NOAA Technical Report NESDIS 102

NOAA Operational Sounding Products From

Advanced-TOVS Polar Orbiting Environmental Satellites

Anthony L. Reale

NOAA/NESDIS

5200 Auth Road

Suitland, Maryland 20746

Washington, DC

August 2001

U.S. Department of Commerce

Donald L. Evans, Secretary

National Oceanic and Atmospheric Administration

Scott B. Gudes, Acting Under Secretary

National Environmental Satellite, Data, and Information Service

Gregory W. Withee, Assistant Administrator
ii
i

1 INTRODUCTION ............................................................................................................................... 1

2 BACKGROUND ................................................................................................................................. 1

3 ATOVS SCIENTIFIC ALGORITHMS............................................................................................... 5

3.1 ORBITAL PROCESSING .................................................................................................................. 5

3.1.1 Preprocessing........................................................................................................................ 5

3.1.2 Radiance Temperature Adjustments...................................................................................... 5

3.1.3 Contamination Detection....................................................................................................... 7

3.1.4 First Guess .......................................................................................................................... 12

3.1.5 Retrieval .............................................................................................................................. 14

3.1.6 Distribution ......................................................................................................................... 16

3.2 OFFLINE PROCESSING........................................................................................................... 17

3.2.1 Satellite Radiance Data Sets................................................................................................ 17

3.2.2 Satellite and Radiosonde Collocations................................................................................ 17

3.2.2.1 Radiosonde Processing.............................................................................................................18

3.2.2.2 Collocation Processing.............................................................................................................18

3.2.2.3 The Matchup Database (MDB).................................................................................................19

3.2.2.4 The First Guess Libraries .........................................................................................................20

3.2.3 Coefficients.......................................................................................................................... 23

3.2.3.1 Limb Adjustment .....................................................................................................................23

3.2.3.2 Cloud Detection .......................................................................................................................23

3.2.3.3 First Guess...............................................................................................................................23

3.2.3.4 ATOVS-A Retrieval.................................................................................................................24

3.2.3.5 ATOVS-B Retrieval.................................................................................................................27

3.2.3.6 Radiance Bias (AMSU-B)........................................................................................................27

3.2.3.7 RFI Correction Tables..............................................................................................................28

4 RESULTS AND ANALYSIS ........................................................................................................ 28

4.1 VERTICAL ACCURACY STATISTICS ............................................................................................. 30

4.2 HORIZONTAL FIELDS .................................................................................................................. 30

5 FUTURE PLANS .............................................................................................................................. 40

6 SUMMARY...................................................................................................................................... 42

7 ACKNOWLEDGMENTS ................................................................................................................ 42

APPENDIX A1......................................................................................................................................... 43

APPENDIX A2......................................................................................................................................... 44

APPENDIX A3......................................................................................................................................... 45

APPENDIX B........................................................................................................................................... 47

REFERENCES ......................................................................................................................................... 51

LIST OF ILLUSTRATIONS .................................................................................................................... 57

LIST OF TABLES.................................................................................................................................... 59
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NOAA Operational Sounding Products From Advanced-TOVS

Polar Orbiting Environmental Satellites

Anthony L. Reale

NOAA/NESDIS

Washington D.C.

1 INTRODUCTION

The National Oceanic and Atmospheric Administration has deployed and operated a fleet of

civilian, polar orbiting, environmental satellites for over 30 years. In 1979, with the launch of the

first Television Infra-Red Operational Sounder (TIROS) Operational Vertical (TOVS)

configuration onboard NOAA-6, the National Oceanic and Atmospheric Administration

(NOAA), National Environmental Satellite, Data, and Information Service (NESDIS) began

operating an extensive ground processing capability at Suitland, Maryland to generate and

distribute operational TOVS sounding products to users around the world in real time

(Werbowetzski 1981). Since then, the NESDIS operational sounding products from polar

orbiting satellites have provided a continuous suite of infrared and microwave radiation sounder

measurements, and derived temperature and moisture sounding products on a global scale. These

data represent a unique source of global, atmospheric, weather information, with a demonstrated

positive impact on Numerical Weather Prediction (NWP) forecasts, NOAA’s primary mission

for sounding products.

The landscape of global weather information from polar orbiting satellites and their application

in global weather monitoring and research has undergone significant changes driven by the

scientific advances of the past 20 years. This report provides a synopsis of the satellite sounders

and derived product systems operated by NESDIS since 1980, followed by a more detailed

review of the current radiometers, scientific algorithms and atmospheric sounding products from

the Advanced TOVS (ATOVS) sensors onboard the (current) NOAA-15 and 16 satellites, and

planned through the year 2010 (NOAA-N`).

2 BACKGROUND

The original TOVS sounder configuration onboard NOAA-6 was the first to simultaneously

deploy a 20-channel High-resolution Infrared Radiation Sounder (HIRS/1), 4-channel

Microwave Sounding Unit (MSU), and 3-channel Stratospheric Sounding Unit (SSU) (Smith et

al. 1979) on a global scale. Each was a cross-track scanner

1

 center of the scan ., and except for

some minor changes with HIRS/2 and HIRS/3 versions, this all-weather2 configuration of

operational, atmospheric radiometers (Kidwell 1998) would remain essentially unchanged until

the launch of NOAA-15 in 1998.

The original NESDIS operational sounding products for NOAA-6 were based on a statistical

retrieval approach (Smith 1968 and Smith et al. 1976). Soundings were produced per 9x7 array

of HIRS fields-of-view (fov), resulting in a nominal horizontal spacing of about 250 km (about

double the MSU and over 10 times larger than the HIRS fov). Procedures to compute soundings

 1

 Scanning from left to right across the orbit track, with a vertical view (nadir) at the center of the scan.

2

 MSU measurements penetrate clouds.
2

included the adjustment of the individual fov across a given scan line to nadir, the spatial

interpolation of MSU and SSU fov to the HIRS, and cloud detection and adjustments on the

HIRS (McMillin and Dean 1982).

Soundings were derived globally and under all weather conditions (except heavy precipitation).

Clear-sky soundings utilized both HIRS and MSU measurements, cloudy soundings the MSU

and upper tropospheric HIRS, and both utilized SSU data for stratospheric coverage. The

measurements used were averages over the 63 HIRS fov (9x7 array), with clear soundings based

on the subset of clear fov (a minimum of five were required to produce a clear sounding), with

the sounding location defined at the centroid of averaged fov. Soundings of temperature and

moisture were retrieved at pre-defined pressure levels (Kidwell et al. 1998), with the lower

twenty levels (1000 to 115 mb) based on regression coefficients which were stratified by latitude

and updated weekly, and the upper twenty levels (100 to .1 mb) based on a fixed, initial set of

global coefficients (Werbowetzki, 1981). Coefficient updating was based on collocated

radiosonde and “clear” satellite observations, which were compiled and updated daily3.

Sounding products were distributed internally to NOAA’s National Meteorological Center

(NMC), now the Environmental Modeling Center (EMC), for assimilation into operational NWP

(primarily in the northwest Pacific region), and over the Global Telecommunications System

(GTS). GTS distribution was at a degraded 500 km horizontal spacing. All products were

archived at the National Climatic Data Center (NCDC) (Kidwell 1998). NESDIS quality control

was minimal, restricted to gross error checks on the measurements and derived products, and a

joint NESDIS/NMC graphic and interactive editing system which provided some quality control

of NESDIS soundings (primarily in the northwest Pacific region) prior to NWP assimilation.

There was little scientific feedback from users outside of NOAA.

The first significant upgrade of the NESDIS operational sounding products occurred in 1986

(Reale et al. 1986). This upgrade replaced the 9x7 HIRS technique with a 3x3 HIRS technique,

including the retrieval of clear soundings at a single HIRS fov (per 3x3) without averaging,

referred to as “hole” hunting. Cloudy soundings retained averaging but over the 3x34. The new

system also introduced internal quality control based on a recursive filter to identify

questionable, bad, and meteorologically redundant soundings. Although the original 500 km

spaced products were retained over the GTS, additional product files containing nominal 50 km

spaced data more consistent with the radiometric measurements became available. NESDIS also

introduced improved quality control, including routine comparisons against operational NWP

analysis fields from NMC, and the first in a series of dedicated graphics systems for monitoring

and evaluating sounding products (Brown et al. 1988).

The original statistical regression approach for deriving operational sounding products was

replaced with a combined physical-statistical retrieval solution in 1988 (Smith et al. 1984 and

Reale et al. 1989), including a separate step to compute the first guess profiles (Goldberg et al.

1988). The new retrieval approach combined sets of globally stratified, atmospheric sounder

weighting function and vertical covariance matrices (Fleming et al. 1986 and Crosby et al. 1973)

to simultaneously retrieve temperature and moisture sounding products. The resulting sets of precomputed retrieval operators replaced the regression coefficients, retaining the weekly updating

 3

 Collocation data sets typically spanned the most recent 50-days.

4

 This was eventually replaced with single fov soundings in 1988.
3

based on the collocated radiosonde and “clear” satellite observations5. Modifications above 100

mb were particularly notable, with the fixed regressions replaced with combined procedures to

construct first guess profiles using a combination of short term and long term climatological

mean profiles, followed by a retrieval step consistent with the lower levels. This implementation

was also the first time that a global NWP forecast model impact study to sanction an operational

upgrade (Dey et al. 1989 and Andersson et al. 1991). NESDIS internal graphical and evaluation

support also improved considerably, resulting in a more complete system for operational

monitoring (Brown et al. 1991).

The late 1980's also marked a period of increased feedback from users outside of NOAA

concerning the impact of polar satellite data in global NWP. This was highlighted by the

European Center for Medium Range Weather Forecasts (ECMWF), who after conducting several

independent experiments on the impact of NESDIS operational soundings on NWP, provided a

series of reports identifying sounding problem areas, particularly for cloudy soundings, and

associated negative impacts on NWP (Kelly et al. 1991). These reports resounded within NOAA,

and marked the beginning of an extensive, 5-year undertaking by NESDIS to identify and correct

problems.

As a result, the early 1990's witnessed a series of upgrades to the NESDIS operational sounding

products which significantly reduced the errors reported by users. These began with the

introduction of cloudy satellite sounding and radiosonde collocations for computing the first

guess and retrievals (Reale 1993) late in 1990, and culminated with the new procedures to use

NWP surface layer data from 6-hour, global forecasts (Kalnay et al. 1990) as a discriminator for

computing cloudy first guess data in April, 1992 (Reale et al. 1994). As a result, a significant

component of the problems reported by the ECMWF was resolved, with positive user feedback

(Eyre and Uddstrom 1993 and Kelly et al. 1992). Major advances were also realized in the area

of quality control with the deployment of enhanced graphical and evaluation systems for routine

operational monitoring (Brown et al. 1992).

Meanwhile, the National Meteorological Center (NMC) of NOAA had embarked on a series of

actions which ultimately led to the replacement of the NESDIS operational soundings with

derived soundings based on an NWP first guess (Daniels et al. 1988) for assimilation into NWP

early in 1995. This would be the predecessor of future trends, both nationally and internationally,

leading to the direct assimilation of sounder radiation measurements into NWP models in place

of derived soundings. The primary motivation for this trend was the reported difficulty (Derber

and Wu 1988) in characterizing the background error structures for derived soundings (Sullivan

and Gruber 1990) compared to the sounder measurements, as required for the optimal

assimilation of observations into NWP (Bergman 1978).

Modification and upgrade of the NESDIS operational sounding products continued through the

middle 1990's, impacting almost every aspect of the processing system, but focusing on methods

to provide more coherent first guess and derived product background errors, and a more

digestible product for NWP assimilation. Upgrades included the expanded use of the HIRS water

vapor channels (Wu et al. 1993) for first guess and retrieval (Reale et al. 1995a) in 1993,

improved cloud detection (McMillin et al. 1994) in 1994, and numerous refinements in the

 5

 The vertical covariance matrices used were from an independent radiosonde sample (Crosby et al. 1973).
4

sampling strategies for compiling collocated satellite and radiosonde observations used to

compute the first guess and derived sounding products (Reale et al. 1995a).

The final set of changes for operational TOVS product systems, referred to as Revised-TOVS

(RTOVS), was implemented into the NESDIS Operations in November, 1997 (Reale et al. 1995b

and 1998). Although originally intended as a system upgrade to accommodate a smooth

transition to the next generation Advanced-TOVS (ATOVS) sounding systems, RTOVS also

included a number of scientific upgrades.

The most notable among the scientific upgrades were the introduction of Advanced Very High

Resolution Radiometer (AVHRR) measurements for cloud detection, and a new approach for

limb adjustments (Wark 1993). RTOVS also included a more subtle change to the retrieval step,

in which the covariance matrix (Crosby et al. 1973) was modified to be based on radiosonde

report minus satellite first guess differences, including routine updating (weekly) using the

radiosonde and satellite collocations. The result was a more consistent approach for using

observed first guess structures in the retrieval solution, a step in the right direction concerning a

better characterization of the final derived product background errors.

System changes were also implemented to include both the raw and processed radiometric

measurements in the operational data stream, to better accommodate users wishing to assimilate

radiance. NESDIS understood that their mission for polar satellite sounding products needed to

be expanded to not only provide derived products, but also the radiometric observations now

being requested by global users.

On May 13, 1998, the ATOVS instrument configuration onboard NOAA-15 was successfully

deployed into a morning orbit (1930 local-time, ascending orbit), followed by NOAA-16 into an

afternoon orbit (1400) on September 21, 2000. ATOVS consisted of the new 15-channel

Advanced Microwave Sounding Unit-A (AMSU-A), which replaced the MSU and SSU, the new

5-channel AMSU-B for atmospheric moisture, the HIRS/3 and the AVHRR (Goodrum et al.

2000); also see Appendices A1 to A3. ATOVS sounding products from NOAA-15 were

operationally implemented by NESDIS in April, 1999 (Reale et al. 1999a and 1999b). This

implementation did not include AMSU-B processing which was delayed until May, 2000

(Chalfant et al.1999 and Reale et al. 2000b).

The deployment of ATOVS also stirred interest among National Weather Service (NWS) field

office forecasters concerning the use of polar sounding products in real-time weather forecast

applications on a regional (and local) scale. The Polar Orbiting Satellite Sounding Evaluator

(POSSE) system (Pettey and Brown 2000), a real-time polar satellite sounding data and graphical

interface available over the Internet, was implemented in 1999 as a first step toward the inclusion

of polar satellite data on the Advanced Weather Interactive Processing System (AWIPS).

The following sections of this report summarize the ATOVS sounding product systems and

scientific algorithms operated by NESDIS.
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3 ATOVS SCIENTIFIC ALGORITHMS

The scientific procedures for processing NESDIS operational sounding products for ATOVS are

presented in the following sections. Two product systems are currently operated, ATOVS-A,

which processes sounding products using the HIRS, AMSU-A and AVHRR radiometers (Reale

et al. 2000a), and ATOVS-B, which processes (moisture) sounding products using the AMSU-B

radiometer (Chalfant et al. 1999).

The scientific procedures for each system are comprised of two primary subsystems for Orbital

and Offline support processing, respectively. Figure 1 illustrates a generic schematic diagram of

these subsystems for ATOVS-A and ATOVS-B, with Orbital processing indicated in red, and

Offline processing in green (daily) and blue (weekly).

3.1 Orbital Processing

The orbital processing system outlined in Figure 1 provides:

Preprocessing

Radiance Temperature Adjustments

Contamination Detection

First Guess

Retrieval

3.1.1 Preprocessing

Preprocessing steps are done on the raw, level-1b satellite data (Goodrum 2000), which contain

the earth located raw satellite measurements from each sounder, and corresponding calibration

data, coefficients and quality flags. Preprocessing steps include the application of the calibration

coefficients, the computation of the radiance and radiance temperature measurements, quality

control, the identification of polar-redundant data (Reale et al. 1999a), and the appendage of

ancillary data such as terrain type (Grody 1991 and Grody et al. 1999), sea surface temperature

(SST) (Walton et al. 1998) and selected NWP forecast data (Kalney et al. 1990 and Reale et al.

1995).

Preprocessing for ATOVS-A preprocessing step also includes a spatial interpolation of the

AMSU-A to the HIRS field of view.

Preprocessing for ATOVS-B includes the correction of AMSU-B measurements for Radio

Frequency Interference (RFI) from data transmitters onboard NOAA-15 (Atkinson 1999 and

2000); AMSU-B from NOAA-16 have no RFI. The RFI correction procedures are presented in

Appendix B.

3.1.2 Radiance Temperature Adjustments

Limb adjustments are applied to the calibrated, radiance temperature measurements from

HIRS/3, AMSU-A and AMSU-B (Wark 1993 and Allegrino et al. 1999), the latter having also

been corrected for RFI for NOAA-15. Each of these radiometers is a cross-track scanner,

resulting in a systematic variation in the vertical and horizontal sensitivity of each channel across

the scan path. Limb adjustments standardize the data from each channel to the vertical (nadir)

view, and greatly simplify the subsequent processing of sounding products with little or no

degradation in data integrity (Goldberg 1999 and Goldberg et al. 2001).
6

Figure 1: System schematic diagram of Orbital processing (red), and the

Daily (green) and Weekly (blue) Offline support systems for ATOVS-A and

ATOVS-B sounding products.
7

The limb (and RFI for NOAA-15) corrected measurements for AMSU-B are also adjusted to

remove perceived bias relative to radiative transfer model calculations (Fleming et al. 1991).

Limb and radiance bias adjustments are summarized in Section 3.2.3.

3.1.3 Contamination Detection

Contamination detection is done separately for microwave and infrared sensor data. Microwave

contamination detection primarily consists of identifying localized anomalies in AMSU sounding

channels sensitive in the lower troposphere6, for example AMSU-A channels 4 and 5, typically

due to precipitation and large ice particles (Grody et al. 1999). Infrared measurements are

screened for clouds.

Precipitation detection and the subsequent editing of AMSU-A and AMSU-B data is done for

each fov in the following manner. First, a Cloud Liquid Water (CLW) amount is computed for

sea fov, and a Precipitation Index (PI) for land fov, respectively (Grody et al. 1999 and 1994). A

median filter analysis is also done for ATOVS-A fov (except high terrain where >1500 m based

on AMSU-A channel 4 for sea and channel 5 for non-sea fov, respectively7. These results are

then combined to identify contaminated AMSU fov. For ATOVS-A, tropical sea fov are flagged

if the CLW exceeds 2 mm, extra-tropical (poleward of 30 latitude) sea fov are flagged if both the

CLW and median filter difference exceeds threshold8, and non-sea fov are flagged if either the PI

or median filter exceed thresholds. For ATOVS-B, sea fov are flagged if the CLW exceeds 2

mm, and non-sea fov are flagged based on the PI.

The four panels of Figure 2 illustrate color graphics of ATOVS-A precipitation detection results

for NOAA-15 using the above approach. Each panel shows a 6-hour composite field across the

Atlantic Ocean and Africa. The upper left panel illustrates CLW values, followed in a clockwise

order by the median filter results, the final edit flag, and the corresponding, limb-adjusted,

measurements ( ̊K) for AMSU-A channel 5. The bright green areas for CLW indicate values

which exceeded the 2.0 mm threshold for contamination over sea. The median filter panel

illustrates green and yellow areas, corresponding to edited observations based on AMSU

channels 4 or 5, respectively, red areas indicating where no testing was done (i.e., due to high

elevation), and grey areas with no contamination. The red areas in lower right panel illustrate

candidate ATOVS-A sounding locations identified as contaminated. Good consistency is

observed among these parameters, and with areas of contaminated AMSU channel 5

measurements, evident across the Tropical Atlantic and Central Africa.

Cloud detection for HIRS measurements in ATOVS-A (Ferguson and Reale 2000) represents a

critical stage of the processing algorithm and determines whether the infrared data can be used in

subsequent first guess and retrieval steps.

Cloud detection consists of a series of tests applied to each HIRS fov, segregated over various

terrain and solar conditions. If at least one test exceeds threshold, the HIRS fov is designated as

cloudy. Tables 1 and 2 list each test, the corresponding test thresholds (OFF indicates the test is

not done), and the constraints under which each test is applied. Table 1 lists the regression based

 6

 The concept of sounding channel sensitivity in the atmosphere is discussed in Section 3.2.3.4.

7

 The use of channel 4 over sea and 5 over land is consistent with their use in subsequent first guess and retrieval

steps.

8

 The median filter threshold is currently 1.5 K for all tests.
8

Figure 2: Precipitation detection parameters of Cloud Liquid Water (mm)

(upper left) and median filter edits (upper right), the final set of edits for

AMSU-A fov (lower right), and AMSU channel 5 radiance temperature ( ̊K)

measurements (lower left), composited from 16Z to 23Z on September 6, 2000

for NOAA-15.
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tests, and Table 2 the remaining tests, including restoral and adjacent spot tests. Although fairly

constant over time, the test thresholds require routine monitoring and are adjusted as necessary to

maintain a consistent global mask throughout the year.

The prefixes “STE” and “P” in Tables 1 and 2 refer to Surface Temperature Estimates and

Predicted parameters, respectively, and are determined using regression coefficients as presented

later in Section 3.2.3.2. SST refers to the NESDIS operational sea surface temperature products

(Walton et al. 1998), which are appended to each HIRS data record during orbital preprocessing.

AVHRR tests consider each of the 17 Global Area Coverage (GAC) (Kidwell 1998) pixels

centered on the HIRS fov, and if one (or more) fails the HIRS fov is designated as cloudy9.

Regression Test Threshold Constraint

STEH8

STEH18

270  ̊K

270  ̊K

SEA, DAY

SEA, NIGHT

STEH18 - STEH8 OFF

OFF

DAY

NIGHT

STEH19 - STEH18 OFF NIGHT

STEH8 - HIRS8 ± 6.0 ( ̊K)

± 5.0

± 4.0

± 5.0

± 4.0

SEA, 30N TO 30S

SEA, POLEWARD 30N, 30S

SEA-ICE

LAND

SNOW COVER

SST - STEH8

SST - STEH18

< -10, > 2.0 ( ̊K)

< - 5 , > 1.5

SEA, DAY

SEA, NIGHT

PAVHRR3 - AVHRR3 OFF NIGHT

PAVHRR4 - AVHRR4 < -2.0, > 1.5 ( ̊K)

< -0.2, > 5.0

< -0.2, > 2.5

< -0.5, > 5.0

SEA

SEA-ICE

LAND

SNOW COVER

PHIRS7 - HIRS7 OFF NIGHT

PAMSU5 (LW) - AMSU5

PAMSU5 (SW) - AMSU5

± 5.0 ( ̊K)

OFF

NONE

NIGHT

PAMSU7 (LW) - AMSU7

PAMSU7 (SW) - AMSU7

OFF

OFF

NONE

NIGHT

Table 1:Regression based cloud detection tests, thresholds and application constraints

for ATOVS-A, NOAA-15, during September-200010. The prefixes “STE” and “P” refer

to Surface Temperature Estimates and Predicted parameters.

 9

 The exceptions are tests of the Standard Deviation, and percent of the (17) GAC pixels which exceed 270 K, based

on AVHRR channel 4.

10

 Thresholds for a given test can vary per satellite and over time.
10

Test Threshold Constraint

ALBEDO: (HIRS20)

ALBEDO: (HIRS20)

OFF

OFF

DAY; LAND (NO SNOW/ICE)

DAY; SEA

AVHRR4 - AVHRR3 < -5.0, > 0 ( ̊K) NIGHT

% AVHRR4 (> 270) OFF NONE

ST. DEV. AVHRR4 > 4.5 ( ̊K) NONE

RESTORAL: (STEH8) > 310 ( ̊K) LAND

ADJACENT SPOT:

 ISOLATED CLOUDY

 STEH8 (ADJ) - STEH8

 STEH18 (ADJ) - STEH18

 STEH8 (ADJ) - STEH8

 STEH18 (ADJ) - STEH18

NA

± 5.0 ( ̊K)

± 5.0

± 3.0

± 3.0

NONE

NONSEA, DAY

NONSEA, NIGHT

SEA, DAY

SEA, NIGHT

Table 2:Non-Regression, Restoral and Adjacent Spot cloud detection tests,

thresholds and application constraints for ATOVS-A, NOAA-15, during

September 2000

The Restoral and Adjacent Spot tests listed at the bottom of Table 2 are done after all the other

tests are completed. The restoral test, also referred to as the hot-land test, resets a cloudy fov to

clear if the “STEH8” value exceeds 315  ̊K. Adjacent spot tests compare clear fov with their

eight adjacent (ADJ) neighbors, and resets to cloudy if surrounded by cloudy scenes, or if the

threshold is exceeded for at least one pair.

The four panels of Figure 3 show color-enhanced, composite fields of NOAA-15, evening, cloud

detection results across the northern and tropical Atlantic. Shown (clockwise beginning upper

left) are the measured HIRS channel 811, the test differences for “SST - STEH18", and for

“PAMSU5 (LW) – AMSU5”, respectively (see Table 1), and the resulting cloud mask (lower

left). The color scale for each panel is shown below, and for test difference panels are set so that

bright green indicates where the cloud threshold was exceeded, or the test not done (i.e., for

“SST - STEH8" over land). Good consistency is seen among the colder HIRS channel 8 values

indicating clouds, the bright green areas where test thresholds were exceeded, and the resulting

cloud mask.

Global cloud cover represents an important component of weather information provided by the

polar satellites. Typically, about 65% of the total number of HIRS fov are declared cloudy.

 11

 HIRS channel 8 is an atmospheric window channel sensitive to clouds.
11

resulting cloud mask (lower left), composited from 18Z on September 6, to 01Z on

September 7, 2000, for NOAA-15.

Figure 3: HIRS channel 8 radiance temperature ( ̊K) measurements (upper left),

the corresponding cloud test differences (K) for “SST - STEH18" (upper right) and

“PAMSU5 (LW) – AMSU5” (lower right) tests as defined in Table 1, and the

resulting cloud mask (lower left), composited from 18Z on September 6 to 01Z on

September 7, 2000 for NOAA-15.
12

3.1.4 First Guess

The first guess is uniquely determined for each ATOVS-A and ATOVS-B sounding using a

library search technique (Goldberg et al. 1988). The first guess libraries consist of approximately

4000 collocations of radiosonde and satellite observations, which are directly accessed during

orbital processing and updated daily. Separate libraries are maintained for the ATOVS-A and

ATOVS-B systems, respectively, and for the clear and cloudy sounding collocations in ATOVSA, respectively. The collocations within each library are also sorted based on terrain and latitude.

First guess library compilation and updating is described in Section 3.2.2.4

The first guess (and retrieval) sequence for ATOVS-A is done for one HIRS fov, selected per

2x2 array across the scan. The overall selection hierarchy is clear, sea, non-sea

12

 (except coast),

and cloudy, meaning that clear/sea soundings have the highest priority, followed by clear/nonsea,

cloudy/sea, and cloudy/nonsea. It is also required that at least 2 of the 4 candidates be designated

as clear to derive a clear sounding, and sea to derive a sea sounding, respectively. If all 4 fov are

designated the same, the fov with the warmest HIRS channel 8 value is selected13.

The first guess (and retrieval) sequence for ATOVS-B products is done for every other AMSU-B

fov across the scan, excluding precipitating fov.

The library search technique utilizes sounder channel measurements as discriminators for

searching the respective libraries and determining the first guess. The channels used, referred to

as the channel combination, are determined by the sounding type. The cloud and terrain

designation of the selected HIRS fov defines the ATOVS-A sounding type, and the terrain

designation the ATOVS-B sounding type.

Tables 3 and 4 list the first guess (FG) channel combinations (CC) for ATOVS-A and ATOVS-B

sounding types, respectively. Special procedures are deployed to not use HIRS channels when

processing ATOVS-A soundings over high terrain (1500 m), and in high southern latitudes. This

is required due to the small number of clear collocations from these regions.

The measurements used to search the libraries are the limb adjusted radiance temperatures for

ATOVS-A, and the limb and radiance bias adjusted radiance temperatures for AMSU-B.

The matrix equation for the library search is given in (1):

D = ( R - Rk )t B-1 ( R - Rk ) (1)

where the subscript t indicates the matrix transpose, -1 the inverse, and

D : scalar closeness parameter,

B : sounding channel radiance covariance matrix; dimension (35 x 35) or (5 x 5),

R : adjusted, observed radiance temperature vector; channels FG (CC), and

Rk : adjusted, library radiance temperature vector; channels FG (CC).

 12

 The Nonsea sounding type includes land, coast, sea-ice, snow cover and high terrain.

13

 If HIRS data are not available, a default location is selected.
13

The dimensions “35” and “5” for the B matrix in Equation (1) denote the total number of

sounder channels available for the ATOVS-A (20 for HIRS plus 15 for AMSU-A) and ATOVSB systems, respectively; not all are used (see Tables 3 and 4). The channels FG (CC) are those

compared in the search to compute “D”, which depend on the sounding type consistent with

Tables 3 and 4. The subscript “k” is the number of library collocations searched, typically about

1500 collocations per sounding type. The channel combination CC, the collocations searched,

“k”, and B matrix all vary consistent with the sounding type.

The first guess temperature, moisture and radiance temperature profiles for a given sounding are

computed by averaging the 10 closest collocations; that is, those with the smallest “D”.

Radiosonde averages are used for temperature and moisture, and the adjusted measurements

from the sounder are averaged for radiance temperature. As discussed later in Section 3.2.2.4, the

radiosonde reports stored in first guess libraries must have originally been complete from at least

950 to 50 mb, and are extended downward to 1000 mb, and upward to 0.1 mb as required.

Sounding Type Channel

Combination (CC)

First Guess

FG (CC)

Retrieval

RE (CC)

Clear, Sea 1 HIRS 7, 10-12

AMSU-A 1, 4-14

HIRS 2-7,10,12,14-16

AMSU-A 4-14

Clear, Non-Sea 2 HIRS 7, 10-12

AMSU-A 5-14

HIRS 2-7,10 12,4-16

AMSU-A 5-14

Cloudy, Sea 3 AMSU-A 1, 5-14 AMSU-A 5-14

Cloudy, Non-Sea 4 AMSU-A 5-14 AMSU-A 5-14

High Terrain:

 > 1500 m

 > 2500 m

5

6

AMSU-A 5-14

AMSU-A 5,14

AMSU-A 6-14

AMSU-A 7-14

Table 3:First Guess (FG) and Retrieval (RE) Channel Combinations (CC) for

ATOVS-A Sounding Types.

Sounding Type Channel

Combination (CC)

First Guess

FG (CC)

Retrieval

RE (CC)

Sea 7 AMSU-B 1-5 AMSU-B 1-5

Non-Sea 8 AMSU-B 3-5 AMSU-B 3-5

Ice and >1500 m 9 AMSU-B 3-5 None

Table 4: First Guess (FG) and Retrieval (RE) Channel Combinations (CC) for

ATOVS-B Sounding Types
14

The panels of Figure 4 show color-enhanced, composite fields of observed and first guess data

across the northern and tropical Atlantic region, illustrating the first guess sequence for one

channel and level. The sequence begins in the upper left panel with the sounder measurements,

in this case AMSU channel 6 which is sensitive near 500 mb. The corresponding first guess

measurement and 500 mb temperature fields are shown in the upper and lower right panels.

Black regions indicate orbit gaps, with grey areas edited due to AMSU contamination (see

Figure 2). Good consistency is observed between the first guess temperature and radiance

temperature data, an important characteristic of the library search approach and pre-requisite for

accurate retrievals (Fleming et al. 1986). Comparison to the concurrent cloud data shown in

Figure 3 indicates that distinctions between clear and cloudy soundings are not evident in the

guess.

3.1.5 Retrieval

The general form of the retrieval equation is given by (2):

T - Tg = C ( R - Rg ) (2)

where T and R are product and measurement parameters, the subscript “g” an apriori estimate,

and C is the solution.

The ATOVS-A retrieval method is the Minimum-Variance-Simultaneous (MVS) solution (Smith

et al. 1984 and Fleming et al. 1986) given by matrix equation (3):

 T - Tg = S A t ( A S At + N)-1 ( R - Rg ) (3)

 = X ( Y )-1 ( R - Rg )

where the subscript t indicates the matrix transpose, -1 the inverse, and:

T: final soundings products vector, (133),

Tg: first guess products vector, (133)

S: first guess covariance matrix, (133 x 133),

A: sounder channel weighting matrix, (35 x 133),

N: measurement noise covariance matrix, (35 x 35),

R: observed radiance temperature vector, channels RE (CC),

Rg : first guess radiance temperature vector, channels RE (CC), and

X,Y: pre-computed retrieval operator components.

The product vector (T) contains 100 levels of atmospheric temperature (1000 mb to .1 mb), 32

levels of moisture (1000 mb to 200 mb), and the surface level14. The dimension 35 for the A and

N matrices denotes all the ATOVS channels; not all are used. The channels RE (CC) denotes the

first guess

15

 and observed channels used in the retrieval solution, depending on the sounding type

consistent with Table 3.

 14

 The 100 levels of temperature and 32 levels of moisture are internal only.

15

 The first guess observations for a given channel are available independent of whether they were used in the first

guess search.
15

Figure 4: The first guess and retrieval sequence for one channel and level,

showing AMSU-A channel 6 radiance temperature measurements ( ̊K)

(upper left), corresponding first guess measurements (upper right) and

500 mb temperatures (lower right) ( ̊K), and 500 mb final sounding

temperatures ( ̊K) at 500 mb, composited from 18Z on September 6, to 01Z

on September 7, 2000.
16

The X and Y components of the MVS retrieval operator are pre-computed offline and updated

weekly. Thirty-two (32) sets of components are computed and selected for a given sounding

based on the latitude, terrain type, and cloud designation. The calculation of the retrieval

operators is described in Section 3.2.3.4.

The lower left panel of Figure 4 shows the retrieved temperature field at 500 mb, the final step in

the first guess and retrieval sequence.

The ATOVS-B retrieval utilizes an ordinary least squares solution of equation 2, where:

T: water vapor mixing ratio sounding products vector (15),

Tg: first guess mixing ratio vector, (15),

C: statistical regression coefficients (15 x RE (CC)),

R: observed radiance temperature vector, channels RE (CC), and

Rg : first guess radiance temperature vector, channels RE (CC).

The product vector (T) includes 15 levels of atmospheric moisture (1000 to 300 mb). The

channels RE (CC) denote the AMSU-B channels used to compute the regression coefficients and

derive sounding products. The channels used depend on the sounding type consistent with Table

4.

Two sets of regression coefficients are available, for sea and non-sea soundings respectively.

Their calculation is addressed in Section 3.2.3.5.

 3.1.6 Distribution

ATOVS-A operational sounding products are distributed to users at the conclusion of orbital

processing, which takes about 20 minutes, with a complete orbit spanning 104 minutes.

The operational products that are routinely distributed to users include

the original calibrated sounder measurements

adjusted sounder measurements

first guess information

derived temperature and moisture soundings

appended ancillary data (i.e., cloud mask, AVHRR, SST, etc.)

Dedicated links for these data include:

NOAA-EMC

United Kingdom Meteorological Office (UKMO, Bracknell, England)

Global Telecommunications System (GTS)

Shared Processing Network (SPN)16

The characteristics of the data provided over the various links include the level-1b data

(Goodrum et al. 2000) and full density operational sounding products over the NOAA-EMC and

 16

 Files are placed into the SPN format and transmitted over the SPN, a dedicated link to the U.S. Navy and Air

Force in support of operational NWP.
17

UKMO links, the full density operational products over the SPN, and a lower density product at

an approximately 250 km spacing over the GTS.

ATOVS-B operational sounding products were only being distributed over the SPN.

The complete set of NESDIS operation sounding products is archived at NCDC (Goodrum et al.

2000).

3.2 OFFLINE PROCESSING

The Offline sub-systems (see Figure 1) comprise a significant portion of the sounding products

system. Offline systems contribute the tuning and validation functions required to maintain the

scientific integrity of the orbital sounding products.

Offline systems routinely compile and maintain data sets comprised of:

Satellite Radiance Data Sets

Satellite and Radiosonde Collocations

Coefficients

3.2.1 Satellite Radiance Data Sets

Satellite Radiance Data Sets are separately maintained for HIRS/3, AMSU-A and AMSU-B

radiometer data. These data sets contain about 100,000 observations of adjusted and nonadjusted (original) radiance temperature measurements at the respective instrument fov, typically

spanning the latest 30 to 60 days. Each data set is updated at the conclusion of orbital processing

at about a 10% sub-sampling rate, excluding polar-redundant and high terrain (>1500 m)

observations.

Since the satellite Radiance Data Sets are compiled at the conclusion of orbital processing, they

can include additional processed data. For example, HIRS/3 data records include interpolated

AMSU-A measurements, the 17 concurrent AVHRR measurements, and are segregated based on

the clear and cloudy designation. The AMSU-A (and AMSU-B) records include CLW amount

and Precipitation edits (Figure 2).

The use of Satellite Radiance Data Sets to generate coefficients for orbital processing are

described in Section 3.2.3.

3.2.2 Satellite and Radiosonde Collocations

Collocated radiosonde and satellite observations are extensively used in the derivation and

validation of the operational sounding products. Their use to derive soundings, referred to as

tuning (Reale et al. 1990), is described in the following sections. Their use in validation is

presented in Section 4.1

Tuning provides routine compensations to reconcile systematic errors among the scientific

algorithms, for example, the radiative transfer model (Uddstrom et al. 1994a and 1994b), the

limb-adjusted (Wark 1993) satellite measurements used to derive products, and ground truth
18

radiosondes (McMillin et al. 1988 and 1992), and is a pre-requisite for maintaining accurate

sounding products.

The steps to compile and utilize collocated radiosonde and satellite observations include:

Radiosonde Processing

Collocation Processing

Matchup Data Base (MDB)

First Guess Libraries

These four steps are updated daily.

3.2.2.1 Radiosonde Processing

Radiosonde reports received over the GTS are processed daily (Tilley et al. 2000). These reports

are accessed from EMC holding files, include radiation correction (McMillin, et al. 1992), and

range from eight to thirty-two hours old when processed by NESDIS, and must pass the

following two sets of screening tests.

First, each report level is tested and designated as missing if:

temperature data are missing or exceed climatological limits,

dewpoint depression data are missing below 500 mb for temperatures > -40° C,

dewpoint depression data exceed 50° C, or

successive significant levels do not decrease in pressure

Radiosonde reports are then rejected if:

any first significant level data are missing,

the vertical extent for temperature does not reach 100 mb,

the vertical extent for moisture does not reach 500 mb (for temperature > -40° C),

there is an extensive gap (Tilley et al. 2000), or

an excessive lapse rate (Tilley et al. 2000).

Accepted radiosonde reports are candidates for collocation with satellite data. Typically, about

1500 northern hemisphere and 200 southern hemisphere radiosonde reports are observed per day,

and about 20% are rejected. Figure 5 shows a global distribution of accepted (o) and rejected (x)

radiosondes from a single day.

3.2.2.2 Collocation Processing

A collocated radiosonde and satellite observation is compiled (Tilley et al. 2000) if the candidate

radiosonde and satellite sounding data:

are within specified time and distance windows,

differ by less then 10° K for any temperature below 100 mb (ATOVS-A only), and

have similar surface pressure

The time (t) and distance windows (x) for ATOVS-A are stratified per geographic category as

defined in Table 5; for ATOVS-B the distance window is 100 km globally, and the time window

is 3-hours for non-sea and 5-hours for sea soundings, respectively. Radiosonde versus satellite

sounding temperature comparisons above which exceed 10 ̊ K at some level above 100 mb,

result in a designation of missing data at and above that level. A surface pressure must be

reported for a radiosonde, and must lie within one TOVS level of the satellite surface pressure

(Reale et al. 1994).
19

Figure 5: A typical global distribution of radiosonde reports from a single day.

Each radiosonde is limited to one collocation per satellite and sounding type, and a given

sounding can only be collocated once. As discussed in Section 3.1, the sounding type is defined

by the terrain of the sounding, namely sea or non-sea, and for ATOVS-A includes the clear or

cloudy designation. Although the non-sea sounding type includes land, sea-ice, snow cover and

coastal designations, only one is selected. Selected collocations are typically those closest in

time, and then in space. The exceptions are coastal collocations, which are only selected if no

other types are available17.

3.2.2.3 The Matchup Database (MDB)

The Matchup Data Base (MDB) supporting the ATOVS-A and ATOVS-B products systems,

respectively, provides the longer term data sets of radiosonde and satellite data collocations used

for the tuning and validation of derived sounding products (Tilley et al. 2000 and Reale et al.

1990). Each MDB is updated daily, with an approximately 80% overlap in sample from week to

week.

The ATOVS-A system maintains separate MDB’s for clear and cloudy collocations, each

containing about 8,000 collocated radiosonde and satellite observations, which are stratified

 17

 Satellite measurements along coastlines are considered undesirable given the mixed sea and non-sea signatures

across the fov (for example, for lower peaking microwave channels).
20

among 23 geographical categories. Each geographic category contains a target sample size, and

sampling constraints which uniquely determine the spatial and temporal distribution of the

collocations.

Table 5 lists the 23 categories (CAT) and associated criteria for the MDB in ATOVS-A. Each

category is segregated by latitude belt (LAT) and terrain type (TERR), and the satellite orbit

node (Ascending, Descending or Both). Each category contains separate distance (x) and time (t)

windows, and target sample sizes (Samp) for the collocations stored, which are also listed, along

with the typical time periods (T) spanned by the clear and cloudy collocations per category.

The MDB update procedure for ATOVS-A includes:

independent sampling for each category,

a chronological pre-sorting of the collocations per category within “500 km” spaced

grid boxes, and

sequential sampling of the most current observation within each grid box

Collocations are stored into each MDB category sequentially, depending upon their age in

relation to a specified reference age. The reference age is initialized at 21-days, and increased (as

necessary) in 7-day increments, up to 105 days, until the target sample size for a given category

is met. This strategy provides a preferential retainment of more recent collocations from

radiosonde dense regions (i.e., northern hemisphere), and older collocations from radiosonde

sparse regions (i.e., southern hemisphere), thus maintaining a complimentary record of recent

weather and complete global coverage.

The ATOVS-B system maintains a single MDB of about 15,000 collocated radiosonde and

satellite observations stratified by sea and non-sea collocations, respectively.

The MDB for ATOVS-B is updated independently for sea and non-sea collocations respectively,

and does not include much of the sampling strategies employed in ATOVS-A. The final sample

of collocations tend to be heavily weighted toward northern hemisphere population centers, with

a global sampling period of about 21 days. As discussed later in Section 5, a merged ATOVS-A

and B system is planned which would inherit the ATOVS-A sampling strategies for MDB

compilation.

The MDB for ATOVS-A is routinely archived by NCDC (Goodrum et al. 2000).

3.2.2.4 The First Guess Libraries

The First Guess Libraries are updated daily based on the MDB, and directly accessed during

orbital processing. The first guess libraries have about half of the capacity of the MDB, with an

additional requirement that the vertical extent of a candidate radiosonde report be complete from

(at least) 950 mb to 50 mb for ATOVS-A, and from 950 mb to 400 mb for ATOVS-B,

respectively.

There are two separate first guess libraries in ATOVS-A, corresponding to clear and cloudy

collocations, respectively, and a single library containing all collocations in ATOVS-B. Each

library contains about 5000 collocations segregated into sea and non-sea sections, and are

updated daily consistent with the order in which they were stored onto the MDB. The right

columns of Table 3 indicate the sample sizes per geographic category, and the associated time
21

CAT Lat Terr Node x

(km)

t

(hr)

Samp

MDB

T-clear

(days)

T-cldy

(days)

Samp

Library

T-clear

(days)

T-cldy

(days)

 118 N Ice/C B 100 3 350 50 30 200 30 21

 219 Ice-45N Sea B 60 4 650 30 25 350 21 20

 3 45-30N Sea B 70 4 650 45 30 350 30 21

 4 30-15N Sea B 80 4 600 55 35 350 35 21

 520 15N-15S All B 60 4 650 30 30 350 21 21

 6 15-30S Sea B 100 5 250 30 40 175 21 25

 7 30-45S Sea B 100 5 250 30 30 175 21 21

 8 45S-Ice All B 100 4 225 35 30 150 21 21

 9 S Ice/C B 100 4 225 — 35 125 — 25

10 90-60N Non-Sea A 100 2 250 40 30 150 30 21

11 60-45N Non-Sea A 60 2 400 25 21 225 20 15

12 45-30N Non-Sea A 50 2 500 21 21 275 15 15

13 30-15N Non-Sea A 70 3 450 30 25 250 21 20

14 15-30S Non-Sea A 100 3 300 25 30 175 20 21

15 30-45S Non-Sea A 100 3 200 25 35 150 20 25

16 60-90S Non-Sea A 100 3 25 — 100 15 — 100

17 90-60N Non-Sea D 100 2 250 30 25 150 21 20

18 60-45N Non-Sea D 60 2 400 25 21 225 20 15

19 45-30N Non-Sea D 50 2 500 21 21 275 15 15

20 30-15N Non-Sea D 80 3 400 50 25 225 30 20

21 15-30S Non-Sea D 100 3 250 35 70 150 21 40

22 30-45S Non-Sea D 100 3 200 35 35 150 21 21

23 60-90S Non-Sea D 100 3 25 — 100 15 — 100

Total Sample 8000 4650

Table 5: The 23 categories (CAT) and associated sampling criteria and characteristics

for the MDB and First Guess Libraries of ATOVS-A on September 6, 2000

periods spanned by the clear and cloudy collocations stored on the first guess libraries for

ATOVS-A.

Figure 6 illustrates a typical global distribution of collocations stored on the first guess libraries

for combined clear and cloudy collocations from NOAA-15. The sounding location is used for

plotting, and the color coding for the collocated sounding is sea (light blue), land (dark green),

coast (yellow), ice (grey) and snow (white). Collocations with ship radiosondes are red.

The compilation of first guess libraries for ATOVS-A includes an upward extrapolation of each

radiosonde temperature profile from the highest report level (i.e., at least 50 mb) to .1 mb, and a

downward extrapolation (if necessary) to 1000 mb.

 18

 Includes Northern Hemisphere Ice, and 60N to 90N coastal, collocations.

19

 Includes Sea collocations from the Ice-edge to 45N.

20

 Includes Sea and Non-sea collocations.
22

Figure 6: The global distribution of clear and cloudy collocations stored on the

First Guess Libraries from NOAA-15 observed on September 6, 2000.

A new approach based on a statistical regression of the collocated AMSU-A measurements

(Goldberg 1999) was deployed for NOAA-15 during April 2000 to extend each radiosonde from

the highest report level to .1 mb. A requirement for extension is that the difference between the

regressed and observed radiosonde temperature profiles must be within 2.5 degrees at some level

between 10 mb and 60 mb. The level at which the absolute difference is minimum is defined as

the merge point, with values as and above this level provided by the regression, and below

provided by the radiosonde. If no merge point is found, the collocation is not stored in the

library. Typically, less than ten (10) percent of the candidate collocations fail these criteria.

The downward extension of radiosonde is only required if the first significant level was between

950 and 1000 mb, and is done using a weighted, averaged, lapse rate and constant dewpoint

depression for temperature and moisture, respectively, based on the lowest reported levels.

Reported and extrapolated radiosonde profile data as described above are stored in the first guess

library, and directly available during orbital processing to determine the first guess (Section

3.1.4).
23

3.2.3 Coefficients

Coefficients for the derivation of operational sounding products are required for

Limb Adjustment,

Cloud Detection,

First Guess,

Retrieval,

Radiance Bias Adjustment (AMSU-B only), and

RFI Correction (AMSU-B for NOAA-15)

Except for limb adjustment, all coefficients are updated weekly.

3.2.3.1 Limb Adjustment

Regression coefficients for limb adjusting the HIRS/3, AMSU-A and AMSU-B (Wark 1993) are

computed from the respective Satellite Radiance Data sets. Coefficients are independent for each

sounder, based solely on the calibrated, radiance temperature, measurements, and include

smoothing constraints across the scan. Three “associated” channels are normally used when

applying corrections, the channel itself and those peaking immediately above and below21.

Associated channels are constrained to not mix temperature, moisture and surface sensitivity

measurements.

Separate sets of limb correction coefficients are generated for sea and non-sea observations,

respectively. In the absence of significant instrument drift, an initial set of coefficients can last

for the operational life-span of the satellite, typically two to three years

22

. NESDIS maintains

operational monitoring of scan position residuals for all sounders and channels, and updates

coefficients as necessary (Allegrino et al. 1999).

3.2.3.2 Cloud Detection

Coefficients for the regression based, cloud detection tests for ATOVS-A (see Table 1) are

updated weekly using the HIRS Satellite Radiance Data set, and an ordinary least squares

regression. Limb adjusted and cloud-free measurements are used, with the AMSU-A

measurements interpolated to the HIRS field-of-view (fov), and the 17 AVHRR centered on the

HIRS fov (Ferguson and Reale 2000). No measurements poleward of 80 degrees latitude or for

terrain heights greater than 1000 m are used.

Table 6 lists the predictands, predictors, parameter names (see Table 1) and sampling constraints

for each set of coefficients. The sample sizes typically used to generate these coefficients are on

the order of 50,000 observations, spanning the most recent 30 to 50 days.

3.2.3.3 First Guess

The radiance covariance “B” matrices (Goldberg et al. 1989) for the library search (see equation

1) are computed using the satellite measurements stored on the MDB. B-matrices are based on

 21

 Except in the case of surface and tropopause channels.

22

 An exception are the limb adjustment coefficients for NOAA-15 which must be updated concurrent with RFI

correction tables (see Appendix B).
24

adjusted radiance temperature observations consistent with those used in the library search and

retrieval steps for each system, respectively, and consistent with the sounding type.

Four (4) “B” matrices are available in ATOVS-A, derived from clear and cloudy measurements,

respectively, over sea and non-sea terrains, respectively.

Two (2) “B” matrices are available for ATOVS-B, derived from sea and non-sea measurements,

respectively.

3.2.3.4 ATOVS-A Retrieval

The retrieval operator components (“X” and “Y” of equation 3) for ATOVS-A temperature and

moisture soundings are updated weekly using the collocations stored on the MDB, with vertical

extent requirements and extrapolation for radiosonde similar to the first guess libraries. As

indicated in Equation 3, the retrieval operator components consist of the:

A-matrix providing sounding channel weights for each retrieval level,

the S-matrix providing the vertical covariance of first guess (or background) error,

and

the N-matrix providing the sounder measurement uncertainty.

Internal matrix calculations are all scaled to 700 cm-123.

Predictand Predictors Parameter Constraint

SST HIRS 6, 7, 10, 11, 8 STEH 8 DAY, SEA

SST HIRS 6, 7, 10, 11, 18 STEH 18 NIGHT, SEA

SST HIRS 6, 7, 10, 11, 19 STEH 19 NIGHT, SEA

AVHRR 3 AVHRR 4 PAVHRR 3 NIGHT, SNOW AND

ICE FREE

AVHRR 4 AVHRR 5 PAVHRR 4 NONE

HIRS 7 HIRS 13-16 PHIRS 7 NIGHT

AMSU-A 5 HIRS 13 -16 PAMSU5 (SW) NIGHT

AMSU-A 5 HIRS 4 - 7 PAMSU5 (LW) NONE

AMSU-A 7 HIRS 13 -16 PAMSU7 (SW) NIGHT

AMSU-A 7 HIRS 4 - 7 PAMSU7 (LW) NONE

Table 6. Predictand, predictor(s), parameter name and sampling constraints for

regression coefficients used in cloud detection.

 23

 Measurements are converted to 700 cm-1 equivalent radiance as originally required to utilize the pre-computed S

matrices from Crosby et al. (1973).
25

Nine (9) “A” matrices provide the vertical weighting function (Fleming et al. 1986) curves for

each ATOVS sounding channel. Each matrix is computed based on combined samples of clear

and cloudy collocations as stored in the MDB geographical categories 1 through 9, respectively

(see Table 5). Individual and mean profiles of the satellite measurements and radiosonde

(temperature and moisture) data, and corresponding atmospheric transmittance profiles

(McMillin et al. 1995) are used to compute an A-matrix for each collocation, which are then

averaged over the category to compute the A-Matrices available for retrieval.

The three (3) differential (d) terms comprising each A-matrix element for a given microwave

sounding channel, CM, and pressure level, P, reduces to (4):

 (CM,P)=(dR(700)/dBT)¥(dT(P)/dR(700)) ¥ dTau(P) (4)

where,

R(700) is the scaled equivalent Radiance at 700 cm-1,

BT is the channel Brightness Temperature ( ̊K),

T is the radiosonde Temperature ( ̊K), and

Tau is atmospheric Transmittance.

Term 1 is computed in the vicinity of the observed sounder channel measurements (BT), whereas

terms 2 and 3 are relative to the radiosonde temperature (T) and transmittance (Tau) profiles in

the vicinity of level (P). The corresponding terms for HIRS channels are more complex given

their nonlinear relationship to temperature.

Figure 7 shows examples of the mean temperature (solid, thick curves) profiles for MDB

categories 1 (blue) and 5 (red), representing polar and tropical atmospheres, respectively, and the

corresponding A-matrix (or vertical weighting function) profiles for HIRS/3, channel 7 (dashed

curves), followed by AMSU-A channels 5, 8, and 13 (thin, solid curves). The set of blue

weighting curves correspond to the polar profile, and the red curves the tropical profile. The

vertical axis indicates the geopotential height (km), the upper horizontal axis indicates

atmospheric temperature ( ̊K), and the lower horizontal axis the weighting function values (from

equation 4). The date of these profiles was from September, 2000.

It can be seen that each channel is sensitive through a different atmospheric layer, and that the

shape of each curve varies per atmosphere. Since the actual height (geopotential) of a given

pressure level varies per atmosphere, plotting the weighting function curves in this manner

accentuates their variation with height on a global scale, and as approximated through the nine

“A” matrices. As discussed in Section 5, future plans include the use of unique A-matrix per

sounding based on the first guess.

Noteworthy are the HIRS channel 7 curves, which illustrate their relatively high vertical

resolution and sensitivity in the lower troposphere compared to AMSU, particularly in the

Tropics.
26

Figure 7: Mean temperature profiles ( ̊K) for Polar (solid blue) and Tropical

(solid red) atmospheres, overlaid by corresponding weighting function curves

for HIRS/3 channel 7 (dashed), and (in ascending order) by AMSU-A channels

5, 8 and 13 for NOAA-16, as computed for the Polar (blue) and Tropical (red)

atmospheres versus geopotential height (m) (vertical axis), with the temperature ( ̊K)

and weight values defined along the upper and lower horizontal axis.
27

Four (4) “S” matrices (Fleming et al. 1986) are computed which contain the vertical covariance

of the “first guess minus radiosonde” differences for temperature and moisture at each pressure

level. Separate matrices are computed based on the respective samples of clear and cloudy

collocations for sea and non-sea terrains as stored on the MDB (see Table 5).

The S-matrix used to derive a given sounding is designed to be consistent with the collocations

searched to determine the first guess, which as discussed earlier depends on the sounding type.

These matrices constrain the retrieval solution based on the expected vertical correlation of the

first guess minus sounding differences for temperature and moisture, including cross correlations

between temperature, moisture and surface terms.

The “N” matrix is tri-diagonal and contains the noise equivalent in radiance (Fleming et al. 1986)

for each channel, based on observed sounder calibration data (from the satellite). The final noise

values depend upon temperature, and are uniquely computed using the mean profiles for MDB

geographical categories 1 through 9, respectively (see table 5).

Thirty-two (32) total sets of retrieval operator components are computed using specific

combinations of the nine (9) A-matrices, and four (4) S-matrices as described above. Seven (7)

operators are available for sea soundings, and nine (9) are available for nonsea soundings, with

separate sets of operators for clear and cloudy sounding types. The retrieval operator and channel

combination used, that is, the specific operator components and the rows and columns of the A

and N matrices for a given sounding, is defined by the sounding type and geographic category

consistent with Tables 3 and 524.

3.2.3.5 ATOVS-B Retrieval

The retrieval coefficients for AMSU-B moisture soundings are computed using the collocations

stored on the MDB and an ordinary least-squares regression solution (Crone et al. 1996). The

radiosondes used must satisfy the vertical extent requirements and require extrapolation similar

to the first guess libraries (for moisture and temperature)

Radiosonde minus first guess water vapor mixing ratios are regressed against the observed minus

first guess radiance temperature values. The radiance temperatures used are both limb and bias

adjusted.

Two sets of global regression coefficients are computed from sea and non-sea collocations

(Goldberg et al. 1998), respectively, consistent with the channel combinations in Table 4.

3.2.3.6 Radiance Bias (AMSU-B)

Radiance temperature bias adjustment coefficients (Fleming et al. 1991 and McMillin et al.

1989) for AMSU-B are computed using a shrinkage-estimator, constrained, regression approach

(Crone et al. 1996). Coefficients are based on radiance temperatures calculated (McMillin et al.

1995) from the radiosondes, versus the collocated, limb-adjusted measurements as stored on the

MDB. The CLW (Grody et al. 1999) is included as a predictor for radiance bias adjustments over

sea.

 24

 Retrieval operators for corresponding, ascending and descending categories over non-sea terrains are identical.
28

Two sets of global regression coefficients are computed based on sea and non-sea collocations,

respectively.

Figure 8 illustrates an example of the magnitude and global characteristics of the radiance bias

adjustments. The upper panel shows bias adjusted radiance temperature measurements for the

AMSU-B 183 ± 1 GHz channel, which is sensitive to upper tropospheric moisture, and the lower

panel the magnitude of the bias adjustments applied. As can be seen, the adjustments for this

channel are on the order of 5 degrees K, with a tendency for positive adjustments in the southern

hemisphere than in the north, perhaps indicative of a seasonal trend. Adjustments over land tend

to be negative, and slightly larger than over sea.

Overall, the bias adjustments appear to enhance the measurement field patterns, an encouraging

aspect, but the large magnitude of the adjustments is troublesome, suggesting significant error in

either the radiative transfer model or radiosondes, the latter particularly suspect concerning upper

tropospheric moisture (Soden et al. 1996).

Upon updating the radiance bias coefficients, all the bias adjusted observations stored on the

MDB are re-calculated. This is done prior to the weekly updating of the first guess libraries,

radiance temperature covariance matrices and retrieval regression coefficients to maintain

consistency between expected and measured observations during the upcoming week.

3.2.3.7 RFI Correction Tables

As discussed in Appendix-B, the RFI correction tables for AMSU-B onboard NOAA-15 require

periodic adjustment (Atkinson 2000). This requires subsequent actions to re-calculate the

limb adjustment coefficients,

limb adjusted observations on the MDB,

bias adjustment coefficients,

bias adjusted observations on the MDB,

radiance covariance “B” matrices, and

retrieval regression coefficients.

These are provided as required.

4 RESULTS AND ANALYSIS

The scientific monitoring and evaluation of operational sounding products is maintained on a

continuous basis at NESDIS. The primary validation strategies consist of

vertical accuracy statistics and

horizontal field analysis
29

Figure 8: Bias adjusted radiance temperature measurements ( ̊K) for the AMSU-B

upper tropospheric moisture channel at 183 ± 1 GHZ., onboard NOAA-15, and the

corresponding magnitude of adjustments (lower), composited from 16Z on September 6

to 05Z on September 7, 2000.
30

4.1 Vertical Accuracy Statistics

Vertical accuracy statistics based on satellite minus radiosondes differences computed from

collocations stored on the MDB are illustrated in Figures 9 and 10, which provide estimates of

the expected performance of NESDIS operational soundings on a global scale. Figure 9 shows

differences for ATOVS-A clear (left) and cloudy (right) temperature soundings, respectively,

from the 60N to 60S latitude belt. Figure 10 shows differences for ATOVS-B moisture

soundings for the 90N to 30N and 30N to 30S latitude belts, respectively. The thicker curves for

each plot are for the final soundings, and the thinner lines for the corresponding first guess

profiles, respectively. Figure 9 provides mean and standard deviation differences in degrees K,

whereas the Figure 10 plots are percent differences in water vapor mixing ratio (g/kg), with the

mean profiles used to compute percentages shown along the inside of the left axis. Pressure (mb)

and sample size are indicated along the left and right vertical axis for all plots.

Figure 9 indicates that uncertainties for the clear and cloudy temperature soundings are

comparable, with satellite minus radiosonde differences approaching 2.5 ̊ K Root Mean Square

(RMS) near the surface and troposphere, 1.5 ̊ K in the middle troposphere where the sounders

are most reliable, and about 2.0 ̊ K in the stratosphere. Clear soundings appear to be slightly

more accurate than cloudy soundings near the surface, and both sets show little or no bias except

at the tropopause.

Figure 10 indicates expected uncertainties for moisture near the surface to range from 15 percent

in the tropics to 25 percent in polar regions. How ever, when multiplied by the mean mixing ratio

values, the actual differences in water mixing ratio units is about 2 g/kg in the Tropics, versus 1

g/kg outside the tropics. Similarly, the steady increase in the percentage uncertainty of moisture

with height is also attributable to the relatively low mixing ratio values aloft. Factoring in the

spatial and temporal windows for each collocation, the inherent variability of moisture

measurements, and radiosonde moisture errors (Schmidlin 1998, Soden et al. 1996, and Wang et

al. 2001), the accuracy estimates shown in Figure 10 are reasonable.

Another feature of these statistics is the convergent aspect of the retrieval solution for ATOVSA, manifested in Figure 9 through the significantly improved accuracy of the final soundings

(thick curves) compared to the guess (thin curves). This confirms the scientific approach, in

particular, the consistency of the first guess radiance temperature and temperature observations

provided for the retrieval step (Fleming et al. 1986).

4.2 Horizontal Fields

Horizontal field analysis provides better meteorological context concerning the characteristics of

the derived satellite products than the vertical accuracy statistics, and is an important component

of the product validation function at NESDIS. Such analysis allows comparisons of sequentially

composite fields of sounder measurements and associated derived products data, and as available

concurrent NWP fields. Figures 11 through 15 illustrate examples of meteorological analysis

over regional and synoptic scales, for various atmospheric levels and satellite data types. All are

concurrent with the 16 Z to 05Z time frame spanning September 6 and 7, 2000, similar to other

horizontal fields presented in this report.
31

Figure 9: Satellite minus radiosonde mean and standard deviation differences (K)

for ATOVS-A Clear (left) and Cloudy (right) for first guess (thin) and final (thick)

temperature soundings, respectively, from 60N to 60S during a 7-day period in

September, 2000. Pressure (mb) is indicated on the left axis with corresponding

sample size on the right.
32

Figure 10: Satellite minus radiosonde mean and standard deviation differences (%)

for ATOVS-B first guess (thin) and final (thick) moisture soundings from the 90N

to 30N (left) and 30N to 30S regions, respectively, during a 7-day period in

September, 2000. Differences are percentages of the mean mixing ratio profiles

(g/kg) shown on the inside of the left axis, with pressure on the outer left axis and

sample size on the right axis.
33

The four panels of Figure 11 illustrate regional scale analysis for a storm system in the N.

Atlantic as it approaches northern Europe. The two upper panels display AMSU measurements

for channel 6, sensitive in the middle troposphere, versus satellite minus NWP difference fields

for the 500 mb to 300 mb layer-mean, virtual temperature. The two lower panels show AMSU

channel 5, sensitive in the lower troposphere, and satellite minus NWP differences for 1000 mb

to 700 mb layer mean virtual temperature. The NWP are from the EMC Aviation Forecast cycle

(Caplan et al 1997) and are the 6-hour forecast valid within ± 3 hours of the satellite observation

time (as appended during Preprocessing). Color scales are shown below each plot; for difference

fields, white indicates values within ± 1 ̊ K. The composite time period for each field is

approximately 18Z to 22Z.

Analysis of Figure 11 indicates that differences between the satellite and NWP data are in

regions of strong temperature advection (Reale 1995c). Agreement elsewhere is quite good (i.e.,

white), including in the warm and cold air cores. These differences also appear to be correlated

with the prevailing advection pattern, with positive (red) values in areas of warm advection, and

negative (blue) values in areas of cold advection25. The vertical structure of the differences is

also meteorologically consistent, for example, the area of positive values (warm advection) aloft

(upper right panel) lie well east of the area of positive values closer to the surface (lower right

panel).

The four panels of Figure 12 illustrate satellite based upper tropospheric structures concurrent

with Figure 11. Shown are the AMSU-A channels 8 (upper left) and 7 (lower left), with peak

sensitivities which on the average lie just below and above the tropopause, around 250 mb and

150 mb, respectively (see Appendix-A1 and Table 8). The corresponding right panels show

derived temperature sounding fields at 250 and 150 mb, respectively.

Figure 12 shows good agreement between the sounder measurement and derived temperature

patterns. Particularly interesting is the anomalous warm region in the AMSU channel 7 and

associated 250 mb temperatures fields. This feature signifies a region of minimum tropopause

heights, and maximum tropopause level change, typically associated with a developing upper air

low pressure trough and located just north of the jet stream-stream core. Combined with the

analysis of AMSU channel 8, both the jet stream and level of maximum wind are indicated just

below the line at which the tropopause cuts the 200 mb surface (Wickham 1970).

The three panels of Figure 13 show NOAA/EMC analysis fields (Caplan et al.1997) for 250 mb

temperature (upper), tropopause pressure (middle), and 250 mb wind (lower), averaged over a

12-hour period beginning at 00Z on September 7, about 6 hours later than the satellite data.

Except for the slight (expected) eastward shift, the EMC analysis supports the satellite analysis

in Figure 12. AMSU-A radiance measurements are directly assimilated into EMC NWP forecasts

(Derber et al. 1998), which accounts for the good agreement between the satellite and EMC data

in this otherwise data devoid region.

 25

 Not attributable to time differences between the satellite and NWP valid times (Reale 1995c).
34

Figure 11: AMSU-A radiance temperature measurements ( ̊K) for channels 6 and 5,

and corresponding Satellite minus NWP forecast difference ( ̊K) fields for upper

(500 to 300 mb) and lower (1000 to 700 mb) tropospheric layers, respectively,

composited from 18Z to 22Z on September 6, 2000 for NOAA-15.
35

Figure 12: AMSU-A radiance temperature measurements ( ̊K) for channels 7

and 8, and corresponding satellite sounding temperature fields ( ̊K) at 250 and

200 mb, composited from 18Z to 22Z on September 6, 2000, for NOAA-15.
36

Figure 13: EMC 12-hour averaged fields for analyzed 250 mb temperature  ̊C

(upper), tropopause pressure (mb) (middle), and 250 mb winds (knots) (lower)

for 06Z on September 7, 2000.
37

The four panels of Figure 14 provide satellite based cloud and moisture data for this case. The

HIRS channel 8 (sensitive to cloud) and experimental cloud-top pressure

26

 (Yang et al 1996) data

in the upper left and right panels illustrate the cloud features for the region. Particularly

interesting is the circular cloud structure in the HIRS 8 and cloud products, which appear to

outline the surface low. Combined with the satellite data shown in Figures 11 and 12, the zonal

plane of a developing baroclinic wave (Wallace and Hobbs 1977) is portrayed. The vertical,

westward tilt of the wave trough is denoted by a curve from the surface low, westward, through

the AMSU channel 6 and 7 warm cores, with the trough at 200 mb situated over the cold air at

lower levels. Maximum cold advection and subsidence in the middle troposphere are typically

observed south and west of this line, with maximum warm advection and lifting to the north and

east. System intensification is probable, consistent with the analysis of the satellite minus NWP

differences from Figure 11.

The lower panels of Figure 14 illustrate the corresponding AMSU-B, mid-tropospheric, moisture

data. Again noticeable is the circular cloud feature, but this time in the AMSU-B 183 ± 3 GHZ

measurements (lower left) reputedly transparent to clouds. This channel also shows warming

(dryness) associated with the “warm core” in AMSU-A channel 7 (Figure 12), as would be

expected in a subsiding cold air mass. Also evident is a dry zone to the east of the surface low,

indicative of residual subsidence from the previous system. The derived AMSU-B moisture field

at 620 mb is shown in the lower right panel. Overall consistency is seen between the AMSU-B

measurements and derived products, although contamination consistent with the infra-red cloud

signature is also evident.

The four panels of Figure 15 show expanded horizontal coverage of the AMSU-B moisture data

for the upper troposphere. Each set of panels displays the AMSU-B 183 ± 1 GHz channel,

sensitive to upper tropospheric moisture, and corresponding derived moisture products at 500

mb. The two upper panels provide an approximately 7-hour composite covering the northern

hemisphere region, and concurrent with the CLW, precipitation, and cloud data shown earlier in

Figures 2 and 3. The two lower panels span 12-hours, covering a larger region from Africa

westward to the middle Pacific. Good consistency is observed among all these data, illustrating

the robust patterns of upper level moisture measurements and derived products on a regional and

global scale. Remote satellite products are a primary source of contiguous and reliable upper

level moisture data.

Satellite derived products are also the primary source of global temperature data in the upper

stratosphere (Finger et al. 1993). The four panels of Figure 16 illustrate 12-hour composite fields

of global coverage for AMSU-A channel 13 adjusted radiance temperature measurements (upper

left) which are sensitive in the vicinity of 5 mb (see Figure 8), corresponding 5 mb derived

temperature fields from NOAA-15 (upper right) and from RTOVS for NOAA-14 (lower left),

and the NOAA-15 minus NOAA-14 temperature differences at 5 mb (lower right). Color scales

are indicated below each panel, with identical spectral scales used for the respective 5 mb

temperature fields.

 26

 Experimental ATOVS-A cloud products include cloud top pressure, cloud top temperature and cloud amount at

HIRS fov.
38

Figure 14: HIRS channel 8 radiance temperature ( ̊K) measurements (upper left),

derived cloud-top pressure (mb) (upper right), and concurrent AMSU-B radiance

temperature measurements ( ̊K) at 183 ± 3 GHz. (lower left) and derived moisture

(g/kg) at 620 mb (lower right), composited from 18Z to 22Z on September 6, 2000

 for NOAA-15.
39

Figure 15: AMSU-B radiance temperature measurements ( ̊K) at 183 ± 1 GHz.

(left panels) and derived 500 mb moisture (g/kg) for composited fields from

approximately 18Z to 0Z (upper), and 16 Z to 4Z (lower) on September 6 and 7,

2000 for NOAA-15.
40

As seen in Figure 16, the 5 mb temperature patterns for NOAA-15, ATOVS, are tightly aligned

with the channel 13 measurements, and differ significantly with their NOAA-14 counterparts.

Absolute differences between these satellites are on the order of 5 ̊ K over most of the Tropics,

and exceed 10 ̊ K in south polar regions27. This is attributable to the new ATOVS approach for

upper stratospheric soundings discussed in Section 3.2.2.4. Comparisons against available

Halogen Occultation Experiment (HALOE) soundings above 10 mb have been used to verify the

improved performance of the ATOVS upper stratospheric products (Goldberg 1999).

5 FUTURE PLANS

The NESDIS operational system provides a dynamic research environment which includes an

ongoing program to develop, evaluate and implement scientific (and system) upgrades for

sounding products based on user feedback and internal monitoring.

The primary scientific activities planned over the next year is the merging of the ATOVS-A and

B systems into a single, simultaneous, temperature and moisture products generation system.

This will include studies to replace the library search with a statistical regression technique based

on AMSU, and if successful, the replacement of pre-computed with real-time retrieval operators.

Referred to as System-2002, the proposed upgrade would result in dual sounding products,

consisting of climate oriented first guess information, and real-time weather oriented derived

products, the later optimized for assimilation into NWP. There are also new requirements to

provide ATOVS processing fall back contingencies, particularly in the event of a HIRS sounder

failure.

Preliminary work on System-2002 has been completed to subsample the AMSU-B onto the

AMSU-A (and HIRS), including averaging of the (nine) corresponding AMSU-B

measurements28. Preliminary design of the regression guess and accompanying physical retrieval

solution is underway. A significant component of these changes entails an increased use of

radiative transfer calculations (McMillin, 1995) during orbital and offline processing. The plan

includes fixed regression coefficients, based on calculated radiance from radiosondes (and

rocketsondes), to compute the orbital guess products, followed by the calculation of associated

first guess radiance29. This would also require the radiance bias adjustment of the AMSU and

HIRS/330 sounder measurements used in the retrieval step for consistency with the calculated

first guess measurements. The requirement to calculate the first guess measurements also means

that radiative transfer parameters required for the retrieval A-matrix would be uniquely available

for each sounding, removing the need for pre-computed retrieval operators.

System-2002 will include a restructuring of the daily and weekly updating procedures. Although

the daily compilation of collocated radiosonde and satellite data (onto the MDB) will continue,

their application in products tuning will be modified and the first guess libraries discontinued.

New radiosonde sampling methods are also anticipated, for example, options to screen the

radiosondes used for tuning based on instrument type31 could significantly improve the global

characteristics of derived product background errors from satellites.

 27

 Vertical analysis also shows significant lapse rate differences as well in most global regions.

28

 Data sets retaining the original AMSU-B resolution would be available upon request.

29

 This was not necessary with the library search approach which provided averaged sounder measurements.

30

 The bias adjustment of HIRS has never been demonstrated operationally and presents unique problems due to

clouds.

31

 The Vaisala RS80 radiosonde is used at about 40 percent of the global operational radiosonde stations.
41

Figure 16: AMSU-A radiance temperature measurements ( ̊K) for channel 13

(upper left) and derived temperatures ( ̊K) at 5mb for NOAA-15 (upper right)

and for NOAA-14 (lower left), and NOAA-15 minus NOAA-14 temperature

difference at 5 mb, composited for the period 16 Z to 05Z on September 6

and 7, 2000.
42

6 SUMMARY

This report presents the current status of the NESDIS operational sounding products systems as

of June, 2001. The report provides a brief history of the operational science algorithms since the

onset of operational sounding products in 1979, leading up to the current ATOVS systems.

ATOVS sounding products actually comprise two (2) separate systems, the ATOVS-A which

processes AMSU-A, HIRS and AVHRR measurements to derive temperature and moisture

sounding, and cloud products, and ATOVS-B which processes AMSU-B moisture sounding

products. The processing systems for each are structured in a similar manner concerning Online

(orbital) and Offline support systems.

Online (orbital) processing systems consist of a series of steps which screen the incoming

sounder measurements, detect the presence of precipitation and cloud, compute first guess

information, and derive sounding products. These steps are outlined with supporting graphical

displays.

Offline support systems operate on a daily and weekly basis to provide data sets and coefficients

which are used to derive the sounding products and for validation. These steps are outlined with

supporting graphics.

Results are presented on the vertical accuracy and meteorological consistency of the derived

sounding products. Statistical comparisons against collocated radiosondes provide estimates of

derived sounding uncertainties on the order of 2.0 degrees RMS for temperature, and

approaching 15 percent for low level moisture, with little or no bias. Meteorological case studies

illustrate the horizontal and vertical consistency of the derived product fields across active

weather systems on a regional and global scale. Both sets of results underscore the reliability of

the NESDIS operational sounding products for global weather, as well as NWS field office

applications.

The report concludes with a summary of future plans to merge AMSU-A and AMSU-B

measurements (along with the infrared) in a proposed System-2002 configuration, which would

provide separate sounding products for climate and weather applications, the latter optimized for

NWP assimilation.

7 ACKNOWLEDGMENTS

I would like to thank Dr. Dieter Klaes (EUMETSAT), Dr. John Bates (NOAA), Mr. Michael

Mignogno (NOAA) and Mr. Mitchell Goldberg (NOAA) for their thoughtful reviews of this

report, and Ms. Joan Reed (NOAA) for final editing and preparation for publication. The work

presented in the report would not have been possible without the continued support of my

NOAA co-workers Mr. Michael Chalfant and Ms. Ellen Brown, and the contractor staff from

Raytheon Corporation.
43

APPENDIX A1

AMSU-A RADIOMETER CHANNEL CHARACTERISTICS

Channel Center Frequency

(GHz)

Peak Sensitivity Product

1 23.8 Surface TPW, CLW

2 31.4 Surface TPW, CLW

3 50.3 Surface

4 50.8 850 mb T

5 53.6 600 mb T

6 54.4 400 mb T

7 54.9 250 mb T

8 55.5 150 mb T

9 57.3 80 mb T

10 57.3, ± .217 50 mb T

11 57.3, ± .322

 ± .048

20 mb T

12 57.3, ± .322

 ± .022

10 mb T

13 57.3, ± .322

 ± .010

5 mb T

14 57.3, ± .322

 ± .004

2 mb T

15 89.0 Surface

TABLE A1

Table A1 shows the channel number, center frequency (GHz), the atmospheric pressure of peak

sensitivity, and atmospheric product retrieved for each of the AMSU-A radiometer channels

onboard NOAA-15. The two frequencies shown for channel 10 (i.e., ± .217), and four

frequencies shown for channels 11 to 14 correspond to the two and four pass bands done for the

respective channels. The peak pressure sensitivities shown are based on calculations using the

OPTRAN radiative transfer model (McMillin et al. 1995), and U.S. Standard Atmosphere

(1976). The products shown correspond only to those derived during the operational processing

of soundings.
44

APPENDIX A2

HIRS/3 RADIOMETER CHANNEL CHARACTERISTICS

Channel Center Frequency

(cm-1)

Peak Sensitivity Product

1 669.1 25 mb

2 678.8 50 mb T

3 690.5 70 mb T

4 703.1 250 mb T, Cloud

5 715.9 400 mb T, Cloud

6 731.7 600 mb T, Cloud

7 747.7 900 mb T, Cloud

8 897.4 Surface Cloud

9 1032 30 mb (O3)

10 801.1 Surface H20, Cloud

11 1362 500 mb H20, Cloud

12 1529 300 mb H20

13 2188 1000 mb T, Cloud

14 2209 600 mb T, Cloud

15 2235 350 mb T, Cloud

16 2242 300 mb T, Cloud

17 2419 Surface

18 2518 Surface Cloud

19 2657 Surface Cloud

20 14,500 (.69 micron) Visible Cloud

TABLE A2

Table A2 shows the channel number, center frequency (cm-1), the atmospheric pressure of peak

sensitivity, and atmospheric product retrieved for each of the HIRS/3 radiometer channels

onboard NOAA-15. The peak pressures are based on radiative calculations using OPTRAN

(McMillin et al. 1995) and U.S. Standard Atmosphere (1976). Products correspond only to those

derived during the operational processing of soundings; for Ozone (O3) products see

Neuendorffer (1996).
45

APPENDIX A3

AVHRR RADIOMETER CHANNEL CHARACTERISTICS

Channel (micron) Center Frequency Peak Sensitivity Product

1 .63 Visible

2 .86 Visible

3a 1.61 Visible

3b 3.74 Surface Cloud

4 10.8 Surface Cloud

5 12.0 Surface Cloud

TABLE A3a

AMSU-B RADIOMETER CHANNEL CHARACTERISTICS

Channel (GHz) Center Frequency Peak Sensitivity Product

1 89 Surface CLW

2 150 Surface CLW

3 183 ± 1 350 mb H20

4 183 ± 3 500 mb H20

5 183 ± 7 650 mb H20

TABLE A3b

The above tables show the channel number, center frequency, the atmospheric pressure of peak

sensitivity, and atmospheric product retrieved for each of the AVHRR radiometer channels

(upper table), and AMSU-B radiometer channels (lower table) onboard NOAA-15. Center

frequency is listed in microns for AVHRR, and GHz for AMSU-B. The two frequencies shown

for AMSU-B channels 3 to 5 (i.e., ± 1, 3 and 7) correspond to the two (2) pass bands done for

each channel. The peak pressure sensitivities (for AMSU-B) are based on calculations using the

OPTRAN radiative transfer model (McMillin et al. 1995) and U.S. Standard Atmosphere (1976).

The products shown correspond only to those derived during the operational processing of

soundings.
46
47

APPENDIX B

RFI CORRECTION PROCEDURE

Preprocessing for ATOVS-B also includes the correction of AMSU-B measurements for Radio

Frequency Interference (RFI) from data transmitters onboard NOAA-15 (Atkinson 2000 and

Chalfant et al.1999); AMSU-B from NOAA-16 have no RFI.

The RFI correction procedures require three basic steps:

determine which transmitters are on for a given scan line,

correct the calibration points and compute “corrected” calibration coefficients,

correct the earth views and apply corrected calibration coefficients.

Figure B1 shows horizontal imagery of color enhanced AMSU-B measurements and the impact

of the RFI. The data are shown for AMSU-B channel 4 at 183 ± 3 GHz, sensitive to atmospheric

moisture in the middle troposphere, and the channel most affected by RFI. The upper panel

shows the original RFI contaminated data ( ̊K), the middle panel the corrected AMSU-B data,

and the lower panel the magnitude of the corrections applied ( ̊K). The graphics were created

special for illustrative purposes, since the actual RFI correction is done prior to the calibration

step (which converts counts to radiance temperature) (Atkinson 1999).

Figure B1 illustrates the complex characteristics of the RFI, and its significant impact on the

AMSU-B channel 4 measurements, up to 37 ̊ K in radiance temperature units.

Figure B2 shows plots of the RFI correction values across the scan for each AMSU-B channel,

with the vertical axis showing the RFI correction in counts, and the horizontal axis each of the

ninety-one AMSU-B scan positions. Each plot represents a different time period.

The upper panel of Figure B2 shows the original RFI values per scan position and channel from

the S-band Transmitter (STX)-1 and STX-3, and the Search And Rescue Repeater (SARR)

observed shortly after launch of NOAA-15 on May 13, 1998 (Atkinson 1999). A maximum RFI

of about 1800 counts is observed for AMSU-B channel 2 (150 GHz).

The middle panel of Figure B2 shows the RFI correction values after September 28th, 1999,

when NOAA turned off the STX-1 and STX-3 transmitters, leaving direct broadcast users reliant

on STX-2 (which was not interfering with AMSU-B). The RFI correction values are significantly

reduced for all channels, with a maximum value of about 600 counts for AMSU-B channel 4

(183 ± 3GHz).

The levels of RFI affecting AMSU-B data are routinely monitored by NOAA and the United

Kingdom Meteorological Office (UKMO), with the UKMO providing updated correction tables

every 2 months along with recommendations to NOAA on whether the current correction tables

need to be updated. The RFI correction pattern updated as of June, 2000 is shown in the lower

panel of Figure B2, for which the vertical axis has been re-scaled to the maximum observed

range of RFI (about 600 counts for AMSU-B channel 4).
48

Figure B1: AMSU-B radiance temperature measurements ( ̊K) for channel 4

(183 ± 3GHz) before (upper left) and after (upper right) RFI corrections, and

the magnitude ( ̊K) of the correction (lower) for NOAA-15 during May, 2001.
49

Figure B2: NOAA-15 correction values in counts (vertical axis) for each of the

ninety-one (91) AMSU-B fov positions across the scan (horizontal axis) for channels

1 (89GHz), 2 (150GHz), 3 (183 ± 1GHz), 4 (183 ± 3GHz) and 5 (183 ± 7GHz)

before (top) and after (middle) September 28, 1999, and for enhanced vertical

scale after June 20, 2000.

-1800

-1600

-1400

-1200

-1000

-800

-600

-400

-200

0

200

400

600

1

5

9

13

17

21

25

29

33

37

41

45

49

53

57

61

65

69

73

77

81

85

89

-1800

-1600

-1400

-1200

-1000

-800

-600

-400

-200

0

200

400

600

1

5

9

13

17

21

25

29

33

37

41

45

49

53

57

61

65

69

73

77

818589

Correction in Counts

-600

-540

-480

-420

-360

-300

-240

-180

-120

-60

0

60

1

5

9

13

17

21

25

2933

3741

45

4953

57

61

656973

77

81

85

89

Spot Number

Ch 1 89 GHz

Ch 2 150 GHz

Ch 3 183+/-1 GHz

Ch 4 183+/-3 GHz

Ch 5 183+/-7 GHz
50
51

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List of Illustrations

Figure 1 - System schematic diagram of Orbital processing (red), and the Daily (green) and

Weekly (blue) Offline support systems for ATOVS-A and ATOVS-B sounding

products

Figure 2 - Precipitation detection parameters of Cloud Liquid Water (mm) (upper left) and

median filter edits (upper right), the final set of edits for AMSU-A fov (lower

right), and AMSU channel 5 radiance temperature ( ̊K) measurements (lower

left), composited from 16Z to 23Z on September 6, 2000 for NOAA-15

Figure 3 - HIRS channel 8 radiance temperature ( ̊K) measurements (upper left), the

corresponding cloud test differences ( ̊K) for “SST - STEH18" (upper right) and

“PAMSU5 (LW) - AMSU5" (lower right ) tests as defined in Table 1, and the

resulting cloud mask (lower left), composited from 18Z on September 6, to 01Z

on September 7, 2000, for NOAA-15

Figure 4 - The first guess and retrieval sequence for one channel and level, showing AMSUA channel 6 radiance temperature measurements ( ̊K) (upper left), corresponding

first guess measurements (upper right) and 500 mb temperatures (lower right)

( ̊K), and 500 mb final sounding temperatures ( ̊K) at 500 mb, composited from

18Z on September 6, to 01Z on September 7, 2000

Figure 5 - A typical global distribution of radiosonde reports from a single day

Figure 6 - The global distribution of clear and cloudy collocations stored on the First Guess

Libraries from NOAA-15 observed on September 6, 2000

Figure 7 - Mean temperature profiles ( ̊K) for Polar (solid blue) and Tropical (solid red)

atmospheres, overlaid by corresponding weighting function curves for HIRS/3

channel 7 (dashed), and (in ascending order) by AMSU-A channels 5, 8 and 13

for NOAA-16, as computed for the Polar (blue) and Tropical (red) atmospheres

versus geopotential height (m) (vertical axis), with the temperature ( ̊K) and

weight values defined along the upper and lower horizontal axis

Figure 8 - Bias adjusted radiance temperature measurements ( ̊K) for the AMSU-B upper

tropospheric moisture channel at 183 ± 1 GHZ., onboard NOAA-15, and the

corresponding magnitude of adjustments (lower), composited from 16Z on

September 6 to 05Z on September 7, 2000

Figure 9 - Satellite minus radiosonde mean and standard deviation differences ( ̊K) for

ATOVS-A Clear (left) and Cloudy (right) for first guess (thin) and final (thick)

temperature soundings, respectively, from 60N to 60S during a 7-day period in

September, 2000. Pressure (mb) is indicated on the left axis with corresponding

sample size on the right
58

Figure 10 - Satellite minus radiosonde mean and standard deviation differences (%) for

ATOVS-B first guess (thin) and final (thick) moisture soundings from the 90N to

30N (left) and 30N to 30S regions, respectively, during a 7-day period in

September, 2000. Differences are percentages of the mean mixing ratio profiles

(g/kg) shown on the inside of the left axis, with pressure on the outer left axis and

sample size on the right axis

Figure 11 - AMSU-A radiance temperature measurements ( ̊K) for channels 6 and 5, and

corresponding Satellite minus NWP forecast difference ( ̊K) fields for upper (500

to 300 mb) and lower (1000 to 700 mb) tropospheric layers, respectively,

composited from 18Z to 22Z on September 6, 2000 for NOAA-15

Figure 12 - AMSU-A radiance temperature measurements ( ̊K) for channels 7 and 8, and

corresponding satellite sounding temperature fields ( ̊K) at 250 and 200 mb,

composited from 18Z to 22Z on September 6, 2000, for NOAA-15

Figure 13 - EMC 12-hour averaged fields for analyzed 250 mb temperature  ̊C (upper),

tropopause pressure (mb) (middle), and 250 mb winds (knots) (lower) for 06Z on

September 7, 2000

Figure 14 - HIRS channel 8 radiance temperature ( ̊K) measurements (upper left), derived

cloud-top pressure (mb) (upper right), and concurrent AMSU-B radiance

temperature measurements ( ̊K) at 183 ± 3 GHz. (lower left) and derived moisture

(g/kg) at 620 mb (lower right), composited from 18Z to 22Z on September 6,

2000 for NOAA-15

Figure 15 - AMSU-B radiance temperature measurements ( ̊K) at 183 ± 1 GHz. (left panels)

and derived 500 mb moisture (g/kg) for composited fields from approximately

18Z to 0Z (upper), and 16 Z to 4Z (lower) on September 6 and 7, 2000 for

NOAA-15

Figure 16 - AMSU-A radiance temperature measurements ( ̊K) for channel 13 (upper left)

and derived temperatures ( ̊K) at 5mb for NOAA-15 (upper right) and for NOAA14 (lower left), and NOAA-15 minus NOAA-14 temperature difference at 5 mb,

composited for the period 16 Z to 05Z on September 6 and 7, 2000

Figure B1 - AMSU-B radiance temperature measurements ( ̊K) for channel 4 (183 ± 3GHz)

before (upper left) and after (upper right) RFI corrections, and the magnitude ( ̊K)

of the correction (lower) for NOAA-15 during May, 2001

Figure B2 - NOAA-15 correction values in counts (vertical axis) for each of the ninety-one

(91) AMSU-B fov positions across the scan (horizontal axis) for channels

1 (89GHz), 2 (150GHz), 3 (183±1GHz), 4 (183±3GHz) and 5 (183±7GHz)

before (top) and after (middle) September 28, 1999, and for enhanced vertical

scale after June 20, 2000
59

List of Tables

Table 1 - Regression based cloud detection tests, thresholds and application constraints for

ATOVS-A, NOAA-15, during September-2000. The prefixes “STE” and “P”

refer to Surface Temperature Estimates and Predicted parameters

Table 2 - Non-Regression, Restoral and Adjacent Spot cloud detection tests, thresholds and

application constraints for ATOVS-A, NOAA-15, during September 2000

Table 3 - First Guess (FG) and Retrieval (RE) Channel Combinations (CC) for ATOVS-A

Sounding Types

Table 4 - First Guess (FG) and Retrieval (RE) Channel Combinations (CC) for ATOVS-B

Sounding Types

Table 5 - The 23 categories (CAT) and associated sampling criteria and characteristics for

the MDB and First Guess Libraries of ATOVS-A on September 6, 2000

Table 6 - Predictand, predictor(s), parameter name and sampling constraints for regression

coefficients used in cloud detection

Table A1 - Table A1 shows the channel number, center frequency (GHz), the atmospheric

pressure of peak sensitivity, and atmospheric product retrieved for each of the

AMSU-A radiometer channels onboard NOAA-15

Table A2 - Table A2 shows the channel number, center frequency (cm-1), the atmospheric

pressure of peak sensitivity, and atmospheric product retrieved for each of the

HIRS/3 radiometer channels onboard NOAA-15

Table A3a - This table shows the channel number, center frequency, the atmospheric pressure

of peak sensitivity, and atmospheric product retrieved for each of the AVHRR

radiometer channels onboard NOAA-15

Table A3b - This table shows the channel number, center frequency, the atmospheric pressure

of peak sensitivity, and atmospheric product retrieved for each of the AMSU-B

radiometer channels onboard NOAA-15