corals to a dominance by algae. Climate 

change is already causing bleaching, disease, and death 

of corals.

This image was taken by Dr. C. Mark Eakin (NOAA Coral 

Reef Watch Coordinator) off the coast of Phuket, Thailand 

on June 22, 2010.
STAR Looks at the Earth | Satellite Measurements of the Atmosphere, Oceans and Land 153

Predicting and Monitoring Coral Bleaching

TOP: CRW’s Seasonal Bleaching Thermal 

Stress Outlook, an experimental product based 

on climate forecast models, provided advance 

warning of bleaching conditions. Map shows 

the 4-month cumulative thermal stress potential 

prediction for June-September 2010.

BOTTOM: CRW’s Bleaching Alert Area product 

indicates areas where thermal stress has currently 

reached levels that can cause bleaching. In 2010, 

major bleaching occurred throughout parts of the 

Indian Ocean, Southeast Asia (the Coral Triangle), 

the central Pacific, and the Caribbean. CRW was 

able to provide coral reef managers and scientists 

with nowcasts of current bleaching environmental 

conditions as the event unfolded. (Map shows a 

composite of the thermal stress for all of 2010).
154 Section 6 | Climate

Vegetation and Surface Temperature 

Trends

Marco Vargas, Felix Kogan, and Wei Guo

A 30-year record of reflected and emitted electromagnetic radiation from the 

Earth’s surface and atmosphere has been accumulated from the Advanced Very 

High Resolution Radiometer (AVHRR) instruments onboard NOAA polar 

orbiting satellites. This long-term satellite record allows us to do land surface 

monitoring and prediction.

Satellite observations from AVHRR are used to derive the Normalized Difference 

Vegetation Index (NDVI) – an index of the coverage and vigor of vegetation – and 

infrared Brightness temperature (BT) – an approximate measure of the surface 

temperature. 

Warming of the climate due to human and natural drivers has been confirmed 

from satellite and in-situ observations. Increases in near surface air temperatures 

could affect surface vegetation and vice versa (see sidebar).

The figure (opposite page) shows linear trends of NDVI and BT over the last three 

decades for each 4 km pixel in the USA, part of Mexico, and Canada. From time 

series such as these, STAR scientists are studying long term vegetation trends and 

their causes. The issue is complex. Surface temperature is not the only variable 

affecting vegetation: changing precipitation and CO2

 are also important factors.

Temperature and vegetation

Satellite observations as well as ground based 

observations have shown that warming of the climate is 

unequivocal. Global surface temperature has increased 

approximately 0.2°C per decade in the last 30 years. 

Temperature affects the growth and productivity of 

plants since rates of photosynthesis rise with increasing 

temperatures. A gradual increase in temperature in a 

region can translate into increasing vegetation growth, 

lengthening of the growing season, and early greening 

and flowering of the vegetation. But with large increases 

in temperature, photosynthesis rates reach a peak 

and then start to decline. These conditions may cause 

vegetation stress, leading to decreased plant growth 

and lower crop yields. Extreme increases in temperature 

may even cause desertification. Changes in precipitation 

rates and the beneficial effect of increasing CO2 levels 

on plant growth may either mitigate or reinforce any 

temperature effects on vegetation.

Changes in vegetation can feedback on the temperature. 

For example, increased vegetation can lead to greater 

evaporation of water from plants – a cooling effect – and 

lower reflection of solar radiation – a warming effect.
STAR Looks at the Earth | Satellite Measurements of the Atmosphere, Oceans and Land 155

Surface Infrared Temperature and Vegetation Trends: 1982-2010

Surface infrared brightness temperature trend (BT: °C/3 decades) – a measure of the surface temperature trend – and the vegetation 

(Normalized Difference Vegetation Index) trend (NDVI/3 decades) for the last three decades over North America. Vegetation (right panel) 

shows a positive trend in the central and eastern regions. Surface temperature (left panel) shows a mostly positive trend in the western 

region and a combination of positive and negative trends in the central and eastern states. Basically, as the brightness temperature 

increased in the western U.S., vegetation vigor has decreased in the same region. Conversely, as the brightness temperature in the eastern 

U.S. has decreased, the vegetation vigor in the same region has increased.
156 Section 6 | Climate

Satellite Vegetation Phenology

Xiaoyang Zhang, Mitchell D. Goldberg, and Yunyue Yu

Vegetation phenology is the study of recurring events in a plant’s life cycle – the 

greening of vegetation in the spring, for example – and how these are influenced 

by seasonal and interannual variations in climate. The seasonal vegetation cycle is 

obviously due to the annual climatic cycle, and shifts in phenology over the long 

term are sensitive indicators of climate change.

Real-time phenologic monitoring enables foresters and farmers to manage crops 

and forests, health officials to determine allergenic pollen occurrences and disease 

outbreaks, and weather modelers to quantify land-surface physical properties. 

STAR scientists have developed a tool to reconstruct long-term phenology records 

and to monitor real-time vegetation phenologic events from satellite data. This 

tool identifies the timing and rates of vegetation growth seasonally and interannually by analyzing the variation of vegetation phenology with time based on 

the Normalized Difference Vegetation Index (NDVI) – a measure of vegetation 

abundance and health – from the observations of the Advanced Very High 

Resolution Radiometer (AVHRR) onboard NOAA’s polar satellites. This dataset 

has been used to generate long-term phenologic metrics and their trends over a 30 

year period. A major advantage of the technique is that since each year’s 

analysis is based only on the phenology cycle that year, it is insensitive to 

unknown drifts in instrument calibration.

The research suggests that global warming may be causing an earlier 

spring bloom in most of North America, but in some mid-low latitude 

regions, warming may actually be leading to delayed plant budburst and 

seed sprout.

Spring flowers and fall colors

Vegetation phenologic metrics – the timing of 

important events in the seasonal cycle of vegetation 

– can be detected from the satellite-based measure 

of vegetation amount and health, the Normalized 

Difference Vegetation Index (NDVI). The four phases 

of the seasonal cycle of NDVI are characterized by 

four phenologic transition dates: the onset of greenup 

(leaf-out), plant maturity, senescence (plant aging), and 

dormancy.

The NDVI time series also reveals the growing season 

length. During the fall senescent phase of deciduous 

plants, foliage coloration occurrence (low coloration, 

moderate coloration, near peak coloration, peak 

coloration, and post peak coloration) can be retrieved 

from an NDVI related index – a brownness index.

Shifts in the phenologic metrics can be traced to climate 

change and human activity.
STAR Looks at the Earth | Satellite Measurements of the Atmosphere, Oceans and Land 157

Earlier Greening

Long-term trend of vegetation greenup onset (plant budburst) is detected from weekly satellite vegetation index (1981-2005) at a spatial 

resolution of 4 km across North America. The trend reveals that spring greenup has been accelerated by an average of 8 days in cold and 

temperate climate regions. In warmer subtropical regions, however, vegetation greenup onset varies progressively from an earlier (north) to 

later (south) date because the number of winter chilling days is insufficient to fulfill vegetation chilling requirements.
158 Section 6 | Climate

Aerosols, Volcanoes and Climate

Istvan Laszlo

Solid and/or liquid particles, called aerosols, are inherent constituents of the 

atmosphere. They are often seen as haze, smoke, and dust. Aerosols exhibit an 

immense range of diversity in size, composition, origin, and temporal and spatial 

distributions. Sizes range by a factor of 10,000 – from a few nanometers (10-9 

meters) to a few tens of micrometers (10-6 meters). Depending on the type, and 

the source, aerosols remain in the atmosphere for less than a day to several years.

Aerosols are produced by both natural processes – smoke from forest fires, 

windblown Saharan sands – and as a result of human activities – biomass burning, 

for example. The latter currently amounts to about 10% of the global aerosol mass. 

Strong volcanic eruptions inject gases and particles into the stratosphere, where 

they spread over most of the globe, remaining for years before they eventually fall 

out. Because they reflect solar radiation back to space, their presence leads to a 

cooling of the Earth, reducing global surface temperatures by about 1 deg C and 

counteracting greenhouse warming for the year or two before they fall out.

Satellites are increasingly used for measuring the amount of aerosols present in the 

atmosphere. This can be done because the intensity and spectral distribution, or 

“color”, of solar radiation reflected back to space by aerosols depend on their amount, 

size, shape and type. STAR scientists use the measurements of the Advanced Very 

High Resolution Radiometer onboard the NOAA satellites to study the changes 

in aerosol amount over the global oceans.

Volcanic dust and sunlight

One of the primary effects of volcanic aerosols is to 

decrease the amount of solar radiation reaching the 

surface. Thus major volcanic eruptions lead to a cooling 

at the surface.

The image shows the aerosol optical thickness – the 

total amount of dust in a vertical column of atmosphere – 

around Iceland immediately following the eruption of the 

Eyjafjallajökull Volcano. (Dark gray area is land; light gray 

and white areas are not observed mostly due to cloud 

cover.) Elevated aerosols (warm colors) are clearly seen 

in the plume. Radiative transfer models predict a decrease 

by over 30% in the solar radiation available for heating the 

surface under the plume.
STAR Looks at the Earth | Satellite Measurements of the Atmosphere, Oceans and Land 159

Aerosol over Oceans

The graph shows the variation with time in the amount of aerosol as represented by the aerosol optical thickness (AOT) over the global 

oceans. The departure of monthly AOT values from their multi-year mean value (anomaly) is plotted. The AOT was determined from the 

Advanced Very High Resolution (AVHRR) instrument flown onboard operational polar orbiting NOAA satellites. The pattern is characterized 

by seasonal fluctuations in AOT and by two maxima corresponding to the eruptions of El Chichon in April 1982 and Mount Pinatubo in June 

1991. Following these volcanic eruptions the aerosol optical thickness increased by 0.1-0.15 globally. This is more than double of the typical 

amount in years without major volcanic events. There is also a hint of a gradual decrease in AOT after about 1996. The question of whether 

this decrease is real or not is an active area of research.
160 Section 6 | Climate

Arctic Climate Interactions

Jeff Key

The Arctic has experienced dramatic changes over the past few decades. Surface 

temperature, a fundamental indicator of climate change, has increased significantly. 

The average extent of summer sea ice cover is declining at a rate that would leave 

most of the Arctic ice-free in summer by mid-century or earlier. Snow cover has 

decreased, permafrost temperatures have increased, and ice sheets have declined.

Satellites can be used to measure most characteristics of Arctic climate, but no 

single sensor is sufficient. Passive microwave, radar, lidar, gravity, visible, and thermal 

sensors are all needed to observe changes in sea ice cover, motion, and thickness, 

snow extent and water content, ice sheet thickness, atmospheric temperature and 

humidity, winds, cloud properties, and sea level.

Satellite climate data records are becoming increasingly important in the analysis 

of interactions between climate components and in quantifying feedbacks – 

amplifications or reductions of an initial climate change. STAR scientists have 

investigated the influence of trends in sea ice and cloud cover on trends in surface 

temperature over the Arctic Ocean. The study showed that changes in sea ice 

account for most of the local summertime warming, while trends in winter cloud 

cover account for most of the cooling in the central Arctic. Of course, large-scale 

changes in temperature are the primary cause for the decline in sea ice, but this 

study quantified the smaller-scale feedbacks. The interactions are complex but 

revealing: if cloud cover had not changed the way it did, the Arctic might have 

warmed even more than what we have observed.

Arctic climate feedbacks and processes

There are many complex interactions in the Arctic 

climate system: (1) Melting snow increases solar 

radiation absorption over land. (2) Ice sheet melting 

contributes to sea level rise. (3) Retreating sea ice 

increases ocean absorption of solar radiation and 

ocean to atmosphere transfer of heat and moisture. 

(4-6) Degrading permafrost increases methane – a 

greenhouse gas – emission; wetland drying increases 

CO2 emission. (6-8) Increasing precipitation plus melting 

snow change the freshwater outflow. Shrinking lake ice 

cover and runoff have ecological impacts. (9) Retreating 

glaciers increase runoff. (10) Changes in cloudiness 

impact the radiative energy budget. (Photo credit: 

Prowse, T.D., 2009. Introduction: hydrologic effects of

a shrinking cryosphere, Hydrol. Process., 23, 1-6, doi:

10.1002/hyp.7215.)
STAR Looks at the Earth | Satellite Measurements of the Atmosphere, Oceans and Land 161

Changes in Sea Ice and Cloud Cover are Related

A decrease in sea ice cover results in greater evaporation from the surface, a more unstable lower atmospheric layer, and an increase in 

cloud amount. This was particularly obvious during the summer of 2007, which saw a record minimum in Arctic ice cover in the satellite era. 

Left: Cloud amount anomalies (departures from normal) from Moderate Resolution Imaging Spectroradiometer (MODIS) observations over 

the Arctic for September-October 2007 relative to the 2002-2007 mean. Right: Sea ice concentration anomalies from Advanced Microwave 

Scanning Radiometer from EOS (AMSR-E) observations for the same period. Large anomalies in ice cover correspond to anomalies in 

cloud cover.
162 Section 6 | Climate

Climate Reconstructions

Thomas M. Smith and Phillip A. Arkin

Today, global sampling of climate variables is nearly complete using satellite-based 

data, which became available around 1980. Prior to that time, global coverage was 

limited, with observations concentrated in populated areas and along the major 

ocean routes. In order to study long-period climate variations we would like to 

have more complete analyses for the pre-satellite period. Climate reconstructions 

are analyses that have been developed to fill the many gaps in the pre-satellite 

period. Reconstructions exploit correlations between the available data and the 

data gaps. The satellite data from the modern period are used to develop those 

correlations, which are then applied to the sparse historical data to fill in the gaps. 

Because of the sparseness of the historical data, reconstructions typically only 

resolve large scale variations on monthly or longer time scales.

STAR scientists and their collaborators have recently reconstructed an important 

climate field: global precipitation. Over most land regions, rain gauge data are 

available since 1900. Over oceans, prior to 1979, measurements of precipitation 

were few and far between, so reconstructions are needed to produce time series of 

historical global variations. A blend of satellite and gauge products – the Global 

Precipitation Climatology Project (GPCP), which began in 1979 – provides the 

high-quality global data needed to establish the statistical connections between land 

plus island gauge data and global ocean precipitation patterns. These relationships 

are applied to the historical land and island rain observations to generate a time 

series of global precipitation anomalies (departures from normal).

Reconstruction of historical oceanic 

precipitation variations

The map on the opposite page shows the global 

distribution of 20th century precipitation trends derived 

from the climate reconstruction method. The trends 

tend to be largest over tropical oceans, where satellitebased analyses indicate that precipitation variations are 

also largest. Patterns in the map indicate relationships 

between oceanic and land trends over the century.

Although there are both positive and negative trends 

in the map, the time series (lower panel) indicates that 

over the course of the 20th century, average monthly 

precipitation over the global ocean has increased by 

about 1 mm. The preliminary uncertainty estimates for 

this change is ±0.6 mm. The 3 to 10 year variations 

are much larger than the multi-decadal variations, so 

a long time series such as this is needed to evaluate 

trends. For the shorter satellite period, the satellitebased analysis shows variations consistent with the 

reconstructed data, lending credibility to the use of the 

method for historical reconstruction.
STAR Looks at the Earth | Satellite Measurements of the Atmosphere, Oceans and Land 163

20th Century Precipitation Trend

Near-global precipitation variations. The geographical distribution of precipitation trends for the 20th century reconstruction is shown in the 

upper panel. The annual 60°S-60°N average over ocean areas is shown in the lower panel for the reconstruction and for the satellite-based 

analysis (heavy dashed line). Over the 20th century, average monthly precipitation over the ocean has increased by about 1 mm.
164 Section 6 | Climate

Checking Climate Reanalyses

Mitchell Goldberg and Likun Wang

There is a growing consensus that increasing industrial emissions of greenhouse 

gases over the past 150 years are causing warming of the atmosphere and oceans, 

rising sea levels, melting ice caps and glaciers, more frequent severe weather, 

and regional shifts in precipitation patterns. To enable policy makers to make 

informed decisions on decreasing future emissions and mitigating and adapting to 

greenhouse warming, it is important to observe, document, understand and predict 

climate change.

A powerful tool for constructing a record of the climate is a climate reanalysis system. 

Similar to a numerical weather prediction (NWP) analysis system, it assimilates 

data from numerous observing systems. It is run on a daily basis for many decades 

to provide a historical description of the atmosphere. However, large differences 

exist between datasets generated by different groups, most likely due to varying 

NWP model physics and data assimilation techniques. Accurate independent 

measurements are needed to check the reanalyses. 

Fortunately, over the past decade, a new generation 

of extremely accurate hyperspectral (observations 

at thousands of wavelengths) infrared instruments 

– NASA’s Atmospheric InfraRed Sounder (AIRS) 

and the European Infrared Atmospheric Sounding 

Interferometer (IASI) – has been launched into space. 

Direct comparisons between observations from these 

two instruments show absolute differences well within 

0.1 °C, making them reliable space-based standards for 

verifying reanalyses, as well as climate prediction models. 

An example of applying the technique is shown in the 

figure on the opposite page.

Hyperspectral infrared spectrum

Spectrally resolved (very high spectral resolution) 

infrared radiances provide the opportunity not only 

to detect climate change but also to understand 

the processes contributing to the change. Do these 

processes involve changes in clouds, temperature, 

water vapor, carbon compounds, ozone, etc? Since 

different parts of the spectrum (see example below of 

Earth’s emission spectrum) are sensitive to different 

gases, layers of the atmosphere, and cloud properties, 

observations of spectrally resolved radiance permit 

dissecting and understanding the root causes of the 

observed climate changes. Also, spectrally resolved 

radiances can be used to validate weather and climate 

models, by simply comparing the observed spectra 

with those calculated from the model’s geophysical 

parameters. (Photo credit: University of Wisconsin)
STAR Looks at the Earth | Satellite Measurements of the Atmosphere, Oceans and Land 165

Differences between Model Simulations and Satellite Observations

The Atmospheric InfraRed Sounder (AIRS) channel with a frequency of 1519.90 wavenumbers (cm-1) is very sensitive to upper 

tropospheric water vapor. The observed AIRS data are compared with simulated AIRS measurements computed from four different climate 

reanalyses of atmospheric states (NASA (MERRA), Japan (JRA), ECMWF (ERA), NCEP (CFSR)), and one weather analysis (ECMWF). 

Upper panel: AIRS brightness temperature (BT) – approximate temperature of the emitting atmospheric layer – observations; Middle 

panel: Simulated AIRS BT observations; Lower panel: Difference in BT between observed and simulated AIRS observations. A variation 

in upper tropospheric water vapor of about 10% results in a BT change of about 1 deg K. The ECMWF weather analysis has the best 

agreement with AIRS and there is high confidence that its upper tropospheric water vapor field is the most accurate. The high accuracy of 

hyperspectral infrared radiances provides a very powerful tool for validating and improving critical data for assessing and predicting climate 

change.
166 Section 6 | Climate

Abrupt Climate Change

Alfred M. Powell, Jr. and Jianjun Xu

Abrupt climate change has been defined by the U.S. Climate Change Science 

Program as a large-scale change in the climate system that takes place over a few 

decades or less, persists, and causes substantial disruptions in human and natural 

systems. When satellite data are merged into a time series of global atmospheric or 

ocean conditions with other historical observations, they provide the tools to study 

environmental changes such as abrupt climate shifts.

The polar atmospheric wave pattern circles the globe and determines regional 

weather and climate patterns. When the polar wave pattern varies, the winds 

around the globe change. The surface wind drives the ocean currents and can cause 

the sea surface temperature (SST) to change. An abrupt climate shift occurred 

in 1977-78 and the decadal state before and after the shift are very different. The 

figure (opposite page) shows the differences in the atmospheric wave pattern and 

its impact on the sea surface temperature. The atmosphere before the shift shows a 

clockwise high pressure (yellow) system in the Gulf of Alaska which advects cold 

water southward along the U.S. Pacific coast. After the shift, a counter-clockwise 

low pressure system (blue) advects water northward along the U.S. West coast. 

For the 1977 climate shift, the eastern Pacific SST was colder before the shift 

and warmer after it. The pre- and post-shift patterns in the atmosphere and the 

sea surface are essentially inverses of each other. The change from cold pre-shift 

temperatures to warm post-shift temperatures affected 5 of the 31 monitored fish 

species caught off the California coast resulting in a 16 percent change in the 

ecosystem.

Environmental changes such as this one can have significant impacts on our 

nation’s food supply with major regional consequences. Similar impacts are also 

associated with events such as El Niño (see sidebar).

El Niño Southern Oscillation (ENSO) 

impacts fishing

Fishermen along the coasts of Peru and Ecuador have 

been affected by the El Niño phenomenon for centuries. 

The key element of the El Niño is the interaction 

between the sea surface and winds. When the winds 

are from the southeast, the water piles up in the western 

Pacific causing broad ocean warming and upwelling 

(deep, colder water rising to the surface) along the 

South American coast. This event brings nutrients to the 

surface for the fishery food chain and fisheries thrive. 

When the wind decreases, the upwelling declines and 

so do the fish. Every few years, fish in these coastal 

waters virtually vanish causing a disruption in the fishing 

industry and regional economic woes.

However, other areas may benefit. The El Niño event 

off the coast of Ecuador forces the mass migration of 

marine life away from the equator in search of food and 

survivable water temperatures. As a result, albacore 

have been found as far north as Oregon, and marlin 

have been caught outside the Golden Gate Bridge area, 

which is far from their traditional habitat.

Left: Marlin (Photo Credit: NOAA Fisheries/Ken Neill); 

Right: Albacore (Photo Credit: NOAA Fisheries).
STAR Looks at the Earth | Satellite Measurements of the Atmosphere, Oceans and Land 167

Synchronous Abrupt Changes in the Atmosphere, Ocean and Ecosystem

The figures show the 1977-78 abrupt climate shift in the atmosphere and the ocean. The top two charts show the decade prior to the shift, 

and the lower two charts show the decade after the abrupt climate shift. The global atmospheric change is shown in the surface pressure 

pattern as indicated by the height of the 1000 hPa pressure surface (left two charts). The pressure pattern indicates the wind direction 

and surface stress before and after the regime shift. The changes in the winds produced the sea surface temperature differences (right 

two charts) in the eastern and central Pacific. A cold SST anomaly (departure from normal) can be seen before the shift and a warm SST 

anomaly can be seen after the shift in the eastern Pacific along the U.S. west coast. The normalized fish catch/stock amounts for the 31 

fish species monitored along the California coast are used to indicate ecosystem change. Five (5) of the 31 fish species monitored were 

identified with greater catch/stock amounts when the sea temperature warmed after the shift. This is a 16% change in the ecosystem fish 

catch which could have significant economic impacts.
168 

Acknowledgments

We thank:

Julie Price, for her unflagging support to the editors, authors, and production team throughout this project,

Marc Pulliam, for making us look good with his magical graphical design, 

Dan Pisut, for his informed advice and guidance on writing level, layout, and image quality, and

Jerry Sullivan, for his eagle-eyed checking of several sections of the manuscript.

The authors and editors 
STAR Looks at the Earth | Satellite Measurements of the Atmosphere, Oceans and Land 169

List of Acronyms and Abbreviations

A-Train Series of Earth observation satellites, one following the other in polar orbit

ABI Advanced Baseline Imager

ACSPO Advanced Clear-Sky Processor for Oceans

AIRS Atmospheric Infrared Sounder

AMSR-E Advanced Microwave Scanning Radiometer from EOS

AMSU Advanced Microwave Sounding Unit

AOD Aerosol Optical Depth

AOT Aerosol Optical Thickness

AQI Air Quality Index

ATMS Advanced Technology Microwave Sounder

ATOVS Advanced TIROS Operational Vertical Sounder

AVHRR Advanced Very High Resolution Radiometer

AWIPS Advanced Weather Interactive Processing System

BT Brightness Temperature

CALIPSO Cloud-Aerosol Lidar and Infrared Pathfinder Satellite

CDR Climate Data Record

CEOS Committee on Earth Observation Satellites

CFCs Chlorofluorocarbons

CFSR Climate Forecast System Reanalysis

CGMS Coordination Group for Meteorological Satellites

CNES Centre National d’Etudes Spatiales (France)

COSMIC Constellation Observing System for Meteorology, Ionosphere and Climate

CrIS Cross-track Infrared Sounder

CRTM Community Radiative Transfer Model

CRW Coral Reef Watch

CZCS Coastal Zone Color Scanner

DMSP Defense Meteorological Satellite Program

DOD Department of Defense

DPI Derived Product Imagery

ECMWF European Centre for Medium-Range Weather Forecasts

EF Enhanced Fujita

ENVISAT Environmental Satellite

EOS Earth Observing Satellite

EPA Environmental Protection Agency

ESA European Space Agency

ESI Evaporative Stress Index

EUMETSAT European Organization for the Exploitation of Meteorological Satellites

FY-2 Chinese geostationary meteorological satellite

FY-3 Chinese polar orbiting meteorological satellite

GEBCO General Bathymetric Charts of the Ocean

GEO Geostationary satellite

GHG Greenhouse gases

GMAO Global Modeling and Assimilation Office
170 

GOES Geostationary Orbiting Environmental Satellite

GOME Global Ozone Monitoring Experiment

GPCP Global Precipitation Climatology Project

GPS Global Positioning System

GPS/RO Global Positioning System/Radio Occultation

GRACE Gravity Recovery and Climate Experiment 

GSICS Global Space-based Inter-Calibration System

GVH Global Vegetation Health

HAB Harmful Algal Bloom

HIRS High-resolution Infrared Radiation Sounder

IASI Infrared Atmospheric Sounding Interferometer

IBCAO International Bathymetric Chart of the Arctic Ocean

ICESat Ice, Cloud, and land Elevation Satellite

IHO International Hydrographic Organization

Insat Indian National Satellite

IOC Intergovernmental Oceanographic Commission

IR Infrared

ISCCP International Satellite Cloud Climatology Project

Jason Ocean altimetry satellite

JCSDA Joint Center for Satellite Data Assimilation

JPSS Joint Polar Satellite System

JRA-25 Japanese 25-year Reanalysis

LEO Low Earth orbiting satellite

LI Lifted Index

LST Land Surface Temperature

MABL Marine Atmospheric Boundary Layer

MERIS Medium Resolution Imaging Spectrometer

MERRA Modern-Era Retrospective Analysis for Research and Applications

METEOR Russian LEO weather satellite

Meteosat European geostationary meteorological satellite

MetOp Meteorological Operational satellite program

MHS Microwave Humidity Sounder

MIRS Microwave Integrated Retrieval System

MODIS Moderate resolution Imaging Spectroradiometer

MSG Meteosat Second Generation

MSPPS Microwave Surface and Precipitation Products System

MTSAT Multi-Functional Transport Satellite

MW Microwave

MYI Multiyear ice

NASA National Aeronautics and Space Administration

NCEP National Centers for Environmental Prediction

NDVI Normalized Difference Vegetation Index

NESDIS National Environmental Satellite, Data, and Information Service

NIC National Ice Center

NIR Near-infrared
STAR Looks at the Earth | Satellite Measurements of the Atmosphere, Oceans and Land 171

NOAA National Oceanic and Atmospheric Administration

NPP National Polar Orbiting Partnership

NPROVS NOAA Products Validation System

NWP Numerical Weather Prediction

NWS National Weather Service (US)

OCO Orbiting Carbon Observatory

OHC Oceanic Heat Content

OLR Outgoing longwave radiation

OMPS Ozone Mapping and Profiler Suite

POES Polar-Orbiting Environmental Satellites

QuikSCAT Quick Scatterometer

R2O Research to Operations

RMS Root-Mean-Square

SAR Synthetic Aperture Radar

SBUV Solar-Backscatter UltraViolet Spectrometer

SeaWiFS Sea-viewing Wide Field-of-view Sensor

SEVIRI Spinning Enhanced Visible and Infrared Imager

SHIPS Statistical Hurricane Intensity Prediction Scheme

SMOPS Soil Moisture Product System

SMOS Soil Moisture-Ocean Salinity

SPC Storm Prediction Center

SSFR Solar Spectral Flux Radiometer

SSM/I Special Sensor Microwave Imager

SSMIS Special Sensor Microwave Imager/Sounder

SSS Sea Surface Salinity

SST Sea Surface Temperature

STAR Center for Satellite Applications and Research

SV Sailing Vessel

SWIR Shortwave infrared

TCI Temperature Condition Index

TIROS Television Infrared Observation Satellite

TOPEX Ocean Topography Experiment

TPW Total Precipitable Water 

TRMM Tropical Rainfall Measuring Mission

TSM Total Suspended Matter

UNESCO United Nations Educational, Scientific and Cultural Organization

USCG U.S. Coast Guard

USDA U.S. Department of Agriculture

USDM U.S. Drought Monitor

USGS U.S. Geological Survey

VCI Vegetation Condition Index

VHI Vegetation Health Index

VIIRS Visible Infrared Imager Radiometer Suite

WMO World Meteorological Organization
In June 2012, Tropical Storm Debby churned off the 

coast of the Florida Panhandle, dumping in excess 

of 20” of rain over a 3 day period. On June 25th, the 

Suomi NPP satellite flew over the storm, collecting 

data from its suite of sensors including VIIRS, which 

captures data from the visible and infrared spectrum. 

174 Section 1 | PRElude

Center for Satellite Applications and Research

www.star.nesdis.noaa.gov