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NISTIR 7047

Satellite Instrument Calibration for Measuring

Global Climate Change

(Report of a Workshop at the University of Maryland Inn and Conference

Center, College Park, MD, November 12-14, 2002)

Edited by

George Ohring

NOAA/NESDIS (Consultant)

Bruce Wielicki 

NASA Langley Research Center 

Roy Spencer

NASA Marshall Space Flight Center

Bill Emery 

University of Colorado 

Raju Datla

NIST

March 2004

U.S. DEPARTMENT OF COMMERCE

Donald L. Evans, Secretary

TECHNOLOGY ADMINISTRATION

Phillip J. Bond, Under Secretary of Commerce for Technology

NATIONAL INSTITUTE OF STANDARDS AND TECHNOLOGY

Arden L. Bement, Jr., Director

Satellite Instrument Calibration for Measuring

Global Climate Change

Report of a Workshop Organized by

National Institute of Standards and Technology

National Polar-orbiting Operational Environmental Satellite System-Integrated

Program Office

National Oceanic and Space Administration

National Aeronautics and Space Administration

At the University of Maryland Inn and Conference Center, College Park, MD,

November 12-14, 2002

Edited by

George Ohring, NOAA/NESDIS (Consultant), Camp Springs, MD

Bruce Wielicki, NASA Langley Research Center, Hampton, VA

Roy Spencer, NASA Marshall Space Flight Center, Huntsville, AL

Bill Emery, University of Colorado, Boulder, CO

Raju Datla, NIST, Gaithersburg, MD

iii
Workshop Organizing Committee

R. Datla, Chair, NIST

G. Ohring, Consultant

M. Weinreb, NOAA

S. Mango, NPOESS IPO

J. Butler, NASA

D. Pollock, UA

iv
Table of Contents

Abstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vi

Importance of Sustained, Long-Term Monitoring of Earth’s Climate Emphasized in

Declaration of the 2003 Earth Observation Summit . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1

Extended Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2

1. Background, Goal, and Scope . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19 

2. Overarching Principles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24

3. Required Absolute Accuracies and Long Term Stabilities for Climate Variables . . . . . . . . . . . . . . . . 34

3.1 Solar Irradiance, Earth Radiation Budget And Clouds . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35

3.2 Atmospheric Variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44

3.3 Surface Variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49

4. Translation of Climate Dataset Accuracies and Stabilities to Satellite Instrument

Accuracies and Stabilities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53

4.1 Solar Irradiance, Earth Radiation Budget, and Clouds . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53

4.2 Atmospheric Variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57

4.3 Surface Variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61

5. Ability of Current Observing Systems to Meet Requirements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64

5.1 Solar Irradiance, Earth Radiation Budget, And Clouds . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64

5.2 Atmospheric Variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69

5.3 Surface variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73

6. Roadmap for Future Improvements in Satellite Instrument Calibration and

Inter-Calibration to Meet Requirements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 74

6.1 Solar Irradiance, Earth Radiation Budget, And Clouds . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 74

6.2 Atmospheric Variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 80

6.3 Surface Variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 86

7. Concluding Remarks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 88

8. Acknowledgments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 89

9. References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 90

Appendix A. Workshop Agenda . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 95

Appendix B. Workshop Participants . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 97

List of Acronyms and Abbreviations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 99

v
Abstract

Measuring the small changes associated with long-term global climate change from space is

a daunting task. The satellite instruments must be capable of observing atmospheric temperature

trends as small as 0.1 ° C/decade, ozone changes as little as 1%/decade, and variations in the

sun’s output as tiny as 0.1%/decade. To address these problems and recommend directions for

improvements in satellite instrument calibration, the National Institute of Standards and

Technology (NIST), National Polar-orbiting Operational Environmental Satellite SystemIntegrated Program Office (NPOESS-IPO), National Oceanic and Atmospheric Administration

(NOAA), and National Aeronautics and Space Administration (NASA) organized a workshop at

the University of Maryland Inn and Conference Center, College Park, MD, November 12-14,

2002. Some 75 scientists, including researchers who develop and analyze long-term data sets

from satellites, experts in the field of satellite instrument calibration, and physicists working on

state of the art calibration sources and standards, participated.

The workshop defined the absolute accuracies and long-term stabilities of global climate

data sets that are needed to detect expected trends, translated these data set accuracies and stabilities to required satellite instrument accuracies and stabilities, and evaluated the ability of

current observing systems to meet these requirements. The workshop’s recommendations

include a set of basic axioms or overarching principles that must guide high quality climate

observations in general, and a roadmap for improving satellite instrument characterization, calibration, inter-calibration, and associated activities to meet the challenge of measuring global climate change. It is also recommended that a follow-up workshop be conducted to discuss implementation of the roadmap developed at this workshop.

vi
Importance of Sustained, LongTerm Monitoring of Earth’s

Climate Emphasized in Declaration

of the 2003 Earth Observation

Summit

We, the participants in this Earth Observation

Summit held in Washington, DC, on July 31, 2003:

Recalling the World Summit on Sustainable

Development held in Johannesburg that called for

strengthened cooperation and coordination among

global observing systems and research programmes

for integrated global observations; 

Recalling also the outcome of the G-8 Summit held

in Evian that called for strengthened international

cooperation on global observation of the environment; 

Noting the vital importance of the mission of organizations engaged in Earth observation activities and

their contribution to national, regional and global

needs; 

Affirm the need for timely, quality, long-term, global

information as a basis for sound decision making. In

order to monitor continuously the state of the Earth,

to increase understanding of dynamic Earth processes, to enhance prediction of the Earth system, and to

further implement our environmental treaty obligations, we recognize the need to support: 

(1) Improved coordination of strategies and systems

for observations of the Earth and identification of

measures to minimize data gaps, with a view to

moving toward a comprehensive, coordinated, and

sustained Earth observation system or systems; 

(2) A coordinated effort to involve and assist developing countries in improving and sustaining their

contributions to observing systems, as well as their

access to and effective utilization of observations,

data and products, and the related technologies by

addressing capacity-building needs related to Earth

observations; 

(3) The exchange of observations recorded from

in-situ, aircraft, and satellite networks, dedicated to

the purposes of this Declaration, in a full and open

manner with minimum time delay and minimum cost,

recognizing relevant international instruments and

national policies and legislation; and 

(4) Preparation of a 10-year Implementation Plan,

building on existing systems and initiatives, with the

Framework being available by the Tokyo ministerial

conference on Earth observations to be held during

the second quarter of 2004, and the Plan being

available by the ministerial conference to be hosted

by the European Union during the fourth quarter of

2004. 

To effect these objectives, we establish an ad hoc

Group on Earth Observations and commission the

group to proceed, taking into account the existing

activities aimed at developing a global observing

strategy in addressing the above. We invite other

governments to join us in this initiative. We also

invite the governing bodies of international and

regional organizations sponsoring existing Earth

observing systems to endorse and support our

action, and to facilitate participation of their experts

in implementing this Declaration.

1
Extended Summary

I. Introduction

Is the Earth’s climate changing? If so, at

what rate? Are the causes natural or humaninduced? What will the climate be like in

the future? These are critical environmental

and geopolitical issues of our times.

Increased knowledge, in the form of

answers to these questions, is the foundation for developing appropriate response

strategies to global climate change.

Accurate global observations from space

are a critical part of the needed knowledge

base. 

Measuring the small changes associated

with long-term global climate change from

space is a daunting task. For example, the

satellite instruments must be capable of

observing atmospheric temperature trends

as small as 0.1° C/decade, ozone changes as

little as 1%/decade, and variations in the

sun’s output as tiny as 0.1%/decade. 

The importance of understanding and

predicting climate variation and change has

escalated significantly in the last decade. In

2001, the White House requested the

National Academy of Sciences (NAS)

National Research Council (NRC) (NRC,

2001a) to review the uncertainties in climate change science. One of the three key

recommendations from the NRC’s report is

“ensure the existence of a long-term monitoring system that provides a more definitive observational foundation to evaluate

decadal- to century-scale changes, including

observations of key state variables and

more comprehensive regional measurements.” To accelerate Federal research and

reduce uncertainties in climate change science, in June 2001, President George W.

Bush created the Climate Change Research

Initiative (CCRI). 

To develop recommendations for

improving the calibration of satellite instruments to meet the challenge of measuring

global climate change, the National Institute

of Standards and Technology (NIST),

National Polar-orbiting Operational

Environmental Satellite System-Integrated

Program Office (NPOESS-IPO), National

Oceanic and Atmospheric Administration

(NOAA), and National Aeronautics and

Space Administration (NASA) organized a

workshop at the University of Maryland Inn

and Conference Center, College Park, MD,

November 12-14, 2002. Some 75 scientists,

including researchers who develop and analyze long-term data sets from satellites,

experts in the field of satellite instrument

calibration, and physicists working on state

of the art calibration sources and standards,

participated in the workshop. Workshop

activities consisted of keynote papers, invited presentations, breakout groups, and

preparation of draft input for a workshop

report. The keynote papers and invited presentations provide extensive background

information on issues discussed at the

workshop and are posted on the NIST website:

http://physics.nist.gov/Divisions/Div844/glo

bal/mgcc.html. (Please Note: To access this

site, you have to input user name: mgccoutline, and password: div844mgcc)

This workshop report has a single clearly

defined goal: 

• Recommend directions for future

improvements in satellite instrument

characterization, calibration, inter-calibration, and associated activities, to

enable measurements of global climate

change that are valid beyond reasonable doubt

Although many of the recommendations

are directed at the NPOESS program, the

nation’s converged future civilian and mili2
tary polar-orbiting operational environmental satellite system, must also apply to sustained space-based climate change observations in general.

To achieve this goal, the report first:

• Defines the required absolute accuracies and long-term stabilities of global

climate data sets

• Translates the data set accuracies and

stabilities to required satellite instrument accuracies and stabilities, and

• Evaluates the ability of current observing systems to meet these requirements 

The focus is on passive satellite sensors

that make observations in spectral bands

ranging from the ultraviolet to the

microwave. The climate change variables

of interest include:

• Solar irradiance, Earth radiation budget, and clouds (total solar irradiance,

spectral solar irradiance, outgoing

longwave radiation, net incoming solar

radiation, cloudiness) 

• Atmospheric variables (temperature,

water vapor, ozone, aerosols, precipitation, and carbon dioxide)

• Surface variables (vegetation, snow

cover, sea ice, sea surface temperature,

and ocean color)

This list is not exhaustive. The variables

were selected on the basis of the following

criteria: 1) importance to decadal scale climate change, 2) availability or potential

availability of satellite-based climate data

records, and 3) measurability from passive

satellite sensors. The workshop breakout

groups were aligned with the above three

groups of climate variables.

While there have been a number of previous reports that have also discussed accuracy and stability measurement requirements for long term climate data sets (for

example, Hansen et al., 1993; Jacobowitz,

1997; NPOESS, 2001) and calibration

issues (Guenther et al., 1997; NRC, 2000;

NRC, 2001b), the present document is an

end to end report. It not only covers the latest thinking on measurement requirements

but also provides general directions to

improve satellite instrument characterization, calibration, vicarious calibration, interinstrument calibration and associated activities to meet the requirements. This general

roadmap provides guidance to the national

agencies concerned with the development

of the space system and related calibration

program to measure global climate change:

NPOESS-IPO, NOAA, NIST, and NASA. 

Measuring small changes over extended

time periods necessarily involves the concepts of accuracy and stability of time

series. Accuracy is defined as the “closeness

of the agreement between the result of the

measurement and the true value of the measurand” (ISO, 1993). It may be thought of

as the closeness to the truth and is measured

by the bias or systematic error of the data,

that is, the difference between the shortterm average measured value of a variable

and the truth. The short- term average is the

average of a sufficient number of successive

measurements of the variable under identical conditions such that the random error is

negligible relative to the systematic error.

Stability may be thought of as the extent to

which the accuracy remains constant with

time. Stability is measured by the maximum excursion of the short- term average

measured value of a variable under identical

conditions over a decade. The smaller the

maximum excursion, the greater the stability of the data set.

It is to be understood that the methods to

establish the true value of a variable (the

measurand) should be consistent with the

internationally adopted methods and standards, thus establishing System of Units

(SI) traceability (BIPM, 1998; NIST, 1995).

3
According to the resolution adopted by the

20th Conference Generale des Poids et

Measures (CGPM) - the international standards body in Paris - “that those responsible

for studies of Earth resources, the environment and related issues ensure that measurements made within their programmes are

in terms of well-characterized SI units so

that they are reliable in the long term, be

comparable world-wide and be linked to

other areas of science and technology

through the world’s measurement system

established and maintained under the

Convention du Metre” (CGPM, 1995).

For this report, the spatial scale of interest is generally global averages. This is not

to say that regional climate change is not

important. On the contrary, just as all politics is local, all climate changes are regional

(e.g., desertification, monsoonal changes,

ocean color (coral death), and snow/ice

cover (retreating snowlines and decreasing

sea ice cover/receding glaciers)). Since

trends in globally averaged data will generally be smaller than those of regional averages, meeting global average requirements

will insure meeting regional climate monitoring requirements. 

It should be pointed out that achieving

the instrument measurement requirements

does not guarantee determining the desired

long- term trends. Superimposed on these

trends is climatic noise - short-term climate

variations - that may mask the signal we are

trying to detect or reduce our confidence in

the derived trend.

II. Overarching Principles

The Workshop developed a set of basic

axioms or overarching principles that must

guide high quality climate observations in

general. The principles include many of the

10 climate observing principles outlined in

the NRC report on climate observing systems (NRC, 1999) and the additional principles for satellite-based climate observations

that were adopted by the Global Climate

Observing System (GCOS, 2003). But in

some cases they go beyond both of those

recommendations, especially relative to the

NOAA, NASA and NPOESS satellite systems. 

Adherence to these principles and implementation of the roadmap for calibration

improvements will ensure that satellite

observations are of sufficient accuracy and

stability not only to indicate any climate

change that has occurred, but also to prove

it beyond reasonable doubt and permit evaluation of climate forcing and feedbacks. 

These key climate observation principles

are given below. Some of these, while

specifically directed at NPOESS, a major

future contributor to the nation’s climate

monitoring program, are also applicable to

all satellite climate-monitoring systems.

SATELLITE SYSTEMS

• Establish clear agency responsibilities

for the U.S. space-based climate

observing system

• Acquire multiple independent spacebased measurements of key climate

variables

• Ensure that launch schedules reduce

risk of a gap in the time series to less

than 10% probability for each climate

variable

• Add highly accurate measurements of

spectrally resolved reflected solar and

thermal infrared radiation to NPOESS

Environmental Data Record (EDR) list

• Increase U.S. multi-agency and international cooperation to achieve a rigorous climate observing system 

4
CALIBRATION

• Elevate climate calibration requirements to critical importance in

NPOESS

• Develop characterization requirements

for all instruments and insure that

these are met

• Conduct pre-launch calibration round

robins (calibrations of different instruments using the same SI traceable

scale) for most NPOESS and

Geostationary Operational

Environmental Satellite - R (GOES-R)

instruments using NIST transfer

radiometers

• Simplify the design of climate

monitoring instruments

• Implement redundant calibration

systems

• Establish means to monitor the stability

of the sensors.

CLIMATE DATA RECORDS (CDRs)

• Define measurement requirements for

CDRs 

• Establish clear responsibility and

accountability for generation of climate

data records 

• Arrange for production and analysis of

each CDR independently by at least

two sources 

• Organize CDR science teams

• Develop archive requirements for

NPOESS CDRs. 

5

Operational environmental satellites can provide sustained long-term climate observations, but clear

agency responsibilities must be established

(Withee, Workshop Invited Presentation).
III. Required accuracies and stabilities for climate variables

The required accuracies and stabilities of

the climate variable data sets were established with consideration of changes in

important climate signals based on current

understanding and models of long-term

climate change. Such signals include:

• Climate changes or expected trends

predicted by models 

• Significant changes in climate forcing

or feedback variables (e.g., radiative

effects comparable to that of increasing

greenhouse gases)

• Trends similar to those observed in

past decades. 

The first step in the process is specifying

the anticipated signal in terms of expected

change per decade. The second step is

determining the accuracies and stabilities

needed in the data set to permit detection of

the signal. Excellent absolute accuracy in

the measurement of the climate variable is

vital for understanding climate processes

and changes. However, it is not as necessary for determining long-term changes or

trends as long as the data set has the

required stability. And, when it comes to

building satellite instruments, stability

appears to be less difficult to achieve than

accuracy. The difficulty arises because of

the many known and unknown systematic

uncertainties that are to be accounted for in

the calibration of the instrument on the

6

detecting 

change 

understanding 

processes 

understanding 

change 

Stability 

Uncertainty 

low 

high low high 

Desired characteristics of a climate observing 

system (After G. Stephens, 2003) 

Excellent absolute accuracy (a component of uncertainty, which also depends 

on precision, or random error) in the measurement of climate variables is vital 

for understanding climate processes and changes. However, it is not as 

necessary for determining long-term changes or trends as long as the data set 

has the required stability.
ground to establish its absolute accuracy

and transfer and monitor the calibration on

orbit. Stability, on the other hand, is the

measure of repeatability and reproducibility

of the metrological characteristics of the

instrument with time. Thus, a key attribute

for the climate data sets is long-term stability. The required stability is some fraction of

the expected signal, assumed to be 1/5 in

this report. If we cannot achieve the above

stability - for example, if we can only

achieve a stability of 0.5 of the signal -

there would be an increased uncertainty in

the determination of the decadal rate of

change.

The factor 1/5, or 20%, is somewhat

arbitrary. It should be periodically reevaluated. If the climate signal is one unit per

decade, a 20% stability would imply an

uncertainty range of 0.8 to 1.2, or a factor

1.5, in our estimate of the signal. One basis

for choosing such a factor is related to the

uncertainty in climate model predictions of

climate change. Thirty-five climate model

simulations yield a total range of 1.4 K to

5.8 K, or factor of about 4, in the change in

global temperature by 2100 (IPCC, 2001).

Thus, a stability of 20% should lead to a

considerable narrowing of the possible

climate model simulations of change.

Achieving the stability requirement does not

guarantee determining these long- term trends.

Superimposed on these trends is climatic

noise - short-term climate variations - that

may mask the signal we are trying to detect or

reduce our confidence in the derived trend.

7

The moon is a stable light source that can be used to monitor and correct for 

changes in satellite sensor stability (McClain, Workshop Invited Presentation).
Although excellent absolute accuracy is

not critical for trend detection, which was

the subject of the workshop, it is crucial for

understanding climate processes and

changes. Continuous efforts should be

undertaken to constantly improve the accuracy of satellite instruments.

Table 1 summarizes the required accuracies and stabilities of the data sets for the

solar irradiance, Earth radiation budget, and

cloud variables; the atmospheric variables;

and the surface variables. The table also

indicates which one of the above climate

signals - climate changes, climate forcings,

climate feedbacks, or trends similar to

recent trends - forms the basis for the

requirement.

IV. Translation of climate data set

accuracies and stabilities to

satellite instrument accuracies

and stabilities

The requirements for the data sets must

be translated into required accuracies and

stabilities of the satellite measurements. In

some cases, for example, solar irradiance

and top of the atmosphere Earth radiation

budget, there is a one to one correspondence. For other climate variables, this

translation is more complex. And for a few

of the variables, additional studies are needed to determine the mapping of data set

accuracies/stabilities into satellite accuracies/stabilities. 

Because of the difficulties in achieving

necessary accuracies (exo-atmospheric total

solar irradiance is one example, (Quinn and

Frohlich, 1999)), a key attribute for the

satellite instruments is long-term stability.

This may be achieved by either having an

extremely stable instrument or by monitoring the instrument’s stability, by various

methods, while it is in orbit. An ideal external calibration source is one that is nearly

constant in time and able to be viewed from

different orbit configurations. If there is

scientific evidence regarding the degree of

stability of such a source, and it is believed

to be at an acceptable level for long termclimate studies, then the stability of the

satellite sensor can be assessed independent

of other reference standards. With such

monitoring, instrument readings can be

corrected for lack of stability. However, this

brings up a measurement challenge for

establishing the degree of stability of the

external reference source. Obviously the

methods and instruments testing the stability

of those sources must have stability requirements far more stringent than given in this

report. One method that has been successfully implemented for the reflected solar

spectral interval is lunar observations, from

orbit, with the sensor. One example is the

ocean color satellite Sea-viewing Wide

Field-of-view Sensor (SeaWiFS), which

used lunar observations to correct for degradation in the near infrared channels (Kieffer

et al., 2003). The required lunar data are

being supplied by a dedicated ground based

facility (Anderson et. al., 1999) 

Since satellites and their instruments are

short-term - NPOESS satellites and

instruments have design lives of about

7 years - satellite programs launch replacement satellites to continue the observations.

Thus, the long-term data record for any climate variable will consist of contributions

from a series of satellite instruments, some

using different techniques. To assess the

reproducibility of the measurement results,

to assist in understanding the differences

that arise even with instruments of similar

design, and to create a seamless data record,

it is essential that the satellites be launched

on a schedule that includes an overlap

interval of the previous and the new

instrument. Acquiring multiple independent

8
space-based measurements of key climate

variables - one of the climate observing

principles listed above - would also help

insure maintenance of stability in the event

of a single instrument failure.

One proposed instrument that may have

very high accuracy and may not require

overlap periods is the proposed spectrally

resolved radiance spectrometer (Anderson

et al., 2003). Sequential flights of copies of

this instrument might maintain the climate

record without overlapping measurements. 

Table 2 summarizes the required accuracies and stabilities of the satellite instruments for solar irradiance, Earth radiation

budget, and cloud variables; the atmospheric variables; and the surface variables. The

table also indicates the types of satellite

instruments used for the measurements. 

V. Ability of current observing systems to meet requirements

Table 3 indicates the ability of current

satellite instruments to meet the requirements for accuracy and stability that are

spelled out in Table 2. Most current observing systems have not been designed to

measure the small changes over long time

periods that are of concern here. The

Clouds and the Earth’s Radiant Energy

System (CERES) instrument appears to be

meeting the accuracy requirements for

Earth radiation budget, but it has not been

in orbit long enough to determine whether it

is meeting the stability requirements.

Stability requirements are being met, or

appear to be close to being met (stabilities

labeled Yes?) for solar irradiance, cloud

cover, cloud temperature, cloud height,

atmospheric temperature, total column

water vapor, ozone, ocean color, snow

cover, and sea ice measurements. Seamless

long term data sets have been assembled for

many of these variables by stitching together

observations from successive satellites and

exploiting satellite overlap periods to

account for systematic differences between

successive instruments. However these have

been major efforts requiring a team of

9

Time series of climate variables have been constructed by stitching together observations 

from series of overlapping operational satellite observations (Defense Meteorological 

Satellite Program F (DMSP F) series and NOAA series). Among the problems: satellite 

drift causing a change in the local time of the observations during each satellite’s lifetime, 

especially for the NOAA satellites (Wentz, Workshop Invited Presentation).
researchers that includes calibration and

instrument experts and geophysicists to

carefully examine the satellite radiances, reevaluate the algorithms and consider the

validation data. In all cases, more than one

reprocessing was required. For most climate variables, current-observing systems

cannot meet both accuracies and stabilities.

In some cases, we don’t know whether current systems are adequate, and studies are

needed to answer the question.

This three part process of going from

requirements for climate variables to the

ability of current systems to meet these

requirements can be illustrated with the

case of sea surface temperature (SST).

Climate models predict an SST increase of

about 0.2 K/decade due to global warming

(see Section 3.3.2). The data set stability

required to detect this change is 1/5 of

0.2 K, or 0.04 K/decade. For infrared imager

observations, SSTs vary approximately as

2.5 x the difference in thermal infrared

brightness temperatures, which leads to a

required stability of about 0.01 K/decade in

brightness temperature (Section 4.3.2).

Currently, none of the available satellite

infrared imagers can meet this requirement

(Section 5.3.2)

10
Table 1. Required accuracies and stabilities for climate variable data sets. Column labeled

signal indicates the type of climate signal used to determine the measurement

requirements.

11

Signal Accuracy Stability (per decade)

SOLAR IRRADIANCE, EARTH RADIATION

BUDGET, AND CLOUD VARIABLES

Solar irradiance Forcing 1.5 W/m2 0.3 W/m2

Surface albedo Forcing 0.01 0.002

Downward longwave flux: Surface Feedback 1 W/m2 0.2 W/m2

Downward shortwave radiation: Surface Feedback 1 W/m2 0.3 W/m2

Net solar radiation: Top of atmosphere Feedback 1 W/m2 0.3 W/m2

Outgoing longwave radiation: Top of atmosphere Feedback 1 W/m2 0.2 W/m2

Cloud base height Feedback 0.5 km 0.1 km

Cloud cover (Fraction of sky covered) Feedback 0.01 0.003

Cloud particle size distribution Feedback TBD TBD

Cloud effective particle size Forcing: Water

Feedback: Ice

Water: 10%

Ice: 20%

Water: 2%

Ice: 4%

Cloud ice water path Feedback 25% 5%

Cloud liquid water path Feedback 0.025 mm 0.005 mm

Cloud optical thickness Feedback 10% 2%

Cloud top height Feedback 150 m 30 m

Cloud top pressure Feedback 15 hPa 3 hPa

Cloud top temperature Feedback 1 K/cloud emissivity 02. K/cloud emissivity

Spectrally resolved thermal radiance Forcing/climate change 0.1 K 0.04 K

ATMOSPHERIC VARIABLES

Temperature

Troposphere Climate change 0.5 K 0.04 K

Stratosphere Climate change 0.5 K 0.08 K

Water vapor Climate change 5% 0.26%

Ozone

Total column Expected trend 3% 0.2%

Stratosphere Expected trend 5% 0.6%

Troposphere Expected trend 10% 1%

Aerosols

Optical depth (troposphere/stratosphere) Forcing 0.01/0.01 0.005/0.005

Single scatter albedo (troposphere) Forcing 0.03 0.015

Effective radius (troposphere/stratosphere) Forcing greater of 0.1 or 10%/0.1 greater of 0.05 or 5%/0.05

Precipitation 0.125 mm/hr 0.003 mm/hr

Carbon dioxide Forcing/Sources-sinks 10 ppmv/10 ppmv 2.8 ppmv/1 ppmv
12

Table 1. (continued)

Signal Accuracy Stability (per decade)

SURFACE VARIABLES

Ocean color 5% 1%

Sea surface temperature Climate change 0.1 K 0.04 K

Sea ice area Forcing 5% 4%

Snow cover Forcing 5% 4%

Vegetation Past trend 3% 1%

Table 2. Required accuracies and stabilities of satellite instruments to meet requirements of 

Table 1. The instrument column indicates the type of instrument used to make the 

make the measurement.

Instrument Accuracy Stability (per decade)

SOLAR IRRADIANCE, EARTH RADIATION BUDGET, AND CLOUD VARIABLES

Solar irradiance Radiometer 1.5 W/m2 0.3 W/m2

Surface albedo VIS radiometer 5% 1%

Downward longwave flux: Surface IR spectrometer and

VIS/IR radiometer

See tropospheric temperature,

water vapor, cloud base height,

and cloud cover

See tropospheric temperature,

water vapor, cloud base

height, and cloud cover

Downward shortwave radiation:

Surface

Broad band solar and

VIS/IR radiometer

See net solar radiation: TOA,

cloud particle effective size,

cloud optical depth, cloud top

height, and water vapor

See net solar radiation: TOA,

cloud particle effective size,

cloud optical depth, cloud top

height, and water vapor

Net solar radiation: Top of atmosphere Broad band solar 1 W/m2 0.3 W/m2

Outgoing longwave radiation: Top of

atmosphere Broad band IR 1 W/m2 0.3 W/m2

Cloud base height VIS/IR radiometer 1 K 0.2 K

Cloud cover (Fraction of sky covered) VIS/IR radiometer See cloud optical thickness and

cloud to temperature

See cloud optical thickness and

cloud to temperature

Cloud particle size distribution VIS/IR radiometer TBD TBD

Cloud effective particle size VIS/IR radiometer 3.7 μm: Water, 5%; Ice, 10%

1.6 μm: Water, 2.5%; Ice, 5%

3.7 μm: Water, 1%; Ice, 2%

1.6 μm: Water, 0.5%; Ice, 1%

Cloud ice water path VIS/IR radiometer TBD TBD

Cloud liquid water path Microwave and VIS/IR

radiometer

Microwave: 0.3 K

VIS/IR: see cloud optical thickness and cloud top height

Microwave: 0.1 K

VIS/IR: see cloud optical thickness and cloud top height

Cloud optical thickness VIS radiometer 5% 1%

Cloud top height IR radiometer 1 K 0.2 K

Cloud top pressure IR radiometer 1 K 0.2 K

Cloud top temperature IR radiometer 1 K 0.2 K

Spectrally resolved thermal radiance IR spectroradiometer 0.1 K 0.04 K
13

Instrument Accuracy Stability (per decade)

ATMOSPHERIC VARIABLES 

Temperature

Troposphere MW or IR radiometer 0.5 K 0.04 K

Stratosphere MW or IR radiometer 1 K 0.08 K

Water vapor MW radiometer

IR radiometer

1.0 K

1.0 K

0.08 K

0.03 K

Ozone

Total column UV/VIS spectrometer 2% (λ independent),

1% (λ dependent) 0.2%

Stratosphere UV/VIS spectrometer 3% 0.6%

Troposphere UV/VIS spectrometer 3% 0.1%

Aerosols VIS polarimeter Radiometric: 3%

Polarimetric: 0.5%

Radiometric: 1.5%

Polarimetric: 0.25%

Precipitation MW radiometer 1.25 K 0.03 K

Carbon dioxide IR radiometer 3% Forcing: 1%;

Sources/sinks: 0.25%

SURFACE VARIABLES

Ocean color VIS radiometer 5% 1%

Sea surface temperature IR radiometer 0.1 K 0.01 K

MW radiometer 0.03 K 0.01 K

Sea ice area VIS radiometer 12% 10%

Snow cover VIS radiometer 12% 10%

Vegetation VIS radiometer 2% 0.8%

Table 3. Ability of current observing systems to meet accuracy and stability requirements.

Accuracy Stability

SOLAR IRRADIANCE, EARTH RADIATION BUDGET, AND CLOUD VARIABLES

Solar irradiance No Yes

Surface albedo Yes TBD

Downward longwave flux: Surface No No

Downward shortwave radiation: Surface No No

Net solar radiation: Top of atmosphere Yes Yes?

Outgoing longwave radiation: Top of atmosphere Yes Yes?

Cloud base height No No

Cloud cover (Fraction of sky covered) No Yes?

Cloud particle size distribution TBD TBD

Cloud effective particle size TBD TBD

Cloud ice water path No No
14

Accuracy Stability

Cloud liquid water path No No (except thicker

clouds over oceans)

Cloud optical thickness No TBD

Cloud top height No Yes?

Cloud top pressure No Yes?

Cloud top temperature No Yes?

Spectrally resolved thermal radiance No No

ATMOSPHERIC VARIABLES

Temperature

Troposphere Yes Yes? (Deep layer means)

Stratosphere Yes Yes? (Deep layer means)

Water vapor

Total column Yes Yes

Profile ? ?

Ozone

Total column No Yes?

Stratosphere No Yes?

Troposphere No No

Aerosols

Optical depth No No

Single scatter albedo No No

Effective radius No No

Precipitation No ?

Carbon dioxide ? ?

SURFACE VARIABLES

Ocean color Yes Yes?

Sea surface temperature No No

Sea ice area Yes Yes

Snow cover Yes Yes

Vegetation ? No
VI. Roadmap for future

improvements in satellite

instrument calibration and intercalibration to meet requirements

It is quite clear from the previous section

that we are currently unable to meet the

measurement requirements for most of the

climate variables. Each of the three workshop panels made recommendations for

improving satellite instrument characterization, calibration, inter-calibration, and associated activities, and these are summarized

here. Action on these recommendations and

on the overarching principles listed above

would permit us to detect climate change

signals at a much earlier stage than is possible now.

Solar Irradiance, Earth Radiation Budget,

And Cloud Variables

Solar irradiance

• Schedule a 1-year overlap in observations of both solar irradiance and

spectral solar irradiance

• Conduct two independent series of

observations to verify accuracy and

stability

Surface albedo

• Implement satellite observations of the

moon for monitoring visible/near

infrared instrument stability

• Maintain the same satellite orbits in

sequential missions

Downward longwave radiation and

downward short wave radiation at the

surface

• Perform studies to assess the sensitivity of downward longwave radiation to

boundary layer temperature and water

vapor changes, and downward short

wave radiation to cloud optical depth,

cloud particle size, and aerosol optical

depth

• Evaluate the capability of 4-D data

assimilation models to constrain

boundary layer temperature and

humidity, and active instruments, such

as Geoscience Laser Altimeter System

(GLAS), Cloudsat, and Cloud-Aerosol

Lidar and Infrared Pathfinder Satellite

Observations (Calipso), to constrain

cloud base for determination of downward longwave radiation

• Assimilate aerosol profile data from

active instruments, such as GLAS and

Calipso, into 4-D NWP models to constrain aerosol effects on downward

short wave radiation

• Expand the Baseline Surface Radiation

Network (BSRN) from the current 20

land sites, especially to ocean locations 

Net solar radiation and outgoing longwave

radiation at top of the atmosphere (Earth

radiation budget)

• Plan minimum satellite overlap periods

of three months for net solar radiation

and one month for outgoing longwave

radiation

• Fully characterize NPOESS Earth radiation budget detectors (Total and Short

Wave channels) for stability with solar

exposure as well as time in vacuum

• Conduct a 2nd set of Earth radiation

budget observations independent of

NPOESS Earth radiation budget measurements. One possibility is full broadband spectrometers for observations of

Earth reflected solar radiation and

Outgoing Longwave Radiation (OLR)

• Enhance NIST spectral sources and

transfer radiometers to cover the full

reflected solar and emitted thermal IR

spectra of the Earth 

15
Cloud base height

• Pursue the development and application of active instruments such as

satellite lidar and cloud radar appear to

be the only methods currently capable

of meeting the cloud base height

requirements

Cloud cover, cloud particle size distribution,

cloud effective particle size, cloud ice water

path, and cloud liquid water path

• Perform additional studies to translate

cloud data set requirements into instrument accuracy/stability requirements

• Verify Moderate Resolution Imaging

Spectroradiometer (MODIS) cloud

measurements against GLAS, Calipso

and Cloudsat observations

• Evaluate NIST standards at 1.6 μm,

2.1 μm, and 3.7 μm to determine if

improvements are needed to meet

accuracy/stability requirements for

cloud effective particle size

• Assess the various instrumental

approaches - VIS/IR, microwave and

active systems - to meet cloud requirements

• Implement multiple calibration references - lunar measurements, calibration lamps, and solar diffusers - for

monitoring on-orbit stability of VIS

radiometers 

Cloud top height, cloud top pressure, and

cloud top temperature

• Insure sufficient overlap to meet

0.2 K/decade stability requirement

• Verify zero radiance levels for IR

radiometers using deep space scanning

• Develop on-board black body radiation

sources whose temperature can be varied over a controlled range

Spectrally resolved outgoing longwave

radiation

• Establish a spectrally resolved absolute

IR radiance scale by laboratory comparisons of “source-based” radiance

scales (the SI traceable standard is a

blackbody source) and “detectorbased” radiance scales (the SI traceable standard is the cryogenic

radiometer that measures optical

power in terms of electrical power in

Watts) 

• Conduct similar measurements independently with instruments that use

different technologies

Atmospheric Variables

Atmospheric temperature

• Plan for satellite overlap periods of

(optimally) one year

Microwave instruments

• Characterize more accurately the nonlinear response of microwave radiometers by pre-launch measurements

• Maintain on-orbit temperature differences across the black body target to

less than or equal to 0.1 K 

• Reduce effects of extraneous

microwave radiation reaching the

detector by performing more accurate

pre-launch measurements of feedhorn

spillover off the antennas and calibration targets

• Maintain spatial and temporal

temperature changes of radiometer

sub-components to less than 0.3 K

• Determine earth incidence angle of

observation to accuracy of 0.3 degrees

Infrared instruments

• Perform careful laboratory measurements of spectral response functions

16
and develop filters that remain stable

in space

• Calibrate laboratory blackbody target

radiances with the NIST portable calibrated radiometer, the Thermal

Transfer Radiometer (TXR)

• Minimize scattered radiation from

solar heated components of the IR

sounder and thermal gradients within

the Internal Calibration Target (ICT) to

increase accuracy of on-orbit radiances

of the ICT

• Accurately characterize in the laboratory non-linearities of instrument

response as functions of instrument

and scene temperatures

• Avoid scan angle effects on instrument

throughput by intelligent instrument

design and/or on-orbit processing

Water vapor

• Microwave radiometer issues for water

vapor are not as stringent as for temperature, but the recommendations

above carry through for water vapor

• IR instrument recommendations for

temperature carry through for water

vapor

Ozone

• Improve the consistency of pre-flight

calibrations of all UV/VIS ozone

instruments and employ standard and

well documented procedures

• Increase the accuracy of pre-flight calibration of albedo (radiance/irradiance)

measurements of UV/VIS ozone

instruments

• Improve pre-flight characterization of

wavelength scales, bandpasses, fields

of view uniformity, non-linearity of

responses, out-of band and out-of-field

stray light contributions, imaging and

ghosting, and diffuser goniometry

• Add zenith sky viewing to pre-launch

instrument testing 

• Calibrate and characterize new instruments (those with advanced technologies such as Ozone Mapping Profile

Suite (OMPS)) more fully in laboratory vacuum, including the temperature

sensitivity of wavelength and radiometric stability, and instrument

response to different ozone amounts

• Develop methods to validate satellite

measured radiances using ground

based measurements

Aerosols

• Aerosol optical depth measurements

are derived from solar spectral

reflectance observations - thus, recommendations concerning VIS/NIR

instruments listed above are applicable

• Develop methods for accurate preflight laboratory calibration and

characterization of polarimetric

instruments

• Develop methods for on-orbit

calibration of polarimeters

Precipitation

• Precipitation measurements are

derived from microwave radiometer

observations - thus, recommendations

concerning microwave radiometers

listed above are applicable

Carbon dioxide

• Assess the capability of hyperspectral

IR instruments such as Atmospheric

Infrared Sounder (AIRS) to detect CO2

variations

• Implement an extensive validation program, including airborne, tall tower,

and ground based Fourier Transform

InfraRed (FTIR) spectrometric measurements to fully characterize spatial

17
and temporal biases in satellite CO2

measurements

• Report and fully document error

characteristics of satellite CO2 measurements to facilitate effective data

assimilation techniques

• Develop new active techniques (e.g.,

lidar) to measure CO2 in the

atmosphere.

Surface Variables

The surface measurements are derived

from VIS/IR and microwave radiometers -

thus, recommendations concerning VIS/NIR

and microwave radiometers listed above are

applicable. In addition, the following recommendations apply to individual surface

variables:

Sea Surface Temperature (SST)

• Characterize more definitively the

accuracy of satellite SST measurements by initiating an on-going

validation program using radiometric

measurements of ocean skin

temperature from ships and other

platforms as ground truth

Ocean color

• Increase confidence in ocean color

measurements by expanding the

Marine Optical Buoy (MOBY) type

surface validation program to more

ocean sites

Normalized Difference Vegetation Index

(NDVI)

• Explore the validation of satellite

based observations of surface

Normalized Difference Vegetation

Index (NDVI) by ground based

observations of NDVI using VIS/IR

instruments similar to the satellite

instruments

It is recommended that a follow-up

workshop be conducted to discuss implementation of the above roadmap developed

at this workshop.

18
1. Background, Goal, and Scope

Is the Earth’s climate changing? If so, at

what rate? Are the causes natural or humaninduced? What will the climate be like in

the future? These are critical science and

geopolitical issues of our times. Increased

knowledge, in the form of answers to these

questions, is the foundation for developing

appropriate response strategies to global climate change. Accurate global observations

from space are a critical part of the needed

knowledge base.

Observing the small signals of long-term

global climate change places enormous

stress on satellite observing systems. Global

temperature changes of tenths of a degree

Centigrade per decade, ozone changes of

1%/decade, and solar irradiance variations

of 0.1%/decade are typical of the kinds of

signals that must be extracted from noisy

time series. Measuring these signals will

require much improved calibration of satellite instruments, and inter-calibration of

similar instruments flying on different satellites. Ability to observe these small signals

of decadal scale climate change will also

give us the capability of measuring the larger signals associated with shorter-term climatic variations, such as those associated

with El Nino.

This report has a single clearly defined

ultimate goal: 

• Recommend directions for future

improvements in satellite instrument

characterization, calibration, inter-calibration, and associated activities, to

enable measurements of global climate

change that are valid beyond reasonable doubt.

This report summarizes the requirements

and general directions for improvements;

future meetings should be planned to

address the specific instrument calibration

issues associated with the requirements.

Although some of the recommendations are

directed at the NPOESS program, the

nation’s converged future civilian and military polar environmental satellite system,

they also apply to space-based climate

change observations in general.

To achieve its goal, the report first:

• Defines the required absolute accuracies and long-term stabilities of global

climate data sets

• Translates the data set accuracies and

stabilities to required satellite instrument accuracies and stabilities, and

• Evaluates the ability of current observing systems to meet these requirements 

The report focuses on passive satellite

sensors that make observations in spectral

bands ranging from the ultraviolet to the

microwave. The climate change variables

of interest include:

• Solar irradiance, Earth radiation budget,

and clouds (total solar irradiance, spectral solar irradiance, outgoing longwave

radiation, net incoming solar radiation,

cloudiness) 

• Atmospheric variables (temperature,

water vapor, ozone, aerosols, precipitation, and carbon dioxide), and

• Surface variables (vegetation, snow

cover, sea ice, sea surface temperature,

and ocean color)

This list is not exhaustive. The variables

were selected on the basis of the following

criteria: 1) importance to decadal scale

climate change, 2) availability of satellitebased climate data records, and 3) measurability from passive satellite sensors.

The report is based on a workshop held

at the University of Maryland Inn and

Conference Center, College Park, MD,

19
20

November 12-14, 2002. NIST, NPOESSIPO, NOAA, and NASA organized the

workshop; the NPOESS-IPO and NIST provided financial support. Some 75 scientists,

including researchers who develop and analyze long-term data sets from satellites,

experts in the field of satellite instrument

calibration, and physicists working on state

of the art calibration sources and standards,

participated in the workshop. 

The workshop agenda included a series

of invited lectures followed by panel sessions. Keynote speakers Richard Goody,

Professor Emeritus, Harvard University, and

Tom Karl, Director, National Climatic Data

Center, NOAA, led off the workshop with

discussions of Issues with Space Radiance

Monitoring, and Improving the Climate

Contribution of Operational Satellites: A

Data Perspective, respectively. Steve

Mango, NPOESS-IPO, presented an

overview of NPOESS/NPOESS Preparatory

Program (NPP) Status/Plans

Calibration/Validation. Viewpoints of two

of the organizing agencies were contained

in papers by Greg Withee, NOAA Assistant

“those responsible for studies of Earth resources, the environment and related issues

[should] ensure that measurements made within their programmes are in terms of

well-characterized SI units so that they are reliable in the long term, be comparable worldwide and be linked to other areas of science and technology through the world’s

measurement system established and maintained under the Convention du Metre”

(CGPM, 1995) ( Semerjian, Workshop Invited Presentation).
21

Administrator for Satellite and Information

Services (presented by Tom Karl) on the

NOAA Perspective on a Global Observation

System, and Hratch Semerjian, Director,

Chemical Science and Technology

Laboratory, NIST, on NIST Activities related to Global Climate Change. Invited

speakers discussed current knowledge of

long term variations of each climate variable, data set accuracy and stability needed

to measure long term changes in the variable, translation of these requirements into

accuracies and stabilities for satellite instruments, current state of the art of satellite

instruments, and required improvements in

instrument characterization, calibration,

intercalibration, and associated activities.

The invited presentations, a rich resource,

are on the NIST web site,

http://physics.nist.gov/Divisions/Div844/glo

bal/mgcc.html. (Please Note: To access this

site, you have to input user name: mgccoutline, and password: div844mgcc)

Following the invited presentations,

three panels met in parallel sessions: 

• Solar irradiance, Earth radiation budget, and clouds. Chair: Bruce Wielicki,

Scribe: Marty Mlynczac 

• Atmospheric variables. Chair: Roy

Spencer, Scribe: Gerald Fraser 

• Surface Variables. Chair: Bill Emery,

Scribe: Dan Tarpley

Each panel included experts on climate

data sets and satellite instrument calibration issues. Panels discussed workshop

issues, drafted material for a workshop

report, and reported to plenary sessions.

After the workshop, panel leaders prepared

draft chapters for the workshop report.

The Workshop agenda and list of participants are included in Appendices A and B,

respectively. 

While there have been a number of

previous reports that have also discussed

accuracy and stability measurement requirements for long term climate data sets (for

example, Hansen et al., 1993; Jacobowitz,

1997; NPOESS, 2001) and calibration

issues (Guenther et al., 1997; NRC, 2000;

NRC, 2001b), this report not only provides

the latest thinking on measurement requirements but also provides general directions

to improve satellite instrument characterization, calibration, vicarious calibration,

inter-instrument calibration, and associated

activities to meet the requirements. This

general roadmap provides guidance to the

national agencies concerned with the development of the space system and associated

satellite instrument calibration program to

measure global climate change: NPOESSIPO, NOAA, NIST, and NASA. 

Measuring small changes over extended

time periods necessarily involves the concepts of accuracy and stability of time

series. Accuracy is defined as the “closeness

of the agreement between the result of the

measurement and the true value of the measurand” (ISO, 1993). It may be thought of as

the closeness to the truth and is measured by

the bias or systematic error of the data, that

is, the difference between the short-term

average measured value of a variable and the

truth. The short- term average is the average

of a sufficient number of successive measurements of the variable under identical

conditions such that the random error is

negligible relative to the systematic error.

Stability may be thought of as the extent to

which the accuracy remains constant with

time. Stability is measured by the maximum

excursion of the short- term average measured value of a variable under identical

conditions over a decade. The smaller the

maximum excursion, the greater the stability

of the data set.
22

It is to be understood that the methods to

establish the true value of a variable (the

measurand) should be consistent with the

internationally adopted methods and standards, thus establishing System of Units

(SI) traceability (BIPM, 1998, NIST, 1995).

According to the resolution adopted by the

20th Conference Generale des Poids et

Measures (CGPM) - the international

standards body in Paris - “that those

responsible for studies of Earth resources,

the environment and related issues ensure

that measurements made within their programmes are in terms of well-characterized

SI units so that they are reliable in the long

term, be comparable world-wide and be

linked to other areas of science and technology through the world’s measurement system established and maintained under the

Convention du Metre” (CGPM, 1995).

For this report, the spatial scale of interest is generally global averages. This is not

to say that regional climate change is not

important. On the contrary, just as all politics is local, all climate changes are regional

(e.g., desertification, monsoonal changes,

ocean color (coral death), and snow/ice

cover (retreating snowlines and decreasing

sea ice cover/receding glaciers)). Since

trends in globally averaged data will

generally be smaller than those of regional

averages, meeting global average requirements will insure meeting regional climate

monitoring requirements. 

It should be pointed out that achieving

the instrument measurement requirements

does not guarantee determining the desired

long-term trends. Superimposed on these

trends is climatic noise - short-term climate

variations - that may mask the signal we are

trying to detect or reduce our confidence in

the derived trend.

The remainder of the report is structured

as follows:

Section 2 presents overarching principles

that must guide high quality satellite climate observations in general. Adherence to

these principles and implementation of the

roadmap for calibration improvements will

ensure that satellite observations are of sufficient accuracy and stability not only to

indicate any climate change that has

occurred, but also to prove it beyond reasonable doubt and permit evaluation of climate forcing and feedbacks. 

Section 3 develops the requirements for

accuracy and stability of the individual climate variables. Various rationales are used

to determine these requirements including

ability to measure:

• Climate changes or expected trends

predicted by models

• Significant changes in climate forcing

or feedback variables (e.g., radiative

effects comparable to that of increasing greenhouse gases) 

• Trends similar to those observed in

past decades 
23

The values for stability are given per

decade. The required accuracies and longterm stabilities in the NPOESS IORD II

(NPOESS, 2001) were a resource for the

workshop panels.

Section 4 discusses the satellite instrument accuracy and stability requirements

for meeting the data set requirements of

section 3. For top of the atmosphere radiation budget variables and for variables that

are linearly related to the satellite measurements, there is a one to one correspondence

with the data set requirements. For variables

that are related to the satellite measurements in a non-linear way, translation of

data set requirements into satellite instrument requirements is more complex.

Section 5 reviews the ability of current

observing systems to meet the instrument

requirements of section 4.

Based on the instrument requirements of

section 4 and the current state of the art in

section 5, section 6 presents recommendations, or a roadmap, for future improvements in satellite instrument characterization, calibration, inter-calibration, and associated activities to meet the requirements.

Almost all of the illustrations in the

report are relevant figures from the workshop’s invited presentations. 

Anthropogenic and Natural Forcings

Significant changes in climate forcing or feedback of a variable (comparable to that of 

greenhouse gases) is one criterion for determining measurement requirements (Cairns,

Workshop Invited Presentation).
24

2. Overarching Principles

The Workshop developed a set of basic

axioms or overarching principles that must

guide high quality climate observations in

general. The principles include many of the

10 climate observing principles outlined in

the NRC report on climate observing systems (NRC, 1999) and the additional principles for satellite-based climate observations

that were adopted by the Global Climate

Observing System (GCOS, 2003). But in

some cases they go beyond both of those

recommendations, especially relative to the

NOAA, NASA and NPOESS satellite systems. 

Adherence to these principles and implementation of the roadmap for calibration

improvements will ensure that satellite

observations are of sufficient accuracy and

stability not only to indicate that climate

change has occurred, but also to prove it

beyond reasonable doubt and permit evaluation of climate forcing and feedbacks. 

These key climate observation principles

are given below. Some of these, while

specifically directed at NPOESS, a major

future contributor to the nation’s climate

monitoring program, are also applicable to

all satellite climate-monitoring systems.

SATELLITE SYSTEMS

1. Establish clear agency responsibilities for the U.S. space-based climate observing system. A major

challenge to achieving a climate

observing system is the current diffusion of responsibility across many

agencies in the U.S. No single

agency has the responsibility, fundThe workshop’s overarching principles include many of the climate monitoring

principles in NRC (1999) and GCOS (2003), but in some cases go beyond these

(Karl, Workshop Invited Presentation). 
ing, and full accountability for success in the climate change “mission.” This leads to great difficulties

in an observing system required to

be diverse and yet accurate and

complete enough to cover oceans,

land, biosphere, cryosphere and

atmosphere. At this point we have

to conclude that a rigorous climate

observing system is not yet in place,

nor is a plan in place to create one

with a high confidence of success.

The current climate observing system is an informal arrangement of

research (e.g. NASA Earth

Observing System (EOS) and operational satellites (e.g. NOAA polar

orbiters) managed by U.S. and

international agencies. It has been

“collected” more than “designed”.

It has a high risk of critical data

gaps and calibration shortcomings

that will seriously degrade the

confidence with which climate

assessments can be made. Clear

agency responsibilities must be

established to insure the success of

the national climate change mission.

2. Acquire independent space-based

measurements of key climate variables. Independent instrument measurements from space of each key climate variable are required to verify

accuracy. This requirement is based

on the experience of NIST and other

national standards laboratories.

Extensive theoretical and laboratory

work is done to establish the uncertainty levels of NIST calibration

standards. But when multiple

nations compare their standards,

usually the differences exceed the

predicted uncertainty. This is a fundamental lesson for climate data,

which, like NIST standards, pushes

the capability of instrument calibration. When climate change surprises

are observed with one instrument,

confidence is increased dramatically

if the signal can be confirmed with

an independent measurement. This

is basic scientific practice. The

measurements should be from different technological approaches. Some

examples already exist: SST from

satellite passive infrared,

microwave, and in-situ buoys.

Surface wind speed from satellite

scatterometer, passive microwave,

and in-situ buoys. Air temperature

from satellite passive microwave

and infrared. But many climate

parameters do not currently have

independent observation approaches.

Cloud amount and layering should

be measured both by active lidar and

radar as well as passive imagers.

Radiation budget should be measured both by simple broadband

radiometers as well as high spectral

resolution spectrometers that cover

the entire (at least 99%) spectrum of

earth emitted and reflected radiation.

Current infrared spectrometers

observe less than 50% of the emitted

radiation. 

3. Ensure that launch schedules

reduce risk of a gap in the time

series to less than 10% for each

climate variable. Most climate

measurements require overlapping

(in time) observations to assure the

calibration record at climate accuracy. This is an especially difficult

requirement since it requires intercalibration of two instruments

before the old instrument fails. In

general it implies the need for hot

25
26

spares in orbit. The current NASA

and NPOESS plans do not include

hot spares. As a minimum, a risk

analysis for instrument and spacecraft failure with time should be

completed to ensure that launch

schedules reduce gap risk to under

10% for each climate variable.

Launch on failure, as currently

planned by NPOESS will assure

unacceptable gaps and insufficient

overlap of climate records from

space-based observations. There are

also likely gaps between the end of

NASA’s responsibilities for climate

variables and the beginning of the

NPOESS measurements. Two

examples are solar irradiance and

radiation budget. NASA radiation

budget data from CERES ends nominally in 2008, while the NPOESS

follow-on ERB instrument begins in

2011. The risk of a gap is currently

estimated at 50%, too high for a climate observing system. We recommend that the solar radiation and

radiation budget gaps be addressed

using the NPP mission planned for

flight in 2006, or by flying small

spacecraft in appropriate orbits.

4. Add highly accurate measurements of spectrally resolved

reflected solar and thermal

infrared radiation to NPOESS

EDR list. Some key climate variThe NPOESS Preparatory Program (NPP) and NPOESS programs will provide 

climate observations from 2006 – 2025 (Mango, Workshop Invited Presentation). 
27

ables are missing from the EDR list.

While beyond the scope of this

workshop to do a comprehensive

list, two examples are given. First,

highly accurate and high spectral

resolution (sometimes referred to as

hyperspectral) radiances that cover

the entire solar and thermal infrared

spectrum of earth reflected and emitted radiation. Such radiances would

be a data source independent of the

broadband radiation data represented

by CERES and Earth Radiation

Budget Experiment (ERBE). They

would likely use coarser spatial resolution (50 km to 100 km) and limited angle sampling (nadir or a few

fixed viewing zenith angles) in order

to achieve high spectral resolution

with high accuracy linear detectors.

In the infrared, such radiances

would also represent independent

confirmation of the temperature and

humidity profile data extracted from

the global imaging spectrometers

such as Cross Track Infrared

Sounder (CrIS). If placed in precessing orbits, they could also

achieve intercalibration with all

other solar and thermal infrared passive sensors, including the ability to

match any spectral response function and to enable orbit-crossing

intercalibration over a complete

range of latitudes from equator to

polar regions. A second example of

a missing CDR is cloud emissivity

in the major infrared window from

8μm to 12μm. Spectrally resolved

thermal radiation from the climate

system is an important and versatile

climate variable that can be very

accurately observed from space

(Goody and Haskins 1998). This

infrared radiance records both the

radiative forcing of the atmosphere

resulting from greenhouse gas emissions and aerosols and the resulting

response caused by the adjustment

of the atmosphere to this radiative

forcing. The Intergovernmental

Panel for Climate Change (IPCC)

predicts increases in greenhouse gas

concentrations and changes in

atmospheric aerosols, which will

manifest themselves as a significant

reorganization of the spectral distribution of outgoing longwave radiation (OLR). The different predictions of future temperature, water

vapor, and cloud amount forecasted

by different climate models will also

cause dramatic differences in the

spectral characteristics of the OLR.

Diagnostic signatures that can

decide issues of model performance

and eliminate competing scenarios

of climate change can be revealed

from the spectrum of accurately

observed OLR. The information provided by spectral resolution allows

us to study individual forcings and

their responses, including those in

cloud formation, which give rise to

much of the variation in model forecasts of future climate.

5. Increase U.S. multi-agency and

international cooperation to

achieve a rigorous climate observing system. This report effectively

focuses concern on the U.S. ability

to produce the CDRs required for a

successful climate research program.

Currently, this situation is symptomatic of a climate research effort

that is doing the best it can with limited resources. Many risky tradeoffs

are justified not by climate requirements but by resource and time limi-
28

tations. As climate change is likely

to continue, there may come a time

when the U.S. and/or the international community decide to attack

climate in an “Apollo”-like mission

mode where requirements drive the

process. It is instructive to imagine

what could be quickly improved or

changed in this scenario.

Computational capacity for modeling could be increased 100 fold in a

few years by purchasing additional

capacity. Additional scientific

expertise would require longer to

transition from other fields: perhaps

5 years. But achieving the required

highly accurate decadal time series

of climate data would take much

longer: 5 years for a crash observation system construction, and another 20 years to collect its first 2

decades of data. This suggests that

the calibration discussions in this

report should be considered very

carefully and given a high priority to

drive improvements in the next

decade of observations from NASA,

NOAA, and NPOESS space-based

systems. It also suggests that

increased U.S. multi-agency as well

as international cooperation through

Committee on Earth Observation

Satellites (CEOS), Integrated Earth

Observing Strategy (IGOS), and the

10-year program adopted at the

Earth Observations Summit in

Washington in July 2003 will be

required to bring the resources to

bear to achieve a rigorous climate

observing system with a high probability of success. Interagency and

international cooperation should

extend across satellite missions,

instruments, instrument calibration,

CDR production, CDR validation,

and archive and distribution. 

CALIBRATION

1. Elevate climate calibration

requirements to critical importance in NPOESS. Calibration must

be done to absolute international

standards similar to NIST standards

both prelaunch and postlaunch.

Also, adopt current international

protocols, definitions, guidelines,

and principles in metrology, including uncertainty assessments for calibration. Calibration must be of such

a high priority that it is capable of

driving instrument cost and

schedule. Currently, instrument calibration and characterization are done

at the end of instrument build when

schedule and budget pressure to finish is very high. The calibration

objective must be high enough priority to drive this final stage. It

typically is not, and many corners

are cut at the end of instrument build

and calibration. This includes solving instrument problems that first

appear during calibration. This will

be a particular challenge for the

NPOESS satellite system. NPOESS

will fly a suite of new sensors that,

while they all have considerable heritage, will require careful pre- and

post-launch calibration/validation.

Painful experience has taught us that

careful in-lab calibration of the sensor avoids many problems that come

up after the launch and on-orbit

operation. Another important lesson

learned is that independent “vicarious” calibration/validation is something that can’t be done only once

early in the life of the new sensor

but must continue periodically

throughout the life of the sensor.

Only in this way can we obtain an

independent estimate of the drift of
29

the instrument over time. Climate

calibration is not as high a priority

as the weather observation mission,

and so given schedule and budget

pressures that are already appearing,

climate quality calibration and,

hence, the credibility of long-term

CDRs, are at very high risk. It is

also important to establish the

veracity of ancillary data (aerosol

networks, radiosondes, etc.) and

measurement systems for vicarious

calibration. In essence, the foundation of CDRs is a three-legged stool:

pre-launch calibration, post-launch

instrument calibration/corrections,

and use of correlative data for validation of the CDRs.

2. Develop characterization requirements for all instruments and

insure that these are met.

Instrument characterization remains

basic to calibration and to the quality of the climate data records. At a

minimum, instrument artifacts in the

data sets, such as residual striping,

banding, or scattered light in the

images, detract from the users’ confidence in the overall quality of the

measurements - even if these artifacts are within the accuracy specifications for the instruments. More

fundamentally, instrument artifacts

may conceal important geophysical

changes or may be misinterpreted as

geophysical properties, themselves.

Satellite instrument calibration begins in the laboratory, continues on-orbit, and includes 

vicarious calibration (McClain, Workshop Invited Presentation).
30

Thus, there is the requirement for

insight into the characterization plan

and reviews of the characterization

data while the instruments are in the

lab to ensure the adequacy of the climate data sets. Once the instruments are out of the lab and on

orbit, the characterization of instrument parameters, such as polarization for example, can be difficult

and expensive, if possible at all.

During the instrument fabrication,

the insight should come from a real

time parallel or collaborative analysis with data provided to a government maintained cal/val archive.

(This is the basic procedure planned

for the government’s procurement of

data for the Landsat Data Continuity

Mission (LDCM)).

3. Conduct and verify prelaunch calibration of NPOESS and GOES-R

instruments using NIST transfer

radiometers. Pre-launch calibration

involving most NPOESS and GOES

R instruments should be conducted

using NIST transfer radiometers,

when available, for appropriate

spectral wavelength ranges and at

climate relevant accuracies. If transfer radiometers are not available,

conduct and verify the accuracy of

the pre-launch radiometric calibration and the adequacy of the characterization of flight sensors by direct

measurement and in conjunction

with available SI traceable transfer

standards from national measurement institutes such as NIST for the

U.S.

Assessing the accuracy of standards for satellite instrument calibration is an 

important component of an overall calibration program

(Johnson, Workshop Invited Presentation). 

• Spectral range: 250 nm to 2500 nm 

• Protocol: Assess accuracy of user 

calibration of working standard 

radiance sources using calibrated 

transfer radiometers (blind study) 

• Key Participants: NIST, University 

of Alabama, NASA 

• Comparisons held: Multiple, since 

1993 

• Characterizations: spatial and 

angular uniformity, temporal 

stability, repeatability 

• Typical agreement: ~3% (visible), 

4% to 10% (near infrared) 

Spectral Radiance Comparisons 
4. Simplify the design of climate

monitoring instruments.

Instruments designed for climate

monitoring should be simple to calibrate and maintain calibration in

orbit. Other objectives such as high

spatial resolution may need to be

sacrificed to attain this goal.

5. Implement redundant calibration

systems. Redundant calibration

systems are critical: both pre-launch

laboratory calibration as well as

post-launch on-orbit calibration.

Such systems allow much more

rigorous estimates of calibration

uncertainty. Redundancy can take

the form of independent on-board

systems, intercalibration of similar

instruments on different satellites,

and vicarious calibration against

lunar or earth targets.

6. Establish means to monitor the

stability of the sensors. As the stability of the sensors is an essential

requirement, their pre-flight stability

is to be monitored during the time

interval between pre-launch calibration and launch. Also, where possible, stable extra-terrestrial sources

proven for their stability (sun, moon,

stars) are to be incorporated for

studies of in-flight sensor degradation and sensor inter-comparison.

CLIMATE DATA RECORDS (CDRs) 

1. Define requirements for CDRs. In

NPOESS nomenclature, EDRs

(Environmental Data Records) are

designed for use at short time/space

scales for applications such as

weather forecasting. CDRs will typically have different requirements

than EDRs, with more stringent calibration accuracy, stability, and

requirements for overlapping

records. Current NPOESS EDR

specifications have tried to add stability requirements for many EDRs

that are meant to respond to CDR

concerns. This is an improvement

but does not fully reach climate

requirements in many cases. This

workshop report attempts to clarify

these problems, where appropriate,

for each NPOESS EDR. Both EDR

and CDR requirements need definition. Clear priorities cannot be

assigned when they are mixed in one

set of requirements as in the current

EDRs. We recommend that CDRs

be generated for all key climate variables that can be measured from

space. It is unlikely that the current

NPOESS EDR data products will be

sufficiently accurate for climate use.

There are two primary reasons for

this assessment. First, EDRs are

designed primarily for weather forecasting, so that data products must

be produced within a few hours of a

measurement taken by satellite.

CDRs can lag observations by

months without serious impact on

long-term climate research. Second,

the weather accuracy requirements

are typically easier to meet than the

more stringent climate accuracy (e.g.

1 K instantaneous temperatures

versus 0.1 K time averaged

temperature). This mismatch of

space/time/accuracy for weather and

climate data products will naturally

lead to simpler and faster analysis

algorithms for EDRs than for CDRs.

CDRs will be required to verify calibration stability and accuracy over

many months of analysis, and will

commonly require reprocessing to

31
remove small artifacts that are not

an issue for weather applications but

are critical to climate use. The optimal CDR data products have historically lagged spacecraft launch by 3

to 4 years. Steps should be taken to

assure that the operational products

will blend seamlessly with EOS and

other mission sensors. EDRs can

potentially meet CDR objectives

through improved algorithms, careful analysis by potential users, and

rigorous use of validation data. The

NPOESS Preparatory Project (NPP)

will be evaluating the use of EDRs,

or enhanced EDRs, as CDRs suitable for climate research.

2. Establish clear responsibility and

accountability for generation of

climate data records. Clear responsibility and accountability must be

established for each climate variable

as a function of time. This has not

yet been achieved for the full range

of climate variables either nationally

or internationally. This is particularly a challenge for the multi-agency

nature of satellite climate data sets,

with NASA, NOAA, and DoD all

playing major roles. 

3. Arrange for production and

analysis of each CDR independently by at least two sources. Each

CDR should be analyzed and produced by at least two independent

sources. Not only instruments, but

also analysis algorithms and code

require validation and independent

confirmation. Scientific remote

sensing algorithms and supporting

code to produce climate quality data

sets can vary from 10,000 to

500,000 lines of code. For large

code developments the question is

not whether code errors exist, but

rather how many. The most robust

method to discover and eliminate

both algorithm and coding problems

in a rigorous fashion is independent

algorithms and coding. Climate signals are often subtle and require

exceptional efforts to attain a high

degree of confidence in results. A

recent example is the difference in

Microwave Sounding Unit (MSU)

analysis results among different

research groups.

4. Organize CDR science teams.

Given the differences in

time/space/accuracy of EDR and

CDR data products there will be a

requirement to organize CDR Science

Teams whose purpose will be to oversee, develop, validate, and carry out

the production of CDRs at climate

accuracy. They will most likely be

required to return to level 0 raw

instrument data and re-calibration to

assure climate accuracy of the products. The algorithms must also

focus on physically based algorithms that will likely require more

processing time than the EDR analysis algorithms. Finally they will

have to account for diurnal cycles to

enable daily mean and monthly

mean data products merging data

from multiple satellites and instruments. Experience with past satellite climate data products indicates

that the CDR Science Teams will

require extensive participation of

climate data users (e.g. climate

modelers) as well as algorithm and

instrument science specialists.

Typically, these teams would be

some combination of agency and

32
33

university scientists. These teams

should be started prior to launch

with sufficient input to assure adequate instrument calibration and

characterization are done pre-launch.

The teams must be able to review

instrument progress as well as to

affect schedules and costs if climate

accuracy is to be obtained. Post

launch team activities would focus

on validation and algorithm

improvement. NPOESS currently

has Operational Algorithm Teams

(OATS) to carry out a review function for EDRs, but no equivalent for

CDRs. There is no current plan to

form CDR teams, or to produce/validate/archive CDR NPOESS data

products. However, two recent initiatives are dealing with this issue.

NOAA, with the assistance of the

National Academy of

Sciences/National Research Council,

is developing a plan for generating

CDRs from operational satellite

observations. This plan will include

recommendations on science teams.

NASA has formed a science team

for NPP to assess the utility of the

EDRs for use as CDRs and to determine additional work that may need

to be done. It is clear that one of the

early functions of the multi-agency

Climate Change Science Program

(CCSP) effort should be to build

upon these initiatives

5. Develop archive requirements for

NPOESS CDRs. CDRs that achieve

validated and science-ready state

will require permanent archive, even

when they are superceded by

improved versions. This is needed

to enable rigorous scientific comparison of results and conclusions in the

published scientific literature over

the decadal time scale of climate

research. While it might seem sufficient to archive the computer code

for generating the CDRs, computer

hardware, operating systems, and

compilers change too dynamically to

achieve a high degree of confidence

that code run 10 years ago can be

made to run on today’s systems

without a major effort and high code

maintenance costs. It also may not

be possible to recreate the same versions of all input data products used

in the CDR product. This is another

fundamental difference between

EDRs and CDRs. EDRs can use the

most recently available and best

“current” processing software with

little concern about consistency with

5 or 10 year old products: the application of such data is over the time

scale of days. This also is a challenge for the NPOESS system.

Weather requirements will likely

lead to a system with a running

archive of the last 3 to 6 months of

data easily available, plus a level 0

raw data archive of all data. There

is not yet a clear NPOESS requirement for CDR products, their permanent archive, or easy access to

earlier versions that may have been

produced 5 or 10 years earlier. Note

that the archive includes not only the

data products themselves, but also

data and documentation on the

instrument, calibration, algorithm,

intermediary data products used for

validation, and validation for each

CDR.
34

3. Required Absolute Accuracies and

Long Term Stabilities for Climate

Variables

This section discusses the required accuracy and stability for each climate variable

data set. These are the accuracies and stabilities needed to detect a climate signal. For

present purposes, the climate signal is a

change in the climate variable over time

and the time scale of interest is a decade. 

The first step in the process is specifying

the anticipated signal in terms of expected

change per decade. The second step is

determining the accuracies and stabilities

needed in the data set to permit detection of

the signal. Excellent absolute accuracy in

the measurement of the climate variable is

vital for understanding climate processes

and changes. However, it is not as necessary for determining long-term changes or

trends as long as the data set has the

required stability. And, when it comes to

building satellite instruments, stability

appears to be less difficult to achieve than

accuracy. The difficulty arises because of

the many known and unknown systematic

uncertainties that are to be accounted for in

the calibration of the instrument on ground

to establish its absolute accuracy and transfer and monitor the calibration on orbit.

Stability on the other hand is the measure of

repeatability and reproducibility of the

metrological characteristics of the instrument with time. Thus, a key attribute for the

climate data sets is long-term stability. The

required stability is some fraction of the

expected signal, assumed to be 1/5 in this

report. If we cannot achieve the above stability - for example, if we can only achieve

a stability of 0.5 of the signal - there would

be an increased uncertainty in the determination of the decadal rate of change. 

Why do we need absolute calibration?

Some possible answers:

A. It is required by a rigorous, physics-based error analysis flow down.

B. We want to force the contractors to do the best they can.

C. We don't, really: we only need long term stability, but

feel that this is the best way to guarantee it.

D. We don't: the satellite instrument only interpolates. The Kelvin

comes from the radiosondes (or buoys, or other vicarious).

E. We haven't decided yet what we will rely on, so we need it just

in case.

F. We don't: we just want pictures.

G. All of the above.

H. None of the above.

Tongue in cheek (Rice, Workshop Invited Presentation).
The factor 1/5, or 20%, is somewhat

arbitrary. It should be periodically reevaluated. If the climate signal is one unit per

decade, a 20% stability would imply an

uncertainty range of 0.8 to 1.2, or a factor

1.5, in our estimate of the signal. One basis

for choosing such a factor is related to the

uncertainty in climate model predictions of

climate change. Thirty-five climate model

simulations yield a total range of 1.4 K to

5.8 K, or factor of about 4, in the change in

global temperature by 2100 (IPCC, 2001).

Thus, a stability of 20% should lead to a

considerable narrowing of the possible climate model simulations of change.

Achieving the stability requirement does

not guarantee determining these long- term

trends. Superimposed on these trends is climatic noise - short-term climate variations -

that may mask the signal we are trying to

detect or reduce our confidence in the

derived trend.

Although excellent absolute accuracy is

not critical for trend detection, which was

the subject of the workshop, it is crucial for

understanding climate processes and

changes. Continuous efforts should be

undertaken to constantly improve the accuracy of satellite instruments.

3.1 Solar Irradiance, Earth Radiation

Budget And Clouds

How Were the Requirements Set?

Overall, the variables in this section are

linked in their role in the energetics of the

climate system. The sun is the dominant

source of energy for the earth’s climate. For

a long time thought of as being a steady,

constant energy source - hence, the term

“solar constant” to express the amount of

solar radiation reaching the Earth - we now

know that it can vary on the decadal time

scales of present interest. Accurate measurements of solar irradiance are key to defining

climate radiative forcing, and its accuracy

requirements are specified in that context.

Changes in surface albedo can represent

both changes in climate forcings - due to

human caused land-cover change - and climate feedbacks - due to changes in

ecosytems and in snow and ice cover resulting from climate changes. Cloud feedback

remains the largest single factor in the current large uncertainty in climate sensitivity

(IPCC, 2001). Cloud properties are critical

to understanding and defining the role of

clouds as feedback mechanisms in the climate system. Earth radiation budget is the

final integral of energetics in the climate

system, and is a key diagnostic for a wide

range of climate forcings (aerosol), feedbacks (clouds, ice/snow), and climate

responses (heat transport). Accuracies for

clouds and radiation budget are defined at

levels sufficient to be at or above estimates

of unforced natural climate variability in

current climate models; these accuracies

must also be sufficient to directly observe

decadal changes in clouds and radiation

budget that would constrain potential cloud

feedback mechanisms in climate models. 

The largest time and space scales will

drive the accuracy and stability requirements. For solar irradiance and surface

albedo, climate radiative forcing drives the

requirements. For clouds and radiation

budget, climate feedbacks drive the requirements. Recent studies of the last two

decades of cloudiness (International

Satellite Cloud Climatology Project

(ISCCP)) and radiation budget data (ERBE,

Scanner for Radiation Budget (ScaRaB),

CERES) from satellites have indicated significant interannual to decadal variability in

the tropics from latitude 20S to latitude

20N. This variability is not shown in current climate model simulations and is representative of changes that are critical to

assess accurately from observations, and to

35
36

be able to predict from climate models. A

climate observing system that cannot rigorously observe such changes with high confidence is very unlikely to be able to constrain and verify cloud feedbacks within climate prediction models. 

Accuracy requirements can also be determined by considering the amount of climate

change likely over the next few decades.

For example, many climate change models

use a 1%/year increase in carbon dioxide to

simulate a nominal doubling of CO2 in 70

years. This doubling is a radiative forcing

of the climate system of about 4 Wm-2, or

about 0.6 Wm-2 per decade. A change in

global average cloud fraction sufficient to

offset this radiative forcing would be about

0.015 if all other cloud properties remained

fixed. This would be a cloud feedback so

strong that climate change due to greenhouse forcing would become negligible. We

suggest that a minimum signal to noise of at

least 5 is needed to detect such change, suggesting a requirement for stability per

decade in global cloud cover of 0.003. This

would be sufficient to detect a cloud feedback. This approach essentially follows

that used by Hansen et al. (1993) in a workshop report that summarized accuracies

required for long-term monitoring of global

climate forcings and feedbacks. The accuracy requirements in this section are in general similar to those in Hansen et al. where

the same climate variable was evaluated.

As in that report, this workshop concluded

that the appropriate scaling for climate

requirements is the radiative flux changes

that can potentially alter climate: either

forcing or feedback. 

The NPOESS project convened a workshop to assess climate measurement

requirements for the Integrated Operational

Requirements Document (IORD) variables

(Jacobowitz, 1997). The report influenced

the IORD to add or change stability requirements, but had little effect on other IORD

requirements, which were focused on

instantaneous observations and, often, high

spatial resolution. Climate space scales run

from 50 km through global, and climate

time scales from a few weeks to centuries

for current global change concerns. The

requirements in this report and in Hansen et

al. (1993) for clouds and radiation budget

are often more stringent than in the

NPOESS climate workshop. The NPOESS

workshop does not appear to have used a

consistent radiative definition of the forcings and feedbacks. Many of its threshold

stability values would not be able to detect

the decadal changes expected for forcings

and feedbacks. Following Hansen et al.

(1993) the current report tries to address the

requirements in a consistent radiative forcing or feedback metric. It also assumes

that the forcing or feedback must be

detected accurately enough to assess

decadal change at the level of 20% of the

anticipated greenhouse gas forcings per

decade. If four forcing and/or feedback

mechanisms are found to be significant at

this level and the data verify that a future

climate model predicts them to this accuracy, then in the simplest sense the uncertainty in future predictions by the climate

model is composed of four likely independent errors, each of which is 20% of

the base greenhouse forcing. We might

anticipate in this scenario that the uncertainty in future predictions would be 20%

(square root (4)) = 40%. This would be a

dramatic improvement over the current

factor of 4 or larger uncertainty. But it

also suggests that the stricter stability

requirements in the current document and

in Hansen et al. (1993) are to be thought of

as thresholds or minimum values, not as

desired objectives. The objectives should

be set even tighter by a factor of 2 to 4

(10% to 5% of the greenhouse forcing). 

The current report does not discuss in

depth spatial, angular, and time sampling

requirements, since the focus of the work-
37

shop was on calibration. But Climate Data

Record (CDR) accuracy includes these

issues as well. For an observing system

with fixed sunsynchronous orbits such as

NPOESS, angular and time sampling biases

are primarily a function of the orbit. Time

sampling for many of the cloud and radiation variables can be augmented by incorporating the geostationary satellite data sets

(imager and sounder), especially where they

can be routinely intercalibrated with the climate instruments to provide consistent data.

Angle sampling errors are being markedly

reduced through the efforts of the new

multi-angle POLarization and Directionality

of the Earth’s Reflectances (POLDER),

Multiangle Imaging SpectroRadiometer

(MISR), and CERES observations. Spatial

sampling errors become significant for

instruments that only view nadir such as the

new active lidar and radar systems. This

primarily limits their climate-monitoring

role to zonal and global means, but in some

cases they can be sufficiently accurate for

1000 km scale annual mean regional values. 

Regional climate change signals will be

larger than zonal or global climate signals.

But internal climate system noise will also

be larger on these regional scales. The

tradeoff of the internal climate noise versus

signal has yet to be clearly defined for all of

the variables in this report. There should be

a continuing effort in the future to estimate

climate noise for each variable at a range of

time and space scales. This information can

then be used to refine the observing system

At least a one-year overlap of solar irradiance observations is needed to remove instrument 

differences in absolute calibration (Lean, Workshop Invited Presentation). 

composite 

total solar 

irradiance 

record: 

Fröhlich & 

Lean, GRL, 

1997

 0.2% 27-day solar rotation 

 0.1% (1000 ppm) 11-year 

solar cycle 

 longer-term variations not 

yet reliably detected

solar 

irradiance 

increases 

when solar 

activity is 

high

Decadal Scale Variations of Solar Irradiance 
38

requirements. There is little justification to

measure more than a factor of 2 more accurately than the background climate noise.

In the current analysis, we have used climate model noise estimates to help set

requirements for several climate variables.

3.1.1 Solar Irradiance 

The IORD-II (NPOESS, 2001) requirements were reviewed and were endorsed for

both total irradiance and spectral irradiance

accuracy and stability. The threshold for

absolute accuracy of total irradiance is

1.5 Wm-2 (0.1%), and for stability

0.02%/decade. As for many instruments,

the stability of the active cavity radiometers

greatly exceeds the absolute accuracy. At

least a one-year overlap of observations is

needed to remove instrument differences in

absolute calibration. A 0.02%/decade stability requirement is sufficient to detect a

0.3 Wm-2 change in solar irradiance over a

decade. This stability will constrain solar

radiative forcing of the Earth’s climate to

within (0.3)(0.25)(0.7) = 0.05 Wm-2 per

decade. The factor of 0.25 converts solar

constant to the global average insolation

over the Earth’s surface, while the factor of

(0.7) is the approximate fraction of energy

absorbed by the Earth. This stability

requirement will also allow rigorous tests of

decade to century time scale variability in

solar output as the length of the data record

grows. The system would be capable of

Each color in the above graph represents a time series of total solar irradiance measured 

by an individual satellite instrument. The total solar irradiance time series in the previous 

figure (Lean, Workshop Invited Presentation) is based on exploiting the overlapped 

observations to adjust for the differing absolute accuracies of the individual instruments 

(Rottman, Workshop Invited Presentation). 
39

detecting 0.5 Wm-2 per century change in

solar forcing. Even this subtle change

would be a significant fraction of anticipated greenhouse gas forcing over the next

century.

Spectral irradiance requirements are in

general about a factor of 10 less stringent,

but details vary with wavelength as indicated in the IORD-II. The spectral irradiance

measurements are crucial for properly specifying the way that the solar radiative energy enters the climate system. Absorption

scattering, and reflection (in the atmosphere, at the surface and in the mixed layer

of the ocean), all depend on wavelength.

Solar radiation at different wavelengths has

different variability. As an example, the UV

radiation that is deposited in the stratosphere, and influences ozone, varies by one

to two orders of magnitude more than the

visible radiation that reaches the earth’s surface. The IR radiation varies least. So solar

radiation at different wavelengths is

deposited in different ways depending on

geography and altitude. The measurements

of total irradiance alone provide no information about the spectral content of the

irradiance variability so a physical understanding of the processes by which climate

responds to solar forcing requires the measurements of the spectral irradiance. Spectral

irradiance observations are also important

for verifying solar physics models. 

3.1.2 Surface Albedo

Land use change is a potential climate

radiative forcing, while ecosystem response

and snow/ice changes are climate feedbacks. The goal is monitoring global average surface albedo change to an equivalent

radiative forcing change of 0.1 Wm-2 per

decade, or about 1/5 of the expected rate of

CO2 forcing. Since one-quarter of the Earth

is covered by land, and about half of the land

is cloud free, this equates to an 0.8 Wm-2

change in the average land surface reflected

flux for a 24-hour average insolation of 342

Wm-2. The resulting change in land albedo

would be 0.8/342 = 0.002. The global average land albedo is roughly 0.2, so that the

stability requirement of 0.002 albedo units

per decade is a relative change of 1%/decade

of the broadband solar energy reflected by

the surface. This 1%/decade will drive the

instrument requirements. Accuracy can be a

factor of 5 less, or 0.01 albedo units. For climate applications, 25 km would be sufficient

horizontal resolution. 

3.1.3 Downward Longwave Radiation

at the Surface

Ideally, we would require surface LW flux

accuracy of 1 Wm-2 and stability of 0.2 Wm-2

per decade, similar to those for TOA (Top of

Atmosphere) LW flux. See section 3.1.6 for

the determination of these values. The TOA

flux changes determine energy input to the

entire column of land/ocean and atmosphere.

The surface radiative fluxes are important in

understanding the vertical redistribution of

changes in TOA flux. Further climate modeling studies are needed to estimate the internal climate system noise in surface radiative

fluxes analogous to that done for TOA fluxes. Recent studies of an 18-year record of

surface LW flux estimates from the ISCCP

(International Satellite Cloud Climatology

Project) have indicated possible large fluctuations in downward LW radiation at the

surface (Zhang and Rossow, 2002).

3.1.4 Downward Shortwave Radiation

at the Surface

Ideally, we would require surface SW

flux accuracy of 1 Wm-2 and stability of

0.3 Wm-2 per decade, values similar to

those for TOA SW flux. See section 3.1.5

for the determination of these values. In

general, surface SW fluxes and TOA SW

fluxes are closely coupled. The exception is

when atmospheric absorption changes. This

can be the case with strongly absorbing
aerosols, but only small variations are

expected for cloud phase, optical depth, particle size and height. Liquid and ice water

cloud particles absorb at similar wavelengths

to water vapor absorption, so that to first

order clouds change the vertical distribution

of solar absorption in the atmosphere.

3.1.5 Net Solar Radiation at the Top of

the Atmosphere

Climate noise represents the unforced

natural variations in the climate system.

Climate models indicate that tropical annual

mean (20S to 20N) shortwave (SW) reflected flux climate noise is roughly 0.3 Wm-2.

This estimate is taken from the NOAA

Geophysical Fluid Dynamics Laboratory

(GFDL) and United Kingdom

Meteorological Office (UKMO) climate

model simulations used in a recent study of

decadal tropical variability (Wielicki et al.,

2002). The previous two decades of Earth

radiation budget measurements of SW

reflected flux at very large time/space scales

indicate that changes of 1 Wm-2 to 3 Wm-2

are possible. From both these perspectives,

a stability requirement/decade is chosen as

0.3 Wm-2 per decade to be able to resolve

changes over a decade to within current

estimates of climate noise, and to be consistent with potential climate variability.

Accuracy is not required at the same level,

and 1 Wm-2 should be adequate. 

3.1.6 Outgoing Longwave Radiation at

the Top of the Atmosphere (TOA) 

Climate models (see 3.1.5) indicate that

annual tropical mean (20S to 20N) longwave (LW) flux climate noise is roughly

0.2 Wm-2. Studies of potential decadal

changes in LW flux at very large time/space

scales indicate that changes of 1 Wm-2 to 3

Wm-2 are possible. From both these perspectives, a stability requirement/decade is

chosen as 0.2 Wm-2 to be able to resolve

changes over a decade to within current

estimates of climate noise. Accuracy is not

required at the same level, and 1 Wm-2

should be adequate.

3.1.7 Cloud Base Height

Cloud base height is not directly

observed from space unless active cloud

lidar and cloud radar are used to rigorously

cover a full range of cloud thickness and

cloud overlap. Estimates from passive

imagers use cloud top height and a parameterization of cloud thickness as a function

of cloud optical depth or cloud liquid/ice

water path. The accuracy in cloud base

height should be sufficient to achieve a surface cloud radiative effect in downward LW

flux of 1 Wm-2, similar to the TOA flux

absolute accuracy. The primary effect is

from low clouds (the opposite of TOA flux),

which are present about 1/4 of the time.

This suggests an accuracy of 4 Wm-2 in

downward LW flux when these low clouds

are present. Using a radiative model, this

equates to a knowledge of global average

cloud base height to roughly 0.5 km.

Stability requirements would be 0.1 km per

decade using a similar scaling for cloud

effects on downward LW flux, and assuming the same 0.2 Wm-2 per decade global

mean analogous to outgoing LW flux at the

TOA (3.1.5). 

3.1.8 Cloud Cover

For climate change, cloud feedbacks

should be monitored to a global average

radiative effect similar to climate model

noise in LW and SW fluxes (3.1.5 and

3.1.6). Cloud cover affects both SW and

LW fluxes. But the largest effect will be for

SW fluxes and therefore should meet a similar 0.3 Wm-2 decadal change stability. The

current global average SW cloud radiative

effect (all-sky reflected flux minus clear-sky

40
reflected flux) is about 50 Wm-2. Using the

average global cloud fraction of 0.5, this

indicates an average overcast SW cloud

radiative effect (overcast minus clear skies)

of about 100 Wm-2. The global average

effect of a change in cloud cover alone (all

other properties constant) would then be

100 Cf, where Cf is cloud fractional coverage in units from 0 to 1. Since cloud radiative effect is roughly linear in cloud cover,

the final stability threshold requirement for

cloud cover is 0.3/100 = 0.003

stability/decade. Accuracy is not required

at this level, and 0.01 accuracy should be

sufficient to be consistent with the 1 Wm-2

accuracy of TOA fluxes in 3.1.5 and 3.1.6).

Note that as clouds become optically thin,

the accuracy of the cloud cover requirement

becomes less stringent proportional to the

SW cloud radiative effect of the thin cloud.

For example, a thin cirrus with a SW radiative effect of only 10 Wm-2 would have a

stability requirement of 0.03/decade for

cloud fraction of these thin clouds.

Therefore cloud fraction accuracy for hard

to detect very thin clouds can be relaxed

from the average value. 

3.1.9 Cloud Particle Size Distribution

No stability or accuracy recommendations are made at this time because of

uncertainties in the effect of cloud particle

size distribution on radiative fluxes. For climate applications, cloud particle size distribution is less critical than cloud effective

radius. This is also true for aerosols (3.2.4).

Since most cloud particles are much larger

than the wavelength of visible radiation,

geometric optics govern, and variations in

the size distribution have little effect on SW

reflected fluxes. For infrared fluxes, effective particle size can be used to predict the

changing absorption optical depth or emissivity with infrared wavelength. The one

exception to this may be thin to moderate

optical depth ice cloud where particle size

is of the order of the wavelength for the far

infrared rotation band of water vapor at

17 μm to 100 μm wavelengths. About half

of the thermal emission of the earth originates in this spectral band, and most of the

water vapor greenhouse effect is in this

spectral band. Typical ice crystal effective

radii for thin to moderate thickness ice

clouds are 20 μm to 50 μm, so that a simple

effective radius may not sufficiently characterize the LW radiative effect of these

clouds in the far infrared. Further analysis

of far-infrared radiative modeling as a function of cloud particle size distributions is

required to clarify this requirement for

climate applications. 

3.1.10 Cloud Effective Particle Size

Cloud effective particle size plays a

potential role in both radiative forcing and

climate sensitivity. The radiative forcing

role is known as the indirect effect of

aerosol forcing. In the simplest sense,

increasing aerosols increase cloud condensation nuclei, which results in smaller cloud

particle size for a given amount of liquid

water condensed during rising motion.

Cloud liquid water path (LWP) is the vertical column amount of liquid water in a

cloud layer. For a given LWP, decreased

effective radius Re results in larger cloud

optical depth Tau and therefore larger cloud

albedo and reflected SW flux. We use the

same simple relation in 3.1.12 of LWP = K x

Tau x Re, where K is a constant, to relate

these three key cloud variables. The driver

for this requirement will be the radiative forcing accuracy desired for the indirect radiative

effect of aerosols. The nominal requirement

is to understand the potential indirect aerosol

radiative forcing at 0.1 Wm-2 per decade:

equivalent to the changes in land albedo

radiative forcing discussed in 3.1.2, and a

factor of two less stringent than solar forcing. This stability is also consistent with

41
42

the 0.12 Wm-2/decade requirement for

direct aerosol radiative forcing in 3.2.4.

Boundary layer water clouds that are considered susceptible to modification by aerosols

cover only about 1/4 of the earth. Therefore,

the most stringent effective particle size limit

will be for liquid water clouds (without overlying thick ice cloud) and will be a stability

requirement of 4(0.12) = 0.5 Wm-2 change

in SW cloud radiative forcing for these

clouds, when present. The relationship of

LWP, Tau, and Re indicates that for constant LWP, a +2% change in Re causes a -

2% change in cloud optical depth. Using the

discussion in section 3.1.13 for Tau, we

conclude that the stability requirement for

water cloud Re is 2% per decade. The

absolute accuracy is 10%. For ice clouds,

instead of the more stringent radiative forcing limits, we use the less stringent cloud

feedback SW radiative flux changes of

0.3 Wm-2 used in 3.1.5 and 3.1.13. We also

assume that these ice clouds cover roughly

1/4 of the Earth. The resulting ice cloud

effective radius absolute accuracy requirement is then 20%, and stability requirement

is 4% per decade. Note that as for cloud

optical depth, these accuracies can be

relaxed for optically thin clouds, proportional to their SW cloud radiative effect. 

3.1.11 Cloud Ice Water Path

Cloud ice water path (IWP) and liquid

water path (3.1.12) are similar variables.

The major difference is that the vertical

variation in particle size can commonly be a

factor of 10 in ice clouds versus a factor of

2 for water clouds. This complicates simple

relationships such as IWP = K x Tau x Re,

where K is a constant, Tau is cloud visible

optical depth, and Re is cloud particle effective radius. But such simple relationships

remain useful for scaling observing requirements, and relating radiative flux changes to

cloud IWP. Following the logic in 3.1.12

for LWP, the IWP absolute accuracy

requirement is 25% and the stability

requirement is 5%/decade. 

3.1.12 Cloud Liquid Water Path

Cloud liquid water path (LWP) is the

vertical column amount of liquid water in a

cloud layer. LWP is related to cloud effective particle size (Re) from 3.1.10 and cloud

optical depth (Tau) from 3.1.13 by the simple approximation LWP = K x Tau x Re,

where K is a constant. It is clear then that

cloud liquid water path is a link between

the water cycle and the energy cycle. But

the time average total amount of liquid and

ice water in clouds is only a very small

fraction of the time integral of precipitation,

or of the column amount of water vapor.

For example, a typical liquid water cloud

with Re = 10 micron, and Tau = 10, has a

LWP of about 0.06 mm of water. Global

average column water vapor is about 30 mm

equivalent, 500 times larger. Annual average precipitation is about 1 m, or 15,000

times larger. This makes it clear that while

there is a link between the water and energy

cycles, large changes in cloud LWP could

occur with little or no changes in precipitation. For this reason, cloud LWP is more

closely linked to the energy cycle than to

the water cycle. But cloud LWP is closely

related to the dynamics of the cloud system

through the moist and dry adiabatic lapse

rates. Therefore it is important to evaluate

independently of cloud optical depth and

effective radius. Changes in the vertical

distribution of particle size within cloud

layers (factor of 2) complicate the simple

approximation of LWP = K x Tau x Re.

The key role of cloud dynamics in cloud

radiative feedbacks indicates that LWP

accuracy and stability goals should be

sufficient to allow direct comparison of

independent measurements of LWP, Tau,

and Re. As a result, we use the cloud radiative effects discussed in 3.1.13 and 3.1.10,

and the simple approximation discussed
43

above to set the LWP absolute accuracy

requirement at 15%, and the stability

requirement at 3%/decade, if all clouds

were water clouds. Using roughly half of

the radiatively important clouds as liquid

water, the final requirement becomes 25%

absolute accuracy and 5%/decade stability

in LWP. The average cloud water content is

about 0.1 mm; hence the required accuracy

and stability are 0.025 mm and 0.005 mm.

3.1.13 Cloud Optical Thickness 

For climate change, cloud feedbacks

should be monitored to a global average

radiative effect similar to climate model

noise in TOA LW and SW fluxes. Cloud

optical thickness is primarily relevant to

SW fluxes and therefore should meet a similar 0.3 Wm-2 decadal change stability.

Global average SW cloud radiative effect

(all-sky minus clear-sky reflected flux) is

about 50 Wm-2. Cloud effects on SW fluxes, however, are highly nonlinear in optical

depth. But using a broadband radiative

transfer model, we can convert accuracy in

TOA SW flux to approximate accuracy in

cloud optical depth. For an average cloud

cover of 50%, a stability of 0.3 Wm-2

equates to a 0.6 Wm-2 change in cloudy

regions. Table 4 shows the percentage stability in cloud optical depth (equivalent to a

TOA change of 0.3 Wm-2) as a function of

cloud optical depth, as predicted by a radiative model. 

Since the majority of clouds have optical

depths between 2 and 32, the requirement is

selected at 2% stability/decade. Absolute

accuracy is not required at this level, and

10% accuracy should be sufficient. Threedimensional cloud effects may dominate

cloud optical depth absolute accuracy while

instrument visible channel stability will

control the stability requirement. At very

low or high cloud optical depths, less accuracy and stability are required in cloud optical depth.

3.1.14 Cloud Top Height

Cloud top effective radiating temperature

is used to set the height requirements.

Temperature is mapped to height through

the temperature profile retrieved by other

EDRs and CDRs. For climate, the cloud

top temperature is the more fundamental

parameter (3.1.16), and the height is a property derived for convenience. Some degradation of accuracy from temperature to

height is expected because of temperature

profile errors, especially in polar regions

with strong temperature inversions. Use of

a typical temperature lapse rate in the

atmosphere allows conversion of the cloud

temperature requirement (3.1.16) to a cloud

top height requirement. For a typical value

of 6 K/km lapse rate, the 1 K accuracy

requirement for cloud temperature converts

to 150 m in global average cloud height.

The stability requirement converts to 0.2/6

= 0.03 km or 30 m per decade. 

3.1.15 Cloud Top Pressure

Similar to cloud height, the cloud top

pressure is typically converted from cloud

top temperature using vertical temperature

profiles. In the lower half of the troposphere, 100 hPa is roughly 1 km in height.

Therefore lower troposphere global accuracies of 15 hPa in cloud top pressure, and

stability of 3 hPa per decade are required.

For upper tropospheric clouds such as cirrus, however, these values will be scaled

down by the decreased change in pressure

with height (not linear) and will be scaled

Table 4. Required cloud optical depth

stability as a function of cloud

optical depth

Cloud Optical Depth 0.1 0.5 2 8 32 128

Cloud Optical Depth

stability (%) 20 5 2 2 3 6
44

up by the 1/Ce cloud emissivity dependence

discussed in 3.1.16 for Cloud Temperature. 

3.1.16 Cloud Top Temperature

For climate change, cloud feedbacks

should be monitored to a global average

radiative effect similar to climate model

noise in TOA LW and SW fluxes. Cloud

top temperature is most relevant to LW

TOA fluxes and therefore should meet a

similar 0.2 Wm-2 decadal change stability.

We use a rough approximation to relate

cloud radiative effect on LW flux CFlw,

cloud fraction Cf, cloud emissivity Ce,

cloud temperature Tc, and surface temperature Ts. The approximation is given by

CFlw = 2 x Cf x Ce x (Tc-Ts). This

approximation leads to a requirement of

about 0.2 K/decade stability in cloud top

temperature, assuming 50% global average

cloud fraction. Note that for climate, the

cloud top effective radiating temperature is

most appropriate and is the most directly

measured quantity by imager or interferometer retrieval techniques. This is the “cloud

top” temperature referred to in these climate

requirements. To match accuracy with outgoing LW flux (3.1.6) at the top of atmosphere, accuracy in cloud top temperature

should be 1 K for optically thick cloud. For

optically thin clouds, to maintain similar

cloud radiation feedback accuracy, cloud

top accuracy should be 1 K/Ce: e.g. 2 K for

cloud emissivity = 0.5, 5 K for cloud emissivity = 0.2. The same dependence on

cloud emissivity also applies to the stability

requirement, which can be stated as

0.2 K/Ce per decade. For thin cirrus of

infrared emissivity 0.2, the stability

requirement would be 1 K/decade. 

3.1.17 Spectrally Resolved Longwave

Radiation

Climate models indicate a lower range of

temperature change of 0.1 K/decade

(Holton et al. 1995). Analysis of the Global

Cloud Imagery (GCI) dataset (Salby and

Callaghan, 1997) of 11 μm radiation indicates typical interannual variability over climatic spatial scales (22.5° x 22.5° grid

boxes) of 0.3 K (Kirk-Davidoff et al.,

2003). The radiation at 11 μm represents a

worst-case in both total variability and diurnal cycle, representing a sound basis for

determining overall dataset requirements.

An absolute accuracy of 0.1 K in a data set

of spectrally resolved longwave radiation

will allow the detection of these low range

climate changes as they become distinct

from the interannual fluctuations. A stability

of 0.04 K/decade is required to resolve estimated 0.2 K/decade global warming. 

3.2 Atmospheric Variables

How Were the Requirements Set?

The expected decadal changes in a variety of atmospheric variables were used to

determine accuracy and stability requirements. This usually involved utilizing the

expected response to global warming estimated from general circulation model

experiments. As in the previous section, we

assume that a signal-to-noise of at least 5 is

required to reliably detect these changes

from an instrument stability standpoint.

The instrument accuracy, as has been discussed above, is less of an issue. As long as

overlapping satellite records can be constructed to determine the calibration offsets

between instruments, we can relax the

absolute accuracy requirements to what is

expected (and indeed already achievable)

from a variety of sensor technologies in the

coming decade. This is not to minimize the

importance of understanding the sources of

absolute accuracy errors, since some of

these sources could conceivably affect the

stability we require for climate monitoring.

For many of the passive microwave or

infrared technologies, instrument absolute

accuracies of 0.5° C can meet our require-
45

ments, as long as these accuracy numbers

are dominated by a systematic bias that can

be removed during satellite overlap periods. 

3.2.1 Atmospheric Temperature

The most stringent climate-monitoring

requirement for atmospheric temperature

would be the observation of the expected

average global warming signal, which,

based on climate model estimates, is about

0.20° C/decade over the next century for

deep-layer tropospheric temperature,

depending somewhat upon latitude.

Expected cooling of the stratosphere is

about 0.40° C/decade, also depending

somewhat upon latitude (IPCC, 2001).

Absolute accuracies of about 0.5° C are

now realistic and achievable, assuming we

are talking about deep-layer averages,

which are probably more pertinent for climate monitoring work. The expected global warming signal of 0.20° C/decade in the

troposphere, assuming the 1/5 factor discussed above, leads to a long-term stability

requirement of 0.04° C/decade.

3.2.2 Water Vapor

Again, the accuracy (bias) associated

with the measured humidity is less important that the long-term stability of that

measurement. We somewhat arbitrarily

assume a 5% accuracy requirement, which

for deep-layer averages or vertically integrated water vapor is already being

achieved from SSM/I. This is considerably

more stringent that listed in IORD II

(20-25%), primarily because of large uncertainties in the retrieval of humidity in shalNo significant water vapor trends have been detected since 1979 

(Bates, Workshop Invited Presentation). 
46

low layers to meet NPOESS weather

forecasting requirements. 

Assuming that constant relative humidity

is maintained during global warming, at

least in the lower troposphere, then an

absolute humidity (or total water vapor)

increase of about 1.3%/decade would be

expected (global average) for a warming of

+0.20° C/decade. Again, very substantial

regional deviations from this average value

would be expected. Utilizing a factor of

(1/5) leads to a 0.26%/decade long-term stability requirement. This is substantially

more stringent than the 2% threshold stability requirement in the NPOESS IORD II

(2001). Again, this is the requirement to

observe the global moistening of the atmosphere associated with global warmingregional changes could be much larger and

would have a much less stringent stability

requirement.

3.2.3 Ozone

Over the next 50 years, stratospheric

ozone should return to its levels of 25 years

ago. Thus, the changes one expects to see

are increases at about half the rate of the

observed decreases. The WMO CEOS

Report on Ozone (WMO, 2001) gives trend

detection goals by atmospheric levels consistent with detecting these trends:

5%/decade for the troposphere, 3%/decade

for the stratosphere, and 1% per decade for

the total column. To detect these trends

requires data set stabilities of 1/5 of the

above values: total column: 0.2%/decade,

stratosphere: 0.6%/decade, and troposphere:

1%/decade. Required accuracies are 3% for

total column ozone, 5% for stratospheric

ozone, and 10% for tropospheric ozone.

The NPOESS IORD II (NPOESS,

2001)) gives threshold long-term stability

requirements of 1% per 7 years for total

ozone and 2% per 7 years for profile ozone

and objective requirements of half these

amounts. These long-term requirements are

on single-instrument stability, not absolute

accuracy. The IORD has threshold requirements only for the total column and stratosphere, not for the troposphere. The OMPS

will have a capability to derive tropospheric

ozone from its observations and pre- and

post-launch instrument calibration should

be considered for reducing errors in this

atmospheric parameter since it is important

to climate science.

3.2.4 Aerosols

The most realistic approach to defining

the required accuracy and long-term stability requirements for aerosols is with respect

to the rate of increase of forcing by wellmixed greenhouse gases. This is because of

the considerable uncertainties in modeling

studies (Haywood and Boucher, 2000) and

poor knowledge of historical changes in this

variable. Unlike greenhouse gases, aerosols

can cause either warming or cooling

depending on their single-scattering albedo.

The magnitude of the aerosol radiative forcing is principally dependent on the aerosol

optical depth, but is also affected by the

vertical distribution of the aerosols, their

size distribution and refractive index. The

expected rate of increase of forcing by wellmixed greenhouse gases is assumed here to

be roughly 0.6 W/m2 per decade. The 0.2

stability factor leads to a stability requirement of 0.12 W/m2 per decade. We estimate

that required accuracies will be approximately 0.01 for aerosol optical depth

(AOD) measurements, 0.02-0.03 for single

scattering albedo, the greater of 0.1 μm or

10% for effective radius, and the greater of

0.3 or 50% for effective variance. Here it is

assumed that the aerosol size distribution is

bimodal since this is typical of aerosol sampling measurements (when optically irrelevant Aitken nuclei are neglected) and of the

majority of AeroNet retrievals (except when
47

stratospheric aerosol yields a trimodal distribution). For aerosols, as before, we

define the necessary long term stability as

0.2 of the change in forcing by well mixed

greenhouse gases, which means that the

long term stability requirements are about

50% smaller than the absolute accuracy

requirements over a decade. These requirements are consistent with IORD-II

(NPOESS, 2001), since the required accuracy and long term stability for aerosol measurements used a similar radiative definition

of the aerosol climate signal. Although the

refractive index only has a small effect on

the radiative forcing of aerosols, it is a crucial diagnostic of the aerosol species and

therefore represents an important constraint

on aerosol transport models and, consequently, the prediction of aerosol forcing.

It should therefore be measured with sufficient accuracy to discriminate between

broad classes of aerosols. An accuracy of

0.02 provides this discrimination and a

long-term stability requirement of 0.01 per

decade is consistent with the radiatively

defined parameters. 

Episodic events (e.g., eruption of Mt.

Pinatubo) can inject large aerosol optical

depths into the stratosphere and cause a substantial cooling on a 1-3 year time scale. The

radiative effect of stratospheric aerosols is

less dependent on the aerosol single scatter

albedo than that of tropospheric aerosols,

because of the significant thermal radiative

forcing, and so is defined principally by the

AOD and secondarily by the aerosol size distribution. We estimate that the required accuracy, using the same radiative definition as

for tropospheric aerosols, is 0.01 for optical

depth, 0.1 μm for effective radius and 50%

for effective variance, where it is assumed

that the stratospheric aerosols are

monomodal. As before, long-term stability

requirements on a decadal time scale are 50%

tighter than the accuracy requirements. 

3.2.5 Precipitation

The globally averaged precipitation rate

is about 3 mm/day, which is 0.125 mm/hr.

It is estimated that only about 5% or less of

the earth is covered by precipitation at any

given instant. Thus, we can say that, where

it is raining, the average rain rate is

(1/0.05) x (0.125 mm/hr) = 2.5 mm/hr. The

consensus of a variety of climate model

simulations suggests that this average precipitation rate is expected to increase about

3% per degree C of warming. Thus, for an

expected decadal warming trend of 0.2° C,

there should be a 0.6%/decade increase in

the precipitation rate. This amounts to

0.015 mm/hr increase in the 2.5 mm/hr

average rain rate. Utilizing the 0.2 factor as

before, this requires a measurement stability

of about 0.003 mm/hr when observing rain.

An absolute accuracy of about 5% of the

mean is somewhat arbitrarily assumed here,

which results in 0.125 mm/hr absolute accuracy requirement where it is raining. Again,

we keep in mind that this accuracy refers to

a systematic bias over many measurements,

as this level of accuracy is probably not

attainable for even the best rain gauges for

individual measurements.

3.2.6 CO2

The secular trend in CO2 is currently

about 1.4 ppmv yr-1, or about 4% per

decade. This variation can easily be detected by a single in-situ station (e.g., Mauna

Loa), and is therefore uninteresting for

satellite applications. 

Much more important for climate projection is the behavior of sources and sinks on

these time scales. The in-situ data can be

inverted to allow the integrated source or

sink to be estimated at continental or ocean

basin scale, but these estimates will be of

little use for understanding mechanisms or

improving predictive models. Satellite data
48

can supplement in-situ data in determining

sources and sinks. 

Mechanisms for current sinks on land are

believed to include CO2 fertilization, nitrogen deposition, re-growth of previously

cleared forests, fire suppression, and a

longer growing season at high latitudes due

to the warming climate. Of these mechanisms, only CO2 fertilization is expected to

strengthen over coming decades. Most terrestrial sink mechanisms are expected to

saturate or even reverse their signs over

time, so huge variations in sources and

sinks are expected over the next 30 years.

The uncertainty in the future behavior of

these sinks is one of the primary drivers of

uncertainty in future climate. 

Since carbon dioxide is measured to a

high level of accuracy at a number of sites

around the globe, any biases in spaceborne

measurements will likely be removable, and

so we arbitrarily assume an absolute accuracy requirement of 10 ppmv, which is

approaching 3% of the average carbon

dioxide concentration of the atmosphere.

The 0.2 factor applied to the current global

trend of 14 ppmv/decade yields a stability

requirement of 2.8 ppmv/decade. 

For estimating sources and sinks on large

spatial (10° by 10°) and temporal (month)

scales, stabilities of about 2 ppmv would

provide information comparable to that of

the current in-situ network. However, the

stability requirement is 1 ppmv (Peylin et

al., 2002; Gurney et al., 2002). Accuracy is

not critical since it is the spatial and temporal gradients that are important for this

problem.

Climate model predictions of precipitation trends as a result of greenhouse 

warming can be used to determine the precipitation measurement requirements 

(Mehta, Workshop Invited Presentation). 
49

3.3 Surface Variables 

Surface variables include land vegetation, snow cover, sea ice, ocean color, and

temperature. One problem with defining the

requirements for the satellite measurements

of the Earth’s surface is the wide range of

surface types covered. Under the final

“roadmap” section we will also discuss the

various concerns that the group had relative

to the future of these satellite measurements. A fundamental concern is the need

for both accurate pre-launch calibration and

post-launch validation of all satellite

instruments. By their very nature satellite

measurements do not directly sense the

parameter of interest and it is only through

these calibration and validation efforts that

we can develop methods to estimate the

desired parameters from the satellite data.

These concerns apply both to the present

and future satellite measurements. 

3.3.1 Ocean Color

About 90% of the signal received by

satellite instruments measuring reflected

visible radiation is contributed by the

atmosphere rather than the ocean. Thus, it

was clear that it will never be possible to

compute accurate “water leaving radiances”

from ocean color sensors without some

method of in-situ calibration. Atmospheric

correction methods alone cannot yield sufficiently accurate ocean color measurements

and it will always be necessary to have

comparisons with in-situ measurements to

derive the appropriate algorithm coefficients. Any future satellite system must be

coupled with in-situ measurements that can

be used to calibrate and validate the satellite

sensor data. With these caveats the requirements for ocean color measurements are set

at 5% for accuracy and 1% for stability.

3.3.2 Sea Surface Temperature (SST)

Climate models predict air temperature

increases of about 0.2 K per decade due to

greenhouse warming. Sea surface temperatures can be expected to increase at about

the same rate. To measure this change

requires a data set stability of 1/5 of

0.2 K/decade or 0.04 K/decade. Accuracy of

0.1 K is considered adequate.

Ocean buoys measure the SST at 1m to

2 m below the surface representative of the

“bulk SST” that were also measured by ship

buckets prior to the 1950s and ship injection SSTs since then. Satellite measured

SSTs are sensitive to the topmost skin layer

of the ocean, but they are generally corrected to bulk SSTs. The Surface Panel

recommends that the satellite SST program

include an in-situ program of

calibration/validation measurements that

combine both skin and bulk SSTs.

3.3.3 Sea Ice

Changes in sea ice area represent potential changes in climate forcing due to the

sea ice- albedo feedback mechanism. We

specify the sea ice area measurement

requirement using the same rationale as for

land surface albedo. We must determine the

change in sea ice area needed to cause a

change in mean global reflected solar radiation of 0.1 W/m2 (about 1/5 of expected

greenhouse forcing in a decade). Since

clouds cover sea ice about half the time, the

change in sea ice area has to be doubled to

achieve the 0.1 W/m2 value. 

0.2 W/m2 = (δAsea ice/earth surface area) x

(αsea ice - αocean) x (Insolation) (1) 

where δAsea ice is the required change in

sea ice area, earth surface area is the total

surface area of the earth, (αsea ice - αocean) is
50

the difference in albedo between sea ice and

open ocean, and Insolation is the average

solar radiation at high latitudes. Taking 0.5

as the difference in albedo between sea ice

and open ocean and 200 W/m2 as the insolation at high latitudes, we obtain a required

sea ice cover change of 106 km2 per decade.

With current average sea ice cover of about

23x106 km2, this represents a change in

total sea ice cover of about 4% per decade.

Thus, stability of the sea ice cover data set

should be about 4% per decade. Absolute

accuracy of 5% would be sufficient. 

3.3.4 Snow Cover

Changes in snow cover represent potential changes in climate forcing due to the

snow- albedo feedback mechanism. We

specify the snow cover measurement

requirement using the same rationale as for

sea ice area. We must determine the change

in snow cover needed to cause a change in

mean global reflected solar radiation of

0.1 W/m2 (about 1/5 of expected greenhouse forcing in a decade). Since clouds

cover snow about half the time, the change

in snow cover has to be doubled to achieve

the 0.1 W/m2 value. 

0.2 W/m2 = (δAsea ice/earth surface area) x

(αsea ice - αocean) x (Insolation) (2)

where dAsnow is the required change in

snow cover, (asnow - aland) is the difference in albedo between snow and snow-free

land, and Insolation is the average solar

Satellite observations indicate that both snow cover and sea ice are decreasing 

(Tarpley and Comiso, Workshop Invited Presentations). 

Yearly Perennial Sea Ice Cover and 

Average North American Snow Cover 

2000

Snow Cover
51

radiation at high latitudes. Taking 0.5 as the

difference in albedo between snow and

snow-free land and 200 W/m2 as the insolation at high latitudes, we obtain a required

snow cover change of 106 km2 per decade.

With current average snow cover of about

25x106 km2, this represents a change in

total snow cover of 4% per decade. Thus,

stability of the snow cover data set should

be about 4% per decade. Absolute accuracy

of 5% would be sufficient.

Experience with over more than 30 years

of photo-interpretive snow mapping by

human analysts show what current capabilities are. We think that automated snow

mapping to be implemented in the near

future would achieve snow cover accuracy

of 5% and stability of 1.5%. These are limits presently met by the human analyst system and should be the expectations for the

future automated systems. 

3.3.5 Vegetation

The type and distribution of vegetation

native to a geographic region are diagnostic

of the area’s climate. This is because vegetation integrates the effects of precipitation and

temperature over all time frames longer than

a few days. In addition the vegetation feeds

back into climate because of the plant

species contribution to the surface energy

and moisture balance and its impact on surface roughness and albedo. For these reasons, observing vegetation changes in the

seasonal to interannual time frame and over

long term is important to climate monitoring. 

The quantity usually derived from satellite observations is normalized difference

vegetation index (NDVI). NDVI is usually

defined as (NIR -VIS)/(NIR + VIS), where

VIS and NIR are albedo measurements at a

visible and near infrared wavelength. More

physically meaningful quantities such as

AVHRR observations suggest that the growing season increased between 1981 and 1994,

but questions remain, mainly with respect to calibration and inter-calibration of sequential 

satellite instruments (Knyazikhin, Myneni, and Shabanov, Workshop Invited Presentation).

Northern Latitude G reening Trends

O ur analysis show s thatduring the 

years 1981 through 1994 for the 

Northern high latitudes

NDVIaveraged over boreal

grow ing season m onths ofM ay to 

Septem ber increased by about

10% ,

the tim ing ofspring green-up 

advanced by about6 days.

Greening trend?

Orbital drift?

Inter-sensor variation?

Noise in the channel data?
52

green vegetation fraction and leaf area

index can be derived from NDVI. Because

NDVI is a relatively robust quantity, that is,

it minimizes some of the noise introduced

by viewing and illumination conditions, we

recommend that it be the basic vegetation

parameter for climate monitoring. There are

related ground truth measurements such as

leaf area index (LAI) that can be measured

and compared with the NDVI estimates.

One could also make direct spectral measurements near the ground that can be used

to directly compute an NDVI for comparison with the satellite estimates. These

would have to be averaged up to a size that

would be relevant to the satellite data.

The needed accuracy and stability for

NDVI for monitoring vegetation at climate

time scales is not clear. There is no ground

truth for NDVI, so translating vegetation

changes as seen from the surface into

equivalent NDVI has not been done.

Likewise, there is no modeled result that

would tell us what kind of vegetation

changes could be expected with global

warming or CO2 doubling. Several

researchers have reported increases in the

Advanced Very High Resolution

Radiometer (AVHRR) NDVI products over

decadal or longer time spans. Such results

have been controversial because of difficulty

in calibration of the AVHRR and problems

introduced into the data record by change in

observation times caused by orbit drift.

However decadal changes in average NDVI

of 5 to 10% at high latitudes have been

reported. In light of these reports, an accuracy requirement of 3% and a stability

requirement of 1% per decade are suggested

for NDVI. 
53

4. Translation of Climate Dataset

Accuracies and Stabilities to

Satellite Instrument Accuracies

and Stabilities

The requirements for the data sets must

be translated into required accuracies and

stabilities of the satellite measurements. In

some cases, for example, solar irradiance

and top of the atmosphere Earth radiation

budget, there is a one to one correspondence. For other climate variables, this

translation is more complex. And for a few

of the variables, additional studies are needed to determine the mapping of data set

accuracies/stabilities into satellite accuracies/stabilities. 

Because of the difficulties in achieving

necessary accuracies (exo-atmospheric total

solar irradiance is one example, (Quinn and

Frohlich,1999)), a key attribute for the

satellite instruments is long-term stability.

This may be achieved by either having an

extremely stable instrument or by monitoring the instrument’s stability, by various

methods, while it is in orbit. An ideal external calibration source is one that is nearly

constant in time and able to be viewed from

different orbit configurations. If there is

scientific evidence regarding the degree of

stability of such a source, and it is believed

to be at an acceptable level for long termclimate studies, then the stability of the

satellite sensor can be assessed independent

of other reference standards. With such

monitoring, instrument readings can be

corrected for lack of stability. 

However, this brings up a measurement

challenge for establishing the degree of stability of the external reference source.

Obviously the methods and instruments

testing the stability of those sources must

have stability requirements far more stringent than given in this report. One method

that has been successfully implemented for

the reflected solar spectral interval is lunar

observations, from orbit, with the sensor.

One example is the ocean color satellite

SeaWiFS, which uses lunar observations to

correct for degradation in the near infrared

channels (Kieffer et al., 2003). The

required lunar data are being supplied by a

dedicated ground based facility (Anderson

et. al., 1999). 

Since satellites and their instruments are

short-term - NPOESS satellites and instruments have design lives of about 7 years -

satellite programs launch replacement

satellites to continue the observations. Thus,

the long-term data record for any climate

variable will consist of contributions from a

series of satellite instruments, some using

different techniques. To assess the reproducibility of the measurement results, to

assist in understanding the differences that

arise even with instruments of similar

design, and to create a seamless data record,

it is essential that the satellites be launched

on a schedule that includes an overlap interval of the previous and the new instrument.

Acquiring multiple independent spacebased measurements of key climate

variables - one of the climate observing

principles listed above - would also help

insure maintenance of stability in the event

of a single instrument failure. 

One proposed instrument that may have

very high accuracy and may not require

overlap periods is the proposed spectrally

resolved radiance spectrometer (Anderson

et al., 2003). Sequential flights of copies of

this instrument might maintain the climate

record without overlapping measurements.

4.1 Solar Irradiance, Earth Radiation

Budget, and Clouds

4.1.1 Solar Irradiance

Accuracy requirements map directly into

instrument requirements. No conversion is

required. 
54

4.1.2 Surface Albedo

From section 3.1.2, the absolute accuracy requirement of surface albedo is 0.01

and the stability requirement is

0.002/decade. Using global average surface

albedo of roughly 0.2, these requirements

are 5% in absolute accuracy and 1% per

decade in stability of the radiometer used to

determine surface albedo. 

4.1.3 Downward Longwave Radiation

at the Surface

Surface radiative fluxes from satellites

are inherently much less accurate than TOA

fluxes, especially for downward LW flux.

The downward LW flux is a function of

near surface air temperatures, water vapor,

and mid to low-level cloud base heights.

Whereas high altitude clouds have the

largest effect on TOA fluxes, low-level

clouds are most important for downward

LW flux at the surface. Estimates for surface LW flux are typically made using

radiative modeling approximations that use

near surface atmospheric temperature and

moisture, cloud base temperature, and cloud

fraction. These parameters will therefore

control the downward LW flux accuracy,

and continuous verification against a global

network of surface validation sites (e.g.

BSRN and Atmospheric Radiation

Measurement (ARM) program) is essential.

Absolute accuracy at global scale is estimated at about 5 Wm-2 for current state of

the art (e.g. CERES on Tropical Rainfall

Measurement Mission (TRMM) or Terra).

Stability will depend primarily on the stability of satellite estimated lower atmosphere

temperature, water vapor, and cloud base

altitudes. For the values from section 3.2, an

air temperature stability of 0.05 K/decade

translates to roughly

0.2 Wm-2 change in downward LW flux.

Water vapor stability of 0.3% per decade

would cause a 0.1 Wm-2 change in downward LW flux. To first order, cloud base =

cloud top - cloud thickness. Cloud thickness

is roughly proportional to cloud optical depth

for a given cloud type. Using the stability

requirements in sections 3.1.13 to 3.1.16, we

predict a rough stability of 0.3 K/decade

in cloud base temperature or 0.25 Wm-2 in

downward LW flux at the surface. We conclude that the required accuracy and stability

requirements for downward LW flux at the

surface will be achieved, if the instrumental

requirements for air temperature, water

vapor, cloud base temperature, and cloud

fraction are met. Examples of the sensitivity

of surface LW flux to these parameters can

be found in Gupta (1989).

4.1.4 Downward Shortwave Radiation

at the Surface

In general, downward SW surface flux

can be predicted as equal to TOA net solar

radiation (3.1.5) minus within-atmosphere

SW absorption. Within-atmosphere solar

absorption is dominated by water vapor,

cloud water droplet, and cloud ice particle

absorption: these are thought to sum to

roughly 20% of TOA incident solar radiation, and can be estimated with sufficient

accuracy by meeting the instrumental

requirements in 4.1.5 (net solar radiation at

TOA), 4.1.10 (effective cloud particle size),

4.1.13 (cloud optical depth), 4.1.14 (cloud

top height) and 4.2.2 (water vapor). But

additional absorption can be present from

aerosols with black carbon and organic

carbon. Unknown aerosol absorption complicates the determination of clear-sky surface SW fluxes and potentially cloudy sky

SW fluxes as well for low clouds embedded

in absorbing aerosol layers. The instrumental requirement for aerosol single scatter

albedo (4.2.4), however, should be sufficient to satisfy this requirement. Given the

difficulty of measuring aerosol absorption

from space, a combination of aerosol assimilation models and satellite aerosol optical
55

depth, particle size, and composition will

likely be required to constrain aerosol

absorption. In addition, a global network of

aerosol and surface SW measurements that

covers ocean, land, and cryospheric climatic

regions and observes varying aerosol types

(biomass burning, industry, dust, etc) will

be a key complement to the satellite observing system, both for independent assessment as well as for validation. 

4.1.5 Net Solar Radiation at the Top of

the Atmosphere

Accuracy and stability requirements for

net solar radiation are directly related to the

accuracy in TOA SW reflected flux. 1 Wm-2

absolute accuracy is 1% of the average

broadband reflected flux and is a 1% instrument calibration requirement. 0.3 Wm-2 per

decade stability equates to 0.3% per decade

calibration stability for the broadband SW

radiance. 

4.1.6 Outgoing Longwave Radiation at

the Top of the Atmosphere

Absolute accuracy of 1 Wm-2 is equivalent to 0.5% in broadband global average

radiance level for calibration. Stability of

0.2 Wm-2 is equivalent to 0.1% per

decade. 

4.1.7 Cloud Base Height

As indicated in 3.1.7, cloud base height

estimates with the NPOESS instruments

will be dependent on estimates of cloud top

height and cloud optical depth or LWP and

IWP. Sensitivity studies should be done to

verify the changes in cloud base height estimates with changes in the imager channels

used to determine these parameters. It is

expected that the primary factor will be

cloud top height, which is in turn specified

by cloud top temperature. This suggests a

requirement for imager infrared window

channel calibration of 1 K absolute and 0.2

K/decade stability as in 4.1.16. These cloud

base heights, however, are only indirect

estimates. Direct estimates of cloud base

will require active lidar and cloud radar

sensors for global conditions, especially for

polar clouds where cloud detection is difficult against bright snow and ice surfaces

during daylight and against small thermal

contrasts and large temperature inversions

in polar night. 

4.1.8 Cloud Cover

Cloud cover is estimated in the NPOESS

system by the cloud imager. The basic

method is to classify each imager field of

view (350 m or 700 m in diameter) as

cloudy or clear. Requirement for global

average cloud cover is 0.01 absolute accuracy and stability of 0.003 per decade (in

units of cloud fraction between 0 and 1).

Cloud detection is usually achieved via a

multi-channel algorithm that detects cloud

as changes from expected clear-sky spectral

reflectance and thermal emission values at a

range of spectral wavelengths. In some

cases ratios of reflectances or differences in

brightness temperature of thermal emission

are used. Because of the complicated decision trees in these algorithms, there is no

easy mapping of this requirement to

individual channels. In order to better

understand this requirement, sensitivity

studies should be performed using the

MODIS data and MODIS cloud mask algorithm to determine the sensitivity to each

channel’s calibration and stability. In most

algorithms, however, application of the calibration requirements for cloud optical depth

(4.1.13) to all solar reflectance channels,

and for cloud top temperature (4.1.16) to all

thermal infrared channels will result in

sufficient stability and accuracy for cloud

fraction determination.
4.1.9 Cloud Particle Size Distribution

There are no current recommendations

for particle size variance accuracy and stability. 

4.1.10 Cloud Effective Particle Size

A radiative transfer adding doubling

model used to derive look up tables for

cloud remote sensing algorithms was used

to convert the cloud particle size requirements into equivalent instrument gain

accuracy and stability for two of the Visible

and Infrared Scanner (VIRS) spectral channels key to particle size retrievals: 3.7 μm

and 1.6 μm. The results indicate the

following requirements for water and ice

clouds at 1.6 μm and 3.7 μm:

4.1.11 Cloud Ice Water Path

At this time it is not clear that IWP can

be measured accurately enough to meet the

requirements in section 3.1.11. The major

difficulty is the large vertical variation in

ice particle size within a single cloud layer

(up to a factor of 10), while the satellite

remote sensing techniques only derive particle size up to an optical depth of 2 or 3 into

the cloud. Since the same channels are

used for ice particle and water particle size

retrieval, and since the water particle

requirements are tighter than for ice, the

calibration requirements in 4.1.10 are sufficient for any future applications to cloud ice

water path, if the vertical cloud particle size

variations can be handled. The cloud optical depth requirements in section 4.1.13 are

also sufficient to meet any future applications

to IWP. In the future advanced methods will

likely be required for IWP climate measurements including cloud radar and/or sub-mm

wavelength radiometers. 

4.1.12 Cloud Liquid Water Path

There are two methods applicable to

measuring LWP using current and near

future satellite instruments. The first was

discussed in section 3.1.12 and relies on the

simple relationship LWP = K x Tau x Re.

The LWP requirement is set to be consistent

with the imager Tau and Re requirements

and therefore is met by the requirements

4.1.13 and 4.1.10. The second method is

use of a passive microwave imager. This is

a multi-channel, multi-polarization retrieval

of LWP, which is usually combined with a

simultaneous retrieval of surface wind

speed and column water vapor. The major

challenge for microwave LWP is poor accuracy for liquid water clouds with optical

depths less than about 6, and poor spatial

resolution for fair weather cumulus cloud

that are a factor of 10 smaller than the

microwave field of view. The advantage of

the microwave is that it does not depend on

any assumptions of the vertical distribution

of cloud particle size within the cloud layer.

The required accuracy of 0.025 mm and stability of 0.005 mm in the cloud liquid water

path translate into a microwave instrument

accuracy of 0.75 K and stability of 0.2 K. 

4.1.13 Cloud Optical Thickness

The relationship of cloud optical depth to

imager visible channel reflectance is nonlinear. As earlier, we use a radiative transfer

model to determine the relationship at a

range of optical depths. On average, a 2%

change in cloud optical depth results from a

1% change in visible imager radiance. The

sensitivity to radiance change is very large

56

Table 5. Instrumental stabilities and

accuracies at 3.7 μm and 1.6 μm 

for water and ice clouds

Stability

(%/decade) Accuracy (%)

Wavelength 3.7 μm 1.6 μm 3.7 μm 1.6 μm

Water cloud 1 0.5 5 2.5

Ice cloud 2 1 10 5
at high optical depths, and small at low

optical depths. We conclude that

1%/decade stability and 5% absolute calibration is required for the visible wavelength channel on the cloud imager.

4.1.14 Cloud Top Height

As discussed in 3.1.14, cloud height

requirements are derived from cloud temperature requirements. As a result, the

instrument requirements in 4.1.16 for cloud

top temperature apply for cloud top height

as well.

4.1.15 Cloud Top Pressure

As discussed in 3.1.15, cloud pressure

requirements are derived from cloud temperature requirements. As a result, the

instrument requirements in 4.1.16 for cloud

top temperature apply for cloud top pressure as well.

4.1.16 Cloud Top Temperature

Absolute accuracy of 1 K in cloud temperature converts to a 1 K temperature

accuracy for the infrared imager and spectrometer atmospheric window channels

used to determine cloud top temperature.

Stability of 0.2 K/decade also converts

directly to a 0.2 K/decade requirement for

the same infrared window channels. These

levels are less stringent in stability than the

surface temperature determined from the

same channels during clear-sky conditions. 

4.1.17 Spectrally Resolved Longwave

Radiance

Absolute accuracy of 0.1 K is equivalent

to 0.21% of the radiance of a 250 K (the

average temperature of the atmosphere)

blackbody at 11 μm (910 cm-1). The

decadal stability requirement of 0.4 K translates to a 0.1% decadal stability for the

spectrally resolved longwave radiance

instrument. To insure adequate sampling

throughout the diurnal cycle, the instrument

orbit should be chosen so that annual average radiance spectra are sampled to an

accuracy of 0.1 K or better at large spatial

scales over a maximum of the globe, with

special attention given to the tropics

because of their importance to the climate

heat engine. An analysis of the Global

Cloud Imagery dataset (GCI), which provides gridded top-of-the-atmosphere (TOA)

radiances at 11 μm (910 cm-1) at 3-hour

intervals (Salby and Callaghan 1997) has

been performed to evaluate the sampling

accuracies of various possible satellite

orbits (Kirk-Davidoff et al. 2003). This

study indicates that for a single satellite in

low earth orbit, either a true polar orbit or

low-precessing orbit is required to obtain

sampling accuracies of 0.1 K or better over

a majority of the tropics. A satellite in sunsynchronous orbit, even with cross-track

scanning, obtains this accuracy over less

than one-quarter of the tropics.

4.2 Atmospheric Variables

4.2.1 Atmospheric Temperature

Tropospheric temperature is profiled by

using atmospheric molecules that are

assumed to be well mixed throughout the

troposphere. In the infrared, carbon dioxide

lines near 4.2 μm and 14 μm are used. In

the microwave, molecular oxygen lines in

the 50-60 GHz region are used. For sensing

near surface temperature, the same radiative

transfer issues noted with sensing near

surface water vapor apply. That is, the

radiative contrast tends to be small in the

infrared since the surface emissivity is generally near unity over land and ocean and

there is relatively more contrast in the

microwave. Again, the microwave weighting functions are stable but the infrared

weighting functions are a function of the

temperature profile itself. Radiance is also

a non-linear function of temperature, pro57
portional to T4 at 15 μm and T12 at 4 μm.

The temperature data set accuracy and stability requirements described in 3.2.1 are

0.5° C accuracy and 0.04° C/decade stability for the troposphere, and 0.5° C accuracy

and 0.08° C/decade stability for the stratosphere.

For both microwave and infrared

sounders, a 1° C change in deep-layer

atmospheric temperature corresponds to on

order of a 1° C change in the instrument

measured brightness temperature (Tb).

Thus, the temperature data set accuracy and

stability requirements translate into similar

instrument accuracy (0.5° C) and stability

(0.04° C/decade) for the troposphere, and

0.5° C accuracy and 0.08° C/decade stability for the stratosphere. These stability

requirements, which were arrived at independently, closely agree with those from the

NPOESS IORD II (NPOESS, 2001). 

4.2.2 Water Vapor

Weak water vapor absorption lines in the

infrared (on the wings of the 6.7 μm band

or in the water vapor continuum at 11μm -

12 μm) or the microwave (around the

22 GHz water vapor line) are used to

observed emission from the lower atmosphere. Discrimination of water vapor in the

lower troposphere is dependent on the relative contrast between the surface emission

and the atmospheric emission. In the

infrared, both ocean and land surfaces have

emissivities near 1.0, creating a low sensitivity to lower tropospheric water vapor. In

the microwave near 22 GHz, the ocean

emissivity ranges from 0.5-0.6 but the land

emissivity is near 1.0. Because of this,

there is good contrast in the microwave and

a greater sensitivity to changes in lower tropospheric water vapor over the ocean versus infrared techniques. In the microwave,

the water vapor weighting function (i.e.,

change in transmittance with change in logarithm of pressure) is stable and the radiance is linearly related to brightness

temperature. In contrast, in the infrared, the

weighting function is more highly variable

(and is a function of the water vapor profile)

and the radiance is a non-linear function of

temperature (about T8 near 6.7 μm).

Both microwave and infrared water

vapor measurements operate at frequencies

where the expected increase in vapor

accompanying, say, a 1 K warming, leads to

a larger instrument response than 1 K, i.e.,

from a 2 K increase at microwave frequencies to 0 K to 4 K decreases at infrared

wavelengths, depending upon the channel

frequency. Thus, the signal magnitude of

increased humidity might be expected to be

larger than the expected global warming

signal, by a factor of 2 to 4. Unfortunately,

since water vapor is not a uniformly mixed

gas like oxygen (for microwave temperature) or carbon dioxide (for infrared temperature), there are significant data interpretation problems when trying to retrieve water

vapor in the atmosphere from passive measurements.

In the microwave, total column vapor

can be measured near the 22.235 GHz water

vapor line, while tropospheric profiles of

vapor can be retrieved with several frequencies near the 183.3 GHz water vapor line.

Using a 2:1 instrument response factor just

described, we can double the temperature

requirements, i.e. 1.0° C absolute accuracy

and 0.08° C/decade stability requirement for

microwave water vapor measurements.

In the infrared, the response of individual

channels varies widely, but we can assume

an average response factor of around 2 to 4.

For the global warming case in which relative humidity remains approximately

constant, the global average brightness

temperature also remains approximately constant. This is because the radiative impact

of the warmer temperature profile offsets

58
the effect of increased specific humidity in

the free troposphere. The approximate simple relation between an infrared channel

brightness temperature and upper tropospheric humidity is

a + bTb = ln (UTH Pref) (3)

where Tb is brightness temperature, UTH

Pref is upper tropospheric humidity at Pref ,

a reference pressure level, and b = -0.115.

Although UTH depends on both water

vapor mixing ratio and atmospheric temperature, observations indicate that the main

variations are due to the water vapor. This

equation indicates that to detect a 0.3%

change in water vapor requires a stability of

0.03 K in brightness temperature.

4.2.3 Ozone

Estimates of atmospheric ozone can be

obtained from satellite instrument measurements of scattered, reflected and emitted

signals from a wide range of the electromagnetic spectrum. This section provides

details only on instruments that measure

scattered sunlight in the UV and visible

parts of the spectrum. The principal measurements of these instruments are ratios of

Earth radiances to solar irradiances, called

albedos or top-of-atmosphere reflectivities

(TOAR).

Total column ozone (TOZ)

The OMPS algorithms will use Total

Ozone Mapping Spectrometer (TOMS)-

style retrievals even though the OMPS has

spectral measurements. The Differential

Optical Absorption Spectroscopy (DOAS)

retrievals require a very high Signal to

Noise ratio (SNR) (not provided by OMPS)

and spectral coverage. They are used in

retrievals for the Global Ozone Monitoring

Experiment (GOME) series of sensors. EOS

Aura Ozone Monitoring Instrument (OMI)

will have both algorithms applied.

The TOMS-style algorithms use combinations of BUV measurements at two or

three individual wavelengths (called pairs

or triplets) with at least one wavelength

having significant ozone absorption for the

viewing conditions and a second wavelength with much smaller absorption. 

Three types of error contribute to the

total instrument error: wavelength-dependent error, wavelength-independent error, and

wavelength scale error (NPOESS, 2000).

To achieve the required 0.2% stability in the

ozone data, the sum of the contributions of

these three errors must be less than 0.2%. 

The hyperspectral total ozone algorithms

use small scale variations in the observed

albedos corresponding to small scale features in the ozone absorption cross section.

They are even less sensitive to wavelengthindependent errors and can adjust for some

wavelength scale errors automatically, but

require higher SNR measurements and

usually better wavelength resolution. They

are more sensitive to wavelength-dependent

calibration errors, unless the errors are

smooth functions of wavelength, but only

need such smoothness over a limited wavelength interval.

Vertical ozone profiles

This discussion will cover two measurement techniques: systems that measure

backscattered ultraviolet radiances (BUV)

and systems that measure limb-scattered

ultraviolet/visible radiances (LUVV). The

discussion material is broken into stratospheric and tropospheric subsections.

Stratospheric ozone

For BUV instruments, the types of error

are calibration errors at a wavelength

(dependent or independent) and wavelength

scale error (NPOESS, 2000; Bhartia, et al.,

1996).. The sum of the contributions of

these errors to the total error must be less

59
60

than 0.6% to achieve 0.6% stability for the

stratospheric ozone profiles. 

For limb-scattered uv-visible measurements, the following error types apply:

wavelength-dependent error, wavelengthindependent error, wavelength scale error,

and pixel-to-pixel error (NPOESS, 2000).

The pixel-to-pixel error is the spatial error of

measurements of limb-scattered uv-vis radiation. The sum of the contributions from these

four error types must be less than 0.6% to

meet the 0.6% stability requirement for

stratospheric ozone profiles. These inequalities assume that satellite pointing and other

errors do not have significant contributions

to the accuracy. Pixel-to-pixel errors are

important in the height normalization step of

the algorithm. Improperly characterized

detector nonlinearity could be an additional

source of pixel-to-pixel errors.

Tropospheric ozone 

Systems using BUV and LUVV measurements have difficulty determining

tropospheric ozone directly. Estimates may

be obtained by “tropospheric residual” techniques in which one subtracts stratospheric

column estimates from total column estimates. Since the ozone in the troposphere

may be as little as 10% of the total column,

the differences will magnify any errors, e.g.,

a 0.5% error in the total column could produce a 5% error in a tropospheric estimate.

The 1% stability requirement on tropospheric

ozone imposes stability requirements on the

total column ozone and stratospheric column

ozone of about 0.1%, and, hence, instrument

stabilities of this order. There are additional

problems with the lack of efficiency of BUV

methods in detecting TOZ changes in the

lower troposphere. For the systems considered in this section, monitoring tropospheric

changes independently is problematic.

4.2.4 Aerosols

Satellite sensors should be capable of

measuring aerosol optical depth and the

aerosol microphysical parameters (single

scattering albedo, refractive index, effective

radius and effective variance) to the

absolute accuracies defined in section 3.2.4.

The absolute accuracies derived there are

based on an evaluation of the radiatively

significant perturbations in the aerosol

parameters. 

For a multi-spectral polarimeter making

measurements over the spectral range of

400 nm-2500 nm, with multi-angle views of

the same location, the instrumental accuracy

that is implied by the required accuracy

with which the aerosol parameters must be

determined is better than 3% radiometric

accuracy and better than 0.5% polarimetric

accuracy. A relative spectral accuracy of

better than 1% and a relative angular accuracy of better than 1% are also required.

Other instruments (MODIS, MISR, POLDER, and A-band spectrometers) may be

capable of meeting the aerosol parameter

accuracies defined in section 3.2.4 over

some surface types, but the requirements

given here are relevant to the NPOESS

Aerosol Polarimetry Sensor (APS) sensor.

Since this sensor provides a complete sampling of the spectral and angular, polarized

signature of aerosols in the atmosphere it is

expected that the required instrumental

accuracies for other sensors would be more

challenging.

The satellite sensor must be inter-calibrated and validated using AeroNet, and

other networks of surface-based sun photometers. AeroNet has provided aerosol

estimates since about 1993 that meet the

present state of the art regarding accurate

AOD retrievals with an accuracy of approximately 0.01. The major deficiency with
61

AeroNet is the sparseness of its global coverage. Other complementary relatively

dense sun photometer networks with limited

coverage also exist and are useful for

aerosol validation over land (e.g., DoE

ARM Southern Great Plains (SGP) MultiFilter Rotating Shadowband Radiometer

(MFRSR) network and NASA’s Solar

Irradiance Research Network (SIRN)

network). 

4.2.5 Precipitation

The most physically direct passive measurements of precipitation come from

microwave radiometers. The physics

underlying this capability is more straightforward and accurate over the ocean than

over land. The frequency range most often

utilized for this is 10-90 GHz, although lower

and higher frequencies also have utility.

Unfortunately, it is difficult to validate these

instruments’ measurements of precipitation

since in-situ validation data for rainfall (rain

gauges and radars) are probably not accurate to better than 5-10%. Instead, the

errors involved in the measurement of rainfall from these satellites can be estimated

with an error model, but the results will

vary widely depending on the assumed

sizes of individual error components and

assumptions about whether any of these

errors are inter-correlated.

Because of the problems inherent in the

validation of rainfall retrievals to better than

5% to 10%, the (arbitrary) absolute accuracy

requirement of 5% we assumed in section

3.2.5 really refers in this case to potential

biases in our retrieved rain rate statistics.

An accuracy of 5% applied to the average

rain rate (where it is raining) of 2.5 mm/hr,

leads to an accuracy requirement of

0.125 mm/hr. The sensitivity of several

microwave frequencies to rain rate

approaches 10° C for each 1 mm/hr in rain

rate, transforming the instrument accuracy

requirement into 1.25° C, which is now

being met with spaceborne window frequency microwave radiometers.

Of greater importance, again, is the

requirement for sufficient long-term radiometric stability to allow us to determine

climate time-scale fluctuations in precipitation, even though we may not know what

average bias (accuracy) exists in the satellite data record. The measurement stability

of about 0.003 mm / hr when observing rain

(from section 3.2.5), multiplied by the

instrument sensitivity to rain (10° C per

1 mm / hr) leads to an instrument stability

requirement of 0.03° C/decade. As we will

see later, even though this is a stringent

requirement, it is possible that existing

technology could meet it.

4.2.6 CO2

The retrieval of variations in dry-air mixing ratio of atmospheric CO2 from spaceborne instruments is very challenging,

requiring spectroscopic measurements of

0.25 to 0.5% (1-2 ppmv) of the background

values (375 ppmv). The measurements must

be sensitive to variations in the

Atmospheric Boundary Layer (ABL). The

estimation of surface sources and sinks

from such data is even more challenging,

yet the potential benefits for carbon cycle

science and concomitant climate effects

makes it imperative to try. 

4.3 Surface Variables

4.3.1 Ocean Color

The ocean color data set requirements in

3.3.1 translate directly to the satellite

requirements. Since it is clearly not possible

to have a satellite-only parameter retrieval

any retrieval requirement will depend on

in-situ calibration measurements. Thus, a

requirement for this application is the addi-
62

tion of a greater number of in-situ measurement systems such as the MOBY-type buoy

system. The satellite requirements are 5% in

accuracy and 1% for stability.

4.3.2 Sea Surface Temperature

The required SST stability and accuracy

are 0.04 K/decade and 0.1 K. Sea surface

temperatures (SST) are generally measured

at IR window wavelengths. The relevant

equation is of the form 

SST = T1 + 2.5(T1 - T2) (4)

where T1 and T2 are IR brightness temperatures at two IR window wavelengths.

Error analysis of this equation assuming

that T1 and T2 have the same absolute errors

leads to a stability requirement of about

0.01 K for each window wavelength brightness temperature. Required accuracy for the

measurements is 0.1 K. For sensors with

additional channels, such as the

Visible/Infrared Imager/Radiometer Suite

(VIIRS) and MODIS, other SST algorithms

may be more effective. The proposed SST

algorithm for VIIRS is a “dual split window” which uses a brightness temperature

difference at the shorter 4 micron channels

together with the longer 11 micron channel

difference to give a more stable SST estimate. But the above error analysis should

hold for any split window type of SST

measurement.

Microwave observations at 6.9 GHz can

also be used to measure SST. A 1 K change

in SST causes about a 0.33 K change in

observed brightness temperature. The

reduction in sensitivity is due to the low

microwave ocean emissivity of about 0.5

and wind roughening effects. Thus, to maintain a stability of 0.04 K in SST requires

about a 0.01 K microwave instrument stability. Required accuracy is 0.03 K.

These values ignore the influence of sensor pointing angle on SST accuracy for the

passive microwave sensors. Both the passive microwave and the thermal infrared

sensors will require an in-situ

calibration/validation program to insure that

these requirements are met. This in-situ

program must include both skin and bulk

measurements of SST and should be continuous.

4.3.3 Sea Ice

Results of sensitivity studies with the

NOAA automated snow cover algorithm

(see 4.3.4) can also be applied to sea ice.

Visible channel accuracy of 12% and stability of 10% would be required to achieve the

required sea ice area data set accuracy of

5% and stability of 4%.

4.3.4 Snow Cover

It is recognized that if the satellite sensor

requirements for ocean color, SST and sea

ice are met the requirements for snow cover

will also be fulfilled. It was also acknowledged that snow cover computation is

transitioning from a “human in the loop”

snow cover product to an automated system

and it was not clear that the automated system would be able to produce the same

accuracy as the man in the loop system.

Sensitivity studies with the NOAA automated snow cover mapping algorithm

indicate that visible channel accuracy of

12% and stability of 10% would be required

to achieve the required snow cover area

data set accuracy of 5% and stability of 4%.

4.3.5 Vegetation

Specifying the tolerable error in NDVI

requires that the albedos from the individual

bands be within certain bounds. Errors in

channel albedo, ai , propagate into the

NDVI in a way that is dependent on the
63

value of NDVI itself. The equation below

gives the relation between relative

uncertainty in NDVI (shortened to N in the

equation) and uncertainty in the channel

calibration:

(∆N / N) = [(1 - N2) / 2N] (∆ai / ai

) (5)

The accuracy and stability of the albedo

observations needed to achieve the specified relative accuracy of 3% and stability of

1% per decade in the NDVI data set is

dependent on NDVI itself. The average

global vegetation index is about 0.35. This

value leads to a needed stability of 0.8% per

decade and accuracy of 2% for the albedo

measurements and, hence, for the visible

and near infrared measurements upon which

the albedo observations depend. The stability value of 0.8% per decade assumes that

the bands used in derivation of NDVI generally drift in the same direction, which is

practically always the case. Study of

desertification would entail looking at low

values of NDVI, so would require greater

accuracy and stability in (∆ai / ai

). Studies

of changes in vegetation, such as of the

greening of the boreal forests, require less

stringent requirements on (∆ai / ai

). 
64

5. Ability of Current Observing

Systems to Meet

Requirements

5.1 Solar Irradiance, Earth Radiation

Budget, And Clouds

5.1.1 Solar Irradiance

Current Total Solar Irradiance

measurements meet the climate stability

requirements but not the absolute accuracy

requirements. Current spectral irradiance

measurements do not meet the absolute

accuracy or the stability requirements.

NPOESS IORD II (NPOESS, 2001) threshold requirements will meet climate needs,

but absolute accuracy of current and nearterm instruments are such that overlap of at

least a year is essential to meet the climate

stability threshold requirement.

5.1.2 Surface Albedo

AVHRR absolute accuracy in the visible

and near IR is estimated at 5 to 10%, and

MODIS and MISR at 3 to 5%, just meeting

the goal of 5%. Stability of AVHRR after

correction using earth viewing targets (no

on board calibration) is estimated to be 3 to

5% per decade (Rossow and Schiffer,

1999), well short of the goal of 1%/decade.

MODIS and MISR instruments have only

been in space for 3 years, too soon to assess

long-term stability. These instruments do

carry diffuser plates, and comparisons to

these have shown changes of 1-2% per year.

But direct MISR/MODIS comparisons

show systematic differences of +/-3% for

bright and dark earth targets in the two

instrument’s radiances for matched

time/space/viewing angle comparisons.

These differences have not yet been

resolved, but may indicate nonlinear

response in one or both of the instruments. 

MODIS has used partial lunar calibration

throughout its mission, and both MISR and

MODIS began full lunar calibration in

March 2003 using a Terra spacecraft

maneuver to scan the moon. The moon

provides a constant low albedo target in the

dynamic range of ocean and land surfaces.

While snow albedo is high, the solar zenith

angles for polar conditions are typically

low, so that radiance signals from snow can

be lower or larger than tropical land and

ocean values. Lunar calibration using

orbital maneuvers, and overlap of instrument time series appear to be critical for

obtaining multi-decade accurate surface

albedo records. Further analysis is needed

in this area, and experience from the routine

SeaWiFS lunar calibration record over

several years should assist in estimating stability achievable using spacecraft pitch

maneuvers to scan the lunar surface at constant libration and phase angles, assuring a

view of the same surface, illumination, and

scattering angles. 

5.1.3 Downward Longwave Radiation

at the Surface

Current downward LW flux measurements do not meet the climate requirement

of 1Wm-2 absolute accuracy and 0.2 Wm-2

stability per decade. Analysis from recent

EOS CERES data products show absolute

accuracy of about 3 to 5 Wm-2 for global

average when compared against a range of

tropical and mid-latitude ARM, BSRN,

Surface Radiation Budget Network

(SURFRAD), and NOAA/ Climate

Monitoring and Diagnostic Laboratory

(CMDL) surface reference sites. Stability

has not yet been established, as the data

products have only been available for about

a year and a long time record is not yet

available. The analysis discussed in section

3.1.3, however, indicates that if the stability

and accuracy goals for 4.2.1, 4.2.2, 4.1.7,

and 4.1.8 (temperature, water vapor, cloud

base height, cloud cover) can be met, then

the stability and accuracy for downward

LW flux may also be met. 
65

5.1.4 Downward Shortwave Radiation

at the Surface

Current downward SW flux measurements do not meet the climate requirement

of 1 Wm-2 absolute accuracy and 0.2 Wm-2

stability per decade. Analysis of recent

EOS CERES data products indicates

absolute accuracy of about 10 Wm-2 (24 hr

average) when compared to a range of

tropical and mid-latitude ARM, BSRN,

SURFRAD, and NOAA/CMDL surface reference sites. The majority of the surface

sites are over land, with a few on islands.

Aerosols are thought to cause at least half

of the problem over land sites, and islandeffect cloudiness may play a similar role

over most island stations. Given that

oceanic aerosols are lower in optical depth

than over land, bias errors over ocean are

likely much less than 10 Wm-2, and global

average may be closer to 5 Wm-2. But

oceanic buoy and ship-based observations

are required to assess global satellite data

accuracy. Improvements in aerosol measurements over most land surfaces are now

becoming available with the EOS MODIS

and MISR instruments, but issues remain

for aerosol absorption. Stability of this

estimate is not currently known but will

rely most heavily on the stability of top of

the atmosphere SW reflected flux, and on

aerosol optical depth and absorption

determination. 

5.1.5 Net Solar Radiation at the Top of

the Atmosphere

Current CERES observations of Net

Solar Radiation at the TOA meet the

absolute accuracy requirement of 1 Wm-2

(1% in instrument calibration, Priestley et

al., 2000) but the instruments have not been

in orbit long enough to fully verify stability

at 0.3 Wm-2 or 0.3% per decade. Early

results from 3 years of Terra observations

by two CERES instruments indicate less

than 0.3% change in SW channel gain

against on-board calibration sources. Most

of the change occurred during the first year

on orbit, but at least a 5-year record of the

new data will be needed to predict decadal

stability values. 

The absolute accuracy of these radiometers, however, cannot meet the stability

requirement without at least a 3-month

overlap of observations. The TRMM and

Terra mission CERES overlap demonstrated

the ability to intercalibrate to within

0.5 Wm-2 using 1 month of data (95% confidence) by rotating one of the CERES

scanners to align its scan plane with the

other during satellite orbit crossings. This

technique could achieve the 0.3 Wm-2

matching requirement for decadal stability

with a 3-month overlap for 95% confidence.

The NPOESS system will fly a copy of

CERES called ERB starting in 2011.

CERES on the recently launched Aqua

spacecraft nominally will remain in orbit

until the Aqua mission is de-orbited in

2008. Risk of a data gap from Terra/Aqua

CERES to the NPOESS ERB is estimated

at 50% probability, including all known

international mission possibilities

(Geostationary Earth Radiation Budget

Experiment (GERB), Megha-Tropique).

NASA is hoping to close the gap by adding

the final CERES instrument in storage onto

the joint NASA/NPOESS NPP gap-filling

mission planned for launch in 2006. 

5.1.6 Outgoing Longwave Radiation at

the Top of the Atmosphere

Current CERES observations of outgoing

LW flux at the TOA meet the absolute accuracy requirement of 1 Wm-2 (0.5%,

Priestley et al., 2000, 2002) but the instruments have not been in orbit long enough to

verify their ability to achieve 0.2 Wm-2

(0.1%) per decade. Instrument gain

changes of 0.1% to 0.2% per year early in

the mission have been corrected using on-
66

board blackbody sources. But, as for the

net solar radiation at the TOA, overlapping

satellite observations are required to meet

the decadal stability requirement. Gaps

between missions will leave 1 to 2 Wm-2

uncertainty in decadal signals (similar to

5.1.5). See the discussion in 5.1.5 concerning the 50% probability gap between EOS

and NPOESS in 2008 to 2011. 

5.1.7 Cloud Base Height

Current estimates of cloud base height

do not meet the accuracy requirements.

Cloud base height stability is unknown.

Largest problems are for multi-layer cloud

systems and for polar clouds. 

5.1.8 Cloud Cover

Current estimates of cloud cover

absolute accuracy do not meet the climate

requirement of 0.01. Accuracy of recent

CERES and MODIS cloud analysis of the

MODIS imager is estimated to be about

0.05. Largest uncertainties are amounts of

very thin cloud, which remain hard to detect

because of small solar and infrared signals.

Polar clouds present a similar problem. But

the radiative climate effect of very thin

cloud is less significant, so that the uncertainty relative to cloud feedback effects is

smaller than 0.05 would imply. Frequency

distributions of optical depth for cirrus and

trade cumulus, two common cloud types are

peaked at optical depth zero, and decrease

monotonically with increasing optical

depth. These clouds have no modal or

“typical” optical depth. Further work is

needed to determine a more radiatively relevant parameter than simple cloud cover for

climate research. Advances should be possible with the upcoming GLAS and Calipso

space-based lidar missions as well as the

Cloudsat cloud radar. Calipso, Cloudsat,

MODIS, and CERES flying in formation

together in the A-train (starting in 2005)

should be capable of assessing the accuracy

much more rigorously for thin, thick, and

overlapped cloud layers. Stability for current AVHRR, GOES, and MODIS cloud

fraction has not been rigorously determined.

Since many clouds are detected by thresholds set near clear-sky background values, it

is thought that cloud fraction is not very

sensitive to small changes in imager calibration. For example, a common threshold

for detection of clouds over ocean is about

3% reflectivity above the ocean background

value: say 5% ocean background and 8%

threshold. A change in instrument gain of

5% would only change these values to

5.15% and 8.4% respectively, for a cloud

“signal” of 3.25% instead of the true value

of 3%. If 0.05 cloud fraction resides

between 3% and 6% above background

then about a 0.005 error in cloud fraction

would occur. But since the albedo of these

clouds is only 0.045 above background, the

actual radiative effect of cloud missed is only

0.005(0.045)(342) = 0.08 Wm-2. This is less

than the 0.3 Wm-2 per decade SW flux stability requirement, so that for studies of

cloud feedback, this would not be a problem. 

But more serious problems arise if

infrared window channels located at wavelengths such as 3.7 μm, 11μm, and 12 μm

vary in calibration. This is because small

signals of a few K in brightness temperature

difference are used to detect low clouds at

night when visible channels are not available, or for clouds over bright surfaces e.g.

snow, ice. These low clouds would have

only small effects on the TOA LW flux, but

large effects on the downward LW flux at

the surface, especially in the polar regions.

More complete sensitivity studies are

necessary. Note that lidar, which can be self

calibrated against Raleigh scattering, would

be a much more rigorous method to determine cloud layering and cloud fraction trends

at zonal to global scales. Lidar and radar
taken together is the only currently available method that could realistically provide

the climate accuracy requirements for a

complete range of climate regimes and

cloud layering. 

5.1.9 Cloud Particle Size Distribution

No accuracy requirements have been

specified. Further study is needed. 

5.1.10 Cloud Effective Particle Size

There has not been sufficient time in

orbit to establish if the current MODIS

instrument can meet the stability requirements in 4.1.10 for the 3.7 μm and 1.6 μm

spectral channels. The accuracy requirements are within the MODIS radiometric

design goals. Accuracy issues remain,

however, with the effect of three-dimensional

cloud radiative transfer on particle size

retrievals, as well as sub-pixel cloudiness. 

5.1.11 Cloud Ice Water Path

Current estimates of cloud IWP cannot

meet the requirements for absolute accuracy

of 25% or for stability of 5% per decade

whether from shortwave or microwave

instruments. Current algorithms use visible

optical depth and effective radius to predict

IWP but also include contributions from

water cloud in multi-layer cloud conditions.

Very limited comparisons with ARM IWP

reference values show average consistency

to within 20% for single level ice clouds.

Multi-layer errors would be larger. Overall

global mean uncertainty is likely to be a

factor of 1.5 to 2. Methods exist to improve

multi-layer cloud conditions over ocean

backgrounds by combining passive

microwave liquid water path with imager

derived total cloud optical depth and ice

particle size when cloud layers are overlapped. Improvements in both the ARM

IWP reference data (many more cases for a

wider range of cloud types and climatological regions) as well as satellite comparisons

will be necessary. It is not clear yet if the

Cloudsat/Calipso/MODIS space based

cloud IWP will be capable of 25% accuracy

and 5% stability, but it is the more physically sound approach and should provide

significantly higher accuracy than current

imager based approaches. Existing spaceborne microwave radiometers operating

near 90 GHz and 183 GHz have substantial

retrieval errors, approaching 50%, due to

unmeasurable variations and uncertainties

in particle size distribution and the intensity

of upwelling microwave radiation at cloud

base. It is hoped that additional, higher frequencies will help alleviate this problem

somewhat, and these frequencies are currently being tested in aircraft experiments.

A final possibility is passive sub-mm wavelength estimates using microwave scatter

from ice particles. These instruments are

still in aircraft demonstration stage but

provide hope along with lidar/radar of

providing the answer for IWP. 

5.1.12 Cloud Liquid Water Path

Current estimates of cloud liquid water

path have not yet demonstrated absolute

accuracy of 25% and stability of 5% per

decade. Estimates are made by two methods: passive microwave (SSM/I, TRMM

Microwave Instrument (TMI), Advanced

Microwave Scanning Radiometer (AMSR))

for ocean background only, and by cloud

imager using visible optical depth and cloud

particle size (land and ocean). A recent

comparison using TRMM TMI passive

microwave matched with VIRS cloud imager

estimates showed consistency in these two

methods of about 10% for monthly averaged single layer water cloud over 40S to

40N. The passive microwave observations

become noisy for optically thin water

clouds (small signal) and cannot provide the

data over land backgrounds. However, for

67
68

thicker clouds over oceans, microwave

observations can meet the requirement.

Optical methods become saturated at very

large optical depths (greater than 50 to 100)

and cannot see water clouds beneath thick

ice cloud. The combination of active and

passive methods, Cloudsat/Calipso/

MODIS/AMSR, should be able to much

more accurately determine cloud LWP for

all global conditions. Imager LWP stability

will be met if the instrument stability for

optical depth in 4.1.13 and effective particle

size in 4.1.10 are met. Current visible

imagers have not yet demonstrated the

1%/decade calibration stability necessary

(see 5.1.2). 

5.1.13 Cloud Optical Thickness

Current AVHRR, GOES, and MODIS

instruments have not yet demonstrated the

ability to reach the requirements of 5%

absolute accuracy and 1% per decade stability. The MODIS radiometer does appear to

have achieved 5% absolute accuracy for its

visible channel, but the instrument has not

been in orbit long enough to demonstrate

1%/decade stability. The key will be lunar

calibrations and the amount of degradation

in the solar diffuser plate. Lunar calibration

can in principle reach this accuracy but

needs further analysis and verification

(Stone et al. 2002; Kieffer et al. 2002).

Recent work suggests the SeaWiFS could

attain a long-term stability of 0.5% or better

through periodic lunar observations (Kieffer

et al, 2003). The absolute accuracy limit is

the understanding of 3-dimensional cloud

structure. Sensitivity of 10 to 30% in cloud

optical depth is common even for stratocumulus layered clouds. The sensitivity is

most obvious in the dependence of imager

derived optical depth on viewing angle

(Loeb and Coakley, 1998). Given the

importance of three-dimensional cloud

structure, this issue should be addressable

using the GLAS lidar as its orbit precesses

across the Terra and Aqua MODIS imager

swaths. It is not clear if the absolute accuracy can be reached without adding active

cloud profiling from cloud radar and/or

lidar. 

5.1.14 Cloud Top Height

Cloud top height is just a function of

cloud top temperature. See 5.1.16.

5.1.15 Cloud Top Pressure

Cloud top pressure is just a derivative of

cloud top temperature. See 5.1.16.

5.1.16 Cloud Top Temperature

Current AVHRR, GOES, and MODIS

cloud top temperatures have not been verified to reach the 1 K absolute accuracy

requirement at very large time and space

scales. More extensive comparisons have

begun with ARM site vertical lidar/radar

cloud profiles that should soon provide more

rigorous analysis of current capability. Early

estimates show mean accuracies of about 2

K for thick clouds and about 6 K for optically thin clouds like cirrus. A much better

accuracy estimate will be possible using

GLAS and Calipso lidar data with MODIS.

Stability of cloud height is primarily a function of stability in the imager and infrared

sounder or spectrometer channel calibration.

For thick clouds, the relationship will be

one-to-one, so that the 0.2 K/decade requirement in cloud height would require a 0.2 K

per decade calibration stability for the

infrared channels used for cloud height. This

will be met if the imager meets SST stability

requirements and the sounder/spectrometer

meets the air temperature stability requirements. Overlap will be key to assuring the

stability, since absolute accuracy is often less

than 0.2 K for infrared radiometers. 
5.1.17 Spectrally Resolved Longwave

Radiation

Spectrally resolved measurements made

by the AIRS instrument (Aumann and

Overoye 1996) had a planned on-orbit validation level of 3% (Chahine et al., 2000),

equivalent to 1.4 K at 250 K and 11 μm

(910 cm-1). Actual validation experiments

under a limited range of conditions indicate

on-orbit performance may be better than

this design absolute accuracy (Aumann,

2003).

5.2 Atmospheric Variables

5.2.1 Atmospheric Temperature

The Microwave Sounding Units (MSU)

on the NOAA polar-orbiting satellites have

yielded a 24 year record so far, made up by

a total of eight satellites (e.g., Christy et al.,

2000). Overlap between successive MSUs

yield monthly global average standard deviations in the inter-satellite difference

approaching 0.01° C - 0.03° C. The

absolute accuracy of these instruments

appears to be around 0.5° C, which is the

same as the required accuracies stated here.

Monitoring of decadal trends to an accuracy

of about 0.04° C/decade - 0.08° C/decade

over the 24-year period of record has been

achieved. Much of this remaining uncertainty is contributed to less by uncertainty

in intercalibration between instruments than

it is to (1) changes in instrument temperature (causing nonlinearity-induced changes

in calibration) and (2) corrections for drift

of the NOAA satellite orbits through the

diurnal cycle.

Clearly, to maintain this (marginal) meeting of the long term stability requirement

would require continued periods of overlap

(preferably at least 1 year) between

successive satellites throughout the coming

decades. Fortunately, the newer Advanced

Microwave Sounding Units (AMSUs)

appear to have much better absolute calibration than did the MSUs. Preliminary work

with the NOAA-15 and NOAA-16 AMSU

data suggests that their difference in calibration could be as small as 0.10° C. This level

of accuracy is difficult to validate. Different

satellites measure at different times of day,

and so the differences between satellites are

partly attributable to diurnal changes in air

mass temperatures. Radiosonde measurements do not have accuracies to this level,

and even if they were, large numbers of

comparisons to satellite measurements would

need to be averaged together to reduce spatial sampling noise. It is still too early to

determine the long-term stability of the

AMSUs, as the maximum overlap between

successive AMSUs amounts to only three

years at this writing.

The utility of the infrared sounders for

climate monitoring has not been explored as

much as the microwave sounders. This is

partly due to a much higher data rate, leading to a much larger volume of data to be

analyzed, and because of the much greater

influence of clouds on the infrared radiances. The primary instruments have been

the High-resolution InfraRed Sounder

(HIRS), flying with the MSUs since 1979.

It is still too early to tell if the stability and

capability for obtaining measurements in

partly cloudy regions of the new

Atmospheric InfraRed Sounder (AIRS)

instrument on NASA’s Aqua satellite is

sufficient for climate monitoring of atmospheric temperature to the required levels.

5.2.2 Water Vapor

The current microwave capability for

monitoring total column tropospheric vapor

comes from the Special Sensor

Microwave/Imager (SSM/I) instruments,

operating since mid-1987. These instruments have allowed the construction of a

continuous record that has been compared

69
to observed sea surface temperatures (SST),

and suggest an increase in oceanic vapor

consistent with the increase in SST during

1987-1998 (Wentz and Schabel, 2000). The

existing series of SSM/Is appear to be

achieving a stability of 0.2%/decade humidity stability, which is approximately equal

to the 0.26% requirement from section

3.2.2.

Water vapor profiles in the troposphere

depend upon measurements near 183.3 GHz,

channels, which have flown on the SSM/T-2

carried by several DMSP satellites and the

AMSU-B instruments flying since early

1998 on the NOAA polar orbiters. There

has as yet been very little work performed

to document the long-term stability or

absolute accuracy of these instruments.

Absolute accuracy is particularly difficult

since standard methods for measuring water

vapor profiles in the atmosphere are notoriously poor (e.g., Elliot and Gaffen, 1991;

Garand et al., 1992). The most accurate

ground-based methods are expensive, and it

would take many match-ups with satellite

measurements to provide validation.

It is still too early to tell if the stability

and capability for obtaining measurements

in partly cloudy regions of the new

Atmospheric InfraRed Sounder (AIRS)

instrument on NASA’s Aqua satellite is

sufficient for climate monitoring of atmospheric water vapor to the required levels.

5.2.3 Ozone

Current atmospheric ozone observing

systems are not designed to meet the

requirements. Two percent differences are

commonly found in comparisons among

TOMS, GOME and Solar Backscattered

Ultraviolet instrument 2 ( SBUV/2) global

mean TOZ time series during overlapping

periods of their records. Some of the adjustments to SBUV/2 calibrations from SSBUV

underflight comparisons led to ozone profile changes greater than 5% (Hilsenrath et

al., 1995). Even for a time series from the

self-calibrating measurements of

Stratospheric Aerosol and Gas Experiment

(SAGE I, II and III), the break in the data

record from SAGE I to SAGE II is viewed

as a large source of uncertainty in determining long-term trends.

Fortunately, through a combination of

good in-flight monitoring of long-term

instrument changes, overlapping missions

and well-managed ground-based observations (see discussions in WMO, 2001 and

Hilsenrath et al., 1998), the existing ozone

data are able to provide researchers with

information on trends at close to the desired

accuracy (WMO, 1998). For in-flight calibration of BUV instruments, the two most

important techniques were those developed

to maintain the radiance/irradiance calibration and the wavelength scale calibration.

The first technique uses multiple diffuser

(used to measure the solar irradiance and

normalize the radiances which remove

some instrument throughput errors in the

TOAR) working together to better characterize both changes in the instrument

throughput and their own degradation. The

second uses solar Fraunhofer lines or

calibration lamp line sources to track the

wavelength scale. The current and planned

instruments will not meet absolute accuracy

requirements for determining trends. But by

using overlap periods with other satellite

instruments and intercomparisons with

well-calibrated ground stations, their longterm stability should allow their products to

be components of multi-instrument atmospheric ozone data records of climate quality.

The current state-of-the-art for satellitebased BUV ozone measurements is the

result of over 30 years of research and

analysis. Efforts need to be implemented to

ensure that post-launch calibration is of

high accuracy to establish climate quality

70
71

data sets. A short list of some of the most

important areas of development includes: 

1. On-board calibration (e.g., diffusers

for solar measurements and lamp

lines sources); 

2. Vicarious calibration (e.g., ice radiances and spectral discrimination); 

3. Algorithmic and internal consistency

checks (e.g., pair justification and

ascending/descending comparisons); 

4. Algorithms with low sensitivity to

measurement errors (e.g., height

normalization, triplets and DOAS);

5. Inter-instrument comparisons with

similar instruments (e.g., SSBUV

underflights); 

6. Inter-instrument comparisons with

other satellite instruments (e.g.,

matchup data sets, methods using

trajectory mapping with sparse but

accurate occultation-instrument

estimates);

7. Comparisons with ground-based

networks (e.g., Dobson and

Umkehr); and 

8. Comparison to other solar

measurements (e.g., SOLSTICE and

Mg II Index work).

5.2.4 Aerosols

Current satellite sensors (e.g., AVHRR,

MODIS, MISR, VIIRS) have difficulty with

aerosol retrievals over land due to backscattered photons through the target aerosol

from the surface, which causes a noise

proportional to albedo and are therefore

predominantly used to retrieve aerosols

over the ocean or dense dark vegetation.

These sensors are also unable to adequately

retrieve the required aerosol model from

measurements alone and must therefore

make prior assumptions about aerosol

refractive indices and the range and mixtures of size distributions. The lack of

surface noise for upward-looking sun

photometers allows this type of measurement to have excellent accuracy in

retrieving aerosol optical depth and reasonable accuracy in the inversions that are

required to derive aerosol microphysical

model parameters. It is therefore important

that satellite sensors be calibrated and validated against these surface measurements to

allow “AeroNet-like” accuracies to be

approached on a global scale. It should

however be emphasized that the inability of

a satellite instrument to measure a particular

aerosol parameter does not mean that the

parameter can be fixed using AeroNet measurements and then used in aerosol retrievals

globally without any reduction in accuracy.

5.2.5 Precipitation

Our current state of the art in precipitation

measurement from space with radiometers is

represented by the Microwave Imager

(TMI) on the Tropical Rain Measurement

Mission (TRMM), and the SSM/I series of

instruments on the DMSP weather satellites. Unfortunately, it is not known how

well any of these instruments measures precipitation because in-situ validation data

for rainfall (rain gauges and radars) are

probably not accurate to better than 5% to

10% at best. Instead, the errors involved in

the measurement of rainfall from these

satellites can be estimated with an error

model, but the results will vary widely

depending on assumptions regarding sizes

of individual error components and whether

any of the error sources are correlated.

Despite these uncertainties, the SSM/I and

TMI data records clearly reveal climatescale changes in rainfall on the order of ±

10% due to the El Nino and La Nina phenomena. The SSM/I data record (since

mid-1987) is still not sufficiently long to
72

reveal a global warming-related increase in

precipitation, partly due to the large interannual variability in the record, and the drift

of the DMSP satellites through the diurnal

cycle.

5.2.6 CO2

Atmospheric CO2 products derived from

thermal emission spectra (AIRS/AMSU,

Tropospheric Emissions Spectrometer

(TES), Infrared Atmospheric Sounding

Interferometer (IASI), and operational follow-ons) will probably be quite useful in

conjunction with the in-situ data for estimating seasonal and interannual variation in

total CO2 sources and sinks at continental

scales. These data will likely be effective

for detecting gross changes in the carbon

cycle, but the resolution of the retrieved

sources and sinks will not be sufficient for

mechanistic interpretation or modeling.

Passive Near InfraRed (NIR) spectroscopy (Orbiting Carbon Observatory

(OCO), SCanning Imaging Absorption

spetroMeter for Atmospheric ChartographY

(SCIAMACHY)) is expected to allow sub

regional source/sink estimation, and in conjunction with other carbon-relevant remote

sensing (vegetation, biomass, and ocean

color) will probably facilitate a revolution

in verifiable process-based models.

Active NIR spectrometry by laser

absorption and/or LIDAR will resolve diurnal and seasonal biases, provide vertical

profiling, and allow rigorous source/sink

modeling and model evaluation at high spatial resolution. In an assimilation system

along with other satellite and in-situ data,

Although not designed for measuring CO2, some current and planned satellite instruments 

could provide useful information. (Denning, Workshop Invited Presentation).
73

these measurements will allow fluxes to be

estimated from process-based models at the

native resolution of the land and ocean

remote sensing, yet be consistent with

atmospheric mass balance.

5.3 Surface variables

5.3.1 Ocean Color

The following current satellite sensors

are capable of making ocean color estimates: SeaWiFS, MODIS, MEdium

Resolution Imaging Spectrometer

Instrument (MERIS), and POLDER. Some

of these may be meeting the accuracy

requirement of 5% for visible measurements. SeaWiFS, using MOBY for

vicarious calibration, is achieving 5%

accuracy, and, using the moon as a stable

reference, it may be meeting the stability

requirement of 1%/decade. Even with a

good instrument, ocean color requirements

will not be met without in-situ and lunar

supplemental measurements. 

5.3.2 Sea Surface Temperature

The following existing satellite sensors

make measurements that can be used for

SST estimation: AVHRR, MODIS, Along

Track Scanning Radiometer (ATSR)

(including Advanced Along Track Scanning

Radiometer AATSR)), GOES, AIRS, TMI,

AMSR. In spite of this wealth of satellite

sensors presently flying, none of the

available SST products can meet the

requirements set out in this report. If ATSR

data were to be reprocessed, it may be possible to approach these requirements. ATSR

has unique “dual-look” capability to correct

for atmospheric water vapor attenuation.

Likewise, MODIS should be reprocessed to

determine its capability for long-term SST

observations. 

5.3.3 Sea Ice

Currently available visible and

microwave radiometers appear capable of

meeting the sea ice requirements. 

5.3.4 Snow Cover

Currently available visible radiometers

appear capable of meeting the snow cover

requirements.

5.3.5 Vegetation

MODIS and MISR provide sufficient

information to make a good estimate of LAI

and Fraction of Photosynthetically Active

Radiation (FPAR). These estimates are

close to an accuracy requirement of 3% and

a stability requirement of 1% set forth in

this document. 
74

6. Roadmap for Future

Improvements in Satellite

Instrument Calibration and

Inter-Calibration to Meet

Requirements

6.1 Solar Irradiance, Earth Radiation

Budget, And Clouds

6.1.1 Solar Irradiance

Major requirements are to assure a

1-year overlap of total and spectral solar

irradiance measurements, to improve accuracy of spectral measurements, and to plan

for at least two independent instruments to

allow verification of accuracy and stability

in orbit. 

6.1.2 Surface Albedo

Routine lunar calibration appears to be

the only viable current method to assure the

long-term stability requirement. This will

require spacecraft to perform calibration

maneuvers that allow the spectral imagers

to scan the lunar surface at scheduled times

to obtain constant lunar phase angle and

libration. Analysis of the SeaWiFS lunar

calibration experience and comparisons to

the new Terra lunar calibration should be

used to assess the accuracy achievable, the

frequency of lunar calibration required, and

the level of constant libration and phase

angle required to reach a given stability

measure. The linearity of the radiometers is

also critical to verify. Sensitivity studies

should be carried out using current MODIS

data to verify the level of linearity and lunar

calibration required to meet the stability

requirement. 

Another approach to improve calibration

is to constrain the imager derived surface

albedo to agree with estimates using a more

accurately calculated broadband radiometer

(CERES, ERB). For either narrowband or

broadband approaches, aerosol scattering

and absorption can cause significant errors

in surface albedo estimates. Aerosol scattering estimates are improving rapidly with

MODIS, MISR, and POLDER observations.

Aerosol absorption remains highly uncertain and is a significant issue for surface

albedo estimates. All surface albedo estimates use radiances to estimate reflected

flux. Recent advances in multi-angle observations from MISR, POLDER, and CERES

appear to be approaching the accuracy

required.

It is also key to keep the same orbit

sampling for successive missions: both to

improve intercalibration as well as to eliminate errors from changed solar zenith,

viewing azimuth, and viewing zenith causing anisotropy changes to be interpreted as

surface albedo change. Studies using the

new Terra surface bidirectional reflectance

models and comparing surface albedo estimates from Terra and Aqua can be used to

assess this sensitivity to orbit. Uncertainties

in aerosol absorption need to be assessed

for the impact on the stability and accuracy

of surface albedo estimates. 

6.1.3 Downward Longwave Radiation

at the Surface

Sensitivity studies are needed to more

rigorously assess the sensitivity to boundary

layer temperature and water vapor profile

changes. Weather prediction accuracy

requirements are for 1 km vertical layers in

temperature and 2 km in water vapor.

These will be too coarse by themselves to

bound downward LW flux change. 4-D

assimilation models using AIRS/AMSU/

Humidity Sounder for Brazil (HSB) are

beginning and when verified against

radiosonde boundary layer temperature and

water vapor profiles, may be sufficient to

constrain the boundary layer temperature

and water vapor by combining improved
75

model surface and boundary layer physics

with the constraint of the satellite spectral

radiances. Finally, the ability to constrain

cloud base height must be addressed using

the new EOS algorithms as well as new

GLAS, Cloudsat, and Calipso active sounding data. 

Satellite measurements of downward

longwave radiation are validated against

measurements at surface stations. Recently,

some of the longer time records from the

stations of the Global Energy and Water

Cycle Experiment (GEWEX) surface radiation budget (SRB) project have extended

beyond a decade in length, but as yet there

is limited overlap with the new reference

Baseline Surface Radiation Network

(BSRN). Even with this overlap in the

future, accuracy of the BSRN network will

need to be improved to 1 Wm-2, and

stability to 0.2 Wm-2, if full verification of

climate trends and accuracy is to be

achieved. Current instruments are estimated to be accurate to 2-5 Wm-2 in both

absolute accuracy and stability. The BSRN

network also needs to be extended from the

current 20 or so primarily land based sites.

Observations are needed in ocean regions,

from oceanographic research vessels, ships

of opportunity, and ocean platforms, and in

polar regions. 

The international BSRN needs to be

expanded into a true global network with

stable institutional support clearly defined,

as opposed to the current essentially volunteer network. Site locations need to be

driven by climate regime sampling, not convenience. 

6.1.4 Downward Shortwave Radiation

at the Surface

Rapid improvements in TOA SW flux

constraints, cloud optical depth, cloud particle size, and aerosol optical depth (MODIS,

MISR, ASP) are being made, but further

advances are needed in aerosol absorption.

Sensitivity studies are needed to map calibration accuracy/stability effects of each of

these parameters (and their instrument

approaches) into downward SW flux at the

surface. It is likely that the CERES,

MODIS, MISR, and APS calibrations may

be sufficient to meet all but the aerosol

absorption. These instruments, together

with aerosol 4-D assimilation models may

be capable of constraining aerosol

absorption and optical depth with sufficient

accuracy in the future, but they cannot

today. This is primarily a key issue for

clear-sky downward SW fluxes, but may

also have a very significant role for boundary layer cloud as well when the aerosol

layer is over or within the cloud layer. This

suggests that the GLAS and Calipso ability

to vertically profile both aerosols and

clouds are very likely to be critical components both the improve 4-D aerosol assimilation where vertical layering is the key to

tying aerosols to source regions using backtrajectory analysis, as well as the large difference in aerosol absorption from an

aerosol layer placed above or below a thick

cloud layer. Finally, as for LW surface

fluxes, improvements in the surface validation network and data are required.

Accuracy and stability of the surface

radiometers need improvement, especially

for diffuse SW fluxes. Greatly improved

sampling over oceans is also required on

ships, buoys, and ocean platforms. The vast

majority of current surface data is at land

and island sites: both of which differ substantially from open ocean conditions.

Polar regions are also inadequately covered.

While climate regimes should dominate the

selection of LW surface sites, the number of

SW sites needs to include additional sites to

cover major aerosol types. Each of these

sites must have Aeronet class aerosol data

available with the surface SW radiation
data. The international BSRN and Aeronet

programs would provide the obvious starting point for expansion. 

6.1.5 Net Solar Radiation at the Top of

the Atmosphere

The NPOESS ERB instrument needs to

at least meet the current CERES absolute

accuracy and to exceed it in stability. Since

the ERB instrument will use new detectors:

these must be characterized for stability

with solar exposure to UV (TOTAL and SW

channels) as well as time in vacuum.

Overlapping observations are key to achieving the stability requirement and the risk of

this must be reduced from the current 50%

between NASA Aqua and NPOESS ERB

observations. The NPP mission is timed

correctly to cover the gap (late 2006

launch), and NASA has a spare copy of the

current CERES instrument in storage. A

3-month overlap of observations is required

to meet the stability requirement.

There currently is only one planned continuous time series of broadband radiation

data: CERES to ERB. There is a European

Organisation for the Exploitation of

Meteorological Satellites EUMETSAT geostationary broadband instrument (GERB) on

Meteosat Second Generation, but its current

instrument lifetime is estimated at 1 to 2

years. There are plans to place GERB on 3

future Meteosat platforms, but the platform

life is nominally 7 years, so that large gaps

in the data record are likely. A second and

independent set of broadband radiation is

needed to allow independent verification of

the CERES/ERB time series. Absolute

accuracy should be at least 1%. The technology should differ from CERES/ERB, the

time series should be overlapped and

continuous, and space/time sampling should

be sufficient to allow continuous inter-calibration with the CERES/ERB record. The

optimal method would be a full broadband

spectrometer (0.3 μm to 4 μm) that covers

at least 99% of the earth reflected solar

spectrum and is linear to better than 0.2%. 

NIST spectral calibration sources and

transfer radiometers are needed to cover the

full reflected solar spectrum from the Earth. 

6.1.6 Outgoing Longwave Radiation at

the Top of the Atmosphere

The discussion on NPOESS ERB instrument requirements and the NPP mission

requirements in section 6.1.5 applies to outgoing longwave radiation measurements

also except a 1-month overlap of observations is sufficient instead of 3 months

overlap to meet the stability requirement. 

Again as discussed in Section 6.1.5, a

second and independent set of broadband

radiation data is needed allow independent

verification of the CERES/ERB time series.

Again the absolute accuracy should be at

least 1%. As noted before the technology

should differ from CERES/ERB, the time

series should be overlapped and continuous,

and space/time sampling should be sufficient to allow continuous inter-calibration

with the CERES/ERB record. The optimal

method would be a full broadband spectrometer (4 μm to 100 μm) that covers at

least 99% of the earth emitted thermal

spectrum and is linear to better than 0.2%.

Aircraft and balloon instruments to cover

the full longwave spectrum at very high

accuracy exist and are being tested and

improved. This development should

continue and evolve into an independent

verification of longwave flux measurements

using global scanners like CERES and ERB.

Sampling must be sufficient to allow intercalibration to 0.2% at 95% confidence in no

longer than 6 months of overlapping data.

Again NIST spectral calibration sources

and transfer radiometers are needed to

cover the full emitted thermal infrared

spectrum from the Earth. 

76
6.1.7 Cloud Base Height

Lidar and cloud radar are the only

methods that currently appear capable of

meeting the cloud base height requirements.

But GLAS was just launched, and Cloudsat

and Calipso launch in 2004. These data,

together with the MODIS estimates of

cloud base, are critical to assess this accuracy and stability capability. Even lidar and

radar, however have their challenges. Lidar

does not penetrate to the base of the lowest

cloud layer in about 20 to 30% of cases

globally. Lidar can, however, be self calibrated against Raleigh scattering, and the

lidar vertical resolution is less than 50 m in

the boundary layer. Cloud base for water

clouds, however suffers from multiple

scatter stretching which needs further

evaluation for accuracy in determinations of

cloud base from lidar. Cloud radar has

much less of an attenuation problem and

will observe most of the multi-layered

cloud that the lidar misses due to attenuation. Cloud radar challenges are: low sensitivity to small particle water clouds,

absolute calibration, and a relatively coarse

500 m vertical resolution. Accurate assessment of cloud base data from space will rely

on a network of surface site lidars. Such an

international network is now in development

but exists at very few sites (Welton et al.,

2001). These sites need to be expanded to

include all significant climate regimes as

defined by cloud height distributions. In

particular, ocean and polar cloud regions

will need to be rigorously sampled. Early

sites are focused on traditional mid-latitude

and tropical land sites. These will not be

typical of open-ocean or polar regions. 

6.1.8 Cloud Cover

Sensitivity studies are needed to map

cloud cover requirements into instrument

requirements. The recently developed

MODIS cloud algorithms could be used in

these studies. A key issue is the wide variability of cloud optical depth and cloud

detection/masking over bright or highly

variable backgrounds. Results must be converted into equivalent SW and LW cloud

radiative effects to avoid unrealistic requirements. For example, very thin high may be

difficult to detect but may have very little

radiative effect. The accuracy and stability

metrics for TOA and surface fluxes can be

used as a guide to determine cloud cover

accuracy and stability requirements for different cloud types. The larger the radiative

impact, the tighter the accuracy constraint. 

A second key study is verification of the

MODIS derived cloud cover and layering

against the GLAS, Calipso, and Cloudsat

active cloud measurements. Calipso and

GLAS should provide the most accurate

cloud cover determinations. It is likely that

meeting the climate accuracy and stability

at large time/space scales (zonal to global)

will require both lidar and radar active

instruments. If this is the conclusion, then

the lidar and radar will be required as a

routine part of the climate observing system. 

6.1.9 Particle Size Distribution

Sensitivity studies are required to further

assess this climate requirement, its instrument requirements, and any further

developments that might be necessary.

6.1.10 Cloud Effective Particle Size

Sensitivity studies are required to further

assess this climate requirement. This may

be one of the most stringent calibration

requirements for imagers like MODIS and

VIIRS to meet. NIST standards at wavelengths near 1.6 μm, 2.1μm, and 3.7 μm

may need improvement to meet this

calibration and stability requirement. 

6.1.11 Cloud Ice Water Path

Several new methods to derive cloud Ice

Water Path need further assessment: the use

77
of only the MODIS imager, a combination

of passive microwave (AMSR or TMI) and

passive imager (MODIS, VIRS) over ocean

backgrounds, lidar/infrared window channels (Icesat, Calipso) for thin ice cloud

layers, lidar/radar/visible channel/near-IR

channel (Calipso, Cloudsat, MODIS) for

moderate to thick ice cloud layers, and

finally sub-mm/far-infrared radiometers and

spectrometers for moderate to thick ice

cloud layers. The best reference for validation of these approaches will be ARM-like

lidar, doppler cloud radar, and radiometer

approaches verified in turn against in-situ

aircraft microphysical data. Further work is

needed to cover a complete range of ice

cloud types, especially for optically thick

clouds. The international community

should also work to expand the ARM surface site network into missing climate

regimes such as tropical land where deep

convection is much stronger than over

ocean backgrounds and is likely to change

cloud IWP and microphysics. It is likely

that only a combined instrument approach

(Cloudsat, Calipso, MODIS) will achieve

climate accuracy and stability from satellite

observations. Further work is also needed

to assess and verify that cloud radar

calibration (ARM sites and Cloudsat) has

sufficient accuracy and stability to meet the

requirement. Improved approaches to

calibration of space-based radar systems

may be required. 

6.1.12 Cloud Liquid Water Path

Imager calibration needs for cloud liquid

water path are the same as those for visible

optical depth and cloud effective particle

size (6.1.13 and 6.1.10). Further work on

imager channel stability is needed using

lunar calibration. But imagers alone are

unlikely to meet the climate requirements.

Passive microwave (SSM/I, TMI, AMSR) is

an independent method over ocean backgrounds that should be capable of the

accuracy and stability for moderate to thick

water clouds. But recent instruments (TMI,

AMSR) have shown worse calibration accuracy than SSM/I and further improvements

are needed in the future NPOESS versions.

Further sensitivity studies are needed to tie

the LWP accuracy/stability goals to passive

microwave calibration values. This is

required because a wide range of channels

is used in LWP derivation from these instruments. Combining passive microwave for

moderate to thick LWP, and imager for low

LWP cases should have the accuracy potential over ocean backgrounds. Over land,

additional cloud radar data is likely to be

required to replace the passive microwave,

which is ineffective over land backgrounds.

Extensive validation will be required to

assess if the imager plus cloud radar can

meet accuracy over land backgrounds. The

r6 sensitivity of radar to particle size means

that combination with imager data will be

critical for LWP over land. Validation of

any of these retrievals requires up looking

passive microwave. Additional sites are

required to validate over a complete range

of water cloud types and boundary layer

conditions. In particular, additional data are

required over ocean from ships and platforms, and in the tropics over land. The

four ARM sites are the current reference for

this validation but do not cover all climate

regions. 

6.1.13 Cloud Optical Thickness

Further assessments of the SeaWiFS and

MODIS attempts at lunar calibration are

key to verifying the ability to reach the

climate stability requirement. Further

assessment is also required for linearity of

response for current imagers. This will be

especially key if lunar calibration is used as

the stability reference. Zero levels can be

verified from nighttime observations, and

78
low reflectivity levels from lunar calibration. But high reflectivity cloud is only verified through linearity of the sensors.

Independent calibration with diffusers and

lamps are key contributions, but may not

achieve the 1%/decade stability. Current

diffusers degrade with exposure, and lamps

degrade with use. Improved stability lamps

and solar diffusers or other solar calibration

sources should also be examined. Multiple

calibration references are critical for

stability. Lunar, lamps, and diffusers used

in conjunction may be sufficient to reach

the stability goal. Another option that

should be assessed is flight in inclined orbit

of a highly calibrated spectrometer that

could be used to intercalibrate the imager

data. This could provide key independent

calibration of a wide range of imager

reflectance channels if it covered the range

from 0.4 μm to 2.5 μm. Overlapping observations will be required to maintain the

stability requirement, since absolute

accuracy is insufficient.

6.1.14 Cloud Top Height

This is a function of cloud top temperature and is discussed in 6.1.16.

6.1.15 Cloud Top Pressure

This is also a function of cloud top temperature and is discussed in 6.1.16.

6.1.16 Cloud Top Temperature

Overlapping observations will be

required to meet the 0.2 K/decade stability

of infrared window and sounder channels

used to determine cloud top temperature.

For infrared sounder multi-channel

retrievals, sensitivity studies need to verify

the interchannel calibration consistency

required to maintain accuracy and stability.

This should be straightforward with current

MODIS and AIRS algorithms. One of the

limitations in calibration of current instruments is the inability of the blackbody to

vary its temperature over a controlled range.

This ability would allow more direct verification of channel gain, linearity, and separation of gain from offset or zero level.

Scanning mirrors can be a problem because

of scan angle dependent emissivity of the

mirror (MODIS). 

In many cases, deep space calibration is

needed to verify zero levels. This is especially important for thermal instruments

since they in essence have “stray light”

emitted from the instrument itself. The

instruments in turn vary in temperature

through the orbit as a result of varying

amounts of solar heating. Unlike solar

reflectance channels, they cannot use the

night side of the earth to verify zero radiance levels. We recommend that all thermal

instruments be capable of verifying zero

radiance levels using deep space scanning.

This type of spacecraft maneuver has been

carried out by ERBS, TRMM, and

SeaWiFS in the past, and will soon be initiated on the EOS Terra mission.

6.1.17 Spectrally Resolved Longwave

Radiation

The NPOESS CrIS instrument has

design accuracies of 0.45% between

650 cm-1 - 1095 cm-1, 0.6% between

1210 cm-1 - 1750 cm-1, and 0.8% over

2155 cm-1 - 2550 cm-1. These design

accuracies correspond to an absolute accuracy in equivalent temperature of 0.29 K -

0.18 K between 650 cm-1 - 1095 cm-1,0.22 K -

0.15 K between 1210 cm-1 -1750 cm-1, and

0.16 K- 0.14 K between 2155 cm-1 -

2550 cm-1.

To meet the spectrally resolved longwave radiation at 910 cm-1, the absolute

accuracy of CrIS would need to improve by

about a factor of 2. Additionally, the diurnal

sampling requirements of this CDR are

79
80

severely impaired by sun synchronous orbit

(Kirk-Davidoff et al. 2003)-either a true

polar orbit or a low precessing orbit is

preferable to meet to the goal of achieving

annual average radiance accuracy of 0.1 K

over large spatial scales, over majority of

the tropics and a large fraction of the rest of

the globe.

To meet the absolute accuracy calibration requirements in the pre-launch phase, a

program of laboratory comparisons between

so-called “source-based” radiance scales

(Fowler et al. 1995) and “detector-based”

radiance scales (Brown et al. 2000) from

NIST is necessary to establish the spectrally

resolved absolute infrared radiance scale

and evaluate the instrument’s “native” radiance scale. An instrument designed to meet

the CDR should then provide a means of

on-orbit evaluation of the drift of the instrument native scale from its pre-launch value.

An inter-calibration with a second spectrally

resolved instrument based on different

sensor technology and meeting the same

standards of pre-launch calibration and

on-orbit diagnostics would provide the maximal demonstration of accuracy achieved

on-orbit, in accordance with one of the

overarching principles listed in Section 2

above: Acquire independent space-based

measurements of key climate variables.

6.2 Atmospheric Variables

6.2.1 Atmospheric Temperature

Microwave instruments

For microwave instruments, it is possible

that the current AMSU design is sufficient

in both long-term stability and absolute

accuracy to meet the climate requirements.

This, however, is difficult to determine

either empirically (due to a lack of sufficient data analysis) or through engineering

Proposed dual interferometer for very accurate spectrally resolved longwave radiation 

observations (Goody, Invited Workshop Presentation). 
81

analysis. Although not driven by climate

requirements, the AMSUs had a very carefully thought out calibration design. In any

event, the AMSU observations will likely

end in another ten years, at which point the

Conically-Scanning Microwave Imager

Sounder (CMIS) and the Advanced

Technology Microwave Sounder (ATMS)

will take over the microwave temperaturemonitoring task. It is not clear whether

engineering analysis has been done to

determine if any of these instruments have

sufficient accuracy and stability designed

into them to meet the climate monitoring

requirements.

Some of the instrument issues that need

to be addressed are discussed in the

following sections. 

6.2.1.1 Microwave instrument linearity

Microwave radiometers are slightly nonlinear devices. These non-linearities need

to be accurately measured prior to launch,

at a range of instrument temperatures.

While, the pre-measurements of the MSUs

were insufficient (Christy et al., 2003), the

measurements of the AMSU instruments

were much more extensive. Multi-point (as

opposed to two-point) calibration strategies

might need to be explored. Better understanding of the causes of radiometer nonlinearity is needed.

6.2.1.2 Target temperature gradients

Calibration targets need better thermal

stability as well as a better understanding

and characterization of the combined thermal and electrical (emissivity or complex

reflection coefficient) properties. On orbit,

it is absolutely essential that the hot calibration target be maintained at a uniform - less

than 0.1° C variation - temperature throughout its extent. A sufficient number of

precision thermistors need to be embedded at

the surface and at various depths within the

target to characterize the calibration targets

thermal field. New techniques for maintaining target temperature uniformity (e.g.

microwave transparent, but infrared-and

solar-opaque enclosures) should be

explored.

6.2.1.3 Microwave antenna patterns, illumination, and feedhorn spillover

The amount of feedhorn energy that does

not come from reflection off the antenna

(during Earth observations and cold space

observations), or from the warm load (during warm calibration target viewing), leads

to substantial uncertainty in absolute

calibration. Pre-launch measurements of

feedhorn spillover off the antennas and calibration target(s) must be more accurate than

have been achieved to date. The feedhorn

spillover needs to be measured to an accuracy of 0.2% in order to meet the absolute

accuracy requirement. New methods of

virtually eliminating spillover in the

antenna design should be explored. 

6.2.1.4 Radiometer sub-component

temperatures

Accurate temperature measurements of

subcomponents in the radiometer are needed. The subcomponents should be enclosed

in a thermally uniform and stable environment with spatial and temporal gradients

less than 0.2 K.

6.2.1.5 Instrument pointing accuracy

Better pointing angle characterization is

required. The Earth incidence angle needs

to be precisely known, i.e., to an accuracy

of about 0.03 degrees.

Infrared instruments

Calibration issues for the infrared

sounders are more complex than those in

the microwave region, and will require substantial work to meet the absolute accuracy

and long-term stability requirements. 
There are numerous potential calibration

problems affecting infrared sounders that

may hinder creation of climate-quality

datasets. Many of them can be avoided or

minimized with intelligent design and careful construction of the sounder instruments.

Residual problems still necessitate careful,

complete instrument characterization before

launch, corrective algorithms in the calibration processing, and continual performance

monitoring and frequent validation on orbit.

The importance of the calibration and

characterization activities before launch

cannot be overstated. Too often budget and

schedule shortfalls are made up by curtailing the effort at the end of the instrument

procurement contract, which is, as luck

would have it, the calibration and characterization activity. It is rare enough that

calibration and characterization are actually

given their pre-planned level of effort. It is

almost unheard of that the level of effort for

calibration and characterization could actually be increased if unexpected problems

cropped up (and unexpected problems

almost always do) and more time was needed to understand and rectify them. Yet,

without such dedication, we pay a penalty

in accuracy and stability of the observations

throughout the lifetime of the sensor in

orbit. 

From our experience with current

sounders, we have found that the following

are major issues that need to be considered

for climate datasets:

1. Lack of knowledge, stability, and

consistency of spectral response.

Application of sounder data requires

knowledge of the spectral response

in all sounder channels. Errors in

spectral response functions cause

errors in calculated radiances, leading in turn to errors in derived

products. Users often invoke empirical corrections for such errors, but

empirical corrections usually do not

accurately reproduce the scenedependencies of Spectral Response

Function (SRF) associated errors.

SRF errors may also be quite different on different satellites, and this

will introduce discontinuities in time

series spanning a sequence of satellites. To avoid these problems, the

SRF must be carefully measured

before launch. If the instrument

vendor provides SRF measurements,

an independent institution should

make corroborative measurements.

Finally, we usually assume that for a

given instrument, the SRF remain

invariant on orbit. If that were not

true, the resulting data set will contain spurious drifts. Therefore, development of filters known to remain

stable under conditions encountered

in space, as verified by testing under

simulated conditions in the laboratory, is essential. In addition, development of on-orbit techniques to

measure spectral response should be

considered. It is likely that SRF

errors will be smaller for the new

generation of hyperspectral sounders

than they are for the current generation of filter radiometers. 

2. Errors in on-board blackbody radiances: The calculated radiance of the

internal blackbody is one of the

anchor points of the on-orbit calibration. During pre-launch testing, the

calibration of a laboratory blackbody is transferred to the internal

blackbody. Therefore, the radiances

of the laboratory blackbody must be

known extremely accurately. Up to

very recently, those radiances were

computed from the temperatures,

measured by embedded thermistors,

82
83

inside the laboratory blackbody.

Traceability to NIST was through

the calibration of the thermistors,

but there was no guarantee that the

radiances were known accurately.

Possible non-blackness, poorly

known thermal gradients, and

scattered radiation could affect the

laboratory blackbody and reduce

confidence in the accuracies of the

calculated radiances. Now, however, NIST has developed the

capability to transfer the NIST radiance scale to a laboratory blackbody

with a portable calibrated radiometer, the Thermal Transfer

Radiometer (TXR). This will

improve the calibration of the laboratory blackbody and thus that of the

internal blackbody as well. 

Despite an accurate pre-launch

calibration, the accuracy of the

radiances of the ICT on orbit can be

compromised by phenomena such as

scattered radiation from solar-heated

components of the sounder during

the calibration process, and thermal

lag within the blackbody during

periods of rapid heating and cooling.

These phenomena, which are worst

in polar orbiters near the terminator

and in geostationary satellites near

local midnight, should be minimized

in the design of the sensor, as they

are extremely difficult to correct for

after the fact. 

3. Inadequate treatment of nonlinearities in response (Response here

means the increment in instrument

output [e.g. counts] resulting from a

unit increment in incident radiance):

Nonlinearities introduce observation

errors whose magnitudes vary with

scene temperature. It would be best

if sounders were built with small or

negligible nonlinearities. Failing

that, the nonlinearities must be accurately characterized as functions of

instrument and scene temperature in

pre-launch testing and that information should be applied during

on-orbit calibrations.

4. Dependence of instrument throughput

on scan angle (e.g. polarizationinduced dependence of reflectance of

scan-mirror on cross-track scan

angle): This phenomenon can cause

significant systematic calibration

errors when calibration sources (and

Earth scenes) are not all at the same

scan position. Best avoided by intelligent design, it can also be accounted

for in on-orbit processing. Best

results require data from both prelaunch measurements and occasional

large-angle scans of space on orbit.

The latter may require special, and

possibly inconvenient and/or

dangerous, spacecraft maneuvers.

5. Random effects-e.g., noise and striping: Normally, effects of noise and

random detector-to-detector striping

are reduced to insignificance by

averaging, and averaging is usually

appropriate with data intended for

construction of long-term or global

datasets. However, extremely high

noise for long periods of time (as

has affected the GOES-8 sounder)

and systematic biases (often resulting from failure of a sounder

component), cannot be overcome by

averaging and thus present a more

serious difficulty.
84

6.2.2 Water Vapor

The microwave radiometer issues for

water vapor are not quite as stringent as for

temperature, but the instrument design

issues remain the same as those described

above for temperature. Design issues for

infrared sounders for water vapor measurements are similar to those for temperature

observations.

6.2.3 Ozone

While current and planned BUV and

LUVV instrument calibration tests make

use of lamps, spheres, and diffusers traceable to NIST standards (Hilsenrath et al.,

1998), there is need for improved consistency from test to test whether for a single

instrument, a series of instruments or instruments with different designs. In other

words, standard and well-documented

practices need to be employed. This is particularly applicable to other instruments

employing BUV techniques such as

GOME-2 flying on MetOp. Data from this

instrument are likely to be compared or

even incorporated into the long-term ozone

record; therefore, common calibrations are

essential. There is also a need for increased

accuracy beyond the current capability at

the less than 3% level of radiance/irradiance

calibration to 1%. Accurate determination is

also needed for characterization of the

wavelength scales, bandpasses, fields-ofview uniformity, nonlinearity of responses,

out-of-band and out-of field stray light

contributions, imaging and ghosting, and

diffuser goniometry. Much can be learned

about BUV and LUVV instrument performance when it used to view the zenith sky

from the laboratory. This procedure should

become part of standard instrument prelaunch testing. Tropospheric residual

techniques (differences between total column and stratospheric column ozone

estimates) require accurate intercalibration

of the instruments or wavelength ranges

producing the two estimates. In particular,

the generation of accurate tropospheric

residuals from the differences between TOZ

from BUV and stratospheric columns from

IR instruments will require improved

characterization of the physical quantities

for ozone absorption and emission.

New instruments such as OMPS, which

have more advanced technologies, must be

further calibrated over what has been done

in the past for TOMS and SBUV/2. These

include full calibration and instrument characterization in vacuum with a measure of

temperature sensitivity of wavelength and

radiometric stability. Because of the

advanced algorithms, instrument characterization should include a measure of

instrument response when viewing a gas

cell containing known amounts of ozone.

The use of new detector technologies in

the form of Charge-Coupled Device (CCD)

and linear array detectors poses additional

challenges for instrument calibration and

characterization work. Instead of a single

shared photomultiplier tube for all the

measurements, different pixels are used for

the different wavelengths. Characterizing

thousands of pixels and monitoring their

behavior in space will require new techniques. The new technology will also

introduce new problems, e.g., the need to

monitor CCD charge transfer efficiency.
85

In addition to direct calibration of the

satellite instruments, ground-based

instruments providing measurements for

comparisons of radiances or atmospheric

ozone estimates need calibration and standards. For the Dobson network, stations

trace their calibration to Instrument #83, the

world standard. A triad of instruments in

Toronto, Canada monitors the stability of

the Brewer network. NIST regularly participates in very useful workshops and

intercomparison campaigns for surface UV

measurements. Methods to use surface

measurements to validate satellite-measured

radiances are under development.

Instruments on NASA’s Earth Observing

System (EOS) satellites will break new

ground in providing space-based measurements. The Spectral Irradiance Monitor

(SIM) of the Solar Radiation and Climate

Experiment (SoRCE) will provide highly

accurate solar spectra. These measurements

can be used to assist in tracking the performance of BUV and LUVV instruments.

6.2.4 Aerosols

Although relative spectral and relative

angular calibration is important in the

retrieval of many of the required aerosol

parameters (size distribution and refractive

index, respectively), the optical depth

retrieval from intensity measurements is

dominated by absolute radiometric calibration. The calibration issues described for

other solar backscattering observational

instruments are therefore also directly appliTo provide more confidence in satellite ocean color observations, more Marine Optical 

Buoys (MOBYs), or similar systems with successor technologies, should be deployed at 

additional ocean locations (McClain, Workshop Invited Presentation). 
cable to aerosol requirements (e.g., VIIRS,

SBUV, SAGE). The future use of

polarization measurements requires accurate

polarimetric calibration and characterization

on the ground with consequent needs for

characterization of any instrumental birefringence and instrumental polarization and

effective methods to polarimetrically

calibrate on orbit.

6.2.5 Precipitation

The microwave radiometer issues for

precipitation are the same as those for

temperature discussed in Section 6.2.1. 

6.2.6 CO2

Any spatially coherent bias in the CO2

retrieval will be misinterpreted by the

assimilation system as a source or sink, so it

is crucial that any such biases be extremely

well characterized and documented through

calibration/validation activities. Expected

sources of such biases are land-sea or vegetation contrasts in surface spectral

reflectance, atmospheric aerosol, cloud

effects, and solar and viewing geometry.

Each of these potential sources of bias must

be characterized by vigorous in-situ measurement campaigns that are designed to

account for the vertical weighting function

of the satellite retrieval. Temporal biases

associated with diurnal and seasonal cycles

and cloud vs. cloud-free columns will also

need to be characterized and documented so

that they can be accounted for in the

assimilation system. 

Measurements necessary to fully characterize these spatial and temporal biases in

the satellite retrievals will include airborne

campaigns to measure vertical structure and

spatial variations, continuous high-precision

measurements from tall towers to characterize diurnal and seasonal cycles and cloud

effects, and upward-looking ground-based

FTIR spectrometry which can retrieve column CO2 simultaneously with the satellite

instrument. Orbiting Carbon Observatory

(OCO) includes a “stare mode” of operation, which will allow the instrument to

observe the column over these fixed

stations for on-board calibration and validation. Airborne campaigns and FTIR

spectrometer siting must be designed to

span possible sources of potential bias

(geographic, solar zenith angle, aerosol,

land/sea placement).

It cannot be emphasized too strongly that

the error characteristics of the satellite CO2

retrievals must be reported and documented

in as much detail as possible! Data assimilation and transport inversions of these data

will have to balance spatially dense satellite

retrievals with sparse but extremely accurate in-situ data. This will be done by specifying an error covariance matrix for all CO2

data, and simply filling this matrix with a

spatially and temporally uniform value

(e.g., CO2 retrievals are uncertain at +/- 2

ppmv) will render the data nearly useless

for the source retrieval. Atmospheric inversions and CO2 assimilation calculations will

be improved by reporting of spectroscopic

errors, vertical weighting functions and

averaging kernels, and cloud masking in

every column retrieval, not just the global

or time mean. These error characteristics as

estimated in retrieval algorithms should be

considered a crucial part of the “product”

suite from any CO2 instrument.

Because of the great difficulties in measuring CO2 with passive instruments, active

systems (e.g., lidar) should be developed.

6.3 Surface Variables 

All the surface variables are measured by

visible/infrared and microwave radiometers.

Recommendations for improving calibration

86
87

and characterization of these instruments

have already been discussed in sections 6.1

and 6.2. However, some surface variables

have several unique validation issues.

Both the IR and microwave measurements are sensitive to a very thin layer of

the ocean’s surface - in the case of IR to a

microns thin skin layer and for microwave

to a layer of centimeters in thickness. But as

discussed earlier, the richest source of

ground truth comes from ships and buoys,

which measure not the skin layer temperature but the ocean temperatures at a depth

of about a meter or more. The satellite

techniques are adjusted to account for the

normal difference between these two

temperatures, but the adjustments are not

perfect. To more accurately validate the performance of satellite radiometers measuring

SST, an on-going program of radiometric

observations of ocean skin temperatures

from ships and other platforms should be

initiated.

The ocean color signal in satellite

observations is masked by the atmospheric

contribution, which accounts for 90% of the

observed radiance over the oceans. Ocean

color observations have been validated by

intensive in-situ radiometric measurements

from a specially designed ocean buoy

(MOBY). To provide more confidence in

satellite ocean color observations, more

MOBYs, or similar systems with successor

technologies, should be deployed at

additional ocean locations.

The satellite observed Normalized

Difference Vegetation Index (NDVI) is a

measure of density and vigor of surface

vegetation, but no ground truth exists to

validate this measurement. Some algorithms

used for processing these satellite observations correct for the atmosphere to derive a

value of the NDVI at the earth’s surface

rather than that observed from space.

Consideration should be given to validation

programs using VIS/IR radiometers similar

to those in space to measure NDVI in areas

with different vegetation conditions. 
88

7. Concluding Remarks

Perhaps for the first time a large group

of climate data set producers/users and

instrument experts assembled to discuss the

problem of measuring global climate

change from space. The group attacked the

problem using an end-to end process: establishing accuracy and long-term stability

requirements for key climate data sets;

translating the data set requirements into

satellite instrument requirements; evaluating

the capabilities of current satellite instruments to meet the observing requirements;

and developing requirements and recommendations for improving satellite instrument calibration and associated activities. In

addition to specific recommendations, the

workshop developed a set of overarching

principles for satellite systems, satellite

instrument calibration, and climate data

records that should guide high quality

climate observations in general. This workshop report should serve as valuable

guidance for the Federal agencies responsible for implementing the nation’s satellite

program for monitoring global climate

change. A follow-up workshop to discuss

implementation of recommendations is in

the early planning stages.
89

8. Acknowledgments

We are grateful to the NPOESS-IPO

and the NIST for their generous financial

support, and to D. Pollock and the administrators of the University of Alabama at

Huntsville for organizational assistance, for

the workshop. We appreciate the careful

and detailed reviews of the complete workshop report by J. Sullivan and S. Bruce.

We thank M. Weinreb for his review of an

early draft of the report. We also thank the

following for their reviews and comments,

or clarifications, of sections of the report:

R. Barnes, J. Bates, B. Cairns, J. Dykema,

L. Flynn, C. Frohlich, E. Hilsenrath, H.

Jacobowitz, C. Johnson, H. Kieffer, C.

Kummerow, J. Lean, C. McClain, G. Pierce,

D. Pollock, J. Rice, W. Rossow, D. Tarpley,

and F. Wentz.
90

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Appendix A. Workshop Agenda

Workshop on Satellite Instrument Calibration for Measuring

Global Climate Change, November 12 - 14, 2002

Inn and Conference Center, University of Maryland, College Park, MD

Time Day 1 Nov 12, 2002 Day 2 Nov 13, 2002 Day 3 Nov14, 2002

7:30 Refreshments (Continuous

from 7:30 AM to 11:00 AM

and from 1:00 PM to 4:30 PM)

Registration/ Help Desk

(open from 8:20 AM to 5 PM)

Refreshments (Continuous

from 7:30 AM to 11:00 AM

and from 1:00 PM to 4:30 PM)

Registration/ Help Desk

(open from 8:20 AM to 5 PM)

Refreshments (Continuous

from 7:30 AM to 11:00 AM

and from 1:00 PM to 4:30 PM)

Registration/ Help Desk

(open from 8:20 AM to 5 PM)

8:20 Registration – No fee 2nd day Opening Remarks -

Hratch Semerjian, Director,

CSTL, NIST

Breakout groups meet:

– Report writing

– Rough draft of workshop

report

8:40 Registration – No fee

9:00 Introductions, Wkshp

Objectives

Opening Remarks -

Greg Withee, NOAA/NESDIS

Jim Anderson, Harvard U.; IR

9:20 Keynote – Richard Goody,

Professor Emeritus, 

Harvard U.

Joe Rice,

NIST;

IR - Absolute Calibration

9:40 Keynote – Tom Karl,

NOAA / NESDIS / NCDC

Frank Wentz , RSS;

Microwave

10:00 NPOESS Plans –

S. Mango, NPOESS-IPO

Gary G. Rottman,

U. Colorado; Total/Spectral

Solar Irradiance

10:20 Judith Lean,

NRL;

Total and Spectral

Solar Irradiance

Kory Priestly, NASA/LaRC;

Earth Reflected Solar

Radiation, and Earth

Emitted Radiation

10:40 Bruce Wielicki, NASA/LaRC;

Earth Radiation Budget

Charge to breakout groups

11:00 Roy Spencer, NASA/MSFC;

Atmospheric Temperature

Breakout groups meet:

1. Solar irradiance, ERB, 

and clouds

2. Atmospheric variables

3. Surface variables

Appropriate climate scientists

and instrument scientists on

each team

– Can we meet

requirements?

11:20 Andy Harris, NOAA/NESDIS/ORA;

Sea Surface Temperature

11:40 Chuck McClain,

NASA/GSFC;

Ocean Color
96

Time Day 1 Nov 12, 2002 Day 2 Nov 13, 2002 Day 3 Nov14, 2002

12:00 Lunch Lunch Plenary: Breakout group

– Summaries of wkshp report 

12:20

12:40

1:00 John Bates,

NOAA/NESDIS/NCDC

Atmospheric Water Vapor

Breakout groups meet:

– Roadmap to meet

requirements

Wkshp Adjourns

1:20 Richard Stolarski,

NASA, GSFC;

Ozone

1:40 Chris Brest,

NASA,GSFC,GISS;

Cloudiness

2:00 Vikram Mehta,

NASA, GSFC;

Precipitation

– Writing assignments

Editing committee

reviews/edits wkshp report

and produces first draft

2:20 Scott Denning,

U. Colorado;

CO2 and other GH Gases

2:40 Brian Cairns,

NASA, GSFC, GISS;

Atmospheric Aerosols

3:00 Dan Tarpley,

NESDIS/ORA;

Snow Cover

3:20 Josefino Comiso,

NASA, GSFC;

Sea Ice

3:40 Juri Knyazikhin,

Boston U.;

Vegetation

4:00 Ernie Hilsenrath/ Scott Janz,

NASA, GSFC;

UV

4:20 Carol Johnson, NIST;

Visible and Near IR –

absolute calibration

4:40 Bob Evans,

RSMAS, U. Miami;

Visible and near IR

Plenary: Breakout group

progress reports

5:00

5:20 Reception : Starts at 5:30 PM Adjourn Editing committee adjourns
97

Appendix B. Workshop Participants

Name Company Email Address Phone Number

Anderson, Jim Harvard University anderson@huarp.harvard.edu 617-495-5922

Ba, Mamoudou Raytheon-ITSS mamoudou_b_ba@raytheon.com 301-794-5514

Barnes, Robert SeaWiFS Project rbarnes@SeaWiFS.gsfc.nasa.gov 301-286-0501

Barnes, William NASA/GSFC william.l.barnes@nasa.gov 301-614-5675

Bates, John NOAA/NESDIS/NCDC john.j.bates@noaa.gov 828-271-4378

Bingham, Gail SDL/USU gail.bingham@usu.edu 435-797-4320

Brest, Chris NASA/GISS cbrest@giss.nasa.gov 212-678-5565

Cairns, Brian Columbia University-NASA/GISS bc25@columbia.edu 212-678-5625

Campbell, G. Garrett CIRA/CSU campbell@cira.colostate.edu 970-491-8497

Comiso, Josefino C. NASA/Goddard Space Flight Center josefino.c.comiso@nasa.gov 301-614-5708

Deland, Mathew SSAI matt_deland@sesda.com 301-867-2164

Denning, Scott Colorado State University denning@atmos.colostate.edu 970-491-6936

Dittberner, Gerald NOAA/NESDIS gerald.dittberner@noaa.gov 301-457-5125 x 113

Duda, James L. IPO/NPOESS james.duda@noaa.gov 301-427-2124 x126

Dykema, John Harvard University dykema@fas.harvard.edu 617-495-5922

Elwell, John SOL/USU john.elwell@sdl.usu.edu 435-797-4600

Emery, William University of Colorado emery@colorado.edu 303-492-8591

Evans, Robert University of Miami bob@rsmas.miami.edu 305-361-4799

Flynn, Lawrence NOAA/NESDIS lawrence.e.flynn@noaa.gov 301-763-8136

Forsythe, John CIRA/Colorado State University forsythe@cira.colostate.edu 970-491-8589

Fraser, Gerald NIST fraser@nist.gov 301-975-3797

Fröhlich, Claus PMOD/WRC cfrohlich@pmodwrc.ch 41 81 4175136

Gasiewski, Al NOAA/ETL al.gasiewski@noaa.gov 303-497-7275

Gelman, Mel NOAA/CPC melvyn.gelman@noaa.gov 301-763-8000 x7558

Goldberg, Mitch NOAA/NESDIS mitch.goldberg@noaa.gov 301-763-8078 x125

Grody, Norman NOAA/NESDIS norman.grody@noaa.gov 301-763-8251

Gruber, Arnold NOAA/NESDIS arnold.gruber@noaa.gov 301-763-8251

Guenther, Bruce UMBC/GEST bruceguenther@comcast.net 301-614-6856

Hilsenrath, Ernest NOAA/GSFC hilsenrath@ventus.gsfc.nasa.gov 301-614-6033

Hoffman, Carl Integrated Program Office carl.hoffman@noaa.gov 301-427-2070 x166

Iacovazzi, Bob NASA/GSFC robert@cloud.gsfc.nasa.gov 301-614-6210

Gordley, Larry Gats Inc larry@gats-inc.com 757-873-5920

Jaross, Glen SSAI jaross@qhearts.gsfc.nasa.gov 301-867-2170

Karl, Tom NOAA thomas.r.karl@noaa.gov 828-271-4476

Kieffer, Hugh USGS hkieffer@usgs.gov 928-556-7015

Kratz, Gene Raytheon ITSS gene_j_kratz@raytheon.com 301-794-5491
98

Name Company Email Address Phone Number

Kirk-Davidoff, Daniel Harvard University davidoff@huarp.harvard.edu 617-495-5922

Larsen, Mark SDL/USU mark.larsen@sdl.usu.edu 435-797-4337

Latvakoski, Harri Space Dynamics Lab harri.latvakoski@sdl.usu.edu 435-797-4695

Lean, Judith Naval Research Laboratory jlean@ssd5.nrl.navy.mil 202-767-5116

Lorentz, Steven L-1 Standards & Technology Inc. lorentz@l-1.biz 301-874-8333

Lottman, Brain TRW brian.lottman@trw.com 310-813-7388

Mango, Stephen NPOESS Integrated Program Office stephen.mango@noaa.gov 301-427-2080 x150

Maslanik, James University of Colorado james.maslanik@colorado.edu 303-492-8974

McClain, Charles R. NASA Goddard Space Flight Center chuck@SeaWiFS.gsfc.nasa.gov 301-286-5377

Mehta, Dr. Vikram The Center for Research on the Changing

Earth System vikram@crces.org 410-992-5282

McReynolds, Katie NIST mreynold@boulder.nist.gov 303-497-7282

Miller, Alvin NOAA/NWS alvin.miller@noaa.gov 301-763-8000 x7552

Mlynczak, Marty NASA Langley m.g.mlynczak@larc.nasa.gov 757-864-5695

Nalli, Nicholas NOAA/NESDIS nick.nalli@noaa.gov 301-763-8053 x 203

Ohring, George NOAA Consultant george.ohring@noaa.gov 301-763-8127 x 352

Parr, Albert NIST parr@nist.gov 301-975-2316

Pierce, Gregory NPOESS/IPO gregory.pierce@veridizn.com 703-251-7489

Pollock, David B. The University of Alabama in Huntsville pollockd@email.uah.edu 256-824-2514

Rice, Joe NIST joe.rice@nist.gov 301-975-2133

Rottman, Gary LASP/University of Colorado gary.rottman@lasp.colorado.edu 303-492-8324

Schneider, Stan NPOES/IPO/NASA stanley.schneider@ noaa.gov 301-427-2124 x169

Seftor, Colin Raytheon ITSS seftor@hoss.stx.com 301-794-5263

Seidel, Dian NOAA/RIARL dian.seidel@noaa.gov 301-713-0295 x126

Sikorski, Richard Raytheon ITSS sikorski@raytheon.com 301-794-5105

Slonaker, Richard Raytheon ITSS richard_l_slonaker@raytheon.com 301-794-5104

Spencer, Roy W. University of Alabama in Huntsville roy.spencer@msfc.nasa.gov 256-961-7960

Stolarski, Richard NASA GSFC stolar@polska.gsfc.nasa.gov 301-614-5982

Tarpley, Dan NOAA/NESDIS/ORA dan.tarpley@noaa.gov 301-763-8042

Vinnikov, Konstantin University of Maryland kostya@atmos.umd.edu 301-405-5382

Walker, David NIST Stop 813.01 dwalker@boulder.nist.gov 303-497-5490

Weinreb, Michael NOAA/NESDIS michael.weinreb@noaa.gov 301-763-8136 x135

Weng, Fuzhong NOAA/NESDIS fuzhong.weng@noaa.gov 301-763-8251

Wentz, Frank Remote Sensing Sys wentz@remss.com 707-545-2904

Wielicki, Bruce NASA Langley b.a.wielicki@larc.nasa.gov 757-864-5683

Xiong, Xiaoxiong MCST/SSAI xiaoxiong.xiong.1@gsfc.nasa.gov 301-809-1222

Zong, Yuqin NIST yuqin.zong@nist.gov 301-975-2332
99

List of Acronyms and Abbreviations

AATSR Advanced Along Track Scanning Radiometer

ABL Atmospheric Boundary Layer

AOD Aerosol Optical Depth

AIRS Atmospheric InfraRed Sounder

AMSU Advanced Microwave Sounding Unit

APS Aerosol Polarimetry Sensor

ARM Atmospheric Radiation Measurement program

ATBD Algorithm Theoretical Basis Document

ATMS Advanced Technology Microwave Sounder

AATSR Advanced Along Track Scanning Radiometer

AMSR Advanced Microwave Scanning Radiometer

ATSR Along Track Scanning Radiometer

AVHRR Advanced Very High Resolution Radiometer

BIPM Bureau International des Poids et Measures

BSRN Baseline Surface Radiation Network

BUV Backscattered UltraViolet - radiances or technique

CALIPSO Cloud-Aerososl Lidar and Infrared Pathfinder Satellite

Observations 

CCD Charge-Coupled Device

CCSP Climate Change Science Program

CEOS Committee on Earth Observation Satellites

CGPM Conference Generale des Poids et Measures

CDR Climate Data Record

CERES Clouds and the Earth's Radiant Energy System

CMDL Climate Monitoring and Diagnostic Laboratory

CMIS Conically-Scanning Microwave Imager

CrIS Cross Track Infrared Sounder

DMSP Defense Meteorological Satellites Program

DOAS Differential Optical Absorption Spectroscopy

DoE Department of Energy

EDR Environmental Data Record

EOS Earth Observing System

ERB Earth Radiation Budget

ERBE Earth Radiation Budget Experiment

EUMETSAT European Organisation for the Exploitation of

Meteorological Satellites

FPAR Fraction of Photosynthetically Active Radiation

FTIR Fourier Transform InfraRed

GAW Global Atmosphere Watch

GCI Global Cloud Imagery

GCOS Global Climate Observing System

GERB Geostationary Earth Radiation Budget Experiment

GEWEX Global Energy and Water Cycle Experiment

GLAS Geoscience Laser Altimeter System
100

GOES Geostationary Operational Environmental Satellite 

GOME Global Ozone Monitoring Experiment

GOMOS Global Ozone Monitoring by Occultation of Stars

GPS Global Positioning System

HALOE HALogen Occultation Experiment

HIRDLS High Resolution Dynamic Limb Sounder

HIRS High-resolution InfraRed Sounder

HSB Humidity Sounder for Brazil

IASI Infrared Atmospheric Sounding Interferometer

ICT Internal Calibration Target

IGOS Integrated Global Observing Strategy

IGACO Integrated Global Atmospheric Chemistry

Observations

IORD Integrated Operational Requirements Document

IPO Integrated Program Office (for NPOESS)

IPCC Intergovernmental Panel for Climate Change

ISCCP International Satellite Cloud Climatology Project

IWP Ice Water Path

IR InfraRed

LAI Leaf Area Index

LW Longwave

LWP Liquid Water Path

LIDAR Light Detection and Ranging

LUVV Limb-scattered Ultraviolet and Visible radiation

MERIS MEdium Resolution Imaging Spectrometer Instrument

Meteosat European Geostationary Meteorological Satellite

MetOp Meteorological Operational satellite

MFRSR Multi-Filter Rotating Shadowband Radiometer

MISR Multiangle Imaging SpectroRadiometer

MLS Microwave Limb Sounder

MOBY Marine Optical Buoy

MODIS Moderate Resolution Imaging Spectroradiometer

MSU Microwave Sounding Unit

MW Microwave

NASA National Aeronautic and Space Administration

NDSC Network for the Detection of Stratospheric Change

NDVI Normalized Difference Vegetation Index

NIR Near InfraRed

NIST National Institute for Standards and Technology

NOAA National Oceanic and Atmospheric Administration

NPOESS National Polar-orbiting Operational Environmental

Satellite System

NPP NPOESS Preparatory Program

NRC National Research Council

NWP Numerical Weather Prediction

OATS Operational Algorithm Teams
101

OCO Orbiting Carbon Observatory

OLR Outgoing Longwave Radiation

OMI Ozone Monitoring Instrument

OMPS Ozone Mapping and Profiler Suite

POAM Polar Ozone and Aerosol Measurements

POLDER POLarization and Directionality of the Earth's

Reflectances

SAGE Stratospheric Aerosol and Gas Experiment

SBUV Solar Backscattered Ultraviolet instrument

SCIAMACHY SCanning Imaging Absorption spetroMeter for

Atmospheric CHartographY

SeaWiFS Sea-viewing Wide Field-of-view Sensor

ScaRaB Scanner for Radiation Budget

SGP Southern Great Plains

SIM Spectral Irradiance Monitor

SIRN Solar Irradiance Research Network

SOLSTICE SOLar STellar InterComparison Experiment

SoRCE Solar Radiation and Climate Experiment

SPARC Stratospheric Processes and their Role in Climate

SNR Signal to Noise Ratio

SRB Surface Radiation Budget

SRF Spectral Response Function

SBUV Space shuttle SBUV

SSM/I Special Sensor Microwave/Imager

SSM/T Special Sensor Microwave/Temperature sounder

SSM/T-2 Special Sensor Microwave/Water Vapor sounder

SST Sea Surface Temperature

SURFRAD Surface Radiation Budget Network

SW Short Wave

TES Tropospheric Emissions Spectrometer

TMI TRMM Microwave Instrument

TOA Top of Atmosphere

TOAR Top-Of-Atmosphere Reflectivity

TOMS Total Ozone Mapping Spectrometer

TOZ Total column Ozone

TRMM Tropical Rainfall Measuring Mission

TSI Total Solar Irradiance

TXR Thermal Transfer Radiometer

UTH Upper Troposphere Humidity

UV UltraViolet

VIRS Visible and Infrared Scanner

VIIRS Visible/Infrared Imager/Radiometer Suite

VIS Visible

WMO World Meteorological Organization

WOUDC World Ozone and Ultraviolet Data Center



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