An Evaluation of Three-Dimensional Weather 

Hazards Using Sounding Data and 

Model Output Data 

bY 

Jeffrey E. Passner 

Computational & Information Sciences Directorate 

Battlefield Environment Division 

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FINAL 

An Evaluation of Three-Dimensional Weather Hazards Using Sounding Data and Model 

krtput Data 

i. AUTHOR(S) 

‘. E. Passner 

‘. PERFORMING ORGANIZATION NAME(S) AND ADDRESS 8. PERFORMING ORGANIZATION 

REPORT NUMBER 

U.S. Army Research Laboratory 

Zomputational & Information Sciences Directorate 

Battlefield Environment Division 

Attn: AMSRL-CI-EW 

White Sands Missile Range, NM 88002-5501 

9. SPONSORING/MONITORING AGENCY NAME(S) AND ADDRESS 

U.S. Army Research Laboratory 

2800 Powder Mill Road 

Adelphi, MD 20783-1145 

10. SPONSORING/MONITORING 

AGENCY REPORT NUMBER 

ARL-TR-1046 

11. SUPPLEMENTARY NOTES 

12a. DISTRIBUTION/AVAILABILITY STATEMENT 12b. DISTRIBUTION CODE 

Approved for public release; distribution is unlimited. A 

13. ABSTRACT (Maximum 200 words) 

The Atmospheric Sounding Program (ASP) is initialized by upper-air observations, either from standard rawinsondc 

observations (RAOB)s or output from a numerical model, the Battlescale Forecast Model (BFM). These data are decodec 

and processed before calculations are performed, giving the forecaster an overview of the atmospheric conditions at or nea 

the ROAB launch site or the BFM grid point. The ASP uses these data to produce a series of weather-hazard products tha 

can be used for analysis or forecasting to 24 h from the initial time of the BFM run. Included in these weather hazards an 

turbulence, icing, and clouds-three parameters, which have major influence in military operations. 

14. SUBJECT TERMS 

weather, hazards, BFM, soundings 

15. NUMBER OF PAGES 

59 

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 

. ..__.^ _. _^^ __^^ s,t.nrlrrd _-.-_. !=““I, _.... ,-a ___ m,.v \ .._.._ ,s9, __, 

prmibd by ANSI Std. 239.18.298.102 

1 
Preface 

. 

The Atmospheric Sounding Program (ASP), developed by the U.S. Army 

Research Laboratory, is part of the U.S. Army Integrated Meteorological 

System Block II software. The program utilizes raw radiosonde data from 

the Automated Weather Distribution System as well as output data created 

by the Battlescale Forecast Model which runs on the Integrated 

Meteorological System. The ASP is employed operationally worldwide on 

the Integrated Meteorological System. 

; I This report briefly principles how three-dimensional weather products are 

developed in the ASP and an evaluation of these products for operations. 
Contents 

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

ExecutiveSummary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 

1.0 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 

2.0 Data Input Methods into ASP ................................................... 9 

2.1 DataSources ............................................................... 9 

2.2 Sounding Data Using Rawinsondes ........................................... 9 

2.3 Sounding Data from BFM Model Output ...................................... 9 

3.0 OverviewoftheWeatherHazards ............................................. 13 

3.1 WeatherHazardsProgram .................................................. 13 

3.2 Turbulence ............................................................... 14 

3.3 Icing ..................................................................... 15 

3.4Clouds ................................................................... 16 

4.0 Statistical Evaluation of the 3-D Weather Hazards ............................... 19 

4.1 Evaluation Results ......................................................... 19 

4.2 Turbulence Evaluation ..................................................... 21 

4.2.1 PIREPsStatistics ..................................................... 22 

4.2.2 Turbulence Evaluation for Sounding Sites ............................... 23 

4.2.3 Turbulence Evaluation for the BFM Output .............................. 25 

4.3 Icing Evaluation ........................................................... 27 

4.3.1 IcingPIxEps ......................................................... 28 

4.3.2 Icing Evaluation for the Upper-Air Observations ......................... 30 

4.3.3 Icing Evaluation for BFM Data ......................................... 32 

4.4 CloudsVerification ........................................................ 34 

4.4.1 Verification of Upper-Air Cloud Forecasts ............................... 36 

4.4.2 Verification of BFM Cloud Forecasts .................................... 39 

5.0 summaly . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43 

References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45 

Acronyms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47 

Distribution...................................................................... 49 

3 
Figures 

1. A 12-h forecast Skew T/Log P diagram from a BFM run ............................... 11 

2. Plot of ASP weather hazards based on sounding observation ........................... 14 

Tables 

1. Contingency table for forecasted and observed weather event .......................... 19 

2. PIREPs and intensity of the turbulence .............................................. 22 

3. PIREPs s per 1000 ft and turbulence intensity ......................................... 23 

4. YES/NO turbulence statistics using upper-air observations ............................ 24 

5. Turbulence statistics and height statistics using BFM data .............................. 26 

6. YES/NO turbulence forecasts by hours using BFM .................................... 27 

7. PIREPsoficingtype .............................................................. 28 

8. PIREPs of icing intensity ........................................................... 29 

9. PIREPs of icing type and intensity by height ......................................... 29 

10. YES/NO icing statistics using upper-air observations .................................. 30 

11. YES/NO icing statistics by height using upper-air observations ......................... 31 

12. YES/NO icing statistics using BFM data ............................................. 32 

13. YES/NO icing statistics by hours using BFh4 data ..................................... 33 

14. Cloud statistics for all levels using upper-air observations .............................. 36 

15. Percentage of low-cloud ceilings in three studies ...................................... 37 

16. Cloud statistics for selected locations in winter 1999 study ............................. 37 

17. Winter 1999 cloud statistics by height ............................................... 38 

18. Cloud statistics for BFM by hours from model initialization time ........................ 39 

19. Cloud statistics for BFh4 by height .................................................. 41 

20. Comparison of upper-air observations and BFM initialhourcloudanalyses...............................................................4 2 
Executive Summary 

Introduction 

The U.S. Army Research Laboratory, Battlefield Environment Division has 

developed the Atmospheric Sounding Program (ASP) to assist the Staff 

Weather Officer in making accurate weather predictions in the battlefield. The 

ASP uses data generated either by a mesoscale model, the Battlescale Forecast 

Model (BFM), or data from conventjonal soundings. The output of the ASP is 

a series of text packages or graphics that display weather products such as icing, 

turbulence, clouds, surface visibility, and thunderstorm probability. 

Purpose 

This report describes the data input and the different weather hazards that 

might interfere with military operations. However, the main emphasis of this 

report is on the evaluation of the derived three-dimensional weather products 

such as turbulence, icing, and clouds, by using both a sounding and output 

from the BPM. 

Overview 

The ASP is initialized by upper-air observations, either from standard 

rawinsonde observations (RAOB)s or output from a numerical model, the 

BPM. These data are decoded and processed before calculations are 

performed, giving the forecaster an overview of the atmospheric conditions at 

or near the RAOB launch site or BPM grid point. The ASP uses these data to 

produce a series of weather-hazard products that can be used for analysis or 

forecasting to 24 h from the initial time of the BFM run. Included in these 

weather-hazard products are thunderstorm probability, turbulence, icing, 

clouds, and surface visibility. 

5 
1 .O Introduction 

The Integrated Meteorological System (IMETS) is a transportable, operational, 

automated weather data receiving, processing, and disseminating system 

utilized by U.S. Air Force weather forecasters in support of U.S. Army 

operations. The U.S. Army Research Laboratory (ARL) has formulated a 

number of weather products that will support the forecaster to make more 

precise and detailed weather decisions. Of most relevance to the military, is the 

impact of weather hazards on military operations. These hazards include 

three-dimensional (3-D) weather effects such as icing, turbulence, clouds, and 

two-dimensional variables such as surface visibility and thunderstorms. Earlier 

efforts at ARL have centered on applying sounding data to exhibit text and 

graphical output of these weather parameters. However, with the development 

and fielding of a mesoscale model, the Battlescale Forecast Model (BFM), shortterm, weather forecasts (<=24 h) of these weather hazards are now being 

produced. [1,2] 

The Atmospheric Sounding Program (ASP) is a program in the IMETS software 

environment that displays meteorological data for the forecaster as well as I 

developing products that are placed into a gridded data base that can be 

accessed by users. The programingests sounding data either from conventional 

radiosondes or from 3-D model output. Once these data are read, the program 

displays a skew T-log P diagram, and a weather hazards product, as well as 

textual information about other weather hazards. 

This report is divided into the following sections, each with a different degree 

of detail. 

l 1.0 - Introduction 

l 2.0 - Data Input Methods into ASP 

l 3.0 - Overview of the Weather Hazards 

l 4.0 - Statistical Evaluation of the 3-D Weather Hazards 

l 5.0 - Summary 

7 
2.0 Data Input Methods into ASP 

2.1 Data Sources 

Data for the ASP comes from two sources, either conventional radiosondes or 

gridded 3-D output from the BFM. Sounding data are delivered into the IMETS 

database by either the Automated Weather Distribution System (AWDS), the 

Automated Weather Network (AWN), or manual ingest. The BFM data are 

placed into a Gridded Meteorological Database (GMDB), where they can be 

accessed by the ASP. Prior to processing the data and performing the 

meteorological calculations, a quality control check of the upper-air sounding 

data is made. Some of the error checks include determining that all mandatory 

levels are available, a complete set of surface data is obtainable, and each level 

has both a pressure and height value. A check for consistency in the data (such 

as temperature and dew point values) is also conducted to ensure that the 

meteorological calculations can be completed without error. 

Once the BFM data are accessed for a grid point, the ASP eliminates the surfacederived data or the first level. Surface temperature and moisture observations 

are normally recorded at 2 m above ground level (AGL); therefore, the ASP 

accepts the 2-m level as the surface level temperature and dew point. This 

eliminates many problems that would occur if the BFM zero-level data were 

used. The wind data at ground level is set to zero by the model and the model’s 

10-m level is used as surface wind. There is no other quality control of BFM 

data because the BFM program has its own quality control program. Reference 

3 details the BFM quality control program. [3] 

2.2 Sounding Data Using Rawinsondes 

This method uses rawinsonde data in World Meteorological Organization 

format (Federal Meteorological Handbook No. 4). These data are commonly 

divided into different groups known as the TTAAs (mandatory levels), TTBBs 

(significant level temperatures), and PPBBs (significant level winds). The actual 

number of levels will vary depending upon the height attained by the balloon 

and the atmospheric structure. [4] 

2.3 Sounding Data from BFM Model Output 

Lee and Henmi describe the mesoscale domain as an area that can range from 

2000 to 2 km. The U.S. Army is concerned with an area of 500 km or less, which 

ARL refers to as the “battlescale.” With this scale in mind, ARL has adapted a 

9 
hydrostatic model, Higher Order Turbulence Model for Atmospheric 

Circulation which has been modified for U.S. Army applications. [5,6] 

One of the main advantages of the BFM is that it takes into account local terrain 

features that assist in producing a fine-tuned forecast. By incorporating these 

features in a specific area, the forecaster does not need to be accustomed with 

the local terrain features and how it might influence nearby weather patterns. 

The BFM calculates intercepted solar radiant energy that can influence 

mesoscale wind fields. It uses the hydrostatic and quasi-Boussinesq 

approximations and has detailed surface boundary layer physics. 

BFM initialization includes all observations from the area of interest, such as 

surface data, upper-air observations, and the 36-h forecasted Naval Operational 

Global Atmospheric Prediction System (NOGAPS) grid, which is issued by the 

U.S. Air Force Weather Agency (AFWA) via the U.S. Air Force AWDS. The 

NOGAPS grid points are spaced 1’ latitudinal distance apart on the mandatory 

pressure surfaces, although much of the work done in this study was completed 

when NOGAPS was delivered with 2.5” data. 

Lateral and time-dependent boundary conditions (large-scale forcing) are 

supplied from grid-point data close to the area of interest taken from NOGAPS 

output valid at analysis and forecast timest. 

The BFM forecast is executed using these boundary conditions and area-ofinterest raw data, as initialization guidance. The forecast solves towards 

forecast solutions dictated by global forecast gridded data, although boundarylayer mesoscale flows can be generated when the local terrain and radiation 

forcing dominate the large-scale dynamics (figure 1). 

10 
The BFh4-generated output for the grid includes the u and v horizontal wind 

vector component, potential temperature, and water vapor mixing ratio. These 

forecast fields are saved at 0 1 3 6 9 12,18, and 24 h from the base time of the I I I 

model run; thus, it is possible to manipulate these data at various intervals over 

the forecast period. 

11 
3.0 Overview of the Weather Hazards 

3.1 Weather Hazards Program 

Today’s weapons and sensors may be even more sensitive to weather than in 

the past. Performance of high-technology weapons such as the Advanced 

Tactical Missile System and the Apache helicopters can be degraded, as can 

many of the intelligence collection systems. The goal of the weather hazards 

program is to optimize weapon performance, assist in troop maneuvers, and aid 

the staff weather officer with weather guidance. [7] 

The weather hazards program provides automated analysis and forecasts of 

what are considered “hazards” to U.S. Army operations. Additionally, many of 

the derived parameters in the ASP program are used by the Integrated Weather 

Effects Decision Aid (IWEDA) program. The IWEDA uses the ASP and BFM 

output to provide detailed information about why, when, and how weather 

impacts weapon systems (as well as their subsystems and components) and 

operations (figure 2). [8] 

Often in weather forecasting, decisions must be made instantaneously; thus, it 

becomes beneficial to implement artificial intelligence techniques. The Weather 

hazards program, however, is not truly artificial intelligence because it uses 

statistical data, conventional computer programming techniques, and basic 

meteorological calculations as a “first guess” at the hazards. However, it 

becomes advantageous to use IF-THEN rules to assist in making weather 

products such as turbulence and clouds. The Weather hazards program makes 

an initial prediction and then gains information as it advances through the 

software in a top-down or forward-training methodology. This is called a 

Heterogenous Expert Systembecause there is an integration of existing software 

with a Rule-Based Expert System. [l] 

13 
Figure 2. Plot of ASP 

weather hazards based on 

sounding observation. 

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3.2 Turbulence 

Treating the atmosphere as a fluid, turbulence is generally a state of fluid in 

which there are irregular velocities and apparently random fluctuations. These 

oscillations in the atmosphere can adversely affect airframe performance and 

endanger U.S. Army aircraft. Turbulence is present in and near thunderstorms, 

as can be expected, based on dramatic updraft and downdraft speeds. 

Typically, a thunderstorm is a warning sign that turbulence will be present, and 

pilots need to make adjustments to their flight plans in the vicinity of convective 

clouds. [9] 

Theoretical studies and empirical evidence have associated clear-air turbulence 

(CAT) with Kelvin-Helmholtz instabilities. Miles and Howard indicate that the 

developments of such instabilities require the existence of a critical Richardson 

number (RI) <=0.25. Keller notes that the RI is expressed as a ratio of the 

buoyancy resistance to energy available from the vertical shear. [lO,ll] 

14 
Of all the methods using a single sounding, the RI proved to make the most 

sense physically, since it included the influence of both the temperature and 

shear in the atmosphere. Additionally, in agreement with McCann, the RI 

displayed the most skill of several methods tested by using a single 

sounding. [ 121 

The U.S. Navy Fleet Numerical Meteorological and Oceanography Center uses 

the Panofsky Index (PI) to forecast low-level turbulence, where the low level is 

considered to be below 4000 ft AGL. 

Based on analyses of raw RAOB data and corresponding pilot reports, early 

results of this work using the PI in the lower levels and the RI above 4000 ft, 

showed a strong bias to underforecasting turbulence. Thus, some additional 

rule checks were developed to ascertain that the derived numerical turbulence 

values made physical sense. . 

3.3 Icing 

One of the most vital hazards forecast is aircraft icing not associated with 

convection. Generally, icing occurs at temperatures between 0 and -40 “C. 

Schultz and Politovich note that the accretion of ice on aircraft surfaces is 

controlled by two processes: 

1. impaction of super-cooled cloud droplets on the aircraft and 

2. the freezing of these droplets onto the airframe. [13] 

In the ASP, three types of icing are considered: 

1. rime, 

2. clear, and 

3. mixed. 

Additionally, there are four icing intensities in the ASP: 

1. Trace icing-the icing becomes noticeable on the aircraft. 

2. Light icing-the accumulation of ice generates a problem for flights in 

excess of 1 h. 

3. Moderate icing-the rate of accumulation presents a problem for short 

flights. 

4. Severe icing-the rate of accumulation is so intense that deicing equipment 

fails to repress it. 

Given the constraints of the single upper-air sounding as the data source, it was 

determined that the best approach to the analysis/forecasting of icing was to 

utilize the RAOB icing tool developed at the AFWA in 1980 (formerly, the U.S. 

15 
Air Force Global Weather Center). The RAOB technique uses the temperature, 

dew-point depression, and temperature lapse rate as a measure of instability of 

the layer. 

The RAOB tool is essentially a “decision tree,” in that it classifies icing by 

temperature, dew-point depression, and lapse rate. There are three main 

temperature groups: 

1. -35 to -16 “C, 

2. -16 to -8 “C, and 

3. -8 to -1 “C. 

These fundamental temperature classes are based on the theory of ice formation, 

with the first case, -35 to -16 “C, resulting in light rime icing in all cases. The 

second class, the -16 to -8 “C group, accounts for the mixed and rime cases, with 

the intensity based on the lapse rate or stability of the layer. The warmest 

group, the -8 to -1 “C group, is often the temperature range when clear icing is 

found; however, when the layer is stable, rime ice usually occurs. Clear ice most 

commonly occurs in layers of the atmosphere that are unstable and undergoing 

lifting. 

In addition to the RAOB tool, a final case is added to account for severe clear 

icing. This situation occurs when there is a strong inversion about 100 mb 

above the surface; thus, the precipitation falls from a liquid state into a layer of 

subfreezing temperatures. This rapid change in temperature causes the 

relatively warm water droplets to spread quickly on the aircraft and cause clear 

icing to form. 

A modification that has been applied in the ASP as compared to the original 

RAOB tool is an allowance for higher dew-point depressions, since the original 

RAOB tool was found to underforecast icing. Cornell’s study showed that in the 

RAOB tool, dew-point depressions were too stringent, and his investigation of 

soundings showed that the mean dew-point depression for all icing types was 

4.5 “C. This modification was made for the RAOB icing chart and is used 

currently in the ASP software. [14] 

3.4 Clouds 

Forecasting of cloud amounts, cloud heights, and cloud depth is essential for 

military operations. Clouds can degrade the effectiveness of many weapon 

systems by limiting flight paths, visibility, and making it impossible to identify 

targets and aircraft. 

16 
It was decided to approach the cloud-forecasting problem with empirical 

techniques, statistical data, and a rule-based IF-THEN set of code. This 

technique was best for using the single upper-air observation in the ASP. The 

main emphasis on the cloud program was to use relative humidity as the basis 

for cloud formation. 

While it may appear that small differences in relative humidities are not 

consequential, work done by Walcek indicated that a two-to-three percent 

increase of the relative humidity could lead to a 15 percent increase in cloud 

cover. He also noticed, as in this study, that ceilings in the middle levels formed 

in lower humidity. Thus, a decision tree, or flow chart, is used to form the IFTHEN rules in the cloud program. [15] 

Mesoscale models often display a dry bias. Schultz and Politovich observed 

that relative humidity values in excess of 55 percent between 500 to 1000 mb 

usually identify regions with widespread cloudiness on the Nested Grid Model. 

The BFM does not display such an extreme bias; however, clouds are often 

observed in layers with relative humidity well below values of saturation. In 

the ASP, a computer routine was developed for the formation of cumulus 

clouds. [13] 

17 
4.0 Statistical Evaluation of the 3-D Weather Hazards 

4.1 Evaluation Results 

The evaluation of the 3-D (vertical and horizontal) weather-hazard products has 

been ongoing for three years. The evaluation has resulted in program 

improvements; thus, this work has been evolving throughout the duration of 

the project. 

A contingency table (table 1) provides a statistical method to display answers 

to binary YES/NO types of forecast evaluations. Some of the commonly used 

evaluation techniques include the probability of detection (POD), false alarm 

rate (FAR), the correct nonoccurrence (CNO), critical success index (CSI), true 

skill score (TSS), and bias. The calculations, based on the contingency table, are 

shown below. 

Table 1. Contingency table for forecasted and observed weather event 

Forecasted YES Forecasted NO 

Observed YES A B 

Observed NO C D 

A 

POD = 

A+B 

c 

FAR = 

C+A 

D 

CNO = 

D+C 

(1) 

(2) 

(3) 

Donaldson developed the CSI, and it was considered a standard in statistical 

evaluation, since it included three of the four elements in the contingency 

table. [16] 

19 
A 

CSI = (4) 

A+B+C 

The CSI, however, does not take into account the null forecast (the D cell in the 

contingency table). Hanseen and Kuipers formulated an equation, which does 

factor in the null event, and it is called the TSS as seen in Eq. (5). This skill score 

is the ratio of observed skill to perfect skill and does not depend on the 

frequency of occurrence and nonoccurrence. [ 171 

TSS = 

WY - PC) 

(5) 

(A + B)(C + D) 

The bias in a forecast is the ratio of the number of positive forecasts to the 

number of observed events as shown in Eq. (6). 

A+C 

Bias = 

A+B 

(6) 

20 
4.2 Turbulence Evaluation 

Evaluation of clear-air turbulence is perhaps the most challenging of all the 

weather hazards. The most effective way to verify clear-air turbulence is to 

compare pilot reports with the forecasted values of turbulence for a layer and 

location in the atmosphere. Kane noted that pilot reports (PIREP)s contain the 

location, time, aircraft type, and turbulence severity and type. Naturally, there 

are variations in each PIREP, since these reports are the subjective judgement 

of the pilot and can vary from pilot to pilot. [18] 

In the study by Kane, it was noted that 72 percent of pilots fail to specify the 

type of turbulence being encountered. Additionally, the reports are sporadic 

in time, location, and season with more PIREPs during the winter months, 

from sunrise to sunset, and closer to major airports. Kane also notes that a 

majority, 86 percent of PIREPs in the Continental United States, are reported 

from 1300 to 0100 UK. Another obstacle in turbulence reporting is that it is 

nearly impossible to know the persistence of turbulence at a given level or 

location. Upon encountering turbulence, most pilots compensate by adjusting 

to another level-trying to avoid damage to aircraft, conserve time and fuel, 

and for the comfort for passengers. [18] 

Another issue with turbulence verification in this study is that the PIREPs are 

compared to RAOB data. RAOBs are typically available only at scattered 

locations and two times a day (1200 and 0000 UTC). For evaluation purposes, 

it is assumed that a location 100 km from a ROAB site has analogous wind and 

temperature profiles as the RAOB site. This supposition makes it necessary to 

verify only those PIREPs close in location and time to the actual RAOB. An 

effort is made in this study not to accept any PIREPs more than 100 km from 

the airport and approximately three h from the time of the RAOB release. As 

an example, any PIREP before 1000 UTC and after 1400 UTC is not used for 

verification of 1200 UTC data because there are few flights before 1000 UK, 

and the actual sounding time is closer to 1100 UTC than 1200 UK; thus, the 

three-h time limit expires at 1400 UTC. 

Using the BFM output, verification is limited to a one-h period surrounding the 

model forecast time. As an example, model forecasts of turbulence at 

1800 UTC are compared to PIREPs from 1730 to 1830 UTC only. 

A “correct” turbulence forecast was one within 100 km of the RAOB site and 

within two h of the RAOB release. Additionally, any PIREPs that included two 

intensities, such as LGT to MDT, were classified as the more extreme intensity 

(moderate in this example). Any turbulence forecast values near the height of 

21 
the PIREP were accepted. For levels below 5000 ft AGL, the forecasted 

turbulence had to be within 1000 ft of the PIREP. From 5000 to 10000 ft AGL, 

the forecast had to be within 1500 ft of the PIREP, and above 10000 ft AGL, the 

forecast had to be within 2000 ft of the actual observed turbulence. As an 

example, a forecast for turbulence at 4000 ft AGL was only “correct” if a pilot 

reported turbulence between 3000 to 5000 ft AGL. 

4.2.2. PIREPs Statistics 

In this study there have been several statistical tests comparing turbulence 

forecasts derived from upper-air observations (the forecast) to PIREPs (the 

verification). The first study was in 1997, from February 25 to May 6, using 501 

PIREPs. This study will be called the “1997 Study.” The second study was 

conducted from November 1998 to April 1999 and is called the “1999 test.” 

However, before comparing the forecasts against the observations, it is 

important to investigate the nature of PIREPs of turbulence. PIREPs of light, 

moderate, and severe turbulence were collected for both the 1997 and 1999 

studies. It should be noted that reports of “neg,” “smooth,” or “no turbulence” 

mean that the pilot reported that there was no turbulence at the time of the 

report. It does not include any PIREPs where no turbulence report was 

submitted. Table 2 displays the results in the two different PIREP studies. 

Table 2. PIREPs and intensity of the turbulence 

1997 Study 1999 Study 

Samples 496 244 

Report no tub 44% 39% 

Report LGT turb 23% 27% 

Report MDT turb 30% 30% 

Report SVR turb 4% 4% 

In the two samples, the number of “smooth”or “neg” turbulence events was 

44 percent in 1997 and 39 percent in 1999. In a similar study, Marroquin 

investigated 17000 PIREPs and found 44 percent “no” turbulence cases and 

56 percent “yes” reports of turbulence. As expected, severe or extreme 

turbulence is not reported often, most likely because it is rare, and pilots avoid 

flying conditions when severe or extreme turbulence is possible. [19] 

22 
. 

Surprisingly, moderate turbulence is reported more often than light turbulence. 

There are probably two reasons for this. 

1. As mentioned previously, all LGT to MDT reports were considered to be 

the more severe of the two (the moderate turbulence). 

2. Pilots, especially in larger aircraft, probably do not report light turbulence 

because there is no harm to such airplanes and little discomfort for the 

passengers. 

Another study was designed to look at the PIREPs by height. Table 3 shows 

a 1999 study of the number of PIREPs per 1000 feet by different layers of the 

atmosphere along with the percentage of light, moderate, severe, and no 

turbulence cases. A total of 242 PIREPs were used in compiling these results 

that indicate that the fewest PIREPs originate in the highest layers of the 

atmosphere and in the lowest layers, just above the surface. 

Table 3. PIREPs Der 1000 ft and turbulence intensitv 

Layer in ft 

0JGT.J 

PIREPs Percent Percent Percent 

Per Percent no light MDT SVR 

1000 ft turbulence turb turb turb 

<=2000 3.0 15 35 40 10 

2000-4000 12.5 41 23 32 5 

4000-8000 17.5 44 30 23 3 

8000-14000 8.2 38 32 26 4 

14000-20000 3.5 39 27 27 8 

> 20000 2.5 33 24 41 2 

Perhaps, this result is not surprising, since most pilots spend very little time in 

the lowest 2000 ft AGL and are often occupied with safely ascending or 

descending the aircraft. While these data in table 3 are limited, it appears that 

the most frequent layer for PIREPs is from the 4000 to 8000 ft AGL layer. 

4.2.2 Turbulence Evaluation for Sounding Sites 

The results of the analyses and forecasts made in the current study are 

displayed in this part of the report. Table 4 shows results from three different 

studies over the past three years, where the 1998 study was conducted from 

December 1997 to 16 April 1998, and the 1997 and 1999 were done 

simultaneously with the PIREP study in section 4.2.1. These three studies used 

the upper-air observations to calculate turbulence using the ASP computer 

routines. 

23 
Table 4. “YES/NO” turbulence statistics using upper-air observations 

<so00 ft sooo- >=10000 ft 

Turb 1997 1998 1999 AGL 10000 ft AGL 

statistics study study study 1999 study 1999 study 1999 study 

Samples 501 100 298 36 86 104 

POD 0.55 0.42 0.30 0.79 0.24 0.45 

FAR 0.16 ' 0.18 0.10 0.00 0.00 0.19 

CNO 0.86 0.85 0.91 1.00 1.00 0.82 

CSI 0.50 0.39 0.29 0.79 0.24 0.41 

TSS 0.41 0.26 0.21 0.79 0.24 0.26 

Bias 0.66 0.50 0.34 0.79 0.24 0.56 

Table 4 shows the difficulty in forecasting and evaluating turbulence. 

Surprisingly, only slight changes have been made to the actual computer 

software to analyze and forecast turbulence over this three-year study. 

However, there is an obvious decrease in skill over the last three turbulence 

seasons. It is difficult to determine whether this is due to any of the software 

changes made or the testing method in this study. The tests each year were 

done at slightly different times of year, with the best results shown in the 1997 

study that extended into early May. While it is not clear, it is possible that 

forecasting turbulence may be more challenging in the winter months. Thus, 

having some of the spring season in the 1997 study may have added some bias 

to the results. Additionally, the samples gathered over the three-year study 

were not standardized; the stations selected were random. Even with these 

uncertainties, these data cast very useful information about turbulence 

forecasts using the ASP. 

The trend for correctly forecasting the nonevent is consistent through the entire 

study, along with a very low FAR. As might be expected with those statistical 

trends, there is a strong bias toward underforecasting the event (turbulence). 

The lower POD over the study is problematic. As mentioned, the techniques 

used, the PI below 4000 ft AGL, the RI above 4000 ft AGL, and simple rules 

based on temperatures and wind speed have not been dramatically changed; 

thus, the lower POD is most likely associated with the testing methods. 

Based on the study in 1999, the third year of testing, the forecasts showed an 

intriguing trend with height. For predictions less than 5000 ft AGL, the POD 

was 0.79, and since there were no false alarms in the study of 36 samples, the 

24 
TSS was also 0.79. However, using the 86 samples between 5000 to 10000 ft 

AGL, the POD lowers significantly to 0.24 with a similar TSS. Above 10000 ft 

AGL, the POD increases to 0.45 with a TSS at 0.26. 

It can be concluded from these data that either the PI is a very accurate tool in 

the lower levels, or turbulent motions are easier to forecast in these lower 

levels. This makes physical sense, since it is possible that most cold-season, 

low-level turbulence is the result of strong boundary-layer wind speed and 

directional shear-two elements that are derived easily from an upper-air 

observation. With an increase in height, more complex factors influence 

turbulence occurrence such as the sudden terrain differences, mountain waves, 

gravity waves, and vertical motions that are not derived easily from a simple 

upper-air observation. 

The RI is one of two parameters used in this layer, along with a set of wind 

speed “rules.” Most of the PIREPs used in the study were from the early 

morning hours in the cold season, so it can be expected that any mixing caused 

by solar radiation and heat transport is not a factor at this level. It is possible 

that turbulence formation is not well understood in this 5000 to 10000 ft AGL 

layer, or the error comes from the speed shear of the wind. However, the most 

likely cause for error in the 5000 to 10000 ft layer is that there were several 

reports of “OCNL LGT TURB” in this layer, which was considered an 

“incorrect” forecast. If these “LGT CHOP” PIREPs were considered as 

“insignificant” as they probably are for U.S. Army aircraft, the revised POD 

becomes 0.46 with a TSS of 0.46 for the layer-a much better skill score. 

Still, the general trend of lower skill with height is not easily explained, but 

may be accounted for by more complex processes in the middle and upper 

atmosphere. Much more work and testing would need to be done along with 

much better data and a field study that could test these data against real-time 

pilot observations. 

4.2.3 Turbulence Evaluation for the BFM Output 

The turbulence routine for the BFM is not very different than the computer 

software used for sounding locations. The PI is employed as the main 

forecasting tool below 4000 ft AGL, while the RI is utilized above that level. 

The only difference is that the “rules” implemented to do consistency checks 

at the end of the program allow for turbulence to begin at lower wind speeds 

25 
due to a slight BFM bias of underforecasting wind speeds. The BFM 

verification set also contains biases, since the model was run in poor weather 

conditions. There were two studies: 

1. 1998 study: December 1997 through April 1998, which used 16 model 

runs and 

2. 1999 study: 16 November 1998 through 16 April 1999, which used 15 

model runs. 

Most of these cases were initialized with 1400 UTC data and run for 24 h using 

1” or 2.5“ NOGAPS data, sounding data, and surface observations. All grids 

were 51*51 with lo-km spacing between the grid points using 16 vertical levels. 

PIREPs were used to verify the forecasts as closely as possible to every grid 

point. Table 5 shows the results of the model runs and turbulence verification. 

Table 5. Turbulence and height statistic+ using BFh4 data 

1998 study 1999 study <5000 ft 5000 to 

BFM “YES/NO” “YES/NO” AGL 10000 ft AGL > 10000 ft AGL 

turbulence forecast forecast turbulence turbulence turbulence 

statistics turbulence turbulence (1999 study) (1999 study) (1999 study) 

Samples 86 154 38 62 47 

POD 0.58 0.50 0.68 0.24 0.64 

FAR 0.18 0.09 0.10 0.00 0.05 

CNO 0.78 0.91 0.85 1.00 0.95 

CSI 0.49 0.48 0.63 0.24 0.62 

TSS 0.36 0.40 0.52 0.24 0.58 

BIAS 0.70 0.55 0.76 0.24 0.68 

The turbulence results using these BFM data show nearly identical results and 

trends as using the sounding data (table 4) with a lower POD in the 1999 study 

than the previous year. A lower FAR and higher rate for correctly forecasting 

the nonevent lead to a slightly higher TSS. A surprising result is that using the 

BFM output data provides a better overall TSS (0.40 versus 0.21 for soundings 

in 1999 study). A possible explanation for this is that cases tested are often 

cases with “bad” weather, thus clear-cut cases of turbulence. 

A second trend is the lower skill scores in the 5000 to 10000 ft range, with the 

same exact skill scores as the soundings (table 4). Again, is assumed that this 

is due to the large number of “OCNL LGT CHOP” reports in these levels that 

26 
I . 

were considered incorrect in the statistics. The model output had lower skill 

scores below 5000 ft AGL but higher skill scores above 10000 ft AGL. It is 

uncertain as to why this occurs; however, it should be noted that the model top 

is generally lower than 25000 ft AGL, and because there are turbulence 

calculation problems near the top of the model, there was no effort to include 

any turbulence above 20000 ft AGL in these statistics. 

While there were limited data for an evaluation of each forecast period, the 

results of the TSS are consistent over the 24-h forecast period. Since most of the 

model runs were started at 1400 UTC, the 18-h forecast often occurred in the 

n-riddle of the night when pilots were not operating. Due to the limitations of 

these data, the “YES/NO” turbulence statistics will not be as complete as 

previous sets. There are no data available for the 18-h forecast in table 6. 

Table 6. “YES/NO” turbulence forecasts by hours using BFh-l 

Forecast hour OOh 03h 06h 09 h 12h 24h 

Samples 18 35 31 33 15 19 

POD 0.55 0.67 0.43 0.47 0.36 0.62 

FAR 0.17 0.13 0.00 0.08 0.00 0.11 

TSS 0.44 0.52 0.43 0.38 0.36 0.45 

Bias 0.67 0.70 0.43 0.52 0.36 0.69 

The hourly results of the BFh4 turbulence forecasts show the same trends that 

exist with the soundings. The forecasts display a very low FAR and a strong 

bias to underforecast turbulence. This bias, is not surprising, given the 

difficulty in detecting and observing turbulence with PIREPs. Still, the trend 

indicates that much more work is required in this area to remove these biases 

through improving the rules or formulating a better understanding of the 

conditions that lead to turbulence. In the future, testing should be done to 

avoid any bias in areas with frequent and reliable PIREPs and numerous 

airports on the grid. 

4.3 Icing Evaluation 

Unlike turbulence, the basic “YES/NO” forecast of icing depends on the 

availability of moisture, formation of clouds, lapse rate, and temperatures 

below 0” C. Using anupper-air observation provides much of this information. 

However, the prime challenge for icing forecasts depends on these upper-air 

data to provide accurate information so that clouds can be forecasted. Once 

clouds have been predicted for a level, then the computer software activates 

27 
the icing routine and can make projections of icing, icing type, and icing 

intensity. Many of the same difficulties encountered with turbulence-forecast 

evaluation exist with icing verification. Pilots tend not to report the “null” 

conditions, so it becomes difficult to know what percentage of flights are 

influenced by icing. Like turbulence, pilots often avoid weather situations 

where icing is expected. 

The icing forecasts are evaluated in the same way as the turbulence forecasts. 

Only PIREPs within 100 km of the sounding site are used within two or three 

hours from the time of RAOB release. If a PIREP is “LGT-MDT,” the moderate 

condition is considered the icing intensity. Any PIREPs of “trace” icing were 

grouped with “light” icing, while extreme icing was never observed in this 

study; thus, making “severe” icing as the most intense icing. For the BFM 

output, the verification period is again a one-hour period centered around the 

forecast hour. A model forecast for 2100 UTC is compared only to PIREPs 

between 2030 and 2130 UTC. All grids used were 51*51 with lo-km spacing 

between the grid points. Pilots report the location of the report in the PIREP; 

therefore, these locations were compared to the nearest grid point to the 

location of the icing event. 

Icing PIREPS 

Like turbulence, icing forecasts were compared to PIREPs, using random 

upper-air sounding locations around the United States at both 1200 and 0000 

UTC. Icing was evaluated at all height levels, although most icing reports 

occur below 25000 ft AGL. In table 7 the icing types reported by the pilots are 

shown for the three study years. 

Table 7. PIREPs of icing fYPe 

1997 study 1998 study 1999 study 

Samples 370 90 132 

Nonevent 27% 33% 31% 

Rime icing 75% 72% 67% 

Mixed icing 18% 23% 23% 

Clear icing 7% 5% 9% 

As displayed in table 7, pilots do not often report the “null” or nonevent for 

icing. The icing types; rime, mixed, and clear icing are listed, assuming that a 

“YES” icing event has been reported. Rime icing is the most frequently 

reported by pilots, with little difference noted in each study year. The most 

28 
infrequent condition is the clear icing, since clear icing usually occurs in unique 

conditions with near-zero temperatures in an atmospheric layer. Two 

additional studies were done to investigate the PIREPs for icing intensity, light, 

moderate, or severe. Table 8 shows the results of this study. 

Table 8. PIREPs of icing intensity 

Icing intensity 1997 study 1999 study 

Samples 264 152 

Nonevent 27% 24% 

Light icing 65% 63% 

Moderate icing 31% 33% 

Severe icing 3% 3% 

The above statistics indicate almost the exact same results for the two study 

years. It is interesting to note that the nonevent numbers are different than 

those in table 7. This is most likely because some pilots report icing type and 

not intensity or, conversely they report icing intensity and not the type. 

Another inquiry involved investigating the PIREPs of icing type and intensity 

with height. This study was done using data from the 1999 winter season. 

These results are shown in table 9. 

Table 9. PIREPs of icing type and intensity by height 

Icing type and 

intensity by <5000 ft 5000-10000 ft10000-15000 ft>15000 ft 

height (%I w w VW 

Rime icing 76 64 78 76 

Mixed icing 14 29 13 18 

Clear icing 10 7 9 6 

Light icing 70 62 53 68 

Mod icing 26 36 44 32 

Severe icing 4 2 3 0 

In table 9 rime icing and light icing are the most commonly reported by the 

pilots, while clear icing and severe icing are the least likely to be reported. The 

trends of these data show a slight increase in mixed icing between 5000 to 

10000 ft AGL, an indication that this layer may contain larger-sized, supercooled water droplets. The “middle” atmosphere between 5000 to 15000 ft 

29 
AGL also shows an increase in icing intensity perhaps related to the lapse rate 

of this layer. In wintertime icing cases, the surface layer is often very stable 

with a steep inversion above the surface. However, just above this inversion 

layer there are often large layers of colder air aloft and steeper lapse rates that 

produce greater instability and more intense lifting of this air. Above 15000 ft 

AGL, the dominating ice type is rime and the intensity becomes lighter due to 

the colder temperatures and perhaps smaller droplet size. 

4.3.2 Icing Evaluation for tie Upper-Air Observations 

By studying the trends in PIREPs it was possible to make minor changes to the 

original RAOB tool developed at AFWA. The RAOB tool (with the 

adjustments) was then used for the three-year study comparing PIREPs to 

proximity upper-air observations. 

There have been three studies for the icing evaluations using upper-air 

soundings as the data source. These are for the same time periods as the 

turbulence studies. 

1. 1997 study, 25 February to 6 May 1997; 

2. 1998 study, December 1997 to 16 April 1998; and 

3. 1999 study, 9 November 1998 to 1 April 1999. 

Over the three-year study, the software for icing has not been changed 

significantly, although an error was found in the lapse-rate calculation and 

corrected for the third study (1999). Using the soundings, the statistical results 

are shown in table 10. 

Table 10. “YES/NO” icing statistics using upper-air observations 

1997 study 1998 study 1999 study 

Samples 264 92 153 

POD 0.86 0.79 0.80 

FAR 0.08 0.06 0.11 

Nonevent 0.82 0.73 0.67 

CSI 0.80 0.76 0.73 

TSS 0.67 0.53 0.47 

Bias 0.93 0.86 0.91 

The data presented in table 10 show only slight differences from year to year 

using the sounding data. The only significant feature in the table is the 

decrease in the TSS during the 1999 study. This seems due to the decrease of 

30 
. 

forecasting the correct nonevent during that season, meaning that there was a 

trend to forecast a “YES” event when.a “NO” event occurred. Because the nonevent is included as part of the TSS, this led to a decrease in the TSS over the 

final year. Explanations for this can most likely be attributed to general 

improvements to the cloud-forecast program over the most recent years. 

Because the icing program depends on the existence of cloud layers, it appears 

that an increase in cloud layers also meant that icing forecasts increased, and 

there were more forecasts for a “YES” event. However, this increase was not 

“significant,” since the overall bias of the icing forecast holds nearly steady 

over the three studies. 

In general, these results give the user excellent guidance for “YES/NO” icing 

forecasts and are only slightly biased to underforecast the icing event. In 

table 11, the icing results are displayed as a function of height for only the 1999 

study. 

Table 11. “YES/NO” icing statistics by height using upper-air observations 

<5000 ft 5000-10000 1000-15000 ft >= 15000 ft 

AGL ft AGL AGL 

Samples 24 68 35 25 

POD 0.83 0.93 0.60 0.65 

TSS 0.66 0.70 0.60 0.45 

Bias 0.89 0.98 0.60 0.70 

Table 11 shows the number of “forecasts” for each level and basic statistics. 

The trend is similar to the turbulence reports with the most PIREPs in the layer 

from 5000 to 10000 ft AGL. This level also displays the best skill using the U.S. 

Air Force RAOB tool and the in-house modifications by ARL. The skill scores 

are lower with increasing height in the atmosphere. Again, this may be related 

to the cloud-forecast program not detecting clouds in the middle 

atmosphere-a region where moisture is not as easily measured by soundings. 

31 
4.3.3 Icing Evaluation for Z3FM Data 

Statistical evaluation for output data of the BFM are displayed in table 12. 

Table 12. “YES/NO” icing statistics using Bl?h4 data 

Icing statistics 1998 study 1999 study 

. 

Samples 78 112 

POD 0.77 0.66 

FAR 0.16 0.13 

Non-event 0.43 0.59 

CSI 0.67 0.61 

TSS 0.21 0.27 

Bias 0.92 0.76 

In both years a relatively high POD was achieved along with a low FAR. 

However, the correct forecast of the nonevent was low. Note that the sample 

size was rather small (only seven nonevents in the sample in the 1998 study); 

thus, the TSS is significantly lower than the CSI in both years. There is a trend 

in the second season (1999) to underforecast the icing events. Compared to the 

skill scores using only upper-air data, the BF.M “YES/NO” icing forecasts do 

deteriorate somewhat. This result can be expected because these data include 

icing forecasts to 24 h after the initial time of the model run. Additionally, the 

icing routine depends on forecasted clouds; therefore, the skill scores will be 

lower, since forecasting clouds is much more difficult using the vertical 

moisture profile with a model than with a sounding. 

Table 13 displays icing forecasts for the forecast periods provided by the BFM 

output. Due to the small number of samples, only the POD is displayed in 

table 13. Additionally, there are not enough data for the 18-h forecast to be 

included in the table. 

32 
33 
4.4 Clouds Verification 

The ASP calculates cloud amounts and heights from a variety of atmospheric 

variables such as relative humidity, season, time of day, location of station, and 

station elevation. If clouds are forecasted, the output includes the base of the 

cloud, the depth of cloud layer, the amount of cloud (scattered, broken, 

overcast), and the predominate type of cloud such as cumulus, stratus, and 

cirrus. In this study, the main emphasis was to evaluate the ceiling or the level 

where more than half the sky is covered by the cloud layer. There was no 

effort to assess parameters that were difficult to verify such as cloud depth or 

cloud type. 

To evaluate the cloud amounts and heights, the ASP cloud forecasts were 

compared to Meteorological Aviation Routine Weather Reports which are 

coded weather observations at selected airports across the world. In the 

United States, many of these observations are taken by automated machines 

called the Automated Surface Observing System (ASOS) which do not report 

clouds above 12000 ft. To compensate for the growing number of ASOS 

stations, satellite photos were used in the 1999 study to account for the clouds 

analyzed or forecasted at the higher levels. 

In the RAOB part of this cloud evaluation, only upper-air observations taken 

at 1200 and 0000 UTC were used. For the 1200 UTC upper-air observations, 

surface observations at 1100 UTC were used to evaluate the cloud forecasts, 

although observations from 1000 and 1200 UTC were often employed if 

weather conditions were changing rapidly. As an example, if the ASP forecast 

was for 8 OVC and the 1100 UTC observation reported CLR, a check of the 

record observations (SA) or special observations (SP) was done to ascertain that 

a ceiling did not form briefly and then dissipate before or after the 1100 UTC 

observation. Similarly, observations surrounding 2300 UTC were examined for 

rapidly fluctuating cloud heights or amounts when compared to the 0000 UTC 

upper-air observations. 

For a forecast to be “correct,” the height of the observed cloud had to be within: 

l 1000 ft of the forecasted cloud height below 5000 ft AGL, 

l 1500 ft between 5000 to 10000 ft AGL, and 

l 2000 ft above 10000 ft AGL. 

Additionally, the cloud forecasts were compared only to the surface 

observation at the upper-air sounding site location or observations within the 

same city area. For example, the Fort Worth, TX, sounding was evaluated 

34 
. 

against the Fort Worth, TX, observation. However, observations at the same 

hour were checked at the Dallas-Fort Worth airport and Love Field in Dallas, 

TX, to see if the general forecast verified across the entire city. A forecast 

for 15 BKN was accepted as “correct” even if Fort Worth, TX, reported clear 

skies, but Dallas-Fort Worth , TX reported 18 BKN. Since many National 

Weather Service offices have moved away from airports, the upper-air 

observations are often released a few miles from the larger airports in some 

cities. In these situations, more than one airport was used to verify the upperair “forecast.” Comments in the SA or SP were not used to verify a forecast. 

An observation of “clouds over mountains NW,” was not accepted as 

verification for a cloud forecasted at the sounding site. 

Finally, since the main emphasis of this study was on ceiling heights and 

amounts, scattered clouds were not considered “wrong” forecasts when there 

was no ceiling forecasted. However, if a ceiling was forecasted and only 

scattered clouds were observed, the forecast was considered wrong, although 

the difference between a scattered layer and a broken layer is often difficult to 

observe. When a broken layer was forecasted and an overcast layer was 

observed, the forecast was still correct as was a forecast for overcast conditions 

where broken clouds were reported. 

Once an overcast layer was reported, there was no way to verify cloud layers 

or amounts above the overcast layer because the observer or ASOS could not 

see it. ASP’s cloud routine continues to forecast and display these layers, but 

they could not be verified when a lower ceiling had already formed. 

For the BFM output, the same verification system was used for the first two 

study periods; however, in the Winter 1999 study it was determined to permit 

a more liberal criteria on cloud height. The BFM is only a 16-layer model, of 

which four layers are within 30 m of the surface, where no clouds are 

permitted to form, and only fog is forecasted. BFM cloud forecasts were 

verified only at the hour of the observation, although SP observations within 

30 min of the forecast was accepted for verification. 

One major difference between the sounding and BFM forecasts should be 

noted. The ASP cloud program uses different relative humidity criteria for 

forecasting clouds when using the upper-air, short-term data and the BFM 

forecasts. For example, the upper-air data uses relative humidity values 

between 90 to 94 percent to form a ceiling between 4000 to 8000 ft AGL. 

However, the program forms a ceiling at the same level using BFM data with 

relative humidity values between 81 to 93 percent. These differences are based 

on a long-term study done by Passner. [2] 

35 
Verification of Upper-Air Cloud Forecasts 

Using the listed criteria in the previous section, three different studies have 

been done for cloud verification. The forecasts were considered either “right” 

or “wrong” based on the cloud amount and cloud height within the time and 

space restraints already noted. These studies were done at 1200 and 0000 UTC 

during both the summer and winter seasons. The study periods were: 

l Winter 1998, December 1997through 16 April 1998 

l Summer 1998,l June - 11 through September 1998 

l Winter 1999,19 November 1998 through 29 March 1999 

Table 14 shows results from the two “winter” studies and one summer study. 

Table 14. Cloud statistics for all levels using upper-air observations 

Hour and studies Winter Summer Winter 1999 

1998 study 1998 study study 

0000 UTC samples 105 183 261 

0000 UTC correct 67% 79% 79% 

0000 UTC wrong 33% 21% 21% 

1200 UTC samples 325 331 203 

1200 UTC correct 65% 66% 74% 

1200 UTC wrong 35% 34% 26% 

The results in table 14 indicate a general skill improvement over the two-year 

study; however, it should be noted that the final study, the one conducted in 

Winter 1999, was done at 16 specific sounding locations around the United 

States. These 16 sites were selected to represent a variety of different winter 

climates, while the Winter and Sumrner 1998 sounding sites selected were 

random and often tested in bad weather situations where it was known that 

the cloud forecast would be challenging. While it is difficult to explain the 

entire winter variation over the two seasons due to the more “standard” test, 

the computer software in the cloud routine program was also adjusted several 

times; so, perhaps the overall performance of the forecasts did improve. 

Another consideration would be the year-to-year and seasonal variety in 

weather patterns with one winter cloudier than another. The only examination 

of this factor was to see how many observations contained low clouds (ceilings 

<=3000 ft in the 1998 studies and ~4000 ft in the 1999 study). Table 15 displays 

results from these investigations. 

36 
Table 15. Percentage of low-cloud ceilings in three studies 

(%) observed Winter 1998 Summer 1998 Winter 1999 

low cloud 

ceiling CW (“/I (“/I 

0000 UTC 39 5 26 

1200 UTC 54 23 32 

ln the first study, the Winter 1998 study, 54 percent of the 1200 UTC 

observations contained low clouds, while in the Winter 1999 study, with sites 

selected in a variety of locations, only 32 percent of the observations verified 

low clouds. At 0000 UTC in the first study (Winter 1998), 39 percent had low 

clouds, while in the third study (Winter 1999) 26 percent verified low clouds. 

Based on these results, it is likely that some of the improvement of the forecasts 

may be attributed to more clear days and fewer difficult cloud forecasts. 

As mentioned above, in the Winter 1999 study, more standardization was 

added to the cloud verification by selecting upper-air locations evenly 

distributed through the United States and in different climate areas. Table 16 

shows some of these cities and the percentage of correct forecasts for these 

stations. 

Table 16. Cloud statistics for selected locations in winter 1999 study 

Loctition Samples Percent correct 

Long Island, NY 30 80 

Tallahassee, FL 30 93 

Miami, FL 31 81 

Bismark, ND 31 77 

Salt Lake City, UT 31 58 

Denver, CO 27 81 

Salem, OR 32 59 

San Diego, CA 32 72 

While the overall number of correct forecasts in the study was 77 percent, there 

is a wide variety of skill on the stations studied. The results include cloud 

forecasts for all levels at both 1200 and 0000 UTC. The highest skill level 

appears to be in the East and South, while the lower scores are noted in the 

Western region of the country, mainly in areas with difficult forecasting 

37 
Western region of the country, mainly in areas with difficult forecasting 

problems. Most interesting was the Salt Lake City, UT site where skill levels 

were low at both 1200 and 0000 UTC. This can be explained by the interaction 

of moisture from the Great Salt Lake which is close to the observing and 

sounding location. The rapid formation of snow showers and varying ceilings 

makes this site, located at a high elevation, a very difficult place to forecast 

clouds and other weather hazards. 

Another challenge was at Salem, OR, located close to the Pacific Ocean and in 

direct line with rapidly moving bands of precipitation from frequent cold 

fronts and upper-air waves. Also, the slightly lower score at San Diego, CA, 

is due mainly to the 62 percent skill level at 1200 UTC. San Diego often 

experiences a marine layer in the morning, which is sometimes so shallow that 

the ASP routines do not forecast it correctly. The marine layer forms at varying 

times of the morning and often forms after the 1200 UTC sounding is 

released. Thus, this shallow, moist layer is not captured by the upper-air 

observation at the actual release time of 1030 UTC (0330 local time). 

While much of this discussion has centered around the low clouds in the 

Winter 1999 study, it was possible to evaluate the higher clouds, since satellite 

photos were used to verify the high clouds. It is nearly impossible to 

determine the height AGL of the clouds using satellite, but it is possible to 

differentiate between high and low clouds using infrared photos. For 

simplicity, all cirrus clouds were assumed to be at 20000 ft AGL. 

Unfortunately, the sample size is smaller than ideal, but table 17 displays the 

cloud verification with height. 

Table 17. Winter 1999 cloud statistics by height 

<4000 4000 to 4000 to 8000 to 8000 to 

Heights number <4000 8000 8000 20000 20000 

by hour, of correct number of correct number of correct 

ceilings samples (“M samples (“/I samples (“W 

0000 UTC 54 76 18 50 12 8 

1200 UTC 65 65 22 55 13 46 

As noted in the winter study, most of the ceilings were low ceilings reported 

at 4000 ft AGL or less. Once a ceiling was formed, it was impossible to verify 

any clouds above that level, thus, resulting in very few ceiling samples at 

higher levels. The skill level does decrease dramatically with height, meaning 

that the ASP using an upper-air observation does not “forecast” ceilings above 

8000 ft AGL with much skill. The most puzzling result is the 8 percent skill of 

38 
the higher ceilings at 0000 UTC. The ASP computer routine is exactly the same 

for 1200 and 0000 UTC in the higher levels; therefore, it is uncertain why this 

occurs. Additionally, upper-air observations have difficulty recording 

moisture in the higher levels, especially during the winter months when the 

moisture is mainly in the form of ice at such levels. However, at the lower 

levels, the cloud routine does continue to be a very valuable guidance tool for 

the user, although the trend of lower skill at 1200 UTC is noticeable due to 

trouble with marine layers and morning stratus clouds. 

4.4.2 Verification of BFM Cloud Forecasts 

Three cloud-forecast studies were conducted using the BFM output; these three 

are similar to the ones done with upper-air observations. Because the BFM 

does not often reach over 25000 ft AGL, there was little effort to test for higher 

level clouds--especially cirrus clouds. With only 16 layers in the BFM, greater 

limits were allowed with respect to the height of the cloud layer in the Winter 

1999 study. For the lower levels (below 10000 ft AGL) the cloud forecast was 

regarded as “correct” if the forecast was within 2000 ft AGL of the observation. 

Above 10000 ft AGL, the forecast was correct if it was within 3000 ft of the 

observed height. Thus, with this change, it might be expected that the Winter 

1999 study might produce higher skill scores. Table 18 shows the evaluation 

of the BFM post-processed ASP cloud output. 

Table 18. Cloud statistics for BFM by hours from model initialization time 

Winter 1998 Summer 1998 Winter 1999 

Hour (“/I C-W (“-1 

00 67 (52) 68 (43) 76 (51) 

03 53 (15) 54 (33) 61 (54) 

06 63 (43) 59 (31) 53 (57) 

09. 42 (26) 59 (33) 50 (52) 

12 43 (43) 65 (32) 64 (45) 

18 79 (19) 62 (47) 51(41) 

24 43 (51) 63 (40) 49 (43) 

Note: Number of samples are in parenthesis 

Noted that the BFM runs were predominately initialized between 1200 to 1400 

UTC. The model was run at a variety of locations, with an effort to examine 

several different climate regions and varying weather situations. Some of the 

runs were completed in areas of clear weather and others were studied in 

39 
difficult weather situations over complex terrain. The number of BFM runs 

was approximately 15 to 20 for each study. 

The results in table 18 indicate that the cloud program performs with the most 

skill at the initial time of the model run, as might be expected. However, some 

differences are noted throughout the study. In the Winter 1998 study, the first 

study completed, there is a rapid decrease in the skill after the six-h forecast, 

a trend not as pronounced in the later studies. One of the reasons for this may 

be the general improvements inboth the cloud program and the BFM software. 

Some changes in the nudging scheme may have helped to preserve the 

moisture from the initial hour to later hours before nudging completely to the 

larger-scale NOGAPS model in the later periods. 

Surprisingly, the lowest overall skill is noted at the nine-h forecast time and not 

the later hours, such as 18 and 24 h after the model run began. A possible 

explanation is due to two complicated model factors. Because the model tends 

to nudge toward the scalar fields, such as temperature and moisture from the 

NOGAPS, a drier environment at those hours tends to reduce the relative 

humidity of each vertical layer of the model output. The ASP cloud program 

uses the relative humidity as a major parameter for the formation of clouds; 

thus, a lower relative humidity will reduce the cloud layers in the forecast 

clouds. Additionally, within the model itself, the lower relative humidity 

values will permit higher amounts of solar radiation to reach the surface grid 

field. This can lead to a higher surface temperature and even lower relative 

humidity values near the boundary, thus, reducing the chance of the ASP 

routine to forecast clouds. 

It is interesting to note in table 18 that the cloud forecasts improve at the 12-h 

forecast period when the boundary layer begins to cool after sunset, especially 

in the winter season. This would reduce the radiation bias reaching the surface 

and would result in higher relative humidity values near the ground and more 

clouds. 

Another trend seen on table 18 is the very consistent forecast skill during the 

summer months and more variable skill in the winter tests. These patterns 

agree with the upper-air studies, but may be due to the ability of the NOGAPS 

model to accurately forecast the moisture fields. In the winter, there are often 

strong synoptic-scale weather systems which bring frequent and rapid changes 

in many weather elements. The summer conditions are more likely to have 

convection and thunderstorm activity. 

40 
Even if the BFM lacks any type of cumulus parameterization routine, the ASP 

forecast cumulus clouds at peak heating hours during the summer months. 

The ASP. does not create cumulus clouds in cooler environments, so in the 

winter storms, any cumulus field might be missed entirely in the forecast and 

could also help to explain the lower skill in the winter season. 

A final look at the ASP model-derived cloud forecasts was to examine how 

accurate the cloud program is with height. To investigate, the atmosphere was 

divided into three layers: 

l low clouds (~4000 ft), 

l middle clouds (4000 to 8000 ft), and 

l higher clouds (>8000 ft). 

Table 19 displays the overall BFM cloud forecasts with height for all forecast 

hours. 

Table 19. Cloud statistics for BFW by height 

Cloud . 

Cloud layer 

forecast Number Number Cloud forecast 

by height clouds clouds Percent layer but not 

w Samples low high correct missed observed 

<4000 246 32 16 64 72 17 

4000to 63 11 6 49 28 4 

8000 

~8000 34 3 3 32 18 5 

In table 19, these data show that the skill of the cloud program decreases with 

height for the 1999 BFM runs. It also shows that the bias in the cloud routine 

is to forecast a ceiling lower than what was observed at the grid points. 

Additionally, much of the error is caused by “missing” a cloud layer (not 

forecasting it), rather than “overforecasting” a layer and not having one 

observed. In the lower levels, approximately 29 percent of all cloud layers 

were missed by the program. Overall, 34 percent of all clouds were missed and 

in only 8 percent of the cases was a layer forecasted and none observed. 

Despite the high number of layers missed in the lower clouds levels, 

forecasting clouds in the lower levels is the strength of the cloud program. 

There is much concern, however, about the cloud forecasts in the middle and 

higher levels. Note that the most of the model runs conducted in the entire 

study used 2.5-NOGAPS data rather than lo NOGAPS data now being used 

41 
by the BFM. It is not certain that this improved resolution would enhance the 

cloud forecasts significantly, although it may provide better initial data and 

better moisture forecasts-especially in the early forecast periods. 

The results displayed in table 19 (the BFM data) do not vary significantly from 

results in table 17 (the upper-air data). This is an indication that the cloud 

routine performs with about the same skill using the sounding data and BFM 

data. Table 20 shows a comparison between the 00-h BFM forecast and the 

sounding data at a variety of stations. The number of correct forecasts are 

almost identical using both methods. 

Table 20. Comparison of upper-air observations and BFSM initial-hour cloud 

analyses 

Winter 1998 Summer 1998 Winter 1999 

% correct % correct % correct 

BFM 00-h 

forecast 

67 68 76 

Upper-air 

obs 

65 66 74 

Results from the cloud forecasts will vary considerably with each different 

BFM run, mainly depending on the quality of initial data, NOGAPS forecast 

data, and how well the radiation parameters are handled. Also, the results 

seem best in cases where there is little variety in moisture fields, and the results 

seem most suspect in cases of rapidly changing weather and in regions of 

complex terrain. 

42 
5.0 Summary 

. 

The IMETS is an automated weather data receiving, processing, and 

disseminating system utilized by U.S. Air Force weather forecasters in support 

of U.S. Army operations. The ASP, a component of the IMETS software, 

calculates, interpolates, and displays meteorological data for the forecaster. 

The ASP uses sounding data either from 1200 or 0000 UTC upper-air 

observations or from BFh4 model output. The influence of 3-D weather 

hazards on tactical operations is of most concern to military leaders. These 

hazards include icing, turbulence, and cloud layers. 

In the ASP, most applications used in the program are either flow chart type 

diagrams, expert system approaches using a set of rules, or regression 

equations designed for general and worldwide use. Turbulence is analyzed 

and forecasted in the ASP by using the PI below 4000 ft AGL and the RI above 

4000 ft AGL. For icing, the RAOB tool originated at AFWA has been modified 

and is now used in the ASP. 

Cloud forecasts were developed through careful investigation of moisture 

properties on skew-T diagrams through many different weather environments. 

This part of the ASP is the most “rule-based” in its design and uses a series of 

IF-THEN rules based on relative humidity, height of level, time of the day, 

season, and location of the station. 

Over the past several years, detailed evaluation of these 3-D weather elements 

has demonstrated how effectively the products are, using both sounding data . 

and output from the BFM. While it is vital to remember that weather forecasts 

of any type should still be in the hands of humans, the guidance provided by 

the BFM and the ASP post-processed parameters does assist the user in most 

military situations. All the forecasts, turbulence, icing, and clouds degrade 

with height using either data source, most likely due to the difficulty in 

measuring the atmosphere and forecasting complex interactions of 

atmospheric motions with more limited data. It is surprising that the tests in 

this study indicate that forecasting skills increase near the surface where there 

is more interaction between land, water, and air, but apparently better 

measurements from the sounding and higher vertical resolution of the BFM 

provide excellent skill scores of turbulence, icing, and clouds in the lower 

levels. 

43 
It can be concluded here that it is essential that meteorologists continue to 

resolve the vertical structure of the atmosphere with even more precision than 

exists today. There is no question that the more layers in a sounding and in a 

model the better the resulting forecasts can be. Additionally, there is a need 

for continued research into the mechanisms of turbulence, icing, and cloud 

formation. This obvious two-pronged approach, improving the observations 

and studying the motions of the atmosphere, can help to improve some of the 

challenging forecasting problems that influence military and nonmilitary 

aviation. 

Still, given the current limitations of atmospheric measurements and difficult 

obstacles of weather forecasting, the results presented in this report do provide 

much confidence that the technology developed from the ASP and BFM 

provide optimal guidance in forecasting for U.S. Army operations. Work can 

be done to upgrade these forecasting tools and subsequent evaluations can be 

completed without the biases specified in these studies, but it is also integral 

to understand the constraints of the forecasting tools currently in use. With 

this knowledge, users can hopefully use these forecasts to the best of their 

ability. 

44 
References 

1. Passner, J.E., “Expert Systems and Empirical Rules Used for Army Operations 

on IMETS,” Proceedings of the 13 ’ Conference on Weather Analysis and 

Forecasting, Vienna, VA, pp. 608-611,1993. (Unclassified) 

2. Passner, J.E., The Atmospheric Sounding Program: An Analysis and Forecasting 

Tool for Weather Hazards on the Battlefield, ARL-TR-1883, U.S. Army Research 

Laboratory, White Sands Missile Range, NM, 1999. (Unclassified) 

3. Henmi T. and R.E. Dumais Jr., Description of the Battlescale Forecast Model, 

ARL-TR-1032, U.S. Army Research Laboratory, White Sands Missile Range, NM, 

1998. (Unclassified) 

4. U.S. Department of Commerce, FederalMeteorological HandbookNo. 4, Second 

Edition, Radiosonde Code, Washington, D.C., 1976. (Unclassified) 

5. Lee, M.E., “U.S. Army Battlescale Forecast Model Concept of Operation,” 

Proceedings of the 1994 Battlefield Atmospherics Conference, White Sands Missile 

Range, NM, 1995. (Unclassified) 

6. Henmi, T., et al., Battlescale Forecast Model and its Evaluation Using White 

Sands Missile Range Meteorological Data, ARL-TR-569, U.S. Army Research 

Laboratory, White Sands Missile Range, NM, 1995. (Unclassified) 

7. Menoher, P.E. Jr., “Weather Support to Army Operations,” Proceedings of the 

Il* Annual EOSAEL/TWI Conference, U.S. Army Sciences Laboratory, White 

Sands Missile Range, NM, pp. 3-51,199O. (Unclassified) 

8. Sauter, David I’., “The Integrated Weather Effects Decision Aid (IWEDA): Status 

and Future Plans,” Proceedings of the 1996 Bafflespace Atmospherics Conference, 

Naval Command, Control, and Ocean Surveillance Center, San Diego, CA, 

pp. 7581,1986. (Unclassified) 

9. American Meteorological Society, Glossary of Meteorology Fifth Edition, 

Boston, MA, 1959. (Unclassified) 

10. Miles, J.W. and L.N. Howard, “Notes on Heterogenous Shear Flow,“]. Fluid 

Mech, 20, pp. 331-336,1964. (Unclassified) 

11. Keller, J.L., “Clear Air Turbulence as a Response to Meso- and Synoptic-Scale 

Dynamic Processes,” Mon. Wea. Rev, 118, pp. 2228-2242,199O. (Unclassified) 

45 
12. McCann, Donald W., “An Evaluation of Clear-Air Turbulence Indices,” 

Proceedings of p International Conference on Aviation Weather Systems, 

American Meteorological Society, Vienna, VA, pp. 449-453,1993. (Unclassified) 

13. Schultz, P. and M.K. Polotovich, “Toward the Improvement of Aircraft-Icing 

Forecasts for the Continental United States,” Weather and Forecasting, 7, pp. 491- 

500,1992. (Unclassified) 

14. Cornell, D., C.A. Donahue, and K. Chan, A Comparison of Aircraft Icing 

Forecast Models, AFCCC/TN-95/004, U.S. Air Force Combat Climatological 

Center, Scott Air Force Base, IL, pp. 40,1995. (Unclassified) 

15. Walcek, Chris J., “Factors Influencing Regional-scale Cloud Cover: 

Investigations Using Satellite-Derived Cloud Cover and Standard Meteorological 

Observations,” Proceedings of the 4’ Symposium on Global Change Studies, 

American Meteorological Society, Anaheim, CA, pp. 235-236,1993. (Unclassified) 

16. Donaldson, R.J., R.M. Dyer, and M.J. Krauss, “An Objective Evaluator of 

Techniques for Predicting Severe Weather Events,” Preprints, p Conference 

Severe Local Storms, Norman, OK, pp. 321-326,1975. (Unclassified) 

17. Woodcock, F., “The Evaluation of Yes/No Forecasts for Scientific and 

Administrative Purposes,” Mon. Wea. Rev, 104 pp. 1209-1214,1976. (Unclassified) 

18. Kane, T, B.G. Brown, and R. Sharman, “A Preliminary Climatology of Upper 

Level Turbulence Reports,” Preprints of 8’ Conference on Aviation, Range, and 

Aerospace Meteorology, Dallas, TX, pp. 363-367,1999. (Unclassified) 

19. Marroquin, A., “Turbulence Forecasting Algorithms: Calibration, Comparison, 

and Verification,” Preprints of 8’ Conference on Aviation, Range, and Aerospace 

Meteorology, Dallas, TX, pp. 72-76,1999. (Unclassified) 

46 
Acronyms 

3-D 

AFWA 

AGL 

ARL 

ASOS 

ASP 

AWDS 

AWN 

BFM 

CAT 

CNO 

CSI 

FAR 

GMDB 

IMETS 

IWEDA 

/ NOGAPS 

PI 

PIREPS 

POD 

RAOB 

RI 

three dimensional 

U.S. Air Force Weather Agency 

above ground level 

U.S. Army Research Laboratory 

Automated Surface Observing System 

Atmospheric Sounding Program 

Automated Weather Distribution System 

Automated Weather Network 

Battlescale Forecast Model 

clear air turbulence 

correct nonoccurrence 

critical success index 

false alarm rate 

Gridded Meteorological Database 

Integrated Meteorological System 

Integrated Weather Effects Decision Aid 

Naval Operational Global Atmospheric Prediction System 

Panofsky Index 

pilot reports 

probability of detection 

rawinsonde observation 

Richardson number 

47 
SA 

SP 

TSS 

record observation 

special observation 

true skill score 

48 
Distribution 

NASA MARSHALL SPACE FLT CTR 

ATMOSPHERIC SCIENCES DIV 

E501 

ATTN DR FICHTL 

HUNTSVILLE AL 35802 

NASA SPACE FLT CTR 

ATMOSPHERIC SCIENCES DIV 

CODE ED 411 

HUNTSVILLE AL 35812 

US ARMY MISSILE CMND 

AMSMI RD AC AD 

ATTN DR PETERSON 

REDSTONE ARSENAL AL 35898-5242 

US ARMY MISSILE CMND 

AMSMI RD AS SS 

ATTN MR H F ANDERSON 

REDSTONE ARSENAL AL 35898-5253 

US ARMY MISSILE CMND 

AMSMI RD AS SS 

ATTN MR B WILLIAMS 

REDSTONE ARSENAL AL 35898-5253 

US ARMY MISSILE CMND 

AMSMI RD DE SE 

ATTN MR GORDON LILL JR 

REDSTONE ARSENAL AL 35898-5245 

US ARMY MISSILE CMND 

REDSTONE SC1 INFO CTR 

AMSMI RD CS R DOC 

REDSTONE ARSENAL AL 35898-5241 

US ARMY MISSILE CMND 

AMSMI 

REDSTONE ARSENAL AL 35898-5253 

PACIFIC MISSILE TEST CTR 

GEOPHYSICS DIV 

ATTN CODE 3250 

POINT MUGU CA 93042-5000 

ATMOSPHERIC PROPAGATION BRANCH 

SPAWARSYSCEN SAN DIEGO D858 

49170 PROPAGATION PATH 

SAN DIEGO CA 92152-7385 

Copies 

1 

3 

1 

1 

1 

1 

49 
METEOROLOGIST IN CHARGE 

KWAJALEIN MISSILE RANGE 

PO BOX 67 

APO SAN FRANCISCO CA 96555 

NCAR LIBRARY SERIALS 

NATL CTR FOR ATMOS RSCH 

PO BOX 3000 

BOULDER CO 80307-3000 

HEADQUARTERS DEPT OF ARMY 

DAM1 PO1 

ATTN LEE PAGE 

WASHINGTON DC 20310-1067 

DEAN RMD 

ATTN DR GOMEZ 

WASHINGTON DC 20314 

US ARMY INFANTRY 

ATSH CD CS OR 

ATTN DR E DUTOIT 

FT BENNING GA 30905-5090 

HQ AFWA/DNX 

106 PEACEKEEPER DR STE 2N3 

OFFUTT AFB NE 681134039 

PHILLIPS LABORATORY 

PL LYP 

ATTN MR CHISHOLM 

HANSCOM AFB MA 01731-5000 

PHILLIPS LABORATORY 

PL LYP 3 

HANSCOM AFB MA 01731-5000 

AFRL/VSBL 

29 RANDOLPH RD 

HANSCOM AFB MA 01731 

US ARMY MATERIEL SYST 

ANALYSIS ACTIVITY 

AMXSY 

ATTN MR H COHEN 

AI’G MD 21005-5071 

US ARMY MATERIEL SYST 

ANALYSIS ACTIVITY 

AMXSY AT 

ATTN MR CAMPBELL 

APG MD 21005-5071 

50 
US ARMY MATERIEL SYST 

ANALYSIS ACTIVITY 

AMXSY CR 

ATTN MR MARCHET 

APG MD 21005-5071 

ARL CHEMICAL BIOLOGY 

NUC EFFECTS DIV 

AMSRL SL CO 

APG MD 21010-5423 

US ARMY MATERIEL SYST 

ANALYSIS ACTIVITY 

AMSXY 

AI’G MD 21005-5071 

ARMY RESEARCH LABORATORY 

AMSRL D 

2800 POWDER MILL ROAD 

ADELI’HI MD 20783-1145 

ARMY RESEARCH LABORATORY 

AMSRL OP CI SD TL 

2800 POWDER MILL ROAD 

ADELPHI MD 20783-1145 

ARMY RESEARCH LABORATORY 

AMSRL CI LL 

ADELPHI MD 20703-1197 

ARMY RESEARCH LABORATORY 

AMSRL SS SH 

ATTN DR SZTANKAY 

2800 POWDER MILL ROAD 

ADELPHI MD 20783-1145 

ARMY RESEARCH LABORATORY 

AMSRL CI 

ATTN J GANTT 

2800 POWDER MILL ROAD 

ADELPHI MD 20783-1197 

ARMY RESEARCH LABORATORY 

AMSRL 

2800 POWDER MILL ROAD 

ADELPHI MD 20783-1145 

NATIONAL SECURITY AGCY W21 

ATTN DR LONGBOTHUM 

9800 SAVAGE ROAD 

FT GEORGE G MEADE MD 20755-6000 

US ARMY RSRC OFC 

ATTN AMXRO GS DR BACH 

PO BOX 12211 

RTP NC 27009 

1 

3 

51 
DR JERRY DAVIS 

NCSU 

PO BOX 8208 

RALEIGH NC 27650-8208 

US ARMY CECRL 

CECRL GP 

ATTN DR DETSCH 

HANOVER NH 03755-1290 

US ARMY ARDEC 

SMCAR IMI I BLDG 59 

DOVER NJ 07806-5000 

ARMY DUGWAY PROVING GRD 

STEDP MT DA L 3 

DUGWAY UT 84022-5000 

ARMY DUGWAY PROVING GRD 

STEDI? MT M 

ATTN MR BOWERS 

DUGWAY UT 84022-5000 

DEPT OF THE AIR FORCE 

OL A 2D WEATHER SQUAD MAC 

HOLLOMAN AFB NM 88330-5000 

PLWE 

KIRTLAND AFB NM 87118-6008 

USAF ROME LAB TECH 

CORRIDOR W STE 262 RL SUL 

26 ELECTR PKWY BLD 106 

GRIFFISS AFB NY 13441-4514 

AFMC DOW 

WRIGHT PATTERSON AFB OH 45433-5000 

US ARMY FIELD ARTILLERY SCHOOL 

ATSF TSM TA 

FT SILL OK 73503-5600 

US ARMY FOREIGN SC1 TECH CTR 

CM 

220 7TH STREET NE 

CHARLOTTESVILLE VA 22448-5000 

NAVAL SURFACE WEAPONS CTR 

CODE G63 

DAHLGREN VA 22448-5000 

US ARMY OEC 

CSTE EFS 

PARK CENTER IV 

4501 FORD AVE 

ALEXANDRIA VA 22302-1458 

52 
US ARMY CORES OF ENGRS 

ENGR TOPOGRAPHICS LAB 

ETL GS LB 

FT BELVOIR VA 22060 

US ARMY TOP0 ENGR CTR 

CETEC ZC 1 

FT BELVOIR VA 22060-5546 

SC1 AND TECHNOLOGY 

101 RESEARCH DRIVE 

HAMPTON VA 23666-1340 

US ARMY NUCLEAR CML AGCY 

MONA ZB BLDG 2073 

SPRINGFIELD VA 22150-3198 

USATRADOC 

ATCD FA 

FT MONROE VA 23651-5170 

ATRC WSS R 

WSMR NM 88002-5502 

ARMY RESEARCH LABORATORY 

AMSRL CI S 

COMP & INFO SC1 DIR 

WSMR NM 88002-5501 

ARMY RESEARCH LABORATORY 

AMSRL CI E 

COMP & INFO SC1 DIR 

WSMR NM 88002-5501 

ARMY RESEARCH LABORATORY 

AMSRL CI W 

COME’ & INFO SC1 DIR 

WSMR NM 88002-5501 

DTIC 

8725 JOHN J KINGMAN RD 

STE 0944 

FT BELVOIR VA 22060-6218 

US ARMY MISSILE CMND 

AMSMI 

REDSTONE ARSENAL AL 35898-5243 

US ARMY DUGWAY PROVING GRD 

STEDP3 

DUGWAY UT 84022-5000 

USTRADOC 

ATCD FA 

FT MONROE VA 23651-5170 

1 

53 
WSMR TECH LIBRARY BR 

STEWS IM IT 

WSMR NM 88002 

US ARMY RESEARCH LAB 

AMSRL D DR D SMITH 

2800 POWDER MILL RD 

ADELPHI MD 20783-l 197 

US ARMY CECOM 

INFORMATION & INTELLIGENCE 

WARFARE DIRECTORATE 

ATTN AMSEL RD IW II’ 

FORT MONMOUTH NJ 07703-5211 

ARMY RESEARCH LAB 

AMSRL CI EW 

J PASSNER 

WSMR NM 88002-5501 

Record copy 

TOTAL 

30 

1 

92 

. 

. 

. 

54