Evaluation Study of an Operational 

Mesoscale Forecast Model Over 

Three Climatologically Different Areas 

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Battlefield Ewi.ronment Division 

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. TITLE AND SUBTITLE 

Evaluation Study of an Operational Mesoscale Forecast Model Over Three 

Climatologically Different Areas 

. AUTHOR(S) 

T. Henmi 

‘. PERFORMING ORGANIZATION NAME(S) AND ADDRESS 

U.S. Army Research Laboratory 

Computational & Information Sciences Directorate 

Battlefield Environment Division 

Attn AMSRL-CI-EW 

WSMR, NM 88002-5501 

8. PERFORMING ORGANIZATION 

REPORT NUMBER 

ARL-TR-1034 

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

U.S. Army Research Laboratory 

2800 Powder Mill Road 

Adelphi, MD 20783-1145 

10. SPONSORING/MONITORING 

AGENCY REPORT NUMBER 

ARL-TR-1034 

II. SUPPLEMENTARY NOTES 

t2a. DlSTRlBUTlON/AVAlLABlLlTY STATEMENT 

Approved for public release; distribution unlimited. 

13. ABSTRACT (Maximum 200 words) 

12b. DISTRIBUTION CODE 

A 

This report describes the evaluation study of the forecast skill of the Battlescale Forecast Model (BFM) over threl 

different regions with different terrain complexities and climates. The model computations are initialized with three 

different sets of initial conditions over Colorado, Washington, and Florida; forecast data are statistically comparec 

with surface observation data. 

Statistical results of the BEM are compared to those of the Navy Operational Global Atmospheric Prediction Systen 

and Navy Operational Regional Atmospheric Prediction System. 

14. SUBJECT TERMS 15. NUMBER OF PAGES 

Mesoscale weather forecast; Battlescale Forecast Model (BFM); statistical evaluation of 66 

BFM; forecast of surface parameters (wind, temperature, and dew point temperature). 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 

NSN7540-01-280-5500 Standard Form 298 (Rev. 2-89) 

i 
Preface 

The Battlescale Forecast Model (BFM) is statistically evaluated by comparing 

the forecast data to observed data. BFM was run for cases of a 30-day 

period over three different model domains having different terrain 

complexities and climate (Colorado, Washington, and Florida). The model 

calculations were initialized with three different sets of initial conditions: 

(1) Navy Operational Global Atmospheric Prediction System (NOGAPS) 

+ upper air + surface data, 

(2) NOGAPS + upper air, and 

(3) NOGAPS. 

For all three model domains, the temperature fields of BFM initialized with 

(1) and (2) above are statistically better than those initialized with (3). For 

Colorado and Washington model domains, the BFM showed clear 

tendencies of forecasting dew point temperatures lower than those observed 

throughout the 24-h forecast period. However, for the Florida model 

domain, forecasts of dew point temperature are higher than observed. 

Three different types of initialization data did not produce significantly 

different wind fields throughout the 24-h forecast period. 

For Colorado and Washington model domains where terrain is more 

complex than Florida, the use of the BFM improves temperature and wind 

forecasts over those of NOGAPS and Navy Operational Regional 

Atmospheric Prediction System (NORAPS). For the Florida domain, no 

significant improvements in temperature forecasts are found. 

1 
Contents 

ExecutiveSummary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 

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

2.0 ModelDomains . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 

3.0 Method.. .................................................................... 15 

3.1 DataUsed ................................................................ 15 

3.2 Initialization Methods ...................................................... 15 

3.3 Statistical Parameters ...................................................... 16 

3.3.1 Mean Difference ...................................................... 16 

3.32 Mean Absolute Difference ............................................. 16 

3.3.3 Root Mean Square Error ............................................... 17 

3.3.4 Root Mean Square Vector Error ......................................... 17 

3.3.5 Correlation Coefficient ................................................ 17 

4.0 Results ....................................................................... 19 

4.1 ScatterDiagrams .......................................................... 19 

4.2 Time Series of the Statistical Parameters ...................................... 26 

4.2.1 Temperature ......................................................... 26 

4.2.2 Dew Point Temperature ............................................... 30 

4.2.3 Wind Speed and Wind Vector .......................................... 37 

4.3 Comparison of the BFM with NOGAPS and NORAPS .......................... 37 

4.3.1 Temperature ......................................................... 38 

4.3.2 Wind Speed and Vector ................................................ 38 

5.0 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51 

References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53 

Acronyms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ..~.......................... 55 

Distribution...................................................................... 57 

3 
Figures 

1. Elevation contours of the Colorado model domain ................................ 12 

2. Elevation contours of the Washington model domain .............................. 13 

3. Elevation contours of the Florida model domain .................................. 14 

4a. Scatter diagram of model calculation versus observation of temperature, 

Colorado model domain ....................................................... 19 

4b. Scatter diagram of model calculation versus dew point temperature, Colorado 

modeldomain ................................................................ 20 

4c. Scatter diagram of model calculation versus wind vector components u 

(left-hand side) and v (right-hand side), Colorado model domain .................... 20 

4d. Scatter diagram of model calculation versus wind speed, Colorado model 

domain ...................................................................... 21 

5a. Scatter diagram of model calculation versus observation of temperature, 

Washington model domain ..................................................... 21 

5b. Scatter diagram of model calculation versus dew point temperature, 

Washingtonmodeldomain ..................................................... 22 

5c. Scatter diagram of model calculation versus wind vector components, u 

(left-hand side) and v (right-hand side), Washington model domain ................. 22 

5d. Scatter diagram of model calculation versus wind speed, Washington 

modeldomain ................................................................ 23 

6a Scatter diagram of model calculation versus observation of temperature, 

Floridamodeldomain ......................................................... 23 

6b. Scatter diagram of model calculation versus dew point temperature, 

Florida model domain ......................................................... 24 

6c. Scatter diagram of model calculation versus wind vector components u 

(left-hand side) and v (right-hand side), Florida model domain ....................... 24 

6d. Scatter diagram of model calculation versus wind speed, Florida model 

domain ...................................................................... 25 

7. Time series of MD and AD of temperature, Colorado model domain ................. 27 

8. Time series of MD and AD of temperature, Washington model domain .............. 28 

9. Time series of MD and AD of temperature, Florida model domain ................... 29 

10. Time series of MD and AD of dew point temperature, Colorado 

modeldomain.. .............................................................. 30 

11. Time series of MD and AD of dew point temperature, Washington 

modeldomain.. .............................................................. 31 

12. Time series of MD and AD of dew point temperature, Florida model 

domain ...................................................................... 32 

13. Time series of MD and AD of wind speed (top), and RMSVE (bottom), 

Coloradomodeldomain ....................................................... 34 

14. Time series of MD and AD of wind speed (top) and RMSVE (bottom), Washington 

modeldomain ................................................................ 35 

15. Time series of MD and AD of wind speed (top) and FUvlSVE (bottom), 

Floridamodeldomain ......................................................... 36 

. 

b 

4 
16a and b. Comparison of BFM temperature forecast performance to NOGAPS and 

NORAPS, Colorado model domain . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39 

16~ and d. Comparison of BFM temperature forecast performance to NOGAPS 

and NORAPS, Colorado model domain . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40 

17a and b. Comparison of BFM temperature forecast performance to NOGAPS 

and NORAPS, Washington model domain . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41 

17~ and d. Comparison of BFM temperature forecast performance to NOGAPS 

and NORAPS, Washington model domain . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42 

18a and b. Comparison of BFM temperature forecast performance to NOGAPS 

and NORAPS, Florida model domain . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43 

1%~ and d. Comparison of BFM temperature forecast performance NOGAPS 

and NORAPS, Florida model domain . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44 

19a and b. Comparison of BFM performance to NOGAPS and NORAPS, for 

wind speed and vectors, Colorado model domain . . . . . . . . . . . . . . . . . . . . . . . . . . . 45 

19c and d. Comparison of BFM performance to NOGAPS and NORAPS, for 

wind speed and vectors, Colorado model domain . . . . . . . . . . . . . . . . . . . . . . . . . . . 46 

20a and b. Comparison of BFM performance to NOGAPS and NORAPS for 

wind speed and vectors, Washington model domain . . . . . . . . . . . . . . . . . . . . . . . . 46 

2Oc and d. Comparison of BFM performance to NOGAPS and NORAPS, for 

wind speed and vectors, Washington model domain . . . . . . . . . . . . . . . . . . . . . . . . . .47 

21a and b. Comparison of BFM performance to NOGAPS and NORAPS, for 

wind speed and vectors, Florida model domain. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48 

21~ and d. Comparison of BFM performance to NOGAPS and NORAPS, for 

wind speed and vectors, Florida model domain. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49 

Tables 

1. Terrain complexities of the three model domains . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 

2. Correlation coefficients between forecast and observation for three model 

domains of Colorado, Washington, and Florida . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19 

3. MD and AD at forecast periods of 0,12, and 24 h between NOGAPS data 

and observation, Colorado model domain . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33 

4. MD and AD at forecast periods of 0,12, and 24 h between NOGAPS data and 

observation, Washington model domain . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33 

5. MD and AD at forecast periods of 0,12, and 24 h between NOGAPS data and 

observation, Florida model domain . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33 
Executive Summary 

The Battlescale Forecast Model (BFM) is statistically evaluated by comparing 

the forecast data to observed data. BFM was run for cases of a 30-day 

period over three different model domains having different terrain 

complexities and climate (Colorado, Washington and Florida). The model 

calculations were initialized with three different sets of initial conditions: 

(1) Navy Operational Global Atmospheric Prediction System 

(NOGAPS) + upper air + surface data, 

(2) NOGAPS + upper air, and 

(3) NOGAPS. 

Forecast data for 24-h periods are statistically compared with surface 

observation data, by calculating parameters such as mean difference (MD), 

absolute difference (AD), root mean square error, and root mean square 

vector error. 

For all three model domains, the temperature fields of BFM initialized with 

(1) and (2) above are statistically better than those initialized with (3). For 

Colorado and Washington model domains, the BFM showed clear 

tendencies of forecasting dew point temperatures lower than those observed 

throughout the 24-h forecast period. However for the Florida model 

domain, forecasts of dew point temperature are higher than observed. 

Three different types of initialization data did not produce significantly 

different wind fields throughout the 24-h forecast period. The values of MD 

for wind speed are in the range of 0 to 1 m/set, and those of AD are also 

between 0 and 1 m/set for three model domains throughout the 24-h 

forecast period. 

For Colorado and Washington model domains where terrain is more 

complex than Florida, the use of the BFM improves temperature forecasts 

over those of NOGAPS and NORAPS. For the Florida domain, no 

significant improvements in temperature forecasts are found. Similarly, the 

BFM produces better wind fields than NOGAPS and Navy Operational 

Regional Atmospheric Prediction System (NORAPS) over Colorado and 

Washington. 

7 
1.0 Introduction 

The Battlescale Forecast Model (BFM) is an operational mesoscale forecast 

model, developed at the U.S. Army Research Laboratory (ARL). It has been 

extensively used to make short-range forecasts of atmospheric conditions as 

a component in both the Integrated Meteorology System (IMETS) and the 

Computer Assisted Artillery Meteorology (CAAM) System. The BFM uses, 

for prognostic calculation, the Higher Order Turbulence Model for 

Atmospheric Circulation (HOTMAC) developed by Yamada. Recently, the 

forecast skill of the BFM was compared with that of the Fifth-Generation 

National Center Atmospheric Research/Penn State Mesoscale Model (MM5), 

by applying the models to the domain of White Sands Missile Range 

(WSMR), NM, which covers an area size of 167 x 167 km (51 x51 grid points 

with grid spacing of 3.33 km). Meteorological parameters forecasted by the 

models were compared with observed data. The comparison study showed 

that the forecast skills of the BFM are comparable to those of the 

MM5-surface temperature forecasted by both the BFM and the MM5 agreed 

well with observed values. Both the BFM and the MM5 showed difficulties 

forecasting the relative humidity. For wind parameters, both models tend to 

predict wind speed less than observation, but the BFM calculations produce 

smaller wind speed than the MM5. For wind-direction forecast, the BFM 

resulted in better forecast than the MM5. [l] 

The BFM in operational mode on the IMETS has been extensively used over 

the model domain of 500 x 500 km with grid spacing of 10 km. So far there 

has been no evaluation study of the BFM in operational mode, applying the 

model for an extended period and comparing model forecasted parameters 

with observations. In this study, the BFM was applied for 30-day periods to 

three different model domains, each having an area size of 500 x 500 km with 

51 x 51 grid points and 10 km grid spacing. 

The first purpose of the present report is to describe the results of evaluation 

studies of BFM forecast skill over three different areas with different terrain 

complexities and climates (Colorado, Washington, and Florida). In this 

study, the model calculations were initialized with three different sets of 

initial conditions of varying completeness, and model values were 

statistically compared with observed data. The second purpose is to 

compare the statistics of BFM forecast data to those of other models, such as 

the Navy Operational Global Atmospheric Prediction System (NOGAPS) 

and the Navy Operational Regional Atmospheric Prediction System 

(NORAPS). [2,3,4] 

9 
2.0 Model Domains 

The following three model domains were chosen for the present study: 

1. Colorado, centered at 38.8”N and 104.7”W, 

2. Washington, centered at 46.8”N and 121.5”W, and 

3. Florida, centered at 28.O”N and 8LO”W. 

Each model domain covers 500 x 500 km area with horizontal grid spacing 

of 10 km and the horizontal grid number of 51 x 51. The vertical depth of 

model is 7 km above the highest point of the model domain with 16 vertical 

layers. 

The complexities of the three model domains are summarized in table 1. 

Table 1. Terrain comulexities of the three model domains 

Mean elevation (m) 

Colorado Washington 

2022 590 

Florida 

7 

Max. height (m) 4053 4105 69 

Min. height (m) 

Standard Dev. (m) 

1001 P 0 

722 526 12 

As seen in table 1, Colorado and Washington model domains are complex 

compared to the Florida model domain. Figures 1,2, and 3 are the elevation 

contour maps of, respectively, Colorado, Washington, and Florida model 

domains. In these figures, the outside squares cover the NOGAPS analysis 

and data composite area of 800 x 800 km, and the inside square covers the 

model domain of 500 x 500 km. In these figures, the characters N, U, and S 

represent the locations of the input data used for initialization of the model 

calculation: 

N - NOGAPS data, 

U - upper-air sounding data, and 

S - surface observation data. 

11 
Figure 1. Elevation contours of the Colorado model domain. 

Note: The outside area covers 800 x 800 km, and the inside area 

covers 500 x 500 km. 

N = NOGAPS data points, 

U = upper-air sounding locations, and 

S = surface observation locations. 

c 

12 
I = 

Figure 2. Elevation contours of the Washington model domain. 

Note: The outside area covers 800 x 800 km, and the inside area covers 500 x 

500 km. 

N = NOGAPS data points, 

U = upper-air sounding locations, and 

S = surface observation locations. 

13 
N N N .( N N 

N 

N 

N 

N 

c 

N 

^., .--_ -2 /- 

N 

Figure 3. Elevation contours of the Florida model domain. 

c 

Y 

Note: The outside area covers 800 x 800 km, and the inside area covers 500 x 

500 km. 

N = NOGAPS data points, 

U = upper-air sounding locations, and 

S = surface observation locations. 

c 

14 
3.0 Method 

3.1 Data Used 

The following data sets were obtained from the period of June 23 to 

July 22, 1998 from the indicated Internet home pages. NOGAPS and 

NORAPS data used for this study were obtained through the Internet home 

page of the Master Environmental Library (MEL) of the Defense Modeling 

and Simulation Office (DMSO) at: htttx / /mel.dmso.mil. 

NOGAPS forecast data were available at every 1 o grid points over the entire 

earth, and NORAPS forecast data were available at every 0.5 ’ grid points 

over the North America region. NOGAPS data we.re used for initialization 

and time-dependent lateral boundary conditions of BFM and, for comparison 

with BFM forecast data. The forecast data for the periods of 12,24, and 36 h, 

initialized at 0000 UTC, were obtained at 13 pressure levels from 1000 mb to 

100 mb. NORAPS data were used for statistical comparison with the BFM. 

Upper-air sounding data are obtained from the University of Wyoming at the 

following Internet home page: http:/ /www-das.uwvo.edu/uuuerair. 

Upper-air data observed at 1200 UTC were used for this study. Surface 

observation data were obtained from Ohio State University at the following 

Internet home page: http: / /rreoerafl.sbs.ohio-state.edu. The data are 

archived for 00,03,09,12,15,18, and 21 UTC. 

3.2 Initialization Methods 

For all three model domains, BFM is initialized at 12 UTC with three 

different data sets as mentioned below, and forecasted data for 24-h period 

are compared with surface observation data: 

(1) NOGAPS + upper air + surface data, 

(2) NOGAPS + upper air data, and 

(3) NOGAPS data only. 

Details of the initialization method of BFM are described in reference 1. 

Briefly, for the three’data sets, the 1” NOGAPS data valid for the forecast 

periods of 0,12, and 24 h are spatially interpolated for the outside square 

areas of 800 x 800 km (see figures 1,2, and 3). For initializations (1) and (2), 

the interpolated data from NOGAPS for the period of 0 h are cornposited 

with upper-air sounding data. For initialization (l), surface data of wind 

vector components, temperature, and mixing ratio at the O-h period are 

15 
3.3 

3.3.1 

assimilated into the model field by a nudging method. The nudging is done 

for 3 h from T,-3 to T,, where T,is the start of the forecast period. For the 

present study, T, is 12 UTC for all three model domains. [1,2] 

Statistical Parameters 

The following verification statistics were calculated using the data for the 30 

day period between June 23 and July 22, 1999. Temperature, dew point 

temperature, wind speed, and vector components at the surface were 

compared by using these parameters at the forecast periods of 0,3,6,9,12, 

15,21, and 24 h. 

Mean Difference 

n 

2X( ‘p,i,j - xo,i,j ) 

MD = j=’ j=l 

m-n 

(1) 

Where 

X = arbitrary meteorological parameter, 

0 = observation, 

P = prediction, 

i = P surface station, 

j = j” forecast day, 

n = surface stations, 

m = total number of forecast days, and 

Nonzero = MD indicates bias. 

For instance, if the mean difference (MD) is positive, it indicates that the 

model tends to overforecast. 

3.32 Mean Absolute Difference 

(2) 

m n 

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

AD = j=l i=l 

m-n 

16 
( 

k 

* 

3.3.3 Root Mean Square Error 

Because the absolute value of the difference is used, good agreements 

between observation and forecast are, in general, related to small values of 

absolute difference (AD). 

4’ 

n 

2X( xo,i, j - ‘p,i, j 1’ 

rmse = 

j=l i=l 

men 

(3) 

Usually, the values of root mean square error (RMSE) is proportional to those 

of AD. [2] 

3.3.4 Root Mean Square Vector Error 

i 

m ” 

cc[( ‘o,i. j - ‘p,i,j)* + (‘o,i,j - ‘p,i,j,‘] 

(4) 

RMsyE = j=l i=l 

m-n 

This parameter measures the difference of both the n-s and e-w components 

of the wind vector giving a result that effectively combines wind speed and 

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

values of the root mean square vector error (RMSVE). 

3.3.5 Correlation Coefficient 

cc=j* (5) 

Here Y,,j,j = x,,~,~ - X, 1 and Yp,i,j 

-- - 

= xp,i,j - xp . x, and x, are the 

means of observed and forecast values, respectively. 

17 
4.0 Results 

4.1 Scatter Diagrams 

The forecast data generated by the BFh4 runs using the NOGAPS + upper-air 

data (2) are used for the plotting. Scatter diagrams obtained for the model 

domains of Colorado, Washington, and Florida are shown in figures 4,5, and 

6, respectively. In these figures, (a) is for temperature, (b) for dew point 

temperature, (c) for wind vector components u and v, and (d) for wind 

speed. The correlation coefficients between forecasted and observed data are 

summarize in table 2. 

Table 2. Correlation coefficients between forecast and observation for three 

model domains of Colorado, Washington, and Florida 

Colorado Washington Florida 

Temperature 0.83 0.77 0.65 

Dew point temp. 0.78 0.44 0.26 

Wind speed 0.62 0.48 0.59 

U 0.38 0.18 0.27 

V 0.34 0.32 0.33 

I .,a , ., I 5 1L 1, ZI 2, 30 32 

Observation 

Figure 4a. Scatter diagram of model ‘calculation versus observation of 

temperature, Colorado model domain. 

19 
jDew Pt. Tip. ICI 

Observation 

Figure 4b. Scatter diagram of model calculation vs. dew point temperature, 

Colorado model domain. 

Observation 

. 

I , I I 1 

v IfI/ 

. 

. . 

. 

. 

. 

l . :; ’ 

. 

. 

: 

a+* 

I I I I I 

10 ld 5 lid 

Observation 

Figure 4c. Scatter diagram of model calculation versus wind vector components u (left-hand side) and 

v (right-hand side), Colorado model domain. 

20 
I 

SPA (I++ : : 1 

*I 

t'* f, 

:,: + t 

t + * l 

Observation 

Fig;ure 4d. Scatter diagram of model calculation versus wind speed, 

C&orado model domain, 

, I I 

Temp IC) 

'f, : 

35- 

30- 

25- 

Y 

. t 

L .,i*’ _ * l - 

I I I 

5 10 15 249 25 CM 35 

Observation 

Figure 5a. Scatter diagram of model calculation versus observation of 

temperature, Washington model domain. 

21 
l 

.**+* 

t i: : 

. ii* 

.;:: 

. 

f 

Dew Pt. Temp. ICI 

[: t rr l 

t 

l . 

:: 

1 

l l 

t 

I I I 

110 i4 5 lk4 15 A[0 

Observation 

Figure 5b. Scatter diagram of model calculation vs. dew point temperature, 

W&ington model domain. 

10- 

. 

5- 1.4 r 

.w 

: 

[+.,..+..-' 

: 0 

I".. *' .* : 

5" 

‘ . . . . . 

2 

t . 

:' 

.-a * 

-5- t ; 

";:'~& ;;: 

. + 

-10 

I 

I I I I 

10 0 , 10 

Observation 

10- 

-5- 

Figure 5c. Scatter diagram of model calculation versus wind vector components, u (left-hand side) 

and v (right-hand side), Washington model domain. 

. 

22 
I I 

SPEED (M/S1 

+ 

I! 

* i 

c 

l 

f 

I 

+ 

lid 

Observation 

Figure 5d. Scatter diagram of model calculation versus wind speed, 

W&hington model domain. 

40- + t 

if 

I I I 

Temp IC) 

* . 

35- 

30- 

25- 

I I I 

13 20 25 .w 35 416 43 

Observat ion 

Figure ‘6a. Scatter diagram of model calculation versus observation of 

temperature, Florida model domain. 

23 
Dew Pt. Temp. ICI 

35 

30- 

25- 

-P 

ul 

6 

; 20- 

L 

LE 

15- 

10- 

5- 

Figure 6b. Scatter diagram of model calculation versus dew point 

temperature, Horida model domain. 

Observation 

I I I I I I 

II 2 !a 5 II 4 

Observation 

Figure 6c. Scatter diagram of model calculationversus wind vector components u (left-hand side) and 

v (right-hand side), Florida model domain. 

. 

. 

24 
I 

SPEED (N/S1 

5- * 

:f 

* i 

+ 

1 1 

l 

+ 

10 lObservation 

Figure 6d. Scatter diagram of model calculation versus wind speed, Florida 

model domain. 

The ranges of variation of temperature and dew point temperature for the 

Florida domain are the narrowest among the three domains, reflecting the 

maritime climate, and those for the Colorado domains are the largest, 

reflecting the continental climate. 

For temperature and dew point temperature, the correlation coefficients for 

the Colorado model domain are the largest, and those for the Washington 

domain follow. It is encouraging to see that the highest correlation values 

for temperature and dew point temperature are produced for the Colorado 

model domain by the BFM despite the large range of values. 

For wind vector components u and v, and speed, the correlation coefficients 

are smaller than those for temperature and dew point temperature. The 

wind speed scatter diagram for the Colorado domain is the most scattered 

of the three domains, again reflecting the complex terrain. On the other 

hand, the wind speed scatter diagram for the Florida domain shows much 

less scattering distribution than the diagrams for the other two domains. 

25 
4.2 Time Series of the Statistical Parameters 

MD, AD, RMSE, and RMSVE are calculated at the forecast periods of 0,3,6,9, 

12,15,21 and 24 h. Time 0 h corresponds to 12 UTC. Forecast calculations are 

performed three times with three different initial conditions. In figures 7 

through 9, three curves are drawn: 

K=l represents the statistics of the BFM initialized with NOGAPS + upper air 

+ surface data, 

K=2 represents those initialized with NOGAPS + upper-air data, and 

K=3 represents those initialized with NOGAPS data only. 

In the figures, only MD and AD for all the meteorological variables, and 

RMSVEs for wind vector are shown in the figures. The RMSEs are calculated 

but are not shown in this report, because the RMSE usually varies 

proportionally to the AD. 

4.2.1 Tempera fure 

Figures 7, 8, and 9 are the MD and AD of temperature for Colorado, 

Washington, and Florida, respectively. It is clear from these three figures that 

the BFM initialized with NOGAPS + upper-air data (K=l and K=2) produced 

better forecast than that with NOGAPS data only (K=3). 

For the Colorado model domain, the initial data without upper-air data 

produced worse temperature forecast than those with upper-air data. The 

effect of using the surface data is seen at the forecast periods between 0 and 

3 h. For very short-term forecast up to 3 h, the nudging of the surface data 

produces improved forecasts of surface temperature. After 3 h, the curves 

with K=l and K=2 are very similar, implying that the effect of surface 

temperature nudging becomes insignificant. The curves of MD indicate that 

during daytime hours the temperatures forecasted by the BFM are lower than 

observed over the Colorado model domain. 

For the Washington model domain, the use of upper-air data for the initial 

condition produced better temperature forecasts, but the influence of upper-air 

data is not as significant as over the Colorado model domain. The effect of 

surface temperature nudging in initialization is seen again in the first 3 h of 

forecast. There are no significant differences between the three MD curves, 

and the values of all three curves for K=l, 2, and 3 stayed around 0” C, 

indicating there is no significant bias. For the Florida model domain, similar 

to the results from the Colorado and Washington domains, the initial condition 

without upper-air data produced inferior forecast of temperature to those with 

upper-air data included. MD curves show that the surface temperature over 

26 
the land of the Florida model domain was underpredicted during daytime 

hours and overpredicted during nighttime hours. The amplitude of daily 

temperature cycle forecasted by the BFM was not large enough to match that 

observed. 

. 

Temperature (CI 

I I I I I I I I I I I I I I I I I I I I I I I I I 

8.0- MD 

6.0- 

4.0- 

2.0- \ 

-4.0- \ 

-6.0- \ K=3- - 

- ~Scc-3’ 

-8.0- 

I I I I I I I I I I I I I I I I I I I I I I I I I 

I I I I I I I I I I I I I I I I I I I I I I I I I 

7.0- AD 

I I I I I I I I I I I I I I I I I I I I I I I I I 

0 2 4 6 8 10 12 14 16 18 20 22 24 

Forecast time [hrs.l 

Figure 7. Time series of MD and AD of temperature, Colorado model domain. 

Note: 

K=l, for initialization with NOGAPS + upper air + surface data, 

K=2, for initialization with NOGAPS + upper-air data, and 

K=3, for initialization with NOGAPS data. 

27 
Temperature IC) 

I I I I I I I I I I I I I I I I I I I I I I I I I 

8.0- MD 

6.0- 

4.0- 

? 

-2.0 

-4.0 

-6.0 

-8.0 

tlllll”llll’llll’lIII”‘II I I I I I I I I I I I I I I I I I I I I I I I I 

7.0- AD 

6.0- 

5.0- 

II 

0 2 4 6 8 10 12 14 16 18 20 22 24 

Forecast time Ihrs. ) 

Figure 8. Time series of MD and AD of temperature, Washington model domain. 

Note: 

K=l, for initialization with NOGAPS + upper air + surface data, 

K=2, for initialization with NOGAPS + upper-air data, and 

K=3, for initialization with NOGAPS data. 

. 

28 
Temperature ICI 

L”“““““““““““‘4 

8.0 MD 

6.0 

4.0 

-6.0 

’ I I I I I I I I I I I I I I I ’ I I I I”” 

III1 I I’ ‘1 ‘I “1 ‘I ‘I 1’ ” “1 

7.0 

I 

AD 

1 

6.0 

5.0 

I”“““““““““““” 

0 2 4 6 8 10 12 14 16 18 20 22 24 

Forecast time lhrs.1 

Figure 9. Time series of MD and AD of temperature, Florida model domain 

Note: 

K=l, for initialization with NQGAPS + upper air + surface data, 

K=Z, for initialization with NOGAPS + upper-air data, and 

K=3, for initialization with NOGAPS data. 

29 
4.2.2 Dew Point Temperature 

Figures 10,11, and 12 are the plotting of MD and AD time series for the three 

model domains. For the Colorado model domain (figure lo), the model 

forecast of dew point temperature averages more than 3” off from the 

observations. Among the three model domains, the AD values for the 

Colorado model domain are the largest. For daylight hours, the model 

forecasted the dew point temperature lower than observed. This is particularly 

significant with K=l and 2, which represent the initial conditions with upperair data. It is not clear why the initial conditions with upper-air data generated 

inferior forecasts of dew point temperature to those only with NOGAPS data. 

Dew Pt TempICl 

” ‘MD’ ” ” ” ” ” ” ” ” ’ “I’ 

3.0- 

2.0- 

1.0- 

0.0- ---+3--L- K=3-- 

I I I I I I I I I I I I I I I I I I I I I I I I I 

I I I I I I I I I I I I I I I I I I I I I I I I I 

AD 

6.0 t 1 

I"""-' I 'I"' I I "I""' III 

0 2 4 6 8 10 12 14 16 18 20 22 24 

Forecast time 1hrs.l 

Figure 10. Time series of MD and AD of dew point temperature, Colorado model 

domain. 

30 
Dew Pt Temp[Cl 

” ‘MD’ ” ” ” ” ” ” ” ” ” ’ ” 

0.01 /- 

/ ‘fg- - - K=3. 

I I I I I I I I I I I I I I I I I I I I I I I I I 

I I I I I I I I I I I I I I I Ii I I I I I I I I 

AD 

I I I I I I I I I I I I I I I I I I I I I I I I I I 

0 2 4 6 ‘3 10 12 14 16 1'3 20 22 24 

Forecast time Ihrs. 1 

Figure 11. Time series of MD and AD of dew point temperature, Washington model 

domain. 

For the Washington model domain, the model generated lower dew point 

temperatures than observed throughout 24-h forecast period. Similar to the 

Colorado model domain, the initial conditions with upper-air data produced 

lower dew point temperatures than those without upper-air data. 

31 
Dew Pt TempICl 

” ‘MD’ ““““““““““‘I 

2.0- 

-2.0- 

-3,0- 

I I I I I I I I I I I I I I ’ , , ’ , ” ‘I,’ 

I I I I I I I I I I I I, , , , , ‘1, ( , ( , , 

AD 

Figure 12. Time series of MD and AD of dew point temperature, Florida model 

domain. 

For the Florida model domain, the model produced greater dew point 

temperatures than observed throughout the 24-h forecast period, except for the 

0 and 3-h periods. The AD values are the smallest among the three model 

domains throughout the 24-h forecast period. There are no significant 

differences among the three forecasts with different initial conditions. 

From the inspection of the moisture values of NOGAPS data, which is used for 

initial and boundary values of the BFM, it is suspected that NOGAPS tends to 

forecast drier atmosphere than observed. Therefore, the dew point 

temperatures by the NOGAPS are compared to the observed values. 

Tables 3,4, and 5 show MD and AD between NOGAPS data and observations 

for dew point temperature, respectively, for the Colorado, Washington, and 

I I I I I I I I I I I I I I I I “I “‘I,’ 

0 2 4 6 8 10 12 14 16 18 20 22 24 

Forecast time Ihrs.) 

P 

. 

32 
Florida model domains. The NOGAPS data used for the initialization and 

boundary conditions of the BFM at 0, 12, and 24 h are compared with the 

surface data. 

Table 3. MD and AD at forecast periods of 0,12, and 24 h between NOGAPS data 

and observation, Colorado model domain 

Forecast hours AD 

Oh -2.2 3.6 

12h -1.2 3.5 

24h -1 3.5 

Table 4. MD and AD at forecast periods of 0,12, and 24 h between NOGAPS data 

and observation for Washington model domain 

Forecast hours AD 

Oh -0.9 1.8 

12h -0.2 2 

24h -1.3 2 

Table 5. MD and AD at forecast periods of 0,12, and 24 h between NOGAPS data 

and observation for Florida model domain 

Forecast hours AD 

Oh -0.7 1.2 

12h 0 1.2 

24h 0.8 1.3 

From these tables, the MD indicates that dew point temperature fields of 

NOGAPS are lower than those observed throughout the 24-h forecast periods 

over the Colorado and Washington domains. For the Florida domain, they are 

lower than observed in the first 12 h. However, the magnitudes of the MD and 

AD between NOGAPS and observation at 0 h given in tables 3 and 4 are smaller 

than those shown in figures 10 and 11. Therefore, it is suspected that in the 

Colorado and Washington domains, the BFM’s prognostic calculations might 

have numerically produced lower dew point temperatures than observed 

during the early stages of forecast periods. 
Figures 13,14, and 15 are time series of MD and AD of wind speed and RMSVE 

respectively, for Colorado, Washington, and Florida. 

Wind speed (M/S1 I I I I I I,, I,, , , , , , , , , , , , I I ’ 4.w- MD 

3.0- 

2.0- 

‘1 .0- 

0.0- _c - - #:3- - - lc=a--- __ -, K-3 

-(c-a- - 

-1.0- 

-2.0- 

-3.0- 

-4.8- 

I I I I I I,, I,, , ( , , , , , , , 

, I , # 

I I1 I , I I , , , , , , ( , 1 , , , , I I I I 

AD 

2.0- 

1 .m- 

/k-e3 ------ 

I I I I, I,, , , * , , , , , , ( , , I I I I 

RMS VECTOR ERROR(M/SI 

I I I I, I,, , , , ) , , , , , , , , , , , I 

RMSVE 

4.0- 

1.0- 

I I I I I ,, , I,, a,, , , , , * , , I I I 

0 2 4 6 B 10 12 16 18 20 22 24 

FOreC;aS t timelfhrs.) 

Figure 13. Time series of MD and AD of wind speed (top), and RMSW (bottom), 

Colorado model domain. 

c 

34 
2.0 

1.0 

I--. 

-- 

_--- 

-- 

-?-RzS - 

u-3 - 

RMS VECTOR ERROR(M/SI 

I ! I I I I I I I I I I I I,, I I I, I I I II 

RMSVE 

4.0- 

3.0- 

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

Forecast tlme [hi-s. I 

Figure 14. Time series of MD and AD of wind speed (top) and RMSVE (bottom), 

Washington model domain. 

35 
4 

3 

2 

1 

0 

-1 

-2 

-3 

-4 

2 

1 

.0 

.0 

.0- 

0- 

.0 

.0 

.0 

.0- 

.0 

RMS VECTOR ERROR(M/Sl. 

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

Forecast tlme Ihrs. > 

Figure 15. Time series of MD and AD of wind speed (top) and RMSVE bottom), Florida 

model domain. 

L 

36 
4.2.3 Wind Speed and Wind Vector 

Unlike temperature and dew point temperature, there are no significant 

differences in MD, AD and RMSVE among the three different initialization 

methods for all three model domains. The values of MD are between 0 and 

1 m/s for the three domains throughout the 24-h forecast period, implying that 

the BFM calculates the wind speeds slightly greater than those of observations. 

For all three domains, AD values are also between 0 and 1 m/set. For wind 

speed forecasts, there are no significant differences among three different model 

domains. The AD values less than 1 m/set are well within the weather forecast 

data criteria used by the U.S. Air Force, which requires that for observed wind 

speed less than 10 m/set, AD is to be within 1 m/s, and for observed wind 

speed greater than 10 m/set, AD is to be smaller than 2.5 m/set. [5] 

For wind vector forecasts, the RMSVE values for the Colorado model domain 

shows the greatest values among the three model domains, and those for the 

Washington model domain show the smallest. 

In the comparison and evaluation study of the MM5 and the BFM over the 

WSMR with unit grid spacing of 3.33 km, the AD values for wind speed are 

between 2 and 3 m/set for both MM5 and BFM. In a similar study over the 

National Training Center area in which BFM was run with unit grid spacing of 

2.5 km, the AD values for wind speed were also between 2 and 3 m/set. For 

both studies, the AD for wind speed are significantly greater than those for the 

current three model domains, which were run with unit grid spacing of 10 km. 

At this time, why the current study shows better results in wind speed forecast 

than these two studies is unknown. Further examination is needed to find the 

exact reasons for the differences and a few items to consider that are: 

1. model performances in different grid spacing, 

2. quality of observed data, and 

3. model performances in different seasons. [1,6] 

4.3 Comparison of the BFM with NOGAPS and NORAPS 

The forecast results of the BFM are compared with those given by the NOGAPS 

and the NORAPS. For the NOGAPS and the NORAPS, data are given every 1 o 

and 0.5”, respectively, and are interpolated bilinearly to the observation 

locations. 

37 
4.3.1 Temperature 

Figures 16,17, and 18 show the comparisons of temperature forecasts for the 

Colorado, Washington, and Florida model domains, respectively. The values of 

RMSE and AD are compared in the form of bar diagrams. In these figures, the 

comparisons for the forecast periods of 0,6,12, and 24 h are shown in (a), (b), (c), 

and (d), respectively. The bar diagram at the left-hand side is for the RMSE, and 

that at the right-hand side is for the AD. In these figures, three BFM forecasts 

with the different initial conditions (see section 3.2) are shown, marked as BFMl, 

BFM2, and BFM3. 

For all the model domains, the initial temperature fields predicted by the BFM 

are calculated best when the surface observed data are used, but at the forecast 

period of 6 h, the influences of use of surface temperature disappears. For 

Colorado and Washington model domains, where the terrain is more complex 

than Florida, the use of the BFM improves the temperature field forecast. 

However, for Florida model domain, the temperature fields forecast by the BFM 

do not significantly improve the comparison statistics over the forecasts by 

NOGAPS and NORAPS. 

4.3.2 Wind Speed and Vector 

Figures 19, 20, and 21 are the comparisons of wind speed and vectors, 

respectively, for the Colorado, Washington, and Florida model domains. In 

these figures, the RMSE is shown at the left-hand side, the AD at the middle, and 

the RMSVE at the right-hand side. For the Colorado and Washington model 

domains, the BFM calculations produced better wind fields than NOGAPS and 

NORAPS throughout the 24-h forecast period, but for the Florida model domain, 

the BFM does not improve the statistics over NOGAPS and NORAPS. A recent 

study by Dumais clearly shows that the ability of BFM to forecast local diurnal 

winds is largely determined by its horizontal resolution and treatment of the 

local surface feature data. In the present study, the horizontal grid spacing is 10 

km, and constant values for surface albedo and soil heat conductivity are used 

over the land. Rao et al. for realistic simulation of sea breeze circulation, the 

horizontal resolutions of -100 m for terrain elevation, and surface features such 

as soil type and soil moisture are needed. It is, therefore, suspected that the 

present forecast calculations by the BFM for the Florida domain had failed to 

simulate the effects of sea-land breeze circulation. BFM showed better forecast 

skills for predictions of wind fields over complex terrain than NOGAPS and 

NORAPS [7,8] 

38 
I i 

. 

Temperat 

FCT PERIOD = 0 h 

6.0 - 

4.0 - 

2.0 - 

.ure (Cl 

8.0 y$ 

6.0 - 

4.0 - 

2.0 - 

E 

m 

FCT PERIOD = 6 h 

6.0 

4.0 

6.0 

(a> 

(b) 

Figures 16(a) and (b). Comparison of BFM temperature forecast performance to NOGAPS 

and NORAPS, the Colorado model domain. 

Note: (a) 0 h, and (b) 6-h forecast period. Left-hand side: RMSE, right-hand side: AD. 

39 
Temperature CC) 

FCT PERIOD = 12 h 

I I I I I I I I I 

t RIISE OF TEMPICI 

8.0 

6.0 

4.0 

2.0 

II 

N 

i ii 

- 

m 

i 

- 

- 

2 

5 

P 

j j- ABOF OF TEtlPlC) 

-I 8.0 t 

b.O 

4.0 

2.0 

N m 

E E E 

m m m 

FCT PERIOD = 24 h 

I I I I 

- Rt!SE OF TEMPtCl 

8.0 - 8.0 

6.0 - b.O 

4.0 - 4.0 

2.0 - 2.0 

- 

I I I I 

- ABDF OF TEtlPfC) 

(4 

(4 

Figures 16(c) and (d). Comparison of BFM temperature forecast performance to 

NOGAPS and NORAPS, the Colorado model domain. 

Note: (c) 12 h, and (d) 24-h forecast period. Left-hand side: RMSE, right-hand side: AD. 

40 
Temperature (C) 

FCT PERIOD = 0 h 

. 

- RtlSE OF TEMPICI 

8.0 - 

6.0 - 

4.0 - 

FCT PERIOD = 6 h 

I I I I 

t 

RHSE OF TEMPICI 

8.0 

t 

6.0 

1 

4.0 

2.0 

ii 

I I I I 

.- ABDF OF TEtlPfC) 

8.0 - 

6.0 - 

4.0 - 

2.0 - 

t ABDF OF TEMP(C) 

8.0 

6.0 

2.0 

Ls 

4.0 

N m 

E E E 

m m q 

(a> 

Figures 17(a) and (b). Comparison of BFM temperature forecast performance to 

NOGAPS qnd NORAPS, Washington model domain. 

Note: (a) 0 h, and (b) 6-h forecast period. Left-hand side: RMSE, right-hand side: AD. 

41 
Temperature 

FCT PERIOD = 12 h 

I I I I 

RHSE OF TEMPIC) 

6.0 

t 

6.0 6.0 - 

4.0 4.0 - 

2.0 2.0 - 

CC) 

I I I I 

ABDF OF TEtlPfC) 

8.0 

FCT PERIOD = 24 h 

I I I I 

- RtlSE OF TEHP[Cl 

8.0 - 

6.0 - 

I I I I 

- ABDF OF TEtlPtC) 

8.0 - 

6.0 - 

4.0 - 4.0 - 

2.0 - 2.0 

(4 

(4 

Figures 17(c) and (d). Comparison of BFM temperature forecast performance to 

NOGAPS and NORAPS, Washington model domain. 

Note: (c) 12 h, and (d) 24-h forecast period. Left-hand side: RMSE, right-hand side: AD. 

. 

, 

42 
Temperature (Cl 

FCT PERIOD = 0 h 

I I I I I f I I I I 

RHSE OF TEMP(CI 

- ABDF OF TEtlPtC) 

8.0 8.0 - 

6.0 - (4 

4.0 - 

I l 

FCT PERIOD = 6 h 

B,Oyj 

6.0 

4.0 

I I I I 

- ABDF OF TEbIPtC) 

8.0 (b) 

b.O 

4.0 

2.0 

Figures 18(a) and (b). Comparison of BFM temperature forecast performance to 

NOGAPS and NORAPS, Florida model domain. 

Note: (a) 0 h, and (b) 6-h forecast period. Left-hand side: RMSk, right-hand side: AD. 

43 
Temperature (C) 

FCT PERIOD = 12 h 

I I I I 

- RHSE OF lEMP(C) 

I I I I 

- ABDF OF TEtlP(C) 

6.0 

4.0 

(4 

FCT PERIOD = 24 h 

I I I I I I I I I 

- RIISE OF TEMPIC) 

8.0 

6.0 

4.0 

2.0 

8.0 

6.0 

4.0 

(4 

E 

m 

Figures 18(c) and (d). Comparison of BFM temperature forecast performance 

NOGAPS and NORAPS, Florida model domain. 

Note: (c) 12 h, and (d) 24-h forecast period. Left-hand side: RMSE, right-hand side: AD 

44 
Wind speed tM/S) 

FCT PERIOD = 0 h 

I I I I 

RtlSE OF WIND SPEEOIWSI / 

RMSE OF WIND VEClORIM/S) 

FCT PERIOD = 0 h 

I ’ 

I I I 1 

4.0 

3.0 

2.0 

1.0 

1 

E 

L m it 

t 

FCT PERIOD = 6 h 

FCT PERIOD = 4 h 

I I I I I 

RHSE OF WIND SPEEDIWS) 

1’ 

4.0 

3.0 

4.0 - 

3.0 - 

2.0 - 

1.0 - 

Figures 19(a) and (b). Comparison of BFM performance to NOGAPS and NORAPS, for wind speed and 

vectors, Colorado model dbmain. 

Note: (a) 0 h, (b) 6-h forecast periods. Left-hand side: Rh4SE of speed, Center: AD of speed, right-hand side: 

RMSVE. 

BFMl: initialized with NOGAPS + upper air + surface data, 

BFM2: initialized with NOGAPS + upper-air data, and 

BFM3: initialized with NOGAPS. 

45 
Wind speed Itl/Sl RMSE OF WIND VECTORlH/S) 

FCT PERIOD = 12 h 

I I I I I 

FCT PERIOD = 12 h 

(,I,,,',,, 

1 I I I 

ABOF OF WIN0 SPEED(H/SI 

4.0 - 

3.0 - 

2.0 - 

1.0 - 

4.0 

3.0 

2.0 

1 .I 

[ 

N 

i 

z 

m 

4.0 

I 

2 iz 4 

(4 

FCT PERIOD = 24 h 

I I I I 

WISE OF WIND SPEED(tl/S) 

FCT PERIOD = 24 h 

I I I I I I I I 

ABLIF OF WIND SPEED(R/SI 

4.0 - 

3.0 - 

2.0 - 

1.0 - 

4.0 4.0 

3.0 

2.0 

1.0 

- 

N 

E 

m 

- 

- 

2 2 

s 

r 

Ill N 0 it 

8 E m 8 2 

Figures 19(c) and (d). Comparison of BFM performance to NOGAPS and NORAPS, for wind speed and 

vectors, Colorado model domain. 

Note: (c) 12 h, (d) 12-h forecast periods. Left-hand side: RMSE of speed, Center: AD of speed, right-hand side: 

Rh4SVE. 

BFMl: initialized with NOGAPS + upper air + surface data, 

BFM2: initialized with NOGAPS + upper-air data, and 

BFM3: initialized with NOGAPS. 

46 
Wfnd speed IM/Sl RHSE OF WIND VECTORlH/S) 

FCT PERIOD = 0 h FCT PERIOD = 0 h 

I I I I I I I I I I I I 

RHSE OF WIND SPEEOIWSI ABOF OF WIND SPEEOtH/Sl 

4.0 - 4.0 - - 4.0- 

3.0 - 3.0 - 

- 3.0- 

2.0 - 2.0 - 2.0 -_- 

1.0 - 1.0 - 

I t1.0 - 

(4 

FCT PERIOD = 6 h 

I I I I 

RICE OF WIND SPEEDIWSI 

4.0 - 

3.0 - 

2.0 - 

1.0 - 

FCT PERIOD = 6 h 

I I I I 

4.0 - 

3.0 - 

2.0 - 

1.0 - 

04 

Figures 20(a) and (b). Comparison of BFM performance to NOGAPS and NORAP!3 for wind speed 

and vectors for Washington model domain. 

Note: (a) 0 h, (b) 6-h forecast periods. L&-hand side: RMSE of speed, Center: AD of speed, right-hand 

side: RhISVE. 

BFMl: initialized with NOGAPS + upper air + surface data, 

BFM2 initialized with NOGAPS + upper-air data, and 

BFM3: initialized with NOGAPS. 

47 
Wind speed ltl/S) 

I 1 I I I I I I _ 

RHSE OF UIND SPEEDIWS) 

I I 

ABDF OF WIND SPEEOIIIISI 

4.0 - 4.0 4.0 - 

3.0 

2.8 

1 .m 

- 2.0 - 

3.0 

1.0 - 1.0 - 

I 

FCT PERIOD = 12 h 

FCT PERIOD = 24 h 

1 I , I 

R”SE OF YlND SPEEDlWSl 

RMSE OF WIND VECTORlh/S) 

FCT PERIOD = 12 h 

I I I I 

FCT PERIOD = 24 h 

I I I I 

4.0 

3.0 

2.8 

1 .e 

IllId 

(4 

(4 

Figures 20(c) and (d). Comparison of BFM performance to NOGAF’S and NORAPS for wind speed 

and vectors, Washington model domain. 

Note: (c) 12 h, (d) 12-h forecast periods. Left-hand side: FU4SE of speed, Center: AD of speed, right-hand 

side: FMSVE. 

BFMl: initialized with NOGAPS +upper ,air + surface data, 

BFM2: initialized with NOGAPS + upper-air data, and 

BFM3: initialized with NOGAPS. 

48 
Wind speed Itl/Sl 

FCT PERIOD = 0 h 

RtlSE OF WIND VECTORltl/S) 

I 8 I I 

Rl!SE OF HIND SPEEDIWS) 

FCT PERIOD = 6 h 

I ’ I , I 1 

RHSE OF WIND SPEEOIWS) 

4.0 

FCT PERIOD = 0 h 

ABDF OF VINtl SPEEO(ll151 

4.0 4.8 

3.0 

2.0 

1.0 

I 

I I I I 

ABDF Gf VINO SPEEDlWSl 

4.8 - 

3.0 - 

2.0 - 

FCT PERIOD = 6 h 

r---l 

(4 

04 

Figures 21(a) and (b). Comparison of BFM performance to NOGAPS and NORAPS for wind speed 

and vectors, Florida model domain. 

Note: (a) 0 h, (b) 6-h forecast periods. 

side: RMSVE. 

Left-hand side: FUSE of speed, Center: AD of speed, right-hand 

BFMl: initialized with NOGAPS +upper,air + surface data, 

BFM2: initialized with NOGAPS + upper-air data, and 

BFM3: initialized with NOGAPS. 

49 
Wrnd speed lH/Sl RMSE OF WIND VECTORlM/S) 

FCT PERIOD = 12 h FCT PERIOD = 12 h 

I I I I 

RHSE OF WIND SPEEDIWS) 

4.0 - 

3.0 - 

2.0 - 

1.0 - 

FCT PERIOD = 24 h 

I I I I I 

RHSE OF WIND SPEED(tl/S) 

4.0 

3.0 

2.0 i I 

I I I I 

ABDF OF WIND SPEED(lt/SI 

4.0 - 

3.0 - 

2.0 - 

1.0 - 

I 

4.0 - 

3.0 - 

2.0 - 

1.0 - 

I 

I I I I 

4.0 - 

3.0 - 

2.0 - 

1.0 - 

FCT PERIOD = 24 h 

I I 1 I 

4.0 

t 

3.0 

2.0 

1.0 

Kll 

Figures 21(c) and (d). Comparison of BFM performance to NodAPS and NORAPS, wind speed 

and vectors, Florida model domain. 

Note: (c) 12 h, (d) 12-h forecast periods. Left-hand side: RMSE of speed, Center: AD of speed, right-hand 

side: RMSVE. 

BFMl: initialized with NOGAPS +upper air + surface data, 

BFM2: initialized with NOGAPS + upper-air data, and 

BFM3: initialized with NOGAF’S. 

50 
5.0 Summary 

BFM forecast calculations were made over the different climatological and 

topographical model domains of Colorado, Washington, and Florida. 

Forecast calculations were made for a 30-day period from June 23 to July 

22, 1998. Three different initial conditions were generated from the 

combination of: 

(1) NOGAPS + upper air + surface observation data, 

(2) NOGAPS + upper air, and 

(3) NOGAPS only. 

1. Scatter diagrams of forecast versus observed data showed the forecast 

data of temperature, dew point temperature, and wind speed and vector 

components u and v over the Colorado model domain correlated better with 

observations than those over Washington and Florida. The reasons why the 

Florida model domain did not produce better correlations than the other two 

model domains are not obvious. The Florida domain is much flatter than the 

Colorado and Washington domains, which implies simpler forecast problem. 

On the other hand, the Florida domain is surrounded by the sea. In the BFM 

on the IMETS, the sea surface temperature distribution is provided by the 

monthly average sea surface temperature data over the globe obtained from 

the U. S. Air Force Combat Climatological Center in 1994. To improve the 

temperature and dew point temperature distributions over the model 

domains surrounded by the sea, the daily sea surface temperature 

distribution may be needed. In the near future, the earth surface skin 

temperature by NOGAPS, which provides daily sea surface temperature 

based on satellite and buoy observations, will be incorporated in the BFM 

operational on the JMETS system. 

2. For all three model domains, the forecast temperature fields initialized 

with (1) and (2) were statistically better than those initialized with (3). For 

the Colorado and Florida domains, the model produced surface 

temperatures lower/higher than observation during daytime/nighttime, 

indicating the daily amplitudes of temperature predicted by the model were 

smaller than what actually occurred. For the Washington model domain, no 

significant bias of temperature was found. The AD values for the 24-h 

forecast period for all three domains varied between 2” and 3” C for the 

BFM calculations using the initial conditions of (1) and (2). Forecast data 

accuracy criteria used by the U.S. Air Force for temperature is 2” C. 

51 
Therefore, the BFM produced slightly worse temperature values than 

those required by the U.S. Air Force criteria. [l] 

3. For the Colorado and Washington domains, MD values of dew point 

temperature were negative during most of 24-h forecast periods. For the 

Colorado domain, this was more significant when the model was initialized 

with (1) and (2) in the above than when initialized with (3). The initial fields 

created by compositing NOGAPS data with upper-air data might have 

caused this numerical drying. Further studies are needed to find the exact 

reasons for this shortcoming of the BFM. For the Florida model domain, on 

the other hand, the values of MD for dew point temperature stayed positive 

throughout the 24-h period. 

4. For wind fields, the three different data initializations combinations did 

not produce significantly different BFM wind fields throughout the 24-h 

forecast periods. There was little difference in the values of MD, AD and 

RMSVE among three different initialization methods for all three model 

domains. The values of MD for wind speed are between 0 and 1 m/set, and 

the values of AD are also between 0 and 1 m/set for the three model 

domains throughout 24-h forecast period. For wind vector forecasts, the 

values of RMSVE for Colorado domain were the greatest among the three 

model domains, and those for the Washington domains were the smallest. 

5. For the Colorado and Washington model domains where the terrain is 

more complex than Florida, the use of the BFM improves temperature field 

forecast over those of NOGAPS and NORAPS. For the Florida model 

domain, there was not significant improvement in the temperature field 

forecast by the BFM over those of NOGAPS and NORAPS. 

6. BFM calculations produced better wind fields than NOGAPS and 

NORAPS throughout the 24-h forecast periods for the Colorado and 

Washington model domains, but the BFM for the Florida domain, did not 

produce improved wind fields over those by NOGAPS and NORAPS. For 

the Florida domain, horizontal grid resolution of 10 km might have been too 

coarse to simulate sea and land breeze circulation. 

Although the present study has shown some limitations of the BFM, the 

BFM is a valuable and useful tool for the short-range weather forecast over 

complex terrains when used with initial and time-dependent lateral values 

provided by a global scale forecast model such as NOGAPS and other 

available meteorological data. 

52 
References 

1. 

2. 

3. 

4. 

5. 

6. 

7. 

8. 

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

with Strong Wind and Temperature Gradients,” Journal of Applied Meteorology, 28, 

pp. 545-554,1989. (UNCLASSIFIED) 

Henmi, T. and R. Dumais, Jr., The BattiescaleForecastModel (BFM), ARL-TR-1032, U.S. 

Army Research Laboratory, WSMR, NM, 1998. (UNCLASSIFIED) 

Haines, P. A., A. J. Blanco, S. A. Lutes, et al., Meteorological Data Processing Methods 

in the Computer-Assisted Artillery Meteorology System (Ba ttlescale Forecast Model), 

ARL-TR-559, U.S. Army Research Laboratory, WSMR, NM, 1997. (UNCLASSIFIED) 

Henmi, T., Comparison and Evaluation of Operational Ihesoscale Models MM5 and 

BFM Over WSMR, ARL-TR-1476, Technical Report, U.S. Army Research Laboratory 

WSMR, NM 1999. (UNCLASSIFIED) 

Cox, R., and B. L. Bauer, and T. Smith, “A Mesoscale Model Intercomparison,” Bulletin 

of American Meteorological Society, 79, pp. 265-283,199s. (UNCLASSIFIED) 

Henmi, T. and R. E. Dumais, Jr., The Battlescale Forecast Model (I3FM~ During the 

TFXXI at Fort Irwin, CA: Statistical Evaluation of 24-h Forecast Fields and Model 

Improvement, ARL-TR-1685, U.S. Army Research Laboratory, WSMR, NM, 1998. 

(UNCLASSIFIED) 

Dumais, R. E, Jr., “Simulating Surface Winds near the Great Salt Lake Using the United 

States Army Battlescale Forecast Model,” in Proceedings of Remote Sensing and 

Hydrology 2000, Santa Fe, NM, 2000. (UNCLASSIFIED) 

Rao, P. A, H. E. Fuelberg and K K. Droegemeir,“High-Resolution Modeling of the Cape 

Canaveral Area Land-Water Circulations and Associated Features,“Monthly Weather 

Review, 127, pp. 1808-1821,1999. (UNCLASSIFIED) 

53 
Acronyms 

AD 

ARL 

BFM 

CAAM 

HOTMAC 

IMETS 

MD 

MEL 

NOGAI’S 

NORAPS 

RMSE 

RMSVE 

WSMR 

MM5 

absolute difference 

U.S. Army Research Laboratory 

Battlescale Forecast Model 

Computer Assisted Artillery Meteorology 

Higher Order Turbulence Model for Atmospheric Circulation 

Integrated Meteorological System 

mean difference 

Master Environmental Library 

Navy Operational Global Atmospheric Prediction System 

Navy Operational Regional Atmospheric Prediction System 

root mean square error 

root mean square vector error 

White Sands Missile Range 

Fifth-GenerationNational Center for Atmospheric Research/ 

Penn State Mesoscale Model 

55 
NASA MARSHALL SPACE FLT CTR 

ATMOSPHERIC SCIENCES DIV 

E501 

ATT’N DR FICHTL 

HUNTSVILLE AL 3 5 802 

NASA SPACE FLT CTR 

ATMOSPHERIC SCIENCES DIV 

CODE ED 41 1 

HUNTSVILLE AL 35812 

US ARMY MISSILE CMND 

AMSMI RD AC AD 

ATTN DR PETERSON 

REDSTONE ARSENAL AL 35898-5242 

US ARMY MISSILE CMND 

AMSMI RD AS SS 

ATTN MR H F ANDERSON 

REDSTONE ARSENAL AL 35898-5253 

US ARMY MISSILE CMN’D 

AMSMI RD AS SS 

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AMSMI RD CS R DOC 

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AMSMI 

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PACIFIC MISSILE TEST CTR 

GEOPHYSICS DIV 

ATTN CODE 3250 

POINT MUGU CA 93042-5000 

ATMOSPHERIC PROPAGATION BRANCH 

SPAWARSYSCEN SAN DIEGO D858 

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SAN DIEGO CA 92152-7385 

Distribution 

Copies 

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METEOROLOGIST IN CHARGE 

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NCAR LIBRARY SERIALS 

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Record copy 

TOTAL 

1 

1 

1 

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