Effectiveness of Two Forecast Models 

for Stratiform Precipitation 

by Jeffrey E. Passner 

ARL-TR-3188 April 2004

Approved for public release; distribution is unlimited.
NOTICES 

Disclaimers 

The findings in this report are not to be construed as an official 

Department of the Army position unless so designated by other 

authorized documents. 

Citation of manufacturer’s or trade names does not constitute an official 

endorsement or approval of the use thereof. 

DESTRUCTION NOTICE−For classified documents, follow the 

procedures in DOD 5220.22-M, National Industrial Security Program

Operating Manual, Chapter 5, Section 7, or DOD 5200.1-R, Information 

Security Program Regulation, C6.7. For unclassified, limited documents, 

destroy by any method that will prevent disclosure of contents or 

reconstruction of the document. 
Army Research Laboratory 

White Sands Missile Range, 88002-5513 

ARL-TR-3188 April 2004

Effectiveness of Two Forecast Models 

for Stratiform Precipitation 

Jeffrey E. Passner

Computational Information Sciences Directorate, ARL 

Approved for public release; distribution is unlimited.
REPORT DOCUMENTATION PAGE Form Approved 

OMB No. 0704-0188

Public reporting burden for this collection of information is estimated to average 1 hour per response, including the time for reviewing instructions, searching existing data sources, gathering

and maintaining the data needed, and completing and reviewing the collection information. Send comments regarding this burden estimate or any other aspect of this collection of information, 

including suggestions for reducing the burden, to Department of Defense, Washington Headquarters Services, Directorate for Information Operations and Reports (0704-0188), 1215 Jefferson 

Davis Highway, Suite 1204, Arlington, VA 22202-4302. Respondents should be aware that notwithstanding any other provision of law, no person shall be subject to any penalty for failing to 

comply with a collection of information if it does not display a currently valid OMB control number. 

PLEASE DO NOT RETURN YOUR FORM TO THE ABOVE ADDRESS. 

1. REPORT DATE (DD-MM-YYYY)

April 2004 

2. REPORT TYPE 

Final 

3. DATES COVERED (From - To)

5a. CONTRACT NUMBER 

5b. GRANT NUMBER 

4. TITLE AND SUBTITLE 

Effectiveness of Two Forecast Models for Stratiform Precipitation 

5c. PROGRAM ELEMENT NUMBER

5d. PROJECT NUMBER 

5e. TASK NUMBER 

6. AUTHOR(S) 

Jeffrey E. Passner 

5f. WORK UNIT NUMBER 

7. PERFORMING ORGANIZATION NAME(S) AND ADDRESS(ES)

U.S. Army Research Laboratory

Computational Information Sciences Directorate 

Battlefield Environment Division (ATTN: AMSRL-CI-EB) 

White Sands Missile Range, NM 88002-5501

8. PERFORMING ORGANIZATION 

 REPORT NUMBER 

ARL-TR-3188 

9. SPONSORING/MONITORING AGENCY NAME(S) AND ADDRESS(ES) 10. SPONSOR/MONITOR'S ACRONYM(S)

U.S. Army Research Laboratory

2800 Powder Mill Road 

Adelphi, MD 20783-1145

11. SPONSOR/MONITOR'S REPORT 

 NUMBER(S)

ARL-TR-3188 

12. DISTRIBUTION/AVAILABILITY STATEMENT 

 Approved for public release; distribution is unlimited.

13. SUPPLEMENTARY NOTES 

14. ABSTRACT 

The U.S. Army Research Laboratory has developed a mesoscale model, the Battlescale Forecast Model (BFM), which is a hydrostatic model 

designed for boundary-layer applications for the Army. Using the basic assumption of Sundqvist in 1989, the model determines precipitation 

rates by the density of clouds. Since ARL receives 15-km Mesoscale Model Version 5 (MM5) output from the Air Force Weather Agency, it 

was decided to study the skill in forecasting stratiform precipitation, precipitation rates, biases, and precipitation types from both the BFM and 

MM5. The precipitation type forecast is a post-processed routine developed by ARL and is used for both models. Results of this study during 

the winter months of 2003 showed that both models forecast much weaker precipitation rates for snow than rain although the models do well 

differentiating between rain and snow. 

15. SUBJECT TERMS

Mesoscale, precipitation, BFM, MM5 

16. SECURITY CLASSIFICATION OF: 

19a. NAME OF RESPONSIBLE PERSON

Jeffrey E. Passner 

a. REPORT

 U 

b. ABSTRACT 

 U

c. THIS PAGE 

 U

17. LIMITATION 

OF 

 ABSTRACT 

 U

18. NUMBER 

 OF 

 PAGES 

38 19b. TELEPHONE NUMBER (Include area code) 

505-678-3193 

Standard Form 298 (Rev. 8/98)

Prescribed by ANSI Std. Z39.18 

ii
Contents 

Preface v

Summary 1

1. Introduction 2

2. Mesoscale Models for the Army 2

2.1 The BFM .........................................................................................................................3 

2.2 The MM5.........................................................................................................................3 

3. Cloud Condensation and Non-Convective Precipitation from the BFM 4

3.1 Total Liquid Water and Cloud Fractions.........................................................................5 

3.2 Precipitation Rates from the BFM ..................................................................................9 

4. Precipitation Rate from the MM5 11

5. Statistical Evaluation of Mesoscale Models and Precipitation 13

5.1. Evaluation of “YES/NO” Forecasts..............................................................................13 

5.2Error Evaluation.......................................................................................................14 

6. Evaluation of Precipitation Forecasts 15

6.1 Results of the BFM and MM5 Precipitation Forecasts.................................................16 

6.2 Precipitation Rates.........................................................................................................16 

6.3 Precipitation Type .........................................................................................................18 

7. Summary and Discussion 20

References 23

Acronyms 25

iii
Distribution List 26

List of Figures 

Figure 1. Relationship Showing the Cloud Fraction as a Statistical Function ...............................8 

List of tables 

Table 1. Contingency Table for Forecasted and Observed Weather Event..................................13 

Table 2. “YES/NO” Forecasts of Precipitation During the Winter Season in 2003....................16 

Table 3. Statistical Analysis of Precipitation Rates from the BFM..............................................17 

Table 4. Statistical Analysis of Precipitation Rates from the MM5 .............................................17 

Table 5. BFM Precipitation-Type Forecasts (Horizontal) and Observations (Vertical) for all

Forecast Hours 499 Samples) ..................................................................................................19

Table 6. MM5 Precipitation-Type Forecasts (Horizontal) and Observations (Vertical) for all

Forecast Hours (461 Samples).................................................................................................19

Table 7. Precipitation Rates and Precipitation Types for the BFM and MM5 (All Hours) .........20 

Table 8. BFM and MM5 Visibility Errors Based on Observed Winter Weather in 2003............22 

iv
Preface

The Integrated Meteorological System (IMETS) is a weather data system utilized by the Air 

Force weather forecasters in support of Army operations. Prediction and forecast products on 

IMETS are achieved through the Battlescale Forecast Model (BFM) and the Fifth-Generation 

Pennsylvania State University/National Center for Atmospheric Research Mesoscale Model 

(MM5), which are used for short-term and long-term forecasts, respectively. Both models 

provide precipitation forecasts although different techniques have been derived to provide 

precipitation output. This report describes the precipitation-forecasting techniques and a 

comparison of the BFM and MM5 products.

v
INTENTIONALLY LEFT BLANK. 
1 

Summary 

The U.S. Army Research Laboratory has developed a mesoscale weather model called the 

Battlescale Forecast Model (BFM). After model initialization, the BFM produces forecast 

variables for a 24-h period. Since the Army required a longer-term prediction, the Mesoscale 

Model Version 5 (MM5) gridded data are received from the U.S. Air Force Weather Agency to 

provide forecast information for up to a 48-h period. Due to the importance of precipitation on 

the tactical decision aids, as well as military operations in general, both models forecast 

stratiform and convective precipitation, which are made available to the user in a database and 

graphically. 

This report describes the basic meteorological theory applied to the precipitation processes and 

forecasts for both the BFM and MM5. The effectiveness of the BFM and MM5 precipitation 

output are analyzed as well. 

Precipitation forecasts are derived from numerical model data, such as the BFM and MM5. 

These data provide the forecaster and users with a detailed overview of the atmospheric 

conditions that might produce precipitation along with the general precipitation rates, amounts, 

and types. These precipitation parameters are later placed into a database so other programs, 

such as the Integrated Weather Effects Decision Aid, can attain this information. 
2 

1. Introduction 

The Integrated Meteorological System (IMETS) is a mobile, operational, automated weather 

data receiving, processing, and disseminating system utilized by Air Force weather forecasters in 

support of Army operations. The U.S. Army Research Laboratory (ARL) is supporting the 

forecaster to make more specific and precise battlefield weather forecasts by producing weather 

products on IMETS. One product to assist in short-term forecasting (<=24 h) is an operational 

mesoscale model, the Battlescale Forecast Model (BFM). For longer-term data, the 

Pennsylvania State University/National Center for Atmospheric Research Mesoscale Model 

Version 5 (MM5) output is available from 6 to 48 hours (1, 2). 

The BFM produces many forecasting parameters, including temperature, pressure, dew point, 

relative humidity, and windspeed and direction as well as precipitation amounts. While these 

outputs provide valuable weather information, Tactical Decision Aids (TDAs) such as the 

Integrated Weather Effects Decisions Aid (IWEDA), have a need for additional precipitation 

parameters such as precipitation rates and precipitation types. The IWEDA has been developed 

to simplify the manner in which environmental impacts on weather systems are displayed to 

users. The IWEDA generates current and forecasted impacts on personnel and approximately 70 

weapon systems, such as attack helicopters and fixed wing aircraft. Both the BFM and the MM5 

can be used to derive precipitation amounts and rates, while a post-processing software package 

has been developed to forecast precipitation types using the model-derived output (3). 

2. Mesoscale Models for the Army

The ARL implemented the Higher Order Turbulence Model for Atmospheric Circulation 

(HOTMAC) as their model for the IMETS platform in response to the Army’s requirement for 

small-scale weather information on the order of less than 500 by 500 km. The HOTMAC was 

selected because it is numerically stable at long-time steps, globally relocatable, emphasizes 

boundary-layer physics, and is platform-independent. In an effort to keep the model run time as 

fast as possible, the BFM contains no convective cloud parameterization or cloud microphysics 

package. The model currently is run to 24 h; however, due to military requirements, it was 

necessary to add the MM5 to the IMETS platform to provide forecast grids out to 48 h from the 

initial forecast time (4, 5). 
3 

2.1 The BFM 

The BFM contains 16 terrain-following vertical levels, a model top of 7000 m above the highest 

elevation, a 10-km horizontal resolution, and a log-linear stagger so that there is greater vertical 

resolution near the surface. The rapid run time for the model can be attributed to a single nest 

and no moist physics or cumulus parameterization routines. However, because of the implicit 

approach, time steps on the order of 200 s (at 10-km resolution) are common for typical 

atmospheric advective speeds and vertical motion fields in the model. Soil temperature on five 

subsurface levels is solved using the heat conduction equation, while long and shortwave 

radiation within a single layer for a stratus cloud is calculated using the method of Hanson and 

Derr. The basic variables that are prognostically forecasted by the model are perturbation 

potential temperature, total water substance mixing ratio, wind speed, wind direction, pressure, 

soil temperature, turbulence kinetic energy and length scale, and non-convective precipitation 

rate (6, 7). 

To initialize the BFM, surface data and upper-air observations are input into the model in the 

area of interest. Additionally, the 36-h forecasted Naval Operational Global Atmospheric 

Prediction System (NOGAPS) package, which is issued by the Air Force Weather Agency 

(AFWA) via the Air Force Automated Weather Distribution System, is utilized as the long-range 

data that the BFM is nudged toward. The NOGAPS grid points are spaced 1o apart, both latitude 

and longitude, on the mandatory pressure surfaces. 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 times of interest. 

The BFM-generated outputs for the grid include the u and v horizontal wind vector components, 

potential temperature, and water vapor mixing ratio. These forecast fields are saved at 0, 3, 6, 9, 

12, 15, 18, 21, and 24 h from the base time of the model run and placed into a Gridded 

Meteorological Data Base (GMDB). 

2.2 The MM5 

The MM5 is a limited-area, non-hydrostatic, terrain-following sigma-coordinate model designed 

to simulate or predict mesoscale and regional-scale atmospheric circulation. 

Terrestrial and isobaric meteorological data are horizontally interpolated from a latitudelongitude mesh to a variable high-resolution domain on Mercator, Lambert Conformal, or polar 

stereographic projection. Since the interpolation does not provide mesoscale detail, these 

interpolated data may be enhanced with observations from the standard network of surface and 

rawinsonde stations using either a Cressman or multiquadric scheme. In the MM5, there is also 

a program that performs the vertical interpolation from pressure levels to sigma coordinates. 

The sigma surfaces near the ground closely follow the terrain, while the higher-level sigma 
4 

surfaces tend to approximate isobaric surfaces. Additionally, the MM5 has a flexible and 

multiple nesting capability, advanced physical parameterization, 3-D data assimilation system 

via nudging, and it can be run on various platforms (8). 

Version 3 of the MM5 was used for this study; it is from AFWA and has a resolution of 15 km 

mesh data on 41 vertical levels. The ARL receives these MM5 data in gridded binary form for 

the Continental United States twice each day, which are initialized at 0600 universal time 

coordinated (UTC) and 1800 UTC, respectively. Due to computational and processing 

constraints, there is a 6-h stagger between the initialization valid time of the 15-km mesh and the 

first forecast output, thus the first MM5 forecast is a 6-h forecast. The frequency of the model 

output is every 3 h, for a time period of 48 h. 

The current AFWA operational version of MM5 places the lowest model vertical level at 20 

magl. To generate data at the standard observation heights of 10 magl and 2 magl, similarity 

theory is being used at ARL to extrapolate to these lower levels from the lowest MM5 sigma 

level. In this fashion, temperature, dew point, and wind data at levels 2 magl and 10 magl are 

produced at ARL in addition to the 41 MM5 sigma levels of data. 

The parameterizations selected by AFWA with this version of the MM5 are as follows: 

• Grell cumulus parameterization − Designed for grid sizes of 10 to 30 km, this 

parameterization accounts for subgridscale convection and compensating subsidence. 

• MRF planetary boundary-layer model − Parameterizes the mixture of heat, moisture, 

and momentum in the boundary layer. 

• Reisner mixed phase explicit moisture microphysics − Cloud and rainwater fields and 

ice processes are predicted explicitly. No graupel or riming processes are calculated. 

• Dudhia cloud radiation − Provides solar and infrared fluxes at the ground and 

atmospheric tendencies resulting from the radiative processes. 

• MM5 five-layer soil model – Temperature predicted in 1, 2, 4, 8, 16 cm layers with fixed 

substrate below using vertical diffusion equation. 

3. Cloud Condensation and Non-Convective Precipitation from the BFM 

The relative humidity of each of the 16 layers produced by the BFM is one key parameter needed 

to calculate the precipitation amount and precipitation rates in the BFM. Since the BFM does 

not directly calculate the relative humidity; it was necessary to derive this value using eq 1: 
5 

SWVAP

TWTR RH =

(1)

where 

RH = relative humidity for each grid point 

TWTR = total water at each grid point 

SWVAP = saturation vapor pressure for the grid point 

These calculations are done for each of the 16 levels at each BFM grid point. 

3.1 Total Liquid Water and Cloud Fractions 

In the BFM, the mixing ratio of total water is a combination of the mixing ratio of water vapor 

and the mixing ratio of cloud water as noted in eq 2: 

Q w= Q v + Q c (2)

where 

 Qw = mixing ratio of total water 

 Qv = mixing ratio of water vapor 

 Qc = mixing ratio of cloud water 

The value of Qv, the mixing ratio of the water vapor, is derived from the initial data of the model. 

In model operation, Qc is initially 0. 

Recalling that the change of total water with time is conserved, the approach suggested by 

Sommeria and Deardoff is used to derive the value of Qc, the mixing ratio of cloud water (9). 

The liquid water potential temperature is defined as: 

c

p

v

l Q C

L

T

θ θ = θ − (3)

where 

θl = liquid water potential temperature 

θ = potential temperature 

T = absolute temperature 
6 

 Lv = latent heat of condensation 

 Cp = specific heat of dry air at constant pressure 

In order to recover the potential (or absolute) temperature and mixing ratios of water vapor (Qv), 

the probability density function, G, defined by Sommeria and Deardoff (9), is used. The density 

function is assumed to be Gaussian such that: 

( ) ( ) 





























 − + − − − = 2

2

2

2

1/2 2 2 1 2 2

1

exp

2 1

1

qw

w

qw

l w

l

l

l qw

q q r

r r

G

l

σ σ σ

θ

σ

θ

πσ σ θ θ θ

 (4) 

where 

θl = fluctuation of liquid water potential temperature 

 qw = fluctuation of total water mixing ratio 

and: 

2 σθl = θl (5)

2 2 σ qw

= q w (6)

l qw

l q w r

σ σ

θ

θ

= (7)

The local condensation is given by: 

Q (Q Q ) H (x) l =

w − s

*

 (8)

where 

 Qs = the saturation mixing ratio, 

H(x) = Heaviside function, 

 x = Qw-Qs 
7 

defined as: 

H(x) = 0, x<0 (9) 

H(x) =1, x>0 

The final equation for cloud water mixing ratio is expressed as: 































 = + − 2 exp 2

1 2

2

l

c s l

Q Q RQ

π

σ (10) 

where 

R = cloud fraction 

s

l

Q Q a

2σ

∆ = (11)

where 

∆Q =Qw-Qsl 

1/4( 2 ) 2 2 2 2 2

s w w l l σ = a q − abq θ +b θ (12) 

The values of a and b in eq 12 are constants as defined in Yamada (10) while the value of R in eq 

10 is a function of cloud coverage for a given volume of air as given by Mellor (11). 

Figure 1 shows how the cloud fraction R varies as a function of Ql, a statistical ratio of super or 

subsaturation. In the figure, clouds can exist even if the mixing ratio of water vapor over a grid 

is not saturated. This is realistic since the grid spacing normally used in mesoscale models is 

larger than the size of small clouds. 
8 

Figure 1. Relationship Showing the Cloud Fraction as a Statistical Function. 

However, after investigating this method for cloud fractions it was determined that it was not an 

effective way to calculate clouds. Some of the reasons for this decision included: 

• The value of cloud liquid water is often too low due to the coarse resolution in both the 

horizontal and vertical in the BFM. 

• Saturation is not well represented in the sounding data used for initialization of the BFM. 

• The BFM nudges to forecasted NOGAPS data, which contains its own biases. 

• Original design of the BFM was for stratiform precipitation in the lowest 4 km. 

• The assumption that the grid spacing is larger than the size of the smaller clouds may be 

accurate; however, stratiform clouds are often in layers and are larger. 

• The Sommeria and Deardorff (9) method to derive cloud fractions was not designed for 

mid-levels or for multiple-layer cases. 

• The technique developed by Sommeria and Deardorff (9) was designed for much smaller 

grid volumes than the current BFM. 

Ironically, the statistical cloud model was a method formulated to avoid coarse grid models in 

which the saturation values are lowered arbitrarily to compensate for the cloud that was not 
9 

resolved; however, the technique of training or “learning” of the data did provide much more 

realistic and accurate cloud fractions in the BFM. While it may appear that small differences in 

relative humidities are not important, Walcek proved that a 2 to 3 percent increase in relative 

humidity could lead to a 15 percent increase in cover. Additionally, Shultz and Politovich 

observed that relative humidity values in excess of 55 percent between 500 to 1000 mbar usually 

identify regions with widespread cloudiness on the Nested Grid Model (12, 13). 

The BFM also had this bias, with clouds observed in layers well below saturation. To 

compensate for this, Passner developed a routine based on a set of IF-THEN rules with an 

emphasis on season, time of day, location, and layer relative humidity. This cloud program was 

used on both the BFM and MM5 with statistical evaluation showing the software to be most 

effective in the lowest 4000 ft of the atmosphere (14).

The importance of the cloud fraction is seen in the next section when discussing the precipitation 

rates produced by the BFM. 

3.2 Precipitation Rates from the BFM 

Since the microphysical processes of stratiform precipitation are not part of the BFM, the 

stratiform precipitation is parameterized as a function of cloud liquid water. The scheme

formulated by Sundqvist et al. for stratiform precipitation is used in the BFM (15). 

The rate of release of precipitation is described by: 















































 = − −

2

* ,

1 exp

c cr

c

o c R Q

Q P C Q (13) 

where 

 Co = characteristic time for the conversion of cloud droplets into raindrops 

 Qc = mixing ratio of cloud water content 

R = cloud fraction 

 Qc,cr = Cloud water content, at which release of precipitation starts to be efficient 

According to Sundqvist (15), Qc,cr should have a value typical of individual cloud types and be 

invariant to grid resolution. He also suggests a value of 0.0001 for Co, which equates to a 

conversion time of approximately 167 min. In his study, Sundqvist worked with the operational 

fine mesh model of the Norwegian Meteorological Institute, which uses a horizontal grid 

resolution of 50 km. In this test, for the BFM, a value of 0.0004 (42-min conversion rate) was 
10 

employed for the model runs. This value, Co, was found to be one of the more influential values 

in determining the accumulated precipitation. 

The rate of precipitation P, at a given z*

 level, is given by: 

( ) * H

z

* g max g *

r (z )Pdz

H

H z z

P z ∫ * ρ 













 + − = (14) 

where 

 z* = vertical coordinate system used in the BFM 

H = depth of the model atmosphere 

Zgmax = highest terrain elevation in the BFM domain 

 Zg = terrain elevation 

Ρ = air density 

To simulate the coalescence process, Sundqvist et al (15) introduced an additional parameter, 

which increases with the rate of precipitation. Additionally, when the temperature is lower than 

-5 °C, to simulate an enhanced release of precipitation in clouds containing a mixture of droplets 

and ice crystals (Bergeron-Findeisen mechanism), he adds another parameter. Finally, since 

cirrus clouds contain mainly ice crystal growth by diffusion, Sundqvist uses another parameter 

that increases with decreasing temperature. 

In the stratiform case, evaporation of precipitating water is assumed to take place according to 

the relation: 

( )( ) r E U CF Pr E = k 1 − 1 − (15) 

where 

 kE = is a coefficient in the expression for evaporation of precipitation (0.00001 in this case) 

 U = relative humidity of the layer 

CF = cloud fraction for the grid volume 

√ Pr = precipitation rate 

The final form of the stratiform precipitation rate at the surface is: 
11 

( ) ( ) ( ) ( )

( ) 













































 − − 













 + − = ∫ *

1/ 2

,

*

*

0

max *

*

1 exp

1 0 dz

CF Q

Q z

z C Q z

H

H z z

P

c cr F

c

OF C

H

w

g g

r ρ

ρ

 (16) 

where 

 COF = Parameter to include Bergeron-Findeisen mechanism for precipitation formation 

ρw = density of water 

The subscript F in the term Q(c,cr)F indicates that the additional parameter for the coalescence and 

Bergeron-Findeisen mechanism are included. The final precipitation rate is expressed in 

millimeter per hour. 

4. Precipitation Rate from the MM5 

The MM5 has many different ways to treat precipitation physics. The explicit schemes treat 

resolved precipitation physics while implicit schemes treat the non-resolved precipitation 

physics. In the MM5 version being discussed here, the explicit scheme is used with the Reisner 

mixed-phase ice scheme. The scheme is activated whenever grid-scale saturation is reached. 

The equations for water vapor, cloud water (ice), and rain water (snow) mixing ratios are based 

on the conservation of moisture but add the effects of the Reisner microphysics package. An 

example of these equations, the equation for rain water (snow if below 0 °C) mixing ratio, is: 

( RE RC RA SM CI qc

f r

nh r

r

r r

r p P P P P P D

V gq

qDIV pq

y

m

pvq

x

m

puq

m

t

pq + + + + + +

∂

∂

+ − ∂

∂

−

























∂

∂

+

∂

∂

=− ∂

∂ *

. *

* *

2

*

σ

ρ δ

σ

σ ) (17) 

where 

m = map factor 

 p* = p star 

 qr = mixing ratio of cloud water 

σ = sigma 

δnh = non-hydrostatic constant 

 DIV = divergence 

 Vf = fall speed of rain or snow 

ρ = density of air 
12 

G = acceleration of gravity 

 PRE = the evaporation of rain and sublimination/deposition of snow 

 PRC = conversion of cloud to rain (ice to snow) 

 PRA = accretion of cloud by rain (ice by snow) 

 Dqc = diffusion term

 PSM = snow melt 

 PCI = heterogeneous freezing of cloud water to cloud ice 

The terms PSM and PCI are the two terms added to the simple ice phase scheme. In the Reisner 

scheme, snow does not melt instantaneously above 0 °C. Additionally, supercooled water can 

exist below 0 °C and unmelted snow can exist above 0 °C. Separate arrays are used to store 

vapor, cloud, cloud ice, and snow. 

The mixing ratio of rain water is used as a key parameter in the fall speed term, which 

determines the rainfall rate at the surface. The equation for the fall speed is: 

b

f

b V aΓ + − = λ

6

(4 )

(18)

where 

 Vf = fall speed 

Ґ = gamma function 

a = 841.9946 for rain or 11.72 for snow 

b = 0.8 for rain or 0.41 for snow 

The value of λ from eq 18 is determined in eq 19: 

1 / 4

r

o w

q

N

















ρ

π ρ λ =

 (19)

where 

π = 3.1416 

 No = Marshall-Palmer intercept parameter 8x106

 m

-4

ρ = mean air density of rain or snow particles (1000 and 100 kg m-3) 
13 

5. Statistical Evaluation of Mesoscale Models and Precipitation 

Two types of evaluations are done in this study. The first will be for “YES/NO” forecasts, where 

the forecast provides information if a certain weather phenomena will or will not occur. A 

second type of evaluation is where the model output is investigated for “error” or how much the 

predicted value differs from the observed value. 

5.1. Evaluation of “YES/NO” Forecasts 

A contingency table provides a statistical method to display answers to binary YES/NO 

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

rate (FAR), the correct non-event (CNE), critical success index (CSI), true skill score (TSS), and 

bias. The calculations are based on the contingency elements listed in table 1, while the 

equations for the evaluation techniques are also shown. 

Table 1. Contingency Table for Forecasted 

and Observed Weather Event. 

Forecast 

YES 

Forecast 

NO 

Observed YES A B 

Observed NO C D 

A B

A POD

+ = (20)

C A

C FAR

+

=

(21)

D C

D CNE

+ = (22)

Donaldson developed the CSI, which considers three of the four elements in the contingency 

table; however, it does not take into account the D element (null element). Hanseen and Kuipers 

formulated an equation that does factor in the null event, and called it the TSS (16, 17). 

A B C

A CSI

+ + = (23) 
14 

( )( )

( ) ( )

A B C D

AD BC TSS

+ +

− = (24) 

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

events, as shown in eq 25: 

A B

A C Bias

+

+

=

(25)

5.2 Error Evaluation 

The three main products used in this study to evaluate model or post-processed derived output 

are mean absolute difference (AD), root-mean square error (RMSE), and correlation coefficient 

(CC). The equations are: 

m n

x x

AD

m

j

n

i

o i j p i j

*

| |

1 1

∑ ∑ , , , ,

= =

−

=

 (26) 

where 

X = meteorological variable 

 O = observation 

P = prediction of variable 

 i = i

th surface station 

 j = j

th forecast day 

n = number of stations, 

m = total number of forecast days 

Small values of AD are related to good agreements between observation and forecast. 
15 

( )

m n

RMSE

m

j

n

i

x x o i j p i j

*

1 1

2 , , , ,

∑ ∑= =

−

=

 (27) 

The values of RMSE are proportional to those of the AD. The CC is displayed in eq 28. The CC 

measures the strength of the relationship between two variables. When CC >0, it indicates a 

positive linear relationship. A value of 1.00 indicates a “perfect” correlation between the 

observed and predicted values of a meteorological forecast. 

∑ ∑ ∑ ∑

∑ ∑

= = = =

= =

∗

=

m

j 1

n

i 1

m

j 1

n

i 1

2

o ,i , j

2

m

j 1

n

i 1

o ,i , j p ,i , j

x * x p , i , j

x x

CC

 (28) 

6. Evaluation of Precipitation Forecasts 

There were approximately 25 model runs done in a variety of locations in the United States; 

however, there was an emphasis on typical wintertime cases and stratiform precipitation since 

the main goal was to study precipitation rates, precipitation type, and the resulting surface 

visibility. 

To verify these data, hourly surface observations were randomly used at a variety of unique 

terrain locations on the grid. This was done so that the influence of terrain could be included on 

the resulting precipitation totals. Each hourly surface observation includes a coded value for the 

accumulated precipitation over the past hour. Unfortunately, the precipitation rates produced by 

the BFM are instantaneous rates, such as 0.06 in/hr, while the MM5 rates are an average rate 

determined by the total precipitation output from the model over a 3-h period. Assumptions 

must be made that the model precipitation is a steady rate, which may be a safe assumption for 

stratiform precipitation, although stratiform precipitation can vary with time. An effort was 
16 

made to eliminate all convective precipitation cases in this study. This is not always feasible as 

even the most uniform storms sometimes contain mesoscale features that can enhance 

precipitation on smaller scales. 

6.1 Results of the BFM and MM5 Precipitation Forecasts 

The most basic evaluation of the model precipitation forecasts was to investigate how well the 

model forecasted precipitation at any hour of the model runs. Table 2 displays these data. 

Table 2. “YES/NO” Forecasts of Precipitation 

During the Winter Season in 2003. 

Model Precip BFM MM5 

Samples 501 463 

POD 0.66 0.82 

FAR 0.35 0.42 

CNE 0.79 0.70 

CSI 0.48 0.51 

TSS 0.45 0.52 

Bias 1.02 1.42 

Cases with precip 37% 33% 

The results indicate that the MM5 has a higher POD of forecasting precipitation, although it does 

have a slightly higher FAR and is biased toward overforecasting precipitation. The BFM data 

set does have a slightly higher percentage of cases with precipitation, although this difference is 

not significant enough to bias the results. 

6.2 Precipitation Rates 

It is impossible to derive the instantaneous precipitation from a surface observation; therefore, 

the precipitation rates, as already mentioned, are not exactly matched. However, these data in 

tables 3 and 4 do give the user a valuable glimpse of rainfall intensity from the models. 
17 

Table 3. Statistical Analysis of Precipitation Rates from 

the BFM. 

BFM 

hours Samples RMSE (mm/h) CC Fcst Ave (mm/h) 

Observed 

Ave 

(mm/h) 

00 31 3.87 0.12 0.38 1.81 

3 22 1.43 0.15 0.56 1.01 

6 26 2.36 0.42 0.63 1.18 

9 22 1.40 0.09 0.65 1.13 

12 17 1.74 -0.15 0.67 1.05 

>12 30 1.28 -0.05 0.61 0.85 

Totals 148 2.01 0.10 0.58 1.17 

Table 4. Statistical Analysis of Precipitation Rates from 

the MM5. 

MM5 

(hours) Samples RMSE (mm/h) CC 

Fcst 

Ave 

(mm/h) 

Observed 

Ave 

(mm/h) 

09 17 3.50 0.02 0.40 1.39 

12 22 2.80 0.21 0.83 1.50 

15 19 1.25 -0.13 0.35 0.78 

18 19 2.86 0.20 1.46 1.22 

21 16 1.57 0.33 1.24 0.70 

>=24 22 0.94 0.19 0.70 0.41 

Total 115 2.15 0.14 0.83 1.00 

Tables 3 and 4 show the results of the precipitation rates. As expected, the CC are very low 

because of the wide disparity in rainfall prediction. This is also seen in the RMSE column, 

where both models show a high error. Overall, the sample size for the hourly data is rather 

small; however, there are interesting trends noted in these data. For both the BFM and MM5, the 

initial time period shows the lowest forecasted precipitation rates. According to Dudhia, the 

precipitation may take several model time steps between production and when it finally reaches 

the ground. In the BFM, this may be a function of slow moistening of the atmospheric column 

and lower mixing ratio, cloud fraction, precipitation rate, along with a high evaporation rate (18). 

After the initial forecast period, the BFM precipitation rates are nearly constant through the 24-h 

model run. Additionally, the observed precipitation shows little variation through the data. This 
18 

is not the case with the MM5 output, which shows more fluctuation in both the forecast averages 

and observed averages. Of great interest is the trend in the MM5, where the precipitation rates 

are less than the observed precipitation rates through the first 15 h of the model runs and then 

suddenly changes at the 18-h period when the forecasted precipitation becomes greater than the 

observed rates. The significance of this trend and its cause is uncertain because of the small data 

sample. 

6.3 Precipitation Type 

An interesting question is: Does the precipitation type have any influence in the rainfall rates, 

snowfall rates or total amounts? In this study, the routine developed at ARL is used to determine 

if the precipitation will reach the surface as rain, snow, freezing rain, or some mixture of rain and 

snow. The routine is implicit, so it is run as part of the post-processor from the BFM and MM5. 

 Using this method, only the lowest 10,000-ft above ground level (AGL) is used, since most 

stratiform precipitation falls from clouds below that level and the temperature is almost always 

below 0 °C in typical wintertime precipitation above that level. Listed below are some of the 

key assumptions of the precipitation-type software: 

• Uses the forecasted wet bulb temperatures rather than temperature. 

• Goes vertically from surface and counts layers above and below 0 °C. 

• If all layers are below freezing, then precipitation will be snow. If all layers are above 0 °C 

then precipitation will be rain at the surface. 

• Freezing rain is forecasted when some layer above the surface is above 0 °C and the 

surface is at 0 °C or less. 

• Calculates the depth of the elevated warm layer, which will help determine if falling snow 

will melt. 

• Calculates the near surface-layer average temperature to know the depth of any warm or 

cold layers near the surface. 

• Does checks to see if snow will melt before reaching ground or rain will freeze at the 

surface. 

• If the routine finds a borderline case between rain and snow, it becomes a “mixed” case. 

During the winter season of 2003, nearly 500 surface observations were collected to coincide 

with areas where the BFM and MM5 were run. The emphasis in the BFM was for all forecasts 

less than 12 h and for the MM5 from 9 to 24 h. In table 5, the results of the precipitation type 

study from the BFM are shown, while table 6 shows similar results from the MM5. 
19 

Table 5. BFM Precipitation-Type Forecasts (Horizontal) 

and Observations (Vertical) for all Forecast Hours 

499 Samples). 

Fcst/Obs None Rain Snow Freezing 

Rain Mixed 

None 249 36 21 0 0 

Rain 49 51 5 0 0 

Snow 13 14 38 0 1 

Freezing 

Rain 8 5 2 0 1 

Mixed 1 6 0 1 0 

Table 6. MM5 Precipitation-Type Forecasts (Horizontal) 

and Observations (Vertical) for all Forecast Hours 

(461 Samples). 

Fcst/Obs None Rain Snow Freezing 

Rain Mixed 

None 218 67 15 0 3 

Rain 20 55 1 0 0 

Snow 13 8 44 0 3 

Freezing Rain 1 6 0 1 0 

Mixed 1 4 0 0 1 

Tables 5 and 6 show encouraging results, especially in the snow forecasts. In 84 percent of the 

BFM snow cases, snow was forecasted, while 98 percent of the snow forecasts were correctly 

predicted in the MM5. There was a higher error in the rain forecasts, although the POD of rain 

was still 67 percent in the BFM and 75 percent in the MM5. However, it should be noted, the 

error of forecasting rain and having freezing rain occur is a function of the models not 

forecasting the surface temperature cold enough. Even an error of 0.1 °C can cause this forecast 

to be incorrect. As noted by Passner (14), the BFM tends to overforecast the surface temperature 

when the boundary layer is moist, thus it is not surprising to see 18 percent of the snow cases 

being forecasted as rain cases due to this high temperature bias. The MM5 has a slight bias to 

underforecast the temperature in moist environments, thus this cold bias helps to drive the MM5 

surface temperature lower and results in a very high POD for snow forecasting. The main bias in 

the precipitation-type software is that too many rain forecasts are actually being observed as 

snow, freezing rain, or mixed precipitation. 
20 

The sample size for freezing rain and mixed precipitation was very small as only about 4 percent 

of all the precipitation observations were freezing rain and approximately 4 percent were mixed 

precipitation. As noted in the tables, the precipitation-type software rarely forecasts freezing 

rain or mixed precipitation, most likely because the models cannot achieve a detailed enough 

profile of the temperature and moisture. 

A final area to investigate was how the precipitation rates varied with the precipitation type in 

each model. Table 7 shows the differences in the forecasted and observed precipitation rates for 

rain and snow with the BFM and MM5. 

Table 7. Precipitation Rates and Precipitation Types for the 

BFM and MM5 (All Hours). 

Model and 

Precip Type Samples 

RMSE 

(mm/h

) 

Forecast 

Avre (mm/h) 

Observed 

Ave(mm/h

) 

BFM Snow 34 0.80 0.54 0.75 

BFM Rain 93 1.32 0.68 1.54 

MM5 Snow 31 0.84 0.37 0.58 

MM5 Rain 63 2.70 1.04 1.31 

The results in table 7 show that precipitation rates and observed rates are significantly lower for 

snow than for rain. Both models underforecast the snowfall rates, with the BFM 

underforecasting snowfall rates by 28 percent while the MM5 underforecast the snowfall rates 

by 36 percent. The BFM does have a more significant error in rainfall rates with an error of 56 

percent in the rates while the MM5 rainfall rates are underforeacsted by 21 percent. The rainfall 

rates are higher than snowfall rates because there is more available liquid water in the 

atmosphere and the mixing ratio values are higher. 

7. Summary and Discussion 

This study was designed to investigate the precipitation rates, precipitation amounts, and 

precipitation types forecasted from two mesoscale models, the BFM and MM5. A description of 

how precipitation is formulated in each model helps to enhance the understanding of how these 

factors influence the model output. The BFM stratiform precipitation rates depend on the 

precipitation rate itself and the evaporation of the precipitation as it falls through the atmospheric 

layers. The most significant terms for the precipitation rate is the cloud liquid water of the layer, 

the cloud fraction of the layer, the coefficient for cloud water content at which the release of 
21 

precipitation becomes efficient, and the coefficient for the conversion rate of cloud particles to 

precipitation size. The evaporation rate in the BFM depends on the relative humidity of the 

layer, the cloud fraction, the precipitation rate for the column, and a constant derived by 

Sundqvist (15). 

In the MM5, the stratiform precipitation routine is an explicit scheme, where the scheme is 

activated when grid-scale saturation is reached. There is an explicit treatment of cloud water, 

rain water, snow, and ice along with feedback to the temperature and moisture field along with 

the radiation scheme. The MM5 contains a microphysics package known as the mixed-phase 

Reisner microphysics package, which builds upon the simple ice routine by permitting 

supercooled water below 0 °C and has a gradual snow melt as it falls. Additionally, unmelted 

snow can exist above 0 °C. The value of the mixing ratio is used in the final fall term in the 

MM5. This fall term is the actual precipitation that reaches the ground. 

The statistical evaluation of the models provided many useful hints on how to improve and 

upgrade the model. The BFM is underforecasting precipitation rate by nearly 50 percent, while 

the MM5 is underforecasting precipitation rates by 17 percent for the overall model sample. The 

MM5 has an interesting trend, where the model underforecasts rates by 43 percent through 15 h 

and then overforecasts the rates by 156 percent from 15 to 48 h after model initiation. Both 

models produce lower precipitation rates in snow than rain, and it was found that the models 

rarely produce snowfall rates (liquid equivalent) greater than 1.00 mm/h. The BFM error is 

logical, given the model’s dry bias and the problems with excessive evaporation below cloud 

base; however, the trends in MM5 precipitation rates are more complex since it contains a 

microphysics package with many assumptions about cloud nuclei sizes, density, and nuclei 

amounts. 

The most vital role of the precipitation rates is that they influence the prevailing surface visibility 

in the post-processing software. Knapp developed regression equations based on 2790 surface 

observations using two types of equations; one with a known ceiling but no precipitation falling 

and another with a ceiling along with precipitation. Passner noted that model biases were 

influencing visibility forecasts and that the equations Knapp formulated were not working well 

with the BFM and MM5 output. To compensate for these results, rainfall and snowfall rates 

were used to help determine precipitation. As an example, when snowfall rates of 1.75 to 2.54 

mm/hr were produced by the model, the forecasted visibility was one mile (19, 14). 

Table 8 shows the performance of the models under different precipitation conditions, which are 

the result of a visibility test. 
22 

Table 8. BFM and MM5 Visibility Errors Based on Observed 

Winter Weather in 2003. 

Model/ 

Obstruction 

Forecast 

Ave (miles) 

Observed 

Ave (miles) 

Mean 

AD Samples 

BFM No 

Precipitation 7.68 9.67 2.18 151 

MM5 No 

Precipitation 8.14 9.67 1.68 198 

BFM Fog 5.50 3.50 4.00 62 

MM5 Fog 5.68 3.68 3.30 50 

BFM Rain 5.76 4.80 3.01 112 

MM5 Rain 5.32 4.90 3.10 83 

BFM Snow 5.49 1.97 3.90 63 

MM5 Snow 6.08 2.45 4.46 72 

The results in Table 8 show the model visibility forecasts are accurate when no precipitation is 

falling. When fog, rain, or snow is observed, the models overforecast visibility in all three cases. 

The fog cases are using the original visibility equations from Knapp (19); however, the rain and 

snow cases are based on the adjustments made for precipitation rates. The mean AD is generally 

the same in all three cases; however, the most significant error appears to be with the snow 

cases, which are overforecasted on average by 3.6 miles. 

In 74 percent of the snow cases, the observed surface visibility was less than 2 miles; however, 

the average snowfall forecast in this study was 5.75 miles. A future step will be to lower the 

snowfall rates and the forecasted visibilities since the models are not able to physically produce 

the precipitation intensity that is often observed. The other major change in the BFM will be to 

use the 70-minute conversion rate, which should enhance the rainfall and snowfall rates. These 

two techniques should make a dramatic improvement in the post-processed visibility routine. 

Additional testing will be conducted to evaluate how these changes work with an independent 

data set in a variety of winter conditions. Additional evaluation of precipitation forecasts must 

also be completed with small-scale models such as a 5-km MM5 to see if the forecast are 

sensitive to grid resolution. 
23 

References 

1. Passner, J.E. Expert Systems and Empirical Rules for Army Operations on IMETS. 

Proceedings of the 13th Conference on Weather Analysis and Forecasting, Vienna, VA, 

608-611, 1993. 

2. Dudhia, J. A Non-hydrostatic Version of the Penn State/NCAR Mesoscale Model: 

Validation Tests and Simulation of an Atlantic Cyclone and Cold Front. Mon. Wea. Rev. 

1993, 121, 1493-1513. 

3. Sauter, David P. The Integrated Weather Effects Decision Aid (IWEDA): Status and Future 

Plans. Proceedings of the 1996 Battlespace Atmospheric Conference, Naval Command, 

Control, and Ocean Surveillance Center, San Diego, CA, 75-81, 1996. 

4. Pielke, R.A. Mesoscale Meteorological Modeling, Academic Press: New York, NY, 1984. 

5. Yamada, T.; S. Bunker. A Numerical Model Study of Nocturnal Drainage Flows with 

Strong Wind and Temperature Gradients. J. Appl. Meteor. 1989, 28, 545-554. 

6. Henmi T.; R.E. Dumais Jr. Description of the Battlescale Forecast Model; ARL-TR-569; 

U.S. Army Research Laboratory: White Sands Missile Range, NM, 1998. 

7. Hansen, H.P.; V.E. Derr. Parameterization of Radiative Flux Profiles with in Layer Clouds. 

J. Appl. Meteor. 1987 26, 1511-1521. 

8. Grell, G.A.; J. Dudhia; D.R. Stauffer. A Description of the Fifth-Generation Penn 

State/NCAR Mesoscale Model (MM5); NCAR Tech. Note NCAR/TN-398+STR; 1995. 

9. Sommeria, G.; J.W. Deardorff. Subgrid Scale Condensation in Cloud Models. J. Atmos. Sci. 

1977, 34, 344-355. 

10. Yamada, T. A Three-Dimensional, Second-Order Closure Numerical Model of Mesoscale 

Circulations in the Lower Atmosphere: Description of the Basic Model and an Application 

to the Simulation of the Environmental Effects of a Large Cooling Pond; ANL/RER-78-1; 

Argonne National Laboratory: Argonne, IL, 19. 

11. Mellor, G.L. The Gaussian Cloud Model Relation. J. Atmos. Sci. 1977, 34, 356-358. 

12. Walcek, C.J. Factors Influencing Regional-scale Cloud Cover: Investigations Using 

Satellite-Derived Cloud Cover and Standard Meteorological Observations. Proceedings of 

the Fourth Symposium of Global Change Studies, Anaheim, CA, 235-236, 1993. 
24 

13. Schultz, P.; M.K. Polotovich. Toward Improvement of Aircraft-Icing Forecasts for the 

Continental United States. Weather and Forecasting 1992, 7, 491-500. 

14. Passner, J.E. Post-Processing for the Battlescale Forecast Model and Mesoscale Model 

Version 5; ARL-TR-2988; U.S. Army Research Laboratory: White Sands Missile Range, 

NM, 2003. 

15. Sundqvist, H.; E. Berge; J.E. Kristja’nsson. Condensation and Cloud Parameterization 

Studies with a Mesoscale Numerical Weather Prediction Model. Mon. Wea. Rev. 1989, 117, 

1641-157. 

16. Donaldson, R.J.; R.M. Dyer; M.J. Krauss. An Objective Evaluator of Techniques for 

Predicting Severe Weather events. Preprints of Ninth Conference Severe Local Storms, 

Norman, OK, 321-326, 1975. 

17. Woodcock, F. The Evaluation of Yes/No Forecasts for Scientific and Administrative 

Purposes. Mon. Wea. Rev. 1976, 104, 1209-1214. 

18. Dudhia, J. Personal Communications, 2003. 

19. Knapp, D.I. Development of a Surface Visibility Algorithm for Worldwide Use with 

Mesoscale Model Output. 15th Conference of Weather Analysis and Forecasting, Norfolk, 

VA, 83-86, 1996. 
25 

Acronyms 

AD absolute difference 

AFWA Air Force Weather Agency 

AGL above ground level 

ARL Army Research Laboratory 

BFM Battlescale Forecast Model 

CC correlation coefficient 

CNE correct non-event 

CSI critical success index 

FAR false alarm rate 

GMDB Gridded Meteorological Database 

GriB gridded binary form 

HOTMAC Higher Order Turbulence Model for Atmospheric Circulations 

IWEDA Integrated Weather Effects Decision Aids 

IMETS Integrated Meteorological System 

mbar millibar 

MM5 Mesoscale Model Version 5 

NOGAPS Naval Operational Global Atmospheric Prediction System 

POD probability of detection 

RMSE root-mean square error 

TDAs Tactical Decision Aids 

TSS true skill score 

UTC universal time coordinated 
26 

Distribution List 

NASA SPACE FLT CTR 1 

ATMOSPHERIC SCIENCES DIV 

CODE ED 41 1 

HUNTSVILLE AL 35812 

US ARMY MISSILE CMND 1 

REDSTONE SCI INFO CTR 

AMSMI RD CS R DOC 

BLDG 4484 

REDSTONE ARSENAL AL 35898 

PACIFIC MISSILE TEST CTR 1 

GEOPHYSICS DIV 

ATTN: CODE 3250 

POINT MUGU CA 93042-5000 

ATMOSPHERIC PROPAGATION BRANCH 1 

SPAWARSYSCEN SAN DIEGO D858 

49170 PROPAGATION PATH 

SAN DIEGO CA 92152-7385 

METEOROLOGIST IN CHARGE 1 

KWAJALEIN MISSILE RANGE 

PO BOX 67 

APO SAN FRANCISCO CA 96555 

NCAR LIBRARY SERIALS 1 

NATL CTR FOR ATMOS RSCH 

PO BOX 3000 

BOULDER CO 80307-3000 

HEADQUARTERS DEPT OF ARMY 1 

DAMI-POB (WEATHER TEAM) 

1000 ARMY PENTAGON ROOM 2E383 

WASHINGTON DC 20310-1067 

HQ AFWA/DNX 1 

106 PEACEKEEPER DR STE 2N3 

OFFUTT AFB NE 68113-4039 

AFRL/VSBL 1 

29 RANDOLPH RD 

HANSCOM AFB MA 01731 
27 

ARL CHEMICAL BIOLOGY 1 

NUC EFFECTS DIV 

AMSRL SL CO 

APG MD 21010-5423 

US ARMY MATERIEL SYST 1 

ANALYSIS ACTIVITY 

AMSXY 

APG MD 21005-5071 

US ARMY RESEARCH LABORATORY 1 

AMSRL D 

2800 POWDER MILL ROAD 

ADELPHI MD 20783-1145 

US ARMY RESEARCH LABORATORY 1 

AMSRD ARL SE EE 

ATTN: DR SZTANKAY 

2800 POWDER MILL ROAD 

ADELPHI MD 20783-1145 

US ARMY RESEARCH LABORATORY 1 

AMSRD ARL CI 

ATTN: J GANTT 

2800 POWDER MILL ROAD 

ADELPHI MD 20783-1197 

US ARMY RSRC OFC 1 

ATTN: AMXRO GS DR BACH 

PO BOX 12211 

RTP NC 27009 

US ARMY CECRL 1 

CECRL GP 

ATTN: DR DETSCH 

HANOVER NH 03755-1290 

ARMY DUGWAY PROVING GRD 1 

STEDP MT DA L 3 

DUGWAY UT 84022-5000 
28 

ARMY DUGWAY PROVING GRD 1 

STEDP MT M 

ATTN: MR BOWERS 

DUGWAY UT 84022-5000 

DEPT OF THE AIR FORCE 1 

OL A 2D WEATHER SQUAD MAC 

HOLLOMAN AFB NM 88330-5000 

PL WE 1 

KIRTLAND AFB NM 87118-6008 

USAF ROME LAB TECH 1 

CORRIDOR W STE 262 RL SUL 

26 ELECTR PKWY BLD 106 

GRIFFISS AFB ROME NY 13441-4514 

AFMC DOW 1 

WRIGHT PATTERSON AFB OH 45433-5000 

US ARMY FIELD ARTILLERY SCHOOL 1 

ATSF TSM TA 

FT SILL OK 73503-5600 

US ARMY OEC 1 

CSTE EFS 

PARK CENTER IV 

4501 FORD AVE 

ALEXANDRIA VA 22302-1458 

US ARMY TOPO ENGR CTR 1 

CETEC ZC 1 

FT BELVOIR VA 22060-5546 

SCI AND TECHNOLOGY 1 

101 RESEARCH DRIVE 

HAMPTON VA 23666-1340 

US ARMY TRADOC ANAL CMND WSMR 

ATRC WSS R 1 

WSMR NM 88002-5502 
29 

US ARMY RESEARCH LABORATORY 1 

AMSRL CI E 

COMP & INFO SCI DIR 

WSMR NM 88002-5501 

US ARMY MISSILE CMND 1 

AMSMI 

REDSTONE ARSENAL AL 35898-5243 

US ARMY DUGWAY PROVING GRD 1 

STEDP3 

DUGWAY UT 84022-5000 

USTRADOC 1 

ATCD FA 

FT MONROE VA 23651-5170 

WSMR TECH LIBRARY BR 1 

STEWS IM IT 

WSMR NM 88002 

US ARMY CECOM 1 

INFORMATION & INTELLIGENCE 

WARFARE DIRECTORATE 

ATTN: AMSEL RD IW IP 

FORT MONMOUTH NJ 07703-5211 

NAVAL RESEARCH LABORATORY 1 

MARINE METEOROLOGY DIVISION 

7 GRACE HOPPER AVENUE STOP 2 

MONTEREY CA 93943-5502 

US ARMY RESEARCH LABORATORY 1 

ATTN: SFAE C3T IE II ROBERT DICKENSHIED 

WSMR NM 88002 

ADMNSTR 1 ELECTRONIC COPY 

DEFNS TECHL INFO CTR 

ATTN: DTIC OCP (ELECT CPY) (W SMITH) 

8725 JOHN J KINGMAN RD STE 0944 

FT BELVOIR VA 22060-6218 
30 

US ARMY RESEARCH LABORATORY 1

ATTN: AMSRL CI IS R (A SMITH) 

MAIL & RECORDS MGMT 

ADELPHI MD 20783-1197 

US ARMY RESEARCH LABORATORY 2 

AMSRD ARL CI OK TL TECHL LIB 

ATTN: KATHLEEN RAPKA 

2800 POWDER MILL ROAD 

ADELPHI MD 20783-1197

Record copy 1 

TOTAL 40