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An Examination of the Quality of Television Infrared

Observation Satellite (TIROS) Operational Vertical

Sounder (TOVS) Extracted Wind and Temperature Data

Over Oklahoma Using the Computer-Assisted Artillery

Meteorology BattleScale Forecast Model (CAAM BFM)

BY

Stephen F. Kirby

Computational and Information Sciences Directorate

Battlefield Environment Division

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ARL-TR-1889 ” “’

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‘. ;a . _ August 2000

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An Examination of the Quality of Television Infrared Observation Satellite (TIROS) Operational Vertical

Sounder (TOVS) Extracted Wind and Temperature Data Over Oklahoma Using the Computer-Assisted

Artillery Meteorology BattleScale Forecast Model (CAAMBFM)

6. AUTHOR(S)

5. F. Kirby

7. PERFORMING ORGANIZATION NAME(S) AND ADDRESS

U.S. Army Research Laboratory

8. PERFORMING ORGANIZATION

REPORT NUMBER

Computational and Information Sciences Directorate

ATTN: AMSRL-CI-EA

White Sands Missile Range, NM 88002-5501

ARL-TR-1889

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U.S. Army Research Laboratory

2800 Powder Mill Road

Adelphi, MD 20783-1145

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ARL-TR-1889

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13. ABSTRACT (Maximum 200 words)

A study centered on the Oklahoma area over a 2-month period in April and May of 1998 has been carried out analyzing the accuracy of

the temperature and wind speed and direction values derived from both the Television Infrared Observation Satellite (TIROS)

Operational Vertical Sounder (TOVS) Analysis Package and the International TOVS Processing Package 5.0. A hydrostatic mesoscale

model, the Battlescale Forecast Model, part of Computer-Assisted Artillery Meteorology Battlescale Forecast Model, was used for the

analysis.

14. SUBJECT TERMS

satellite, TOVS, mesoscale model, wind profiling radar, raob

15. NUMBER OF PAGES

6 1

r

16. PRICE CODE

17. SECURITY CLASSIFICATION 18. SECURITY CLASSIFICATION 19. SECURITY CLASSIFICATION 20. LIMITATION OF ABSTRACT

OF THIS REPORT OF THIS PAGE OF ABSTRACT

UNCLASSIFIED UNCLASSIFIED UNCLASSIFIED SAR
Preface

In an effort to find new sources of input data for the mesoscale

model portion (Battlescale Forecast Model [BFM]) of ComputerAssisted Artillery Meteorology Battlescale Forecast Model

(CAAMBFM), two Television Infrared Observation Satellite

(TIROS) Opera ti ‘onal Vertical Sounder (TOVS) processing

packages were utilized to look at the quality of the temperature

and wind speed and direction values extracted from TOVS data.

BFM, run in two modes, 3-d objective analysis (3dobj) only and

the full model, was used to quantify the accuracy of the TOVS

data. The 3dobj output wind speed and direction values were

compared to data from five wind profiling radars within the

Oklahoma region. The objective analysis used TOVS data as

input. For the model output, the temperature and wind speed

and direction values were compared to the 1200 Universal Time

Coordinates (UK) radiosonde observation from Norman,

Oklahoma. The model runs also used TOVS data as input.

1

Preceding Page’s Blank
Y

Acknowledgements

The author would like to thank Dr. Pat Haines (U. S. Army

Research Laboratory) for providing the Oklahoma profiler data

and Dr. Teizi Henmi (U. S. Army Research Laboratory) for

providing guidance on the interpolation technique used to create

gridded fields of the raw surface data.
Contents

Preface . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .................... 1

Acknowledgements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3

Executive Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7

1.0 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. 9

2.0 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 3

3.0 Theory ................................................................................................................................... 1 7

3.1 TOVS Preprocessor......................................................................................................... 17

3.2 Atmospheric Radiance Module.. .................................................................................. 1 8

3.3 Stratospheric Mapper.. ................................................................................................... 1 8

3.4. Retrieval Module ........................................................................................................... 1 8

4.0 Processing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .... 1 9

5.0 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .......... 21

6.0 Discussion. . . . . . . . . . . ..I.............................................................................................................. 2 9

7.0 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ..f........................................... 33

References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ........... 33

Acronyms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35

Appendices

Appendix A. Earth Location of Satellite Data .................................................................. 37

Appendix B. Limb Correction.. .......................................................................................... .43

Appendix C. Physical Retrieval Model ............................................................................. 47

Distribution ..I.............................................................................................................................. 5 1

5
1 .

2 .

3 .

4 .

5 .

6 .

7 .

8 .

Tables

3dobj run with profiler radar winds; NOGAPS data not used . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21

3dobj run with profiler radar winds; NOGAPS data were used . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22

CAAMBFM run with OUN raob, using climatology;

NOGAPS data were not used . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 3

CAAMBFM run with OUN raob, using regression;

NOGAPS data were not used . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24

CAAMBFM run with OUN raob, using climatology;

NOGAPS data were used . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25

CAAMBFM run with OUN raob, using regression;

NOGAPS data were used . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 6

Sdobj run with wind profiling radar, based on climatology;

NOGAPS data not used . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27

3dobj run with wind profiling radar, based on regression;

NOGAPS data not used . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 7
Executive Summary

A study centered on the Oklahoma area over a 2-month period in

April and May of 1998 has been carried out analyzing the

accuracy of the temperature and wind speed and direction values

derived from both the Television Infrared Observation Satellite

(TIROS) Operational Vertical Sounder (TOVS) Analysis Package

and the International TOVS Processing Package 5.0. A hydrostatic

mesoscale model, the Battlescale Forecast Model (BFM), part of

Computer-Assisted Artillery Meteorology Battlescale Forecast

Model (CAAMBFM), was used for the analysis. The study was

composed of two parts.

1. The accuracy of the wind speed and direction data from TOVS

was analyzed using output from the 3-d objective analysis

portion of BFM compared to up to five wind profiling radars in

Oklahoma.

2. The accuracy of temperature and wind speed and direction

data from TOVS was analyzed using the O-h forecast from

CAAMBFM compared to the radiosonde observation taken at

1200 Universal time coordinates (UTC) in Norman, Oklahoma.

Theory behind the TOVS extraction process is given in three

appendices to this report.

7
- ---1

1 .O Introduction

In an effort to investigate new sources of data to serve as input

into the Computer-Assisted Artillery Meteorology Battlescale

Forecast Model (CAAMBFM), an analysis of the quality of the

temperature and wind profiles extracted from Television Infrared

Observation Satellite (TIROS) Operational Vertical Sounder

(TOVS) data has been carried out. This analysis has encompassed

TOVS data sets derived from two different versions of the TOVS

analysis software. The first software version is the TOVS Analysis

Package (TAP) and was part of the software on the commercial

SeaSpace computer at the time of this test. The second version is

the International TOVS Processing Package (ITPP) 5.0.

The University of Wisconsin Cooperative Institute for

Meteorological Satellite Studies (CIMSS) developed both of these

software packages. One difference between the ITPP 5.0 software

and the SeaSpace TAP is the inclusion of the Thermodynamic

Initial Guess Retrieval (TIGR) database in ITPP 5.0. French

scientists at their Laboratoire de Meterologie Dynamique du

Centre National de la Recherche Scientifique developed TIGR.

TIGR is a set of 1800 archived atmospheric profiles classified by

latitude and season that were derived from a starting set of

150,000 samples by statistical methods. From these data,

coefficients for the regression relations can be determined, thus,

yielding better first guess profiles. Another improvement is the

ability to incorporate upper air data, and Advanced Very HighResolution Radiometer (AVHRR) data into the analysis, all of

which should generally improve the first guess profiles. The

AVHRR data are of considerably higher resolution (1 km) than

either the microwave or infrared sounding data. TOVS data have

a horizontal resolution of approximately 75 km [1,2,3].

9
The CAAMBFM can, broadly speaking, be run in two different

modes: with or without large-scale initialization data. The TOVS

profile extraction process requires the user to decide,

l how the initial guess profiles will be created (climatology or

regression) and

l whether the geostrophic/gradient wind component is desired.

Thus, a number of data combinations are possible when TOVS

profiles are used as input to the CAAMBFM. Before the TOVS

data could be used as input to the CAAMBFM, it was reformatted

as a list of soundings, identified by latitude, longitude, and

elevation, each with the following parameters.

0 measurement

l height

l pressure

l temperature

l dew point temperature

l wind speed

0 wind direction

Navy Operational Global Atmospheric Prediction System

(NOGAPS) model data were used for the large-scale initialization.

When NOGAPS data were used, the CAAMBFM first composites

the surface data and TOVS soundings with the NOGAPS data. In

addition, an initial field is created consisting of winds with

constant direction (derived by averaging the NOGAPS wind

directions) but with speeds that vary in the vertical based on the

log wind profile. 3-d objective analysis (3dobj) is run on both the

large-scale initialization data (in this case NOGAPS) temperature

and mixing ratio fields to create the initial potential temperature

and moisture fields respectively. This initial field then undergoes

a 3-hour (model time) spin-up, whereby the equations of motion

are used to “nudge” the initial field toward the composite of the

TOVS and NOGAPS data. This creates a so-called O-h analysis

field. It is this field of temperatures and wind speed and direction

data, which were then compared to the Norman, Oklahoma

radiosonde observation (raob) data, which are taken to be truth.

Fortunately, there were National Oceanic and Atmosphere

Administration (NOAA)-12 and NOAA-14 satellite passes with

footprints over the Oklahoma area close to 1200 Universal time

10
coordinate (UTC) matching up with the daily 1200 UTC raob

taken at Norman, Oklahoma.

In addition, wind fields output by the 3dobj portion of the

CAAMBFM initialized with TOVS data, were compared with

profiler wind data (the number of profilers used for comparisons

each day varied between 4 and 5).

1 1
I ”

2.0 Background

Onboard the NOAA polar-orbiting satellites is the TOVS, which

consists of three sounders, the High-resolution Infrared Radiation

Sounder (HIRS), the Microwave Sounding Unit (MSU) and the

Stratospheric Sounding Unit (SSU). Profiles of various

meteorological parameters (the ones of interest here are

temperature and wind speed and direction) are generated from

the TOVS data by a multi-step process:

1 .

2 .

3 .

4 .

5 .

6 .

Create TOVS sensor datasets (TIROS Information Processor

[TIP] files) from High Resolution Picture Transmission (HRPT)

telemetry data

Decommutate HIRS and MSU data from TIP data

Calibrate and earth-locate (appendix A) HIRS and MSU data,

perform limb-corrections (appendix B) and transform

calibrated and earth-located HIRS/MSU data into data sets

that can be displayed and retrieved.

Use a physical retrieval model (appendix C) to obtain vertical

profiles of atmospheric temperature, humidity, geopotential

height, and other parameters.

Eliminate soundings of questionable reliability by objective

analysis of differences between infrared and microwave

retrievals for the same location and of variability in 1000 to 500

mb thickness and long wave window versus surface

temperature.

Determine the geostrophic or gradient wind for “good”

soundings in retrieval file by least-squares objective analysis of

the height fields. [4]

The domain used for the CAAMBFM runs was a grid of 41 by

41 points, 400 by 400 km, with a grid resolution of 10 km. It is

centered in Oklahoma at 35.5 N, 97.5 W. The raob used as truth is

the one from University of Oklahoma-Norman (OLIN) taken daily

at 1200 UTC located at 35.2 N, 97.5 W. The analysis period for the

CAAMBFM runs using TOVS data compared with raob data

cover 14 days in April and May 1998. Wind data were available

1 3
from five profiling radar sites within this domain. These radar

systems can measure up to 17 km.

Gridded surface data needed to be created so that some of the

TOVS profile extractions within ITPP 5.0 would have “tie-down”

points for the first guess profiles. Raobs taken off the Integrated

Meteorological System (IMETS) were interpolated to a Mercator

grid (one version of a latitude/longitude-based grid) and the

weighting factor scaled as

1 (distance porn surface report to grid point)2 .

This method was used to create grids of both ambient temperature

and dew point depression. Within the 400 x 400-km Oklahoma

grid, approximately a dozen surface stations were reporting.

Navy Operational Regional Atmospheric Prediction System

(NORAPS) O-h analysis model (gridded) data (0.5” resolution)

were used for the 1000 mb heights. Again, the data were

interpolated to a Mercator grid using the same type of weighting

function. The Mercator grid was created using a FORTRAN

program available from the Naval Research Lab (NRL), at

Monterey, California accessible through the Internet site, the

Master Environment Library (MEL). The final step was to

consolidate the Mercator gridded 1000 mb heights, ambient

temperature, and dew point depressions into one file. The format

required for this surface data are that used by the Man computer

Interactive Data Access System (McIDAS) weather information

processing and display computer system.

The first step was to extract TOVS sounding data using either the

TAP or the ITPP 5.0 software package. As mentioned earlier, the

user has several options when starting the extraction process.

Among them is whether the user wants the first guess profile to be

based on climatology or regression. The wind component

generated can be either geostrophic or gradient. The geostrophic

component assumes a balance between the horizontal pressure

gradient force and the Coriolis force. For the gradient component,

a three-way balance between the horizontal pressure gradient

force, the Coriolis force, and the centrifugal force is assumed.

Also, when using the ITPP 5.0 software the user has the choice of

whether or not they want the TOVS extraction to utilize surface

data for the first guess profiles as described above. Furthermore,

1 4
1

the user has the option of using the TIGR database. The author

has created a shell script called “Run” which makes symbolic

links to the requisite data files and to the parameter files which

designate whether surface data are used or not, whether the TIGR

database is used or not, etc. This shell script causes a list of data

files from NOAA-12 and NOAA-14 satellite passes residing on the

SeaSpace computer hard disk to be displayed. Upon choosing

one, all of the TOVS processing algorithms are run.

1 5
3.0 Theory

TOVS soundings are the result of integrating data taken from

three sounders: the High-resolution Infrared Radiation Sounding

Unit (HIRS/IL), the MSU, and the SSU. Four modules control the

processing:

1. Preprocessor

2. Atmospheric Radiance Module

3. Stratospheric Mapper

4. Retrieval Module

3.1 TOVS Preprocessor

1 . Sensor digital data are converted to radiances.

2 . Each scan spot is earth-located.

3 . Stratospheric Sounding data are passed on to the Stratospheric

Mapper. The SSU does not have as wide a field-of-view as the

Infrared and Microwave Sensors. This is compensated for by

the Stratospheric Mapper, which holds a global map of SSU

radiances (in continual update) on a latitude/longitude grid.

4. The infrared and microwave data are adjusted. Specifically,

the microwave data are corrected for antenna side-lobes.

Limb correction is also applied to both data types so that each

scan can be treated as if taken at nadir. This limb correction is

based on regression equations that have been built on

synthesized radiance data.

5 . The microwave data are interpolated to the scan spots of the

HIRS/2 sounder to compensate for its poorer spatial

resolution.

6 . Initial guess values of skin (i.e., the surface) temperature and

surface albedo are made; also, terrain elevation and solar

zenith angle values are held for later stages of the processing.

1 7
3.2 Atmospheric Radiance Module

1. Provides spatially averaged, clear column radiances to the

Retrieval Module.

2 . The data are split into boxes:

a. HIRS/2 cross-track spots

b. HIRS/2 along-track spots.

3 . One sounding is derived from this group of 63 scan spots.

4 . The 63 scan spots are tested for cloud contamination.

5 . If 4 or more of the 63 scan spots are deemed clear, the clearcolumn radiances are calculated to be a weighted average of

the observed radiances from the clear scan spots.

6. If less than four clear scan spots are located, a more

complicated scheme, the “adjacent-pair” technique is

employed. It essentially looks for variances in cloud amounts

in adjacent scan spots. If four or more good adjacent pairs are

located, clear-column radiances are calculated. These

soundings are termed “partly cloudy” or “second path”

soundings.

7. Finally, if less than four good adjacent pairs are found, then

the sounding is “overcast” and the HIRS/2 tropospheric data

are not used. Rather, a sounding will be derived based on the

MSU, SSU, and the stratospheric HIRS/2 channels. Soundings

derived via this branch are called “cloudy” or “third path”

soundings.

3.3 Stratospheric Mapper

1. The stratospheric mapper maintains a global map of SSU

radiances on a latitude/longitude grid.

2. This map is in a process of continual update pending the

arrival of newly sensed data.

3.4 Retrieval Module

.

This module comes up with the final product, the retrievals, by

using the clear column radiances determined from the

Atmospheric Radiance Module and the Stratospheric Mapper. It

is an iterative scheme developed by Chahine (1970) (appendix C).

1 8
4.0 Processing

II ’

Once the TOVS binary file is created, the temperature, dew point

temperature, and wind information is extracted and reformatted

into a file that CAAMBFM can ingest. The CAAMBFM model was

then run two different ways so that two comparisons could be

made. One method was to run the “full-fledged” model (i.e., all

model physics come into play; heretofore termed a CAAMBFM

run) and the resultant temperature and wind field was compared

to the OUN raob. In the other mode, only the preliminary 3dobj

portion was run (heretofore referred as a 3dobj run) and the

resultant wind field was compared to data from up to five wind

profiling radars.

For each of the CAAMBFM runs, the model was spun-up to the Oh analysis field as described earlier. Then a shell script would

automatically perform the following actions:

1 .

2 .

3 .

Extract constant pressure levels of the u- and v-components of

the wind and the temperature field.

Pick out the pressure, height, temperature, wind speed, and

wind direction fields from the OUN raob using a patternmatching script language called “AWK”.

Calculate the root mean square vector error (rmsve) for the uand v-components and the root mean square error (rmse) in

temperature with the OUN raob being used as truth. These

rmsve and temperature rmse values are user-selectable as to

what height levels are to be averaged over. Assuming there

are good input values at all these heights, one can select from

the following height ranges:

l “low”: 925,850 mb

l “raid”: 700,500,400 mb

l “high”: 300,250 mb

l “all”: 925 - 250 mb

19
The equations for the rmsve and the temperature rmse are as

follows:

msve =

$

&((u; - u,,,)~ + (vi -v,,)2N/N

i=l

where

‘i =the i-th u-component measured by the raob or

profiler

u,,, =the corresponding u-component output by the model

(CAAMBFM)

Vi =the i-th v-component measured by the raob or

profiler

v,,, =the corresponding v-component output by the model

(CAAMBFM)

N = number of measurements

temperature rmse =

\i

C&T -T,,12UN

where

& = the i-th temperature measured by the raob

7;,, = the corresponding temperature output by the model

(CAAMBFM)

N = number of measurements

Again, a shell script controlled the processing for the 3dobj runs

versus profiler data comparison.

1. Calling a FORTRAN program, which extracts the wind

profiler data within our domain of interest and compares it to

the wind data from the binary file created by 3dobj.

(1)

(2)

2 . Calling a C program to calculate rmsve errors in the u- and vcomponents using the profiler data as truth.

20
5.0 Results

.

I .

Two software packages were used in this analysis, TAP and

ITPP 5.0. Tables 1 through 6 show the results of the analysis using

the TAP software, and Tables 7 and 8, the results with the ITPP 5.0

software.

Table 1 shows the comparison of u-, v-components derived from

3dobj run with profiler radar winds, using TAP. NOGAPS data

were not used. TOVS profiles are used as input to 3dobj.

Climatology and regression refer to the method used to create the

first guess profiles.

Table 1. 3dobi run with profiler radar winds;

c

I

t

NOdAPS data Aot used

Climatology Regression

rmsve (m/s) rmsve (m/s)

04-16-98 17.41 ’ 15.08

04-21-98 8.40 9.17

04-22-98 5.97 6.38

04-23-98 5.48 5.12

04-30-98 6.23 7.01

05-04-98 12.03 11.76

05-05-98 27.90 27.68

05-07-98 19.43 15.63

05-11-98 8.95 8.81

05-13-98 8.09 11.55

05-19-98 11.75 12.85

05-20-98 5.66 6.24

Mean rmsve 11.44 11.44

2 1
Table 2 shows the comparison of u-, v-components derived from

3dobj run with profiler radar winds. NOGAPS data were used in

the 3dobj run, using TAP. TOVS profiles are used as input to

3dobj. Climatology and regression refer to the method used to

create the first guess profiles.

Table 2. 3dobj run with profiler radar winds;

NOGAPS data were used

Climatology Regression

rmsve Cm/s) rmsve (m/s)

04-16-98 9.40 ’ . ’ 10.13 - . ’

04-21-98 11.92 14.91

04-22-98 7.93 9.89

04-23-98 6.70 5.93

04-30-98 6.34 9.56

05-04-98 13.06 12.31

05-07-98 17.18 9.23

05-11-98 8.95 9.00

05-13-98 8.20 12.23

05-19-98 12.02 12.58

1 Meanrmsve 1 10.17 1 10.58

22
Table 3 shows the comparison of temperature and u-, vcomponents derived from CAAMBFM run with OUN raob.

TOVS profiles are used as input to CAAMBFM and the TOVS first

guess profiles derived using climatology, using TAP. NOGAPS

data were not used in the CAAMBFM runs.

Table 3. CAAMBFM run with OUN raob, using climatology; NOGAPS data were not used

Low’ T Low Mid* T Mid High” T High Al14T All

rrnse rmsve rxnse rmsve rrnse nnsve rmse rmsve

(deg Cl (m/s) (deg C) (m/s) (deg C) (m/s) (deg C) (m/s)

04-06-98 8.15 17.69 3.84 16.19 3.28 46.93 5.33 28.83

04-07-98 1.35 11.08 .81 2.37 1.55

04-08-S18 1 5.79 1 3.89 ( 2.98 115.23 1 3.46 122.67 1 4.10 115.-83

5 I 1.53 I 14.78 I 2.55 I 3.81

34-23-98 1 1.23 I 3.90 1 0.76 1 5.32 1 1.60 I 13.91

7.32 7.07 4.96 5.28 7.54

Mean 1 4.00 1 9.93 1 2.17 10.10 3.30 16.38 3.28 12.70

Notes:

‘Low=925,850 mb

*Mid=700,500,400 mb

3High=300,250 mb

4A11=925,850,700,500,400,300,250 mb

.

23
Table 4 shows the comparison of temperature and u-, vcomponents derived from CAAMBFM run with OUN raob.

TOVS profiles are used as input to CAAMBFM and the (TAP)

TOVS first guess profiles derived using regression, using TAP.

NOGAPS data were not used in the CAAMBFM runs.

05-05-98 0.52 16.77 3.39 14.80 1.45 6.86 2.37 13.70

05-07-98 5.78 8.69 2.78 16.22 4.59 4.34

05-11-98 2.59 13.93 1.63 8.27 1.81 10.80 2.00 10.87

05-13-98 6.61 11.88 3.26 10.72 5.59 8.80 5.10 10.57

Mean 4.19 9.90 1.88 9.90 2.28 12.07 3.15 10.86

Notes:

‘Low=925,850 mb

2Mid=700,500,400 mb

3High=300,250 mb

4All=925,850,700,500,400,300,250 mb

24
Table 5 shows the comparison of temperature and u-, vcomponents derived from CAAMBFM run with OUN raob.

TOVS profiles are used as input to CAAMBFM and the (TAP)

TOVS first guess profiles derived using climatology, using TAP.

NOGAPS data were used in CAAMBFM runs.

Notes:

‘Low=925,850 mb

*Mid=700,500,400 mb

3High=300,250 mb

4All=925,850,700,500,400,300,250 mb

25
Table 6 shows the comparison of temperature and u-, vcomponents derived from CAAMBFM run with OUN raob.

TOVS profiles are used as input to CAAMBFM and the TOVS first

guess profiles derived, using TAP. NOGAPS data were used in

the CAAMBFM runs.

05-07-98 4.71 7.61 2.28

05-11-98 2.28 14.19 1.57

05-13-98 6.14 12.47 2.91

Mean 3.65 9.68 1.89

Notes:

‘Low=925,850 mb

2Mid=700,500,400 mb

3High=300,250 mb

‘A11=925,850,700,500,400,300,250 mb

15.45 5.11 4.00

8.49 1.79 9.66 1.86 10.73

11.86 5.31 9.48 4.74 11.42

10.44 3.30 12.27 3.13 10.98

26
Table 7 shows the comparison of u-, v-components from 3dobj run

with wind profiling radar, using ITPP 5.0. TOVS profiles with

initial guess profiles based on climatology are inputs to 3dobj.

NOGAPS data not used. Also the TOVS first guess profiles have

gridded surface data for tie-down points.

Table 7. 3dobj run with wind profiling radar, based on climatology; NOGAPS data

not used

Geostrophic wind Gradient wind component

component rmsve (m/s) rmsve (m/s)

04-07-98 24.86 24.96

04-08-980921 UTC 15.26 17.42

04-08-981301 UTC 11.83 12.61

04-13-98 23.71 22.66

04-14-98 11.54 11.35

04-21-98 11.95 11.92

04-22-98 19.62 16.94

05-05-98 31.00 28.14

05-07-98 26.06 14.64

mean 19.54 17.85

Table 8 shows the comparison of u-, v-components from 3dobj run

with wind profiling radar, using ITPP 5.0. TOVS profiles with

initial guess profiles based on regression are inputs to 3dobj.

NOGAPS data not used. Also the TOVS first guess profiles have

gridded surface data for tie-down points.

Table 8. 3dobj run with wind profiling radar, based on regression; NOGAPS data not

used

27
6.0 Discussion

Tables 1 and 2 provide the results (in terms of rmsve using up to

five wind profiling radars as truth) of running 3dobj initialized

with TOVS data from the TAP. Tests were done running 3dobj

both with and without 12-h forecast NOGAPS data for large-scale

initialization. Also the TOVS initial guess profiles were created

using two different methods: climatology and regression. When

NOGAPS data were not used, the mean rmsve was 11.44 m/s for

both the climatology and the regression initial guess profile

methods. This rmsve is for the entire profile from the surface to

11,250 m above ground level (45 levels). Introducing NOGAPS

data into the 3dobj runs reduced the rmsve to 10.17 m/s for the

climatology method and to 10.58 m/s for the regression method,

which is an improvement of 11 percent and 8 percent respectively.

Tables 3 and 4 show comparisons between OUN raob data (used

as truth) and the results of CAAMBFM runs using TOVS profiles

as input that use either climatology or regression as the method to

derive the first guess profiles. Note that regression is the superior

method as improvement is seen in the mean rmse for all variables

except temperature right at the surface. The largest improvement

is at “high” (300, 250 mb) levels where the temperature rmse is

decreased by 31 percent and the rmsve by 26 percent.

Tables 5 and 6 are again CAAMBFM runs compared to OUN raob

data; however, NOGAPS data were used for these cases. Here the

opposite trend is seen. Regression yields larger errors than

climatology at all levels when NOGAPS is used in the CAAMBFM

run.

Comparing the four different combinations possible (CAAMBFM

runs with/without NOGAPS initialization and TOVS first guess

profiles made using climatology or regression), the optimal

combination in this study was the case where NOGAPS is used

and the first guess method is climatology (table 5). For this case,

the mean temperature rmse for “all” levels was 2.8” C and the

mean rmsve for “all” levels was 10.4 m/s.

When the methods used for table 3 (CAAMBFM run without

NOGAPS and climatology first guess profiles for TOVS) and table

5 (CAAMBFM run with NOGAPS and climatology first guess

29
profiles for TOVS) are compared, it is seen that NOGAPS reduces

the temperature rmse and the rmsve at all levels. The biggest

reduction in error is at “high” levels (300, 250 mb) where the

temperature rmse and the rmsve are decreased by 28 percent and

15 percent respectively.

Comparing the methods of (1) CAAMBFM run without NOGAPS

using regression for the TOVS first guess profiles with (2)

CAAMBFM run with NOGAPS using regression for the TOVS

first guess profiles as shown in tables 4 and 6 respectively, one

sees that the introduction of NOGAPS data have decreased the

temperature rmse at low levels only. However, the decrease is by

such a relatively large amount, 13 percent, that the overall change

when NOGAPS data are used is a 0.6 percent reduction in

temperature rmse for “all” levels. Over “all” levels, rmsve

increased by 1 percent.

Tables 7 and 8 display the rmsve when profiler wind data are

compared to 3dobj output using TOVS data created with gridded

surface data for tie-down points and either climatology or

regression. The inclusion of surface data in the TOVS profile

extraction process has actually increased the rmsve by 6 to

10 m/s. Climatology is the better method in this case, in

particular, the gradient wind component, which has a mean

rmsve of 17.85 m/s.

30
7.0 Conclusions

The worth of including NOGAPS 12-h forecast data are seen when

3dobj runs using TAP TOVS data as input were compared to

profiler data. The rmsve is decreased, albeit by a small amount,

when either climatology or regression is used as the method for

derivation of first guess profiles in TOVS.

Four different combinations of CAAMBFM runs were made.

They were:

1. without NOGAPS large-scale initialization data and

climatology for the TOVS first guess profile

2 . without NOGAPS large-scale initialization data and regression

for the TOVS first guess profile

3 . with NOGAPS large-scale initialization data and climatology

for the TOVS first guess profile

4. with NOGAPS large-scale initialization data and regression

for the TOVS first guess profile

The optimum scheme in terms of smallest mean temperature rmse

and mean rmsve (2.8” C and 10.4 m/s respectively) was case 3.

This is logical, as the NOGAPS 12-h forecast will generally

provide good guidance. Case 1 produced the largest mean

temperature rmse, 3.28” C and the largest mean rmsve, 12.70 m/s.

One interesting result was that for case 3, the mean temperature

rmse for “all” levels was 15 percent smaller than for case 1. The

mean rmsve for “all” levels was 18 percent smaller for case 3 than

for case 1. For case 4, the mean temperature rmse for “all” levels

was 0.6 percent smaller for “all” levels than for case 2 while the

mean rmsve for “all” levels for case 4 was 1 percent larger. Thus,

when climatology was used for the TOVS first guess profiles,

NOGAPS had a significant impact. When regression was used for

the TOVS first guess profiles, NOGAPS had virtually no impact.

The fact that the mean rmsve increased significantly, when surface

data were used as tie-down points for the ITPP 5.0 TOVS initial

guess profiles was disappointing. Horizontal interpolation of

surface meteorological is inherently risky as one is “blindly”

3 1
spreading the parameters across varying terrain heights.

Although the relief in Oklahoma is not dramatic, this may have

played a role in the large errors.

Before TOVS data can be used as good model initialization data,

the errors in the wind data must be decreased. It is hoped that the

much higher resolution of infrared and microwave sensors set to

be launched on both polar-orbiting and geostationary satellites

will help reach that goal.

32
References

1 . Smith, W.L., H.M Woolf, S.J. Nieman, and T.H. Achtor, ITPP5-The Use of AVHRR and TIGR in TOVS Data Processing,

Technical Proceedings of The Seventh International TOVS

Study Conference, Igls, Austria, lo-16 February 1993,

pp 443-453. (UNCLASSIFIED)

2. Francis, J. A. Improvements to TOVS retrievals Over Sea Ice

and Applications to Estimating Arctic Energy Fluxes. Journal

of Geophysical Research 99 (D5): 10,395 - 10,408, 1994.

(UNCLASSIFIED))

3. Terascan 2.6 Reference Manual Volume 1, Seaspace

Corporation, San Diego, CA, 19% pp 379-408.

(UNCLASSIFIED))

4. Kidder, S. Q. and T. H. Vonder Haar, Satellite Meteorology,

pp 190-193,1995. (UNCLASSIFIED))

5. Chahine, M.T, Inverse Problems in Radiative Transfer:

Determination of Atmospheric Parameters, J. Atmos. Sci., 27,

pp 960-967. (UNCLASSIFIED))

33
Acronvms

3dobj 3-dimensional objective analysis

ARL Army Research Laboratory

AVHRR Advanced Very High Resolution Radiometer

BFM Battlescale Forecast Model

CAAMBFM Computer-Assisted Artillery Meteorology

CIMSS

HIRS

HIRS/2

HRPT

IMETS

ITPP

McIDAS

MEL

MSU

NOAA

NOGAPS

NORM’S

NRL

OUN

rmse

rmsve

roab

BattleScale Forecast Model

Cooperative Institute for Meteorological Satellite

Studies

High-resolution Infrared Radiation Sounder

High-resolution Infrared Radiation Sounding Unit

High Resolution Picture Transmission

Integrated Meteorological System

International TOVS Processing Package

Man computer Interactive Data Access System

Master Environmental Library

Microwave Sounding Unit

National Oceanic and Atmospheric Administration

Navy Operational Global Atmospheric Prediction

System

Navy Operational Regional Atmospheric Prediction

System

Navy Research Laboratory

University of Oklahoma - Norman

root mean square error

root mean square vector error

radiosonde observation

35
- - . -...-. .--.--. - -

TAP TOVS Analysis Package

TIGR Thermodynamic Initial Guess Retrievals

TIP TIROS Information Processor

TIROS Television Infrared Observation Satellite

TOVS TIROS Operational Vertical Sounder

UTC Universal time coordinates

36
Appendix A

Earth Location of Satellite Data

37
Earth location, also referred to as “geolocation”, is the

determination of a latitude and longitude for each pixel in a

satellite view. The expression “navigating the data” may be used

in this context as well. In order to do this, one needs the satellite

orbit and attitude parameters as well as the scanning geometry of

the instrument used.

First, a right ascension-declination coordinate system is adopted.

The right ascension is the angular displacement, measured

counterclockwise from the x-axis, of the projection of a point in

space in the equatorial plane. The declination of this point is its

angular displacement measured northward from the equatorial

plane.

The ascending node is the point where the satellite crosses the

equatorial plane going north (ascending).

A satellite’s orbit is described by its orbital elements defined as

follows:

a = semimajor axis = the distance from the center of the ellipse

to the perigee or apogee

& = eccentricity = the distance from the center of the ellipse to

one focus (to the center of the Earth) divided by the

semimajor axis

i = inclination = angle between the equatorial plane and the

orbital plane

0, = argument of perigee = the angle measured in the orbital

plane between the ascending node (equatorial plane) and

the perigee at time I,

R, = right ascension of ascending node at time t,

A4, = mean anomaly at time t,

to = epoch time (valid time of the elements)

39
To get the position of the satellite at time t, one needs the current

orbital elements. a, E , and I are constants and are available from

bulletins. M, R , and CO are calculated as:

M= M,+dM/dt(t-t,,) (A-1)

Q = S2, + dsZ / dt(t - t,) (A-2)

o = w, +do ldt(t -to) (A-3)

Next, the satellite position is calculated using the vector rotation

method.

First, solve for e, the eccentric anomaly using equation (A-4),

n = mean motion constant = 27~ / T where T is the satellite

period.

t, = time of perigeal passage

M=n(t-t,)=e-&sin(e) (A-4)

Next, calculate 8 , using

case =(cose-E)/ (l- ~cos(e)) (A-5)

Then calculate r using

r=(U(l-E*))/(l+&COS(e)) (A-6)

Form a vector pointing from the center of the earth to the satellite

in right ascension-declination coordinate system.

(A-7)

The orbital ellipse lies in the x-y plane with perigee on the positive

x-axis. Rotate the vector about the z-axis through the argument of

perigee. Multiplying the vector by a rotation matrix does this.

40
Next, rotate the vector about the x-axis through the inclination

angle.

the ascending node.

The satellite radius is then

The last rotation is about the z-axis through the right ascension of

J(x”j2 + (y”‘)2 + (2”j2 = I-.d (A-11)

The declination of the satellite is given by

sin-’ (2”’ / r.Y) = 6,$ (A-12)

Right ascension of the satellite is

tarP(y’” / x’“) = R, (A-13)

The latitude and longitude of the subsatellite point may now be

determined. The latitude is simply given by the declination. The

longitude is the difference between the right ascension of the

satellite and the right ascension of the prime meridian

(0”Zongitude) that passes through Greenwich, England (a value

available in bulletins).

4 1
Appendix B

Limb Correction

43
Limb Correction is applied to the Microwave Sounding Unit and

the High Resolution Infrared Sounder data in order to make the

data as if it were taken at nadir. Regression equations based on

synthesized radiance data are used.

At each step a correction is added to the radiance,

where

Lj,,, = radiance in channel j at scan angle m

L,,,,, = radiance in channel n at scan angle m

45
Appendix C

Physical Retrieval Model

47
The physical retrieval model is an iterative process by which

temperature at a given level is determined as follows:

1 . A first guess temperature profile is selected.

2 . Weighting functions are calculated.

3 . Solve the forward problem to yield estimates of the radiance

in each channel of the radiometer.

4. If the computed radiances match the observed radiances

within the noise level of the radiometer, the current profile is

accepted as the solution.

5. If convergence was not achieved, adjust the profile as in

equation (A-15).

6 . Repeat steps 3 to 5 until a solution is reached.

Bj(q(“+y = Bj(T,‘“qLj- / ,@)I (C-1)

where,

T,‘“’

J

= the nth estimate of the temperature at the jth

level

Bj (T,‘“‘) = the resultant Plank radiance at level j at the

wavelength of channel j.

Lj(“) = the nth estimate of the radiance in channel j,

calculated using the Tj(*)

Lj- = the observed radiance in channel j.

The temperature at level j can then be calculated using the inverse

Planck function.

There is a one-to-one correspondence between channel j of the

radiometer and level j where the weighting function of channel j

peaks. This formulation is logical in that if the calculated radiance

is greater than the observed radiance, then the Planck radiance

should be adjusted downward, which in turn implies a

downward adjustment in temperature.

49
Moisture profiles can be similarly derived as follows.

qjk

@+I) _

- qk O’) [1+ (Lj- - L,‘“‘) / ,q (C-2)

qjk w = the mixing ratio for level k at the wavelength of channel j

qk

OJ) = the nth estimate of the mixing ratio at the kth level

rJ@) is a factor that estimates the mixing ratio change necessary

to correct for a given radiance imbalance.

50
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WARFARE DIRECTORATE

ATTN AMSEL RD IW II’

FORT MONMOUTH NJ 07703-5211

1

55
ARMY RESEARCH LABORATORY

AMSRL CI EA

MR S KIRBY

COMP & INFO SC1 DIR

WSMR NM 88002-5501

Record copy

Total

3

1

64 .

.

56