Army Research Laboratory 

^ ‘,, ., ,^. 

Comparison and Evaluation 

of 

Operational Mesoscale MM5 and BFM 

Over WSMR 

BY 

Teizi Henmi 

Information Science and Technology Directorate 

Battlefield Environment Division 

I * :,.-. :,_I __ 

ARL-TR-1476 

. . 

.> G 

’ * ^ ‘, .,’ .’ 

May 2000 

I ’ 

Approved for public release; distribution unlimited. 

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4. TITLE AND SUBTITLE 5. FUNDING NUMBERS 

Comparison and Evaluation of Operational Mesoscale Models MM5 and BFM 

Over WSMR 

6. AUTHOR(S) 

T. Henrni 

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

U.S. Army Research Laboratory REPORT NUMBER 

Computational & Information Sciences Directorate 

Battlefield Environment Division AFU-TR- 1476 

Attn: AMSRL-CI-EW 

WSMR, NM 88002-5501 

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AGENCY REPORT NUMBER 

U.S. Army Research Laboratory 

2800 Powder Mill Road ARL-TR- 1476 

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Approved for public release; distribution unlimited. A 

13. ABSTRACT (Maximum 200 wom’sl 

Forecast data of both Fifth-Generation Mesoscale Model (MM5) and the Battlescale Forecast Model (BFM) were statistically 

compared with the Surface Atmosphere Measuring System (SAMS) data at the White Sands Missile Range covering the period 

of April and May 1999. Archived forecast data from MM5 and SAMS, and output data from the BFM are used for this study. 

Statistical parameters such as mean difference, absolute difference, root mean square error, and root mean square vectdr 

error are calculated between forecast data and observed data for both models. Surface meteorological parameters, 

temperature, relative humidity, horizontal wind vector components, and wind speed are used for the present study. 

Statistics for individual stations as well as all the SAMS stations covering the 42-day period are analyzed. 

This study shows that both models predicted the surface temperature fields well. MM5 tends to over predict relative 

humidity, whereas BFM tends to under predict it. Both models tend to under.predict wind speed, but the BFM calculation 

produces smaller wind speeds than MM.5 The BFM produced a better vector wind than did MM5. 

$4. SUBJECT TERMS k5. NUMBER OF PAGES 

mesoscale, forecast, numerical model, WSMR 65 

16. PRICE CODE 

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

OF REPORT OF THIS PAGE OF ABSTRACT 

UNCLASSIFIED UNCLASSIFIED UNCLASSIFIED SAR 

NSN 7540-01-280-5500 Standard Form 298 (Rev. 2-89) j 

Prescribed by ANSI Std.Z39-18 
Preface 

. 

This report describes the results of statistical comparisons of surface 

meteorological parameters forecasted by both the Fifth-Generation 

National Center for Atmospheric Research/Penn State Mesoscale Model 

(MM5) and the U.S. Army Battlescale Forecast Model (BFM) to the 

Surface Atmosphere Measuring System data at the White Sands Missile 

Range (WSMR), covering the period of April and May 1999. 

. 

MM5 is now employed operationally at the WSMR weather station for 

short-range (up to 24 hr) forecasting. The BFM, developed at the U.S. 

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

Meteorological System. 
. 

. 

Contents 

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

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

I. 

2. 

3. 

3. 

4 

5. 

6. 

7. 

Introduction ........................................................................................................................................ 9 

1.1 Purpose ........................................................................................................................................ 9 

Review of the Mesoscale Model Comparison Studies ........................................................... 11 

MM5, SAMS Data, and BFM Forecast Data .............................................................................. 13 

3.1 MM5 Data ................................................................................................................................... 13 

3.2 SAMS Data ................................................................................................................................. 14 

BFM Data .......................................................................................................................................... 17 

Statistical Parameters ...................................................................................................................... 21 

BFM Forecast and SAMS Data Comparison ............................................................................. 23 

Comparison of MM5 and BFM Forecast .................................................................................... 27 

6.1 Scatter Diagrams of Forecast Versus Observation ............................................................... 27 

6.2 Station Statistics ......................................................................................................................... 33 

6.3 Time Series of Statistical Parameters ...................................................................................... 41 

6.4 Mean Wind Vector Fields ........................................................................................................ 42 

Summary ........................................................................................................................................... 47 

References .............................................................................................................................................. 49 

Acronyms & Abbreviations ............................................................................................................... 53 

Distribution ............................................................................................................................................ 55 

3 
Figures 

Figure 1. Map showing the locations of WSMR SAMS ................................................................... 14 

Figure 2. Contour plot of terrain data ................................................................................................ 18 

Figure 3. BFM model domain ............................................................................................................. 20 

Figure 4. BFM and SAMS comparisons ............................................................................................. 23 

Figure 5. BFM and SAMS comparisons except for relative humidity (percent) .......................... 24 

Figure 6. BFM and SAMS comparisons except for wind speed (m/set) ...................................... 24 

Figure 7. Time series of RMSVE (m/set) ........................................................................................... 25 

Figure 8a. Scatter diagram of MM5 vs. SAMS observation for temperature ............................ 27 

Figure 8b. Scatter diagram of BFM vs. SAMS observation for temperature ............................. 28 

Figure 9a. Scatter diagram of MM5 vs. SAMS observation for relative humidity ................... 28 

Figure 9b. Scatter diagram of BFM vs. SAMS observation for relative humidity .................... 29 

Figure 10a. Scatter diagram of MM5 forecast vs. SAMS for wind speed .................................... 29 

Figure lob. Scatter diagram of BFM forecast vs. SAMS for wind speed ..................................... 30 

Figure lla. Scatter diagram of MM5 forecast vs. SAMS observation for wind vector 

component, u ................................................................................................................... 30 

Figure lib. Scatter diagram of BFM forecast vs. SAMS observation, for wind vector 

component, u ................................................................................................................... 31 

Figure 12a. Scatter diagram of MM5 forecast vs. SAMS observation, for wind 

vector component, v ....................................................................................................... 31 

Figure 12b. Scatter diagram of BFM forecast vs. SAMS observation, for wind vector 

Figure 13. 

Figure 14. 

Figure 15. 

Figure 16. 

Figure 17. 

Figure 18. 

Figure 19a. 

Figure 19b. 

Figure 20a. 

Figure 20b. 

Figure 21a. 

Figure 21b. 

component v ................................................................................................................... 32 

Time series of the statistical parameters, MD, AD, and RMSE for 

temperature. Thin and thick lines represent the MM5 and BFM, 

respectively ...................................................................................................................... 38 

Time series of the statistical parameters, MD, AD, and RMSE for relative 

humidity. Thin and thick lines represent the MM5 and BFM, 

respectively ...................................................................................................................... 39 

Time series of the statistical parameters, MD, AD, and RMSE for 

x-component of wind vector, u. Thin and thick lines represent the 

MM5 and BFM respectively ........................................................................................... 39 

Time series of the statistical parameters, MD, AD, and RMSE for 

y-component of wind vector v. Thin and thick lines represent the 

MM5 and BFM, respectively ......................................................................................... 40 

Time series of the statistical parameters, MD, AD, and RMSE for 

wind speed. Thin and thick lines represent the MM5 and BFM, 

respectively ...................................................................................................................... 40 

Time series of the RMSVE .............................................................................................. 41 

Mean wind vectors calculated by MM5 for 00 1st ...................................................... 43 

Mean wind vectors calculated by MM5 for 12 1st ...................................................... 43 

Mean wind vectors calculated by BFM for 00 1” ........................................................ 44 

Mean wind vectors calculated by BFM for 12 1st ....................................................... 44 

Mean wind vectors by observation for 00 1”’ ............................................................... 45 

Mean wind vectors by observation for 12 1”’ ............................................................... 45 

4 
Tables 

Table 1. Elevation, latitudes and longitudes of WSMR SAMS ...................................................... 15 

Table 2. Correlation coefficients between forecast and observation ............................................. 32 

Table 3. Statistical parameters, MD, AD, and RMSE with statistics for MM5 and BFM ........... 34 

Table 4. Statistical parameters, MD, AD, and RMSE with statistics for MM5 and BFM 

except for relative humidity ................................................................................................. 35 

Table 5. Statistical parameters, MD, AD, and RMSE with statistics for MM5 and BFM 

except for wind speed ........................................................................................................... 36 

Table 6. RMSVE for individual stations for MM5 and BFM .......................................................... 37 
Executive Summary 

Forecast data of both Fifth-Generation Mesoscale Model (MM5) and the 

Battlescale Forecast Model (BFM) were statistically compared with the 

Surface Atmosphere Measuring System (SAMS) data at the White Sands 

Missile Range covering the period of April and May 1999. Archived 

forecast data from MM5 and SAMS, and output data from the BFM are 

used for this study. 

Statistical parameters such as mean difference, absolute difference, root 

mean square error, and root mean square vector error are calculated 

between forecast data and observed data for both models. Surface 

meteorological parameters, temperature, relative humidity, horizontal 

wind vector components, and wind speed are used for the present study. 

Statistics for individual stations as well as all the SAMS stations covering 

the 42-day period are analyzed. 

This study shows that both models predicted the surface temperature 

fields well. MM5 tends to over-predict relative humidity, whereas BFM 

tends to under-predict it. Both models tend to under-predict wind speed, 

but the BFM calculation produces smaller wind speeds than MM5. The 

BFM produced a better vector wind than MM5 did. 

7 
1. Introduction 

The White Sands Missile Range (WSMR) weather station, “C” station, is 

now using the Fifth-Generation National Center of Atmospheric 

Research (NCAR)/Penn State Mesoscale Model (MM5) as a tool for 

short-range (24 h) weather forecast. MM5 is a limited area, 

nonhydrostatic, terrain following, sigma-coordinate model to predict 

mesoscale and regional-scale atmospheric circulations. Details about the 

model can be found on its internet MM5 home web page: 

http://www.ucar.edu/mm5/mm5-home.html. Forecasts are routinely 

made twice a day initialized at 0000 and 1200 UTC over the model 

domain covering WSMR and the surrounding area. These forecasts and 

the Surface Atmosphere Measuring System (SAMS) data are being 

archived by the “C” station. [l] 

The Battlescale Forecast Model (BFM), developed at the U. S. Army 

Research Laboratory (ARL), is used to make short-range forecasts of 

atmospheric conditions in the Integrated Meteorology System (IMETS) 

and the Computer Assisted Artillery Meteorology (CAAM) system. The 

BFM is designed to forecast atmospheric conditions over a battlescale 

area (500 x 500 km or less). The BFM is globally relocatable except for 

high latitude regions and has been used for different parts of the world. 

Detail of the BFM is described by T. Henmi and R. Dumais. [2,3,4] 

Archived data of MM5, SAMS, and the BFM forecast over the WSMR 

domain provide excellent data sets with which to evaluate and compare 

MM5 and BFM. There have been several reports of mesoscale model 

comparison studies, as reviewed in the next section, but the past studies 

were based on the results of a limited number of cases. The present 

study is based on 42, 24-h forecast calculations of MM5 and BFM 

compared with observational results. 

1.1 Purpose 

The purpose of this report is to present the results of this study. A review 

of preceding comparison studies of mesoscale models is given in 

section 2. In section 3, archived MM5 and SAMS data along with BFM 

applications to WSMR are discussed. Comparison methods are 

introduced in section 4, while the results of this study are shown in 

section 5. Section 6 summarizes the present study. 
2. Review of the Mesoscale Model Comparison Studies 

The U.S. Army Atmospheric Science Laboratory (early designation of the 

Battlefield Environment Division, Information Science & Technology, 

ARL) sponsored a Mesoscale Model Comparison workshop in 1992. Two 

sets of 24-h observation data obtained in project WIND were used to 

compare model simulation results. The model domain was an area of 

200 x 200 km (41 x 41 horizontal grid points with grid spacing of 5 km) 

located in Northern California. Four different models participated in the 

workshop: 

1. the numerical simulation model for Flow Over Irregular Terrain with 

Natural and Anthropogenic Heat sources (FITNAH) developed by 

Gross, 

2. the Fourth-Generation NCAlVPenn State Mesoscale Model (MM4) 

operated by the group at Tel Aviv University, 

3. the Colorado State University (CSU) Regional Atmospheric 

Modeling System (RAMS), and 

4. the Higher Order Turbulence Model for Atmospheric Circulation 

(HOTMAC). [4,5,6,7,8,9,10,11] 

Of these models, FITNAH was the only nonhydrostatic and the other 

three models were hydrostatic. The results of the workshop were 

published, and statistical evaluations of simulation results by these four 

models were presented by Gross. In general, for the summertime data 

set, all the models were able to reproduce the diurnally-forced upslope 

and downslope winds, including their times of onset. All models were 

successful in producing temperature changes. However, for the 

wintertime data set, all four models failed to reproduce the range of 

observed wind speeds due to an inability to resolve the rapid changes 

associated with the frontal passage. However, all the models reproduced 

the narrow range of observed wind directions. [12,13] 

Cox, et al performed the intercomparison study of four mesoscale models 

by applying them to five different theater scale regions. A theater scale 

region is a rectangular region of 2800 to 6000 km on a side. In their study, 

the model simulations were made over the five different climatological 

11 
areas of 3220 x 3220 km (71 x 71 horizontal grid points with 46 km grid 

spacing). [ 141 

The five areas were: 

1. Continental United States, 

2. Alaska region including parts of Siberia and Canada, 

3. Central American region, 

4. Korean region including parts of China and Japan, and 

5. Middle East region. 

The models studied were: 

1. Fifth Generation NCAR/Penn State Mesoscale Model (MM5), 

2. CSU RAMS (nonhydrostatic version), 

3. Navy Operational Regional Prediction System Version 6 (NORAPS6), 

and 

4. the U.S. Air Force mesoscale model, the Relocatable Window Model 

(RWM) 

The versions of MM5 and RAMS used in the study are nonhydrostatic. 

NORAPSG and RWM are hydrostatic models. Initial conditions and 

time-dependent boundary conditions were provided by the U.S. Air 

Force’s Global Spectral Model at a 2.5 E grid resolution. Surface and 

rawinsonde data, and the National Meteorological Center (NMC) global 

analysis 2.5 E gridded data fields were used to compare the model 

simulation results. The simulation calculations were performed for three, 

three-day periods in five regions, and two independent 36-h forecasts 

were made during each three-day period. Comparison of model 

simulation results with observational data indicated that RAMS forecasts 

were statistically closer to the observed values most often, with MM5 

following next. 

12 
3. MM5, SAMS Data, and BFM Forecast Data 

3.1 MM5 Data 

The WSMR MM5 forecasts are done on three nests : 

Grid Grid numbers Grid distance (km) 

1 84 x 98 30 

2 67x 71 10 

3 61 x 61 3.3 

and 31 vertical layers are used. The vertical o-coordinate used in MM5 is 

defined as: 

~ = PO-PI 

Ps - Pt 

(1) 

where 

ps and pt are the surface and top pressures, respectively, of the 

reference state and are independent of time 

p0 is the reference state pressure and dependent on height only. [15] 

The initial and time dependent lateral-boundary values, for model 

forecast calculations, are provided by the forecast data of Eta model run 

by the National Center for Environmental Prediction (NCEP). The center 

of all the model domain is located at 33.0” N and 106.0” W. 

The grid 3 domain is located to cover WSMR. Grid 3 data were 

interpolated to the locations of the SAMS sites and the interpolated data. 

Forecast data of MM5 (PSAMS) are archived. PSAMS data are composed 

of hourly forecasts over two 12-h forecast periods initialized at 0000 and 

1200 UTC that includes temperature and relative humidity values at the 2 

m level, wind speed, and direction at the 10 m level. Hourly data are 

displayed at 1 to 11, and 13 to 23 UTC. Few or no data at 0, 12, and 24 

UTC are given in data set. The surface data were obtained by 

extrapolating the data at the lowest level of model (about 40 m above 

ground surface) to the surface level, using the similarity theory 

13 
relationships. These surface data are statistically compared to the SAMS 

data. [16,17] 

3.2 SAMS Data 

The locations of the WSMR SAMS sites are shown in figure 1. Table 1 

displays the elevation, and latitudinal and longitudinal locations. Both 

figure 1 and table 1 were obtained from the internet home page of WSMR 

Weather Station (http://weather.wsmr.armv.com). In the table, the 

stations in bold characters, which are located within the BFM model 

domain, are used for the present study. 

Figure 1. Map showing the locations 

of WSMR SAMS. I- SAMS TOWER LOCATIONS 

<; 

@19 

13 i 

‘._C .A! 

14 
Table 1. Elevation, latitudes and longitudes of WSMR SAMS 

Station Elevation Latitude Longitude Station 

Number (Feet) (” N) (” W) Name 

1 4005 32.36 106.38 ‘C station’ 

2 3953 32.66 106.40 ‘Apache Site’ 

3 4272 32.37 106.49 ‘WSMR Post’ 

4 3911 32.90 106.41 ‘Northrup 

Strip’ 

5 5901 32.40 106.50 ‘San 

Augustine 

Pass’ 

6 5356 33.50 106.54 

7 4058 32.90 106.13 

‘Mockingbird 

Gap’ 

‘Ninninger 

Site’ 

8 4745 33.47 106.58 ‘Zumwalt 

Track’ 

9 8941 33.30 106.53 

11 4877 33.80 106.68 

12 4785 33.80 106.59 

13 5290 33.74 106.43 

14 4530 33.50 106.21 

15 4057 33.17 106.49 

17 9169 32.79 105.82 

‘Salinas Peak 

‘LBTS’ 

‘Zurf Site’ 

‘4 Mile Site’ 

‘Oscura Range 

Camp’ 

‘Jallen Site’ 

‘Sacramento 

Peak’ 

18 4180 32.40 106.15 ‘Orogrande 

Gate’ 

19 4917 33.63 106.48 ‘Duquette’ 

Note Stations in bold characters are used for the present 

comparison studies. 

15 
Table 1. Elevation, latitudes and longitudes of WSMR SAMS 

(continued) 

30 3978 32.88 106.50 ‘Radar 449’ 

31 4255 32.80 106.50 ‘Radar 551’ 

Note Stations in bold characters are used for the p‘resent 

comparison studies. 

16 
3. BFM Data 

BFM is applied to a domain similar to the MM5 grid 3 area. The BFM 

domain consists of 51 x 51 grid points with a grid spacing of 3.33 km; this 

means a total model area of 166.7 x 166.7 km. The model vertical depth 

was set to 7 km above the highest elevation point in the domain and 

includes 16 layers. Forecast calculations are made twice daily initialized 

at 00 and 12 UTC. Figure 2 shows terrain data for the area of 533.3 x 

533.3 km which contains the model domain at the central area. Also 

shown are the locations of input data for initial and lateral boundary 

conditions. In the figure, N represents the location of the Naval 

Oceanographic Global Atmospheric Prediction System (NOGAPS) 

forecast data points. The data are given at every 1” in both latitudinal 

and longitudinal directions. U represents the radiosonde stations. The 

data obtained at El Paso (located at 31.90” N and 106.70” W), and 

Albuquerque (located at 35.05” N and 105.62“ W) are used. 

The NOGAPS data were obtained through the internet homepage of the 

Master Environmental Library (MEL) addressed at: http:/ /wwwmel.nrlmry.navy.mil/homepage.html Horizontal wind vector 

components, temperature, dew point temperature, and geopotential 

height at 13 different pressure levels (1000, 975, 925, 900, 850, 700, 500, 

400, 300, 250, 200, 150, and 100 mb) ,are obtained for the forecast periods 

of 0,6,12,18, and 24 h. These data are interpolated to the 161 x 161 grid 

with the grid spacing of 3.33 km at each pressure level for the above 

forecast periods. With the exception of the 0 hour data, the data is 

vertically interpolated from the pressure levels to the BFM’s height levels 

to produce three-dimensional fields of the input data. At 0 hrs, the 

NOGAPS data and upper air sounding data are cornposited and 

interpolated to the BFM vertical levels. [2] 

17 
Figure 2. Contour plot of 

terrain data. 

18 

Note - For the area covering of 533.3 x 533.3 km, centered at 32.80” N 

and 106.30” W, BFM model domain is located in the central area. Also 

shown are the locations of input data for initial and lateral boundary 

conditions. N represents NOGAPS data point, U for upper air, and S for 

SAMS stations. 

The BFM vertical coordinate z’ is defined as 

z* = jq z - z, 

H - z, 

. 

(2) 

where 

z is the Cartesian vertical coordinate, 

zg the ground elevation, 
Hth e material surface top of the model in the z’coordinate, and H is the 

- 

corresponding height in the z coordinate defined by H= H + zg max. zg may is 

the maximum value of terrain elevation in the BFM model domain. 

The data fields covering the 51 x 51 grid points are used for forecast 

calculation. Details of the data analysis is described in reference 2. 

Forecast calculation of the BFM is done as follows. 

Suppose a forecast calculation is initialized at time t,. 

Precalculation will start at time t, -3 hour, and for 3 hours from t,-3 to 

t, to b the model fields are dynamically adjusted to the initial fields 

by the nudging method. 

The hourly lateral boundary condition data between two different 

forecast periods are calculated by a linear interpolation method. 

From t time t to t + 1, the data for t + 1 hour are assimilated in for 

1 hr, and this process is repeated for an entire forecast period. For 

the present study, surface data are not used for initialization. 

After the forecast calculation is completed, the following bilinear 

interpolation is conducted to obtain the BFM data at the SAMS locations. 

Suppose a SAMS location (x’, and y’) is surrounded by 4 BFM grid points. 

An interpolated value y’ of an arbitrary variable y’ at (x’, y’) is calculated 

using a bilear interpolation method as 

fh=cp(i,j>+ W-x)*[cp(i+1 ,j)-cp(i,j)l (3) 

(p2 =cp(i,j+l)+(x’-x)*[cp(i+l,j+l)-cp(i,j+l)] (4) 

cp’(x’,y’) =(P1 + (Y’-Y)+P, - %I (5) 

Here (i,j) is the southwest grid point of the 4 grid points surrounding a 

SAMS location (x’,y’), and (x, y) is the distance location for the grid point 

(i,j); $(i,j) is an arbitrary variable at (x,y). Figure 3 shows the terrain 

elevation data for BFM, covering the domain of 51 x 51 grid points with 

grid spacing of 3.33 km. In the figure, the locations of SAMS stations are 

represented by the station number listed in table 1. As can be seen.in the 

figure, the majority of the SAMS are located in the central portion of the 

model domain and are not distributed evenly. Only a few stations are 

placed in the mountainous areas. Thus, the data set is, by no means, 

19 
ideal for mesoscale model evaluation. The results obtained in the 

comparison study should be regarded as qualitative. 

Figure 3. BFM model domain. ;_,.;, ., .I .- :,p‘;.,_ .,, - ; : . ., __ . . 

,t;: - 14 ,,_ .: 

:,'.x" 

. .' i ,_ 

i ;, 

/ \, ‘.. 

Y_/ .Ii 

_’ 

;, 

.i .:. .i.....‘. .:._ . . . . . . . . .I.LLi.. . ../... L.L.. I__. . ..I._... ^...... II. ‘L-1. 1.: ^ ..^ _ . .._.._ ._....:.., ..;.. 

. 

. ‘,,’ !_. : “_ :- : ‘.‘. ,_ 

.--mmNote Covers the area of 51 x 51 grid points with grid 

spacing of 3.33 km, centered ai 32.8” N and 

106.3” W. 

present 

20 
I - 

. 

4 Statistical Parameters 

The following statistical parameters between the forecast data, MM5 and 

BFM, and the observed SAMS data are calculated using the data for 42 

days in the period between April 1 and May 31, 1999. Statistical 

parameters are calculated for temperature, relative humidity, wind speed 

and vector components at every forecast hour. 

1. Mean Difference 

n fC( ‘p,i,j - xo,i,j ) 

MD = j=l j=’ (6) 

m-n 

Here the subscripts O and r represent observation and prediction, 

respectively. The subscript i represents the ith SAMS station, and the 

subscript j the j’” forecast day. n is the number of SAMS stations, and m 

the total number of forecast days. A nonzero ‘mean difference (MD) 

indicates bias. For instance, if MD value is positive, it indicates that the 

model tends to over-forecast. 

2. Mean Absolute Difference 

m n 

CCI xp,i,j - xo,i,j 

AD = j=1 i=l 

(7) 

21 
3. Root mean square error (Rh4SE) 

\; 

n fC( 'o,i,j - xp,i,j )’ 

RMSE = j=l i=l 

rn.r-2 

Good agreements between observation and forecast are, in general, 

related to smaller values of absolute difference (AD) and RMSE. 

4. Root mean square vector error (RMSVE) 

II’ f f [('o,,,j - 'p,,,j)' + (vo,i,j -‘p,i,jj21 

RMSVE = i=l i=l 

m-n 

(9) 

This parameter measures the differences of both wind speed and 

direction. Again, good agreements of wind vectors are related to small 

values of the RMSVE. 

5. Correlation Coefficient 

Here, Y,,~,~ = x,,~,~ - <, and yP,i,j = x,,~,~ - <. < and <are 

the means of observed and forecast values, respectively. 

22 
5. BFM Forecast and SAMS Data Comparison 

As mentioned previously, the BFM forecast calculations are made twice 

daily initialized at 00 and 12 UTC. The purpose of this section is to 

compare forecast statistics of the two forecast calculations for the period 

of 12 and 24 UTC. Figure 4 through 7 shows the time series of the 

statistical parameters which are calculated every forecast hour. Forecast 

calculations initialized at 00 UTC are made for 24 h, and those initialized 

at 12 UTC for 12 h. 

Figure 4. BBM and SAMS comparisons. 

Note Time series of MD, AD, and RMSE for surface 

temperature (” C) are shown. Thin and thick 

lines represent forecast calculations initialized 

at, respectively, 00 and 12 UTC. 

23 
Figure 5. BFM and SAMS 

comparisons except for relative 

humidity (percent). 

Figure 6. BFM and SAMS 

comparisons except for wind speed 

(m/set). 

RMSE 

48 

Forecant time 1hrs.L 

24 
Figure 7. Time series of FWSVE 

(m/set). 

RMS VECTOR ERRORiM/SI 

I’ 1 ’ 1 ’ 11 11 I”? 11 1 ” ” ’ “1 1 I 

RMSVE 

8.8 

These figures show the following clearly 

For temperature and wind speed and vector, the forecasts initialized 

at 12 UTC produce better results than those at 00 UTC for the same 

period of day. For relative humidity, the forecast calculations 

initialized at 12 UTC does not produce significant improvement over 

the ones at 00 UTC. 

The time series of MD for relative humidity initialized at 00 UTC 

shows that the BEM forecast tends to produce lower relative humidity 

than observation except for several hours of the local afternoon 

period. 

For temperature, the BFM tends to produce forecast values greater 

than observation for most of the forecast periods. 

For wind speed, the BFM tends to produce lighter wind speeds than 

, 

those of observation. 

The time series of RMSVE shows that the forecast initialized at 

12 UTC represents improved wind vector values over those 

initialized at 00 UTC for the same period of day. 

25 
Thus, it can be concluded for surface meteorological parameters over 

WSMR that the 12-h forecast calculation initialized at 12 UTC represents 

an improvement over the 24-h forecast calculation initialized at 00 UTC. 

This simply demonstrates that the BFM starts losing the forecasting skills 

with time. 

26 
6. Comparison of MM5 and BFM Forecast 

As previously mentioned, the MM5 forecast data of surface parameters 

PSAMS are composed of two subsets of 12-h forecast calculation. 

Therefore, in the following, the corresponding data subsets of BFM are 

used for comparison. 

6.1 Scatter Diagrams of Forecast Versus Observation 

Figures 8a through 12b shows the scatter diagrams of forecast calculation 

versus observation. In these figures, A is for MM5 forecast, and B 

for BFM. 

Figure 8a. Scatter diagram of MM5 vs. SAMS 

observation for temperature. 

Observation 

27 
Figure 8b. Scatter diagram of BFM vs. 

SAMS observation for temperature. 

Figure 9a. Scatter diagram of MM5 vs. 

SAMS observation for-relative 

humidity. 

28 
Figure 9b. Scatter diagram of BFM vs. 

SAMS observation for relative humidity. 

Figure 10a. Scatter diagram of MM5 

forecast vs. SAMS for wind speed. .*:.;.. 

. 

).I* *: 

1 . * 

f’ * ri : f 

: - 

. 

ii. 

; : *. * 

1 * 

:!; 

:, 

Observation 

29 
Figure lob. Scatter diagram of BFM 

forecast vs. SAMS for wind speed. 

I I 

SPEED Iti/ 

10- . 

Observation 

Figure lla. Scatter diagram of MM5 

forecast vs. SAMS observation for 

wind vector component, u. 

30 
Figure lib. Scatter diagram of BFM 

forecast vs. SAMS observation, for wind 

vector component, u. 

Figure 12a. Scatter diagram of MM5 

forecast vs. SAMS observation, for wind 

vector component, v. 

U lM/Sl' 

* 

. - 

10- 

5- 

2 

,” 0 

k 

LL 

-5- 

. . 

-10 

t 

31 

. 
Figure 12b. Scatter diagram of BFM 

forecast vs. SAMS observation, for 

wind vector component v. 

5- 

In 

* 

:0 

‘ 

0 

LL 

-5- 

-10- 

I , I , 10 5 0 b 1'0 rl 

Observation 

I----- 

These scatter diagrams are composed of all the data points including 18 

SAMS stations, for 24 h, and 42 forecast days. The correlation coefficients 

between the model forecast and the SAMS observation are calculated as 

appears in table 2. 

Table 2. Correlation coefficients between forecast and observation 

Parameter MM5 vs. SAMS BFM vs. SAMS 

Temperature ( C ) .88 .88 

R.H. (%) .72 58 

Wind speed (m/set) .38 .53 

u (m/set) .44 .62 

v (m/set) .004 .15 

32 
The following trends in the data can be seen. 

. From figures 8A and B, there is good agreement for temperature 

between forecasts and observation for both MM5 and the BFM with 

the correlation coefficients 0.88. 

l Reflecting dry atmospheric conditions over WSMR and the study 

period of April and May, 1999, data points of relative humidity are 

concentrated in lower left part of figures 9A and 8. The figures 

show considerable scatter, indicating that both models have 

difficulties in simulating the moisture parameter near the ground 

surface. The correlation coefficient for MM5 forecast versus SAMS 

observations is 0.72, slightly the BFM’s 0.58. 

. For wind parameters, the correlation coefficients between BFM and 

SAMS are greater than those between MM5 and SAMS, but figure 

10B clearly shows that BFM tends to under forecast the surface 

wind speed. 

6.2 Station Statistics 

The statistical parameters, MD, AD, RMSE, and RMSVE are also 

calculated for each SAMS station; these results appear in tables 3 

through 6. 

In tables 3 through 6, the statistical parameters calculated for MM5 are 

given in the left side of table and those for BFM are in the right side. 

33 
34 

Table 3. Statistical parameters, MD, AD, and Rh4SE with statistics for MM5 

and BFM. 

rl ote xatistical parameters, MU, AU, and KMSE for temperature tor eacn 

station. Statistics for MM5 are given in the left-hand side of table, 

and those for BFM in the right-hand side. 
Table 4. Statistical parameters, MD, AD, and RMSE with statistics for MM5 

and BFh4 except for relative humidity 

1 

Station 

# 

,MM5 * < 

mIYDpJ~~ IRMSE IIMD IAD IRMSE 

2 I 4.4 8.6 I 11.1 11 -2.1 I 12.1 

3 6.4 

4 

* 

-0.4 

5 2.8 

9.6 I 11.9 II -0.6 I 7.2 I 10.9 

10.2 I 13.6 11 -6.4 1 11.1 I 15.7 

8.2 1 11.3 11 -3.7 1 8.5 1 13.4 

I I I 6.1 9.8 1 12.5 11 0.6 I 7.6 I 10.4 

9.4 I 11.9 II -2.4 I 9.0 I 12.9 

10.7 

9.8 

10.0 12.6 

9.2 12.3 

12.8 ~ 15.2 

I 7.6 

I II I I 

II I I 

9.8 I 12.3 11 -1.8 1 9.5 I 13.7 

22 I 7.7 9.5 11.9 0.9 6.8 10.0 

25 9.1 12.7 1 8.3 1 11.3 I 7.0 

each station. Statistics for MM5 and are given in the left-hand side 

of table, and those for BFM in the right-hand side. 

35 
Table 5. Statistical parameters, MD, AD, and Rh4SE with statistics for MM5 

Note Statistical parameters, MD, AD, and RMSE for wind speed for each 

station. Statistics for MM5 and are given in the left-hand side of 

table, and those for BFM in the right-hand side. 

36 
Table 6. RMSVE for individual 

stations for MM5 and BFM 

Table 3 shows that both the MM5 and BFM had a difficult time with the 

temperature for station 9 (Salinas Peak). Both models consistently 

calculated the temperature warmer than observed values. The terrain 

elevation height for this station is 2,709 m (8941 ft) according to table 1. 

The height reduced from the terrain elevation data for BFM is 1,963 m, 

746 m lower than the real height. The poor forecast of temperature for 

the station 9 by BFM may be caused by this discrepancy of height. For 

MMS, the terrain elevation data set is not available, but a similar reason 

may have applied to the poor forecasts. Otherwise, MM5 and BFM 

produced similar statistics for temperature. 

37 
For relative humidity (refer to table 4), the MM5 tends to forecast higher 

relative humidity than what is observed, while the BFM tends to forecast 

slightly lower than observation. 

Table 5 and 6 show the statistics of wind speed and wind vector, 

respectively. It is noteworthy that both models produced lower statistical 

values for some stations located in mountainous site, # 5 (San Augustine 

Pass) and #9 (Salinas Peak) than other stations. In analysis of WSMR 

SAMS data using the data comprised of a 60-day period of March and 

April, 1990, Brock (1990) showed that #5 station has mountain peaks to 

the north and to the south so it rarely reports strong winds from the 

north or south and, instead, exhibits a wind rose showing predominantly 

east and west winds. The unit grid distance of 3.33 km used for both 

models may not be fine enough to resolve the local effect such as the 

above. [X3] 

According to table 5, both models tend to under-forecast wind speed, but 

the BFM under-forecasts wind speeds for all the stations more than MM5. 

In contrast, for RMSVE, the values for BFM are smaller for all the stations 

except #5 than those by MM5. 

Figure 13. Time series of the 

statistical parameters, MD, AD, 

and RMSE for temperature. Thin 

and thick lines represent the MM5 

and BFM, respectively. 

38 
Figure 14. Time series of the 

statistical parameters, MD, AD, 

and RMSE for relative humidity. 

Thin and thick lines represent 

the MM5 and BFM, respectively. 

Figure 15. Time series of the 

statistical parameters, MD, 

AD, and RMSE for 

x-component of wind vector, 

u. Thin and thick lines 

represent the MM5 and BFM, 

respectively. 

Relative humidity(%) 

_~~I 

” b ” ” ” ” ” ” ” ” ” ’ ” 

x.0- 

X-camp. of wind, u (M/S1 

:i-l;---i 

39 
Figure 16. Time series of the 

statistical parameters, MD, AD, and 

FUviSE for y-component of wind 

vector v. Thin and thick lines 

represent the MM5 and BFM, 

respectively. 

0 2 4 6 8 10 12 14 16 1B 20 22 24 

Forecast t,me Ihrs.! 

Figure 17. Time series of the 

statistical parameters, MD, AD, 

and FUviSE for wind speed. Thin 

and thick lines represent the MM5 

and BFM, respectively. 

4.8 

t -I 

0 2 4 6 E 10 12 14 16 1E 20 22 24 

40 
Figure 18. Time series of the RMSVE . 

0 2 4 6 E 10 12 14 16 18 20 22 24 

Forecast time Ihrs.1 

6.3 Time Series of Statistical Parameters 

MD, AD, RMSE, and RMSVE are calculated every hour of the 24-h 

forecast period using 42 days of forecast data. For the MM5 .forecast, 

PSAMS data files contain very few data at 00,12, and 24 UTC. In figures 

13 through 18, the values for MM5 are drawn with thin lines, and those 

for BFM with thick lines. 

Inspection of these figures reveals that AD and RMSE vary 

proportionally. Therefore, in the following, AD is chosen for 

consideration. The following can be seen from these figures:. 

0 For surface temperature, the statistics of both models are very 

similar, except that MM5 under-forecasts temperature slightly, and 

that BFM over forecasts the temperature. Mean absolute 

differences for both models are about 3.0” C. 

. MM5 forecasts produces a slightly higher relative humidity than 

observation throughout the 24-h forecast period. As shown in the 

previous section, the BFM tends to produce a lower relative 

humidity than observed from the evening to morning hours, and 

41 
from 00 to 12 UTC, the BFM produced smaller AD than MM5, and 

for the second 12-h period, both models produced similar AD 

values. The AD ranged within 15 percent. 

. MD, AD, and RMSE of wind vector components u and v, and wind 

speed respectively in figures 15, 16, and 17, and the RMSVE in 

figure 18 show that both MM5 and BFM under-predicted wind 

speed throughout the entire forecast period. The under-prediction 

of wind speed is more significant for BFM than by the MM5. The 

RMSVE values of MM5 are greater than those of BFM throughout 

the 24-h forecast period. This is mainly due to larger errors of 

MM5 in calculation of the y-component of wind vector v. 

l In a study of the BFM during June and July, 1998, over three 

different model domains (Colorado, Washington, and Florida) 

encompassing 500 x 500 km area with grid spacing of 10 km by 

Henmi (2000), the AD values for surface temperature are in the 

range of 2 to 3” C, which are similar to the AD values obtained by 

both MM5 and BFM in this study. However, for wind speed, the 

above study showed AD smaller than 1 m/set, and RMSVE in the 

ranges of 2 to 3 m/set for three different model domains. The 

present study shows that by the BFM AD values are in the range of 

2 to 3 m/set, and RMSVE in the range of 4 to 5 m/set. In a BFM 

evaluation study over Colorado which covers a 500 x 500 km with 

a grid spacing of 10 km, D. Knapp and R. Dumais obtained the 

results of AD of 3.0” C for temperature and 1.8 m/set for wind 

speed. Both results are similar to the results of the present study 

for temperature, but smaller for wind speed. At this point, it is not 

clear why both MM5 and BFM calculations over WSMR with a 3.33 

km grid spacing have resulted in larger values of AD than in the 

previous studies. [19,20] 

6.4 Mean Wind Vector Fields 

In figures 19, 20, and 21 (A and B ), are mean wind vectors, respectively, 

at 00 and 12 local standard time (1st). The wind vectors calculated by 

MM5 are shown in figure 19, those by BFM in figure 20, and those 

observed in figure 21. 

42 
Figure 19a. Mean wind vectors 

calculated by MM5 for 00 1st. 

Figure 19b. Mean wind vectors 

calculated by MM5 for 12 1st. 

43 
Figure 20a. Mean wind vectors 

calculated by BFM for 00 1st. 

Figure 20b. Mean wind vectors 

calculated by BFM for 12 1st. 

44 
Figure 21a. Mean wind vectors by 

observation for 00 1”‘. 

Figure 21b. Mean wind vectors by 

observation for 12 1”‘. 

45 
Both models were able to produce predominantly southwesterly flow 

patterns. 

At 00 lst, the mean BFM wind vectors are more similar in direction to 

those observed; however, the BFM wind speeds are markedly less than 

the observations. On the whole, the MM5 wind speeds at 00 1st are closer 

to the observations. 

At 12 lst, with the exception of two stations to the east of the Organ/San 

Andres mountain chain, observed wind speeds are quite light. The 

observed directions are all WSW. MM5 shows considerably higher wind 

speeds at this time than observed and shows a southwest wind direction 

for the more northerly stations. The BFM wind speeds appear to be 

intermediate between those observed and those given by MM5. In 

general, the BFM wind directions are quite similar to the observations. 

46 
7. Summary 

MM5 and BFM forecast data over the WSMR covering the 42 days during 

April and May, 1999 are statistically compared with SAMS data. MM5 

data interpolated to SAMS locations and archived on the computer of 

WSMR weather station are used for the study. Since the majority of 

archived daily data of MM5 are composed of two 12-h forecast data, the 

BFM calculations were correspondingly done twice a day, initialized at 

00 and 12 UTC. Although forecast calculations initialized at both 00 and 

12 UTC produced comparable results for the same period of day between 

12 and 24 UTC, those initialized at 12 UTC produced slight improvement 

over those initialized at 00 UTC. The data for initialization and timedependent boundary conditions were provided by the U.S. Navy’s 

NOGAPS 0,6,12,X3, and 24-h forecast data, and upper-air sounding data 

at El Paso, Texas and Albuquerque, New Mexico. 

The following results are obtained from the present study: 

0 From the scatter diagrams of forecast and observed data for all of 

the forecast days, it can be seen that the surface temperature 

forecast by both MM5 and BFM produced good agreement with 

SAMS data. On the other hand, both MM5 and BFM showed 

difficulty in forecasting the relative humidity at the surface. The 

scatter diagram of wind speed for BFM versus the observations 

shows that the model tends to under-forecast wind speed. The 

correlation coefficients for wind speed, and vector wind 

components (u and v) for both models are comparable, except that 

the correlation coefficient of v for MM5 is negative. 

. The statistics at individual stations show that the temperature at 

station # 9 (Salinas Peak) was poorly forecast about 5” C higher 

than observation by both models. A major cause of this appears to 

be the large difference in elevation between the actual height and 

that effectively given by the models as their grid point data is 

interpolated to the station # 9 location. For wind speed and vector 

components, both models produced poor statistics for mountain 

stations #5 and 9, probably because the grid spacing of 3.33 km 

may not be fine enough to resolve local effects such as channeling 

and blocking effects of complex terrain. 

. The 12-h forecast calculation by the BFM initialized at 12 UTC 

resulted in, for same time period of day, slight improvements over 

the 24 h forecast calculation initialized at 00 UTC. 

47 
. The time series of statistical parameters of both the MM5 and BFM 

for temperature are very similar, except that MM5 under forecasts 

and BFM over forecasts slightly. It may be concluded that both 

MM5 and BFM predicted the surface temperature fields over the 

WSMR model domain reasonably well. The BFM tended to 

produce a lower relative humidity than observed from the evening 

to the morning hours and slightly higher values in the afternoon; 

whereas, MM5 tended to produce a slightly higher relative 

humidity than observed throughout the 24-h forecast period. 

0 Wind speeds predicted by the BFM are less than those observed 

throughout the 24-h forecast period. However, probably due to 

poor performance of MM5 in simulating the y-component of wind 

vector, v, the RMSVEs for BFM are smaller than those for MM5 

throughout the 24-h forecast period. 

a The majority of SAMS stations in WSMR are located in the valley 

areas and are not distributed evenly in the model domain. 

Observed data at a few mountain stations are influenced by 

localized effects that could not be resolved by the grid spacing 

(3.33 km) used in the present study. Therefore, this study’s 

statistics should be regarded as qualitative. 

. Up to this time, the BFM has been mainly used with grid spacings 

greater than 5 km. This study is the first systematic attempt to use 

the terrain elevation data with grid spacing of 3.33 km. It is 

encouraging to see that a hydrostatic and a single-nested forecast 

model, the BFM, has produced comparable statistical results of 

surface meteorological parameters to the non-hydrostatic and 

triply-nested MM5. 

. The present study was performed for the period of April and May, 

1999 over WSMR. Further study is needed to cover different 

seasons and model domains, particularly the winter season in 

which synoptic scale weather patterns are important to local 

weather. 

48 
References 

1. 

2. 

3. 

4. 

5. 

6. 

7. 

8. 

Haines, P. A., A. J. Blanco, S. A. Lutes, and J. B. Spalding, 

Meteorological Data Processing Methods in the Computer-Assisted 

Artillery Meteorology System (Battlescale Forecast Model), TR-559, 

U. S. Army Research Laboratory, WSMR, NM, 1997. 

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

Model, TR-1032, U. S. Army Research Laboratory, WSMR, NM, 1998. 

Henmi; T. and R. Dumais, _Tr., The Battlescale Forecast Model (BFM) 

during the TFXXI at Fort Irwin, CA: Statistical Evaluation of 24 h 

Forecast Fields and Model Improvement, TR-1685, U.S. Army 

Laboratory, WSMR, NM, 1998. 

Cionco, R. M., Overview of the Project WIND Data, in “Mesoscale 

Modeling of the Atmosphere, edited by Pielke and Pearce, 63-72, 

Meteorological Monograph 25, No. 47, American Meteorological 

Society, 1994. 

Gross, G., On the Wind Field in the Loisach Valley- Numerical 

Simulation and Comparison with the LOWEX III Data, Meteor. 

Atmos. Phys. 42,231-247,199O. 

Gross, G., Project WIND Numerical Simulations with FITNAH, in 

Mesoscale Modeling of the Atmosphere, edited by Pielke and Pearce, 

73-80, Meteorological Monograph 25, No. 47, American 

Meteorological Society, 1994. 

Alpert, I’. and M. Tsidulko, Project WIND Numerical Simulations 

with the Tel Aviv Model: PSU-NCAR Model Run at Tel Aviv 

University, in Mesoscale Modeling of the Atmosphere, edited by 

Pielke and Pearce, 81-95, Meteorological Monograph 25, No. 47, 

American Meteorological Society, 1994. 

Tremback, C. J., G. J. Tripoli, R. Arritt, W. R. Cotton, and R. A. Pielke, 

The Regional Atmospheric Modeling System, Proc. Int. Conf. of 

Development and Application of Computer Techniques to 

Environmental Studies, 601 - 607, Computer Mechanics Institute, Los 

Angeles, CA,.1986. 

. 

49 
Walko, R. L., and R. A. Pielke, Simulation of project WIND Cases 

with RAMS, in Mesoscale Modeling of the Atmosphere, edited by 

Pielke and Pearce, 97 - 121, Meteorological Monograph, 25, No. 47, 

American Meteorological Society, 1994. 

Yamada, T. and S. Bunker, A Numerical Model Study of Nocturnal 

Drainage Flows with Strong Wind and Temperature Gradients, 

Journal of Applied Meteorology, 28,545-554,1989. 

9. 

10. 

11. 

12. 

13. 

14. 

Yamada, T. and T. Henmi, HOTMAC: Model Performance Evaluation 

by Using Project WIND Phase I and II Data, in Mesoscale Modeling of 

the Atmosphere, edited by Pielke and Pearce, 123-135, Meteorological 

Monograph, 25, No. 47, American Meteorological Society, 1994. 

Pielke, R. A., and R. P. Pearce, Mesoscale Modeling of the 

Atmosphere, Meteorological Monograph, 25, No. 47, American 

Meteorological Society, 1994. 

Gross, G., Statistical Evaluation of the Mesoscale Model Results, in 

Mesoscale Modeling of the Atmosphere, edited by Pielke and Pearce, 

Meteorological Monograph, 137-154, 25, NO. 47, American 

Meteorological Society, 1994. 

Cox, R., B. L. Bauer, and T. Smith, A Mesoscale Model 

Intercomparison, Bulletin of American Meteorological Society, 79, 

265-283,1998. 

15. Grell, G. A., J. Dudhia, and D. R. Stauffer, A Description of the FifthGeneration Penn State/NCAR Mesoscale Model (MM5), NCAR/TN398+STR, NCAR Technical Note, 1994. 

16. Davis, C., Email addressed to T. Henmi, WSMR, NM, 1999. 

17. Businger, J. A., Turbulent Transfer in the Atmospheric Surface Layer, 

in Workshop on Micro Meteorology, edited by Haugen, 67-100, 

American Meteorological Society, 1973. 

18. Brock, F. V., Analysis of the Surface Atmospheric Measurement 

System, Report for Contract No. DAAL03-86-D-001, Delivery Order 

1923, US Army Atmospheric Sciences Laboratory, WSMR, NM, 1990. 

50 
19. Knapp, D. I., and R. E. Dumais, Jr., Technical Validation of the 

Ba ttlescale Forecast Model, Proceedings of the 1995 Battlefield 

Atmospheric Conference, Battlefield Environment Directorate, U. S. 

Army Research Laboratory, WSMR, NM, 1995. 

20. Henmi, T., Evaluation Study of Opera fional Mesoscale Forecast 

Model Over Three Clima tologically Different Areas, to be published, 

ARL Technical Report, 2000. 

51 
Acronyms & Abbreviations 

AD 

AFWC 

ARL 

BFM 

CAAM 

csu 

FITNAH 

GM 

HOTMAC 

IMETS 

MD 

MM4 

MM5 

NMC 

NCAR 

NCEP 

NOGAPS 

NORAPS 

PSAMS 

RAMS 

RMSE 

RMSVE 

absolute difference 

Air Force Weather Central 

Army Research Laboratory 

Battlescale Forecast Model 

Computer Assisted Artillery Meteorology 

Colorado State University 

numerical simulation model for flow over irregular 

terrain with natural and anthropogenic heat 

sources 

Global Spectral Model 

Higher Order Turbulence Model for Atmospheric 

Circulation 

Integrated Meteorological System 

mean difference 

Fourth-Generation NCAR/Penn State mesoscale 

Model 

Fifth-Generation NCAR/Penn State Mesoscale 

Model 

National Meteorological Center 

National Center of Atmospheric Research 

National Center for Environmental Predictions 

Naval Oceanographic Global Atmospheric 

Prediction System 

Navy Operational Regional Prediction System 

Version 6 

archived forecast data of MM5 

Regional Atmospheric Modeling System 

root mean square error 

root mean square vector error 

53 
RWM 

SAMS 

WIND 

WSMR 

54 

Relocatable Window Model 

Surface Atmosphere Measuring System 

Nonuniform Domain 

While Sands Missile Range 
Distribution 

Copies 

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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 

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APG MD 21005-5071 

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CSTE EFS 

PARK CENTER IV 

4501 FORD AVE 

ALEXANDRIA VA 22302-1458 

US ARMY CORPS 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 IS S 

INFO SC1 & TECH DIR 

WSMR NM 88002-5501 

ARMY RESEARCH LABORATORY 

AMSRL IS E 

INFO SC1 & TECH DIR 

WSMR NM 88002-5501 

ARMY RESEARCH LABORATORY 

AMSRL IS W 

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 
INFO SC1 & TECH DIR 

USTRADOC 

ATCD FA 

FT MONROE VA 23651-5170 

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 20783-1197 

US ARMY CECOM 

INFORMATION & INTELLIGENCE 

WARFARE DIRECTORATE 

ATTN AMSEL RD IW IP 

FORT MONMOUTH NJ 07703-5211 

Record copy 

TOTAL 

60 

1 

62