Different Initialization Data and the Performance 

by the BFM 

by Jeffrey E. Passner 

ARL-TR-3362 November 2004

Approved for public release; distribution is unlimited.

 
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Army Research Laboratory 

White Sands Missile Range, NM 88002-5501 

ARL-TR-3362 November 2004

Different Initialization Data and the Performance 

by the BFM 

Jeffrey E. Passner 

Computational and Information Sciences Directorate 

Approved for public release; distribution is unlimited.

 
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Different Initialization Data and the Performance by the BFM 

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14. ABSTRACT 

The U.S. Army Research Laboratory supports the forecaster by producing weather forecasts on the Integrated Meteorological System 

(IMETS). On IMETS, a mesoscale model known as the Battlescale Forecast Model (BFM) outputs many weather parameters, including 

temperature, pressure, dew point, relative humidity, and wind speed. Additionally, many other elements, such as icing, turbulence, clouds, 

and visibility, are produced in the post-processor. This report investigates how these model outputs are influenced by the initial data that the 

model incorporates. These influences are studied by evaluating the model’s temperature, wind, moisture, and cloud forecasts from 0 to 12 h 

after model base time. In this study, two different models are used to initialize the BFM: the Pennsylvania State University/ National Center 

for Atmospheric Research Mesoscale Model Version 5 and the Naval Operational Global Atmospheric Prediction System model. The results 

are discussed in this report. 

15. SUBJECT TERMS

BFM, MM5, model initialization 

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Jeffrey E. Passner 

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Standard Form 298 (Rev. 8/98) 

Prescribed by ANSI Std. Z39.18 
Contents 

List of Tables iv

Preface v

Summary 1

1. Introduction 2

2. The BFM 2

3. BFM Initialization and Objective Analysis 3

3.1 Initialization Using NOGAPS Model Data.....................................................................3 

3.2 Initialization Using the MM5 Data .................................................................................9 

4. Statistical Evaluation of Mesoscale Models 11

5. Evaluation of the Different BFM Initialization Methods 12

6. Conclusions and Discussion 18

References 20

List of Acronyms 22

Distribution List 23

iii 
List of Tables 

Table 1. Sample of NOGAPS data received from AFWA. ............................................................5 

Table 2. Sample of MM5 data used to initialize the BFM. ..........................................................11 

Table 3. Surface temperature error for the BFM-NOGAPS and the BFM-MM5. .......................13 

Table 4. Surface dew-point error for the BFM-NOGAPS and the BFM-MM5. ..........................14 

Table 5. Surface wind-direction errors for BFM-NOGAPS and the BFM-MM5. .......................15 

Table 6. Surface wind-speed errors for the BFM-NOGAPS and the BFM-MM5. ......................15 

Table 7. Accuracy of cloud forecasts using the BFM-NOGAPS and the BFM-MM5.................17 

iv 
v 

Preface

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

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

are achieved through the Battlescale Forecast Model (BFM) and the Pennsylvania State 

University/National Center for Atmospheric Research Mesoscale Model Version 5 (MM5), 

which are used for short-term and long-term forecasts, respectively. The BFM forecast 

calculations are made using Naval Operational Global Atmospheric Prediction System 

(NOGAPS) fields, upper-air radiosonde observations, and surface data as initial input. However, 

in this study, the input data was adjusted so that forecasted grid data from the MM5 was used as 

BFM initial input instead of data from the NOGAPS. This report describes the processes used to 

achieve this and the results produced in the temperature, wind, and moisture fields. 
INTENTIONALLY LEFT BLANK. 
1 

Summary 

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

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

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

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

provide forecast information for up to a 48-h period. The BFM develops its forecast based on 

initial data fields received from Naval Operational Global Atmospheric Prediction System 

(NOGAPS) fields data, upper-air sounding data, and surface data. However, to meet other 

requirements, a second version of the BFM was developed that uses the 15-km output of the 

MM5 to initialize the BFM and runs the BFM for a 12-h period. 

This report describes the theoretical and basic principles of the BFM initialization processes and 

how they vary based on different initial data. The influences of these initial data are shown by 

examining the model temperature, wind, moisture, and cloud forecasts from 0 to 12 h after the 

model base time. Overall, the results show that there is little significant statistical variation in 

using either initialization scheme. In fact, the only difference found was in the post-processed 

cloud forecasts, which followed the biases of each model. 
2 

1. Introduction 

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

receives, processes, and disseminates weather data utilized by Air Force weather forecasters in 

support of Army operations. The U.S. Army Research Laboratory (ARL) supports forecasters 

by producing weather products on IMETS, which in turn enables them to make more specific 

and precise battlefield weather forecasts. One product that assists in short-term forecasting (≤ 24 

h) is an operational mesoscale model, the Battlescale Forecast Model (BFM). For longer-term 

data, the Pennsylvania State University/National Center for Atmospheric Research Mesoscale 

Model Version 5 (MM5) provides output to cover periods from 6 to 48 h (1, 2). 

The BFM uses many forecasting parameters, including temperature, pressure, dew point, relative 

humidity, and wind speed, as well as various other parameters in the post-processor that are used 

by Tactical Decisions Aids, such as the Integrated Weather Effects Decisions Aid (3). 

It is interesting to see how these initial data parameters, in a mesoscale model, influence the 

model output and the post-processor results. This report examines these influences by evaluating 

the model temperature, wind, moisture, and cloud forecasts from a 0 to 12-h period after the 

model base time. The results are discussed in section 6. 

2. The BFM 

ARL implemented the Higher Order Turbulence Model for Atmospheric Circulation 

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

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

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

emphasizes boundary-layer physics, and it is platform independent. Currently, the model is run 

to 24 h; however, due to military requirements, it was necessary to add the MM5 to the IMETS 

platform in order to provide forecast grids out to 48 h from the initial forecast time (4, 5). 

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

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

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

and the absence of moist physics or cumulus parameterization routines. However, because of the 

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

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

subsurface levels is solved using the heat conduction equation, while long-wave and short-wave 
3 

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

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

potential temperature; the total water substance mixing ratio; wind speed; wind direction; 

pressure; soil temperature; the turbulence kinetic energy and length scale; and the nonconvective precipitation rate (6, 7). 

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

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

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

Agency (AFWA) via the Air Force Automated Weather Distribution System, is utilized as the 

long-range data that the BFM is nudged toward. The NOGAPS grid points are spaced 0.5o apart, 

both in latitude and longitude, on the mandatory pressure surfaces. Lateral and time-dependent 

boundary conditions (large-scale forcing) are supplied from grid-point data close to the area of 

interest, taken from NOGAPS output valid at analysis and forecast times of interest. 

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

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

6, 9, 12, 15, 18, 21, and 24 h from the base time of the model run and placed into a Gridded 

Meteorological Data Base. 

3. BFM Initialization and Objective Analysis 

Before initializing the forecast, the user must select an area of interest and center point for the 

grid, as well as the base time for the model by using a graphical user interface. The BFM then 

receives surface, upper-air, and numerical model data for objective analysis from the IMETS 

database. These data come from an expanded area, typically 1600 by 1600 km for a 10-km 

resolution, although surface data are received only for a smaller (500-by-500 km) model domain. 

Typically, BFM forecast calculations can be made using some combination of NOGAPS gridded 

forecast fields, upper-air radiosonde observations, and surface sensor observations as initial input 

and time-dependent, lateral boundary condition data. In this study, model runs were done only 

with a complete set of initial data: NOGAPS or MM5 data, upper-air data, and surface input. 

3.1 Initialization Using NOGAPS Model Data 

The NOGAPS model produced by the Naval Research Laboratory is a complete global spectral 

model containing 30 vertical levels to 1 mbar and includes data quality control, tropical cyclone 

bogusing, data analysis and initialization, and a forecast model. The data analysis is a 
4 

multivariate statistical technique patterned after the volume method developed by Lorenc for the 

European Centre for Medium-Range Weather Forecasts (8). 

The analysis is performed on the Gaussian grid of the global spectral forecast model at the 16 

mandatory pressure levels from 1000 to 10 mbar. The NOGAPS is a hybrid system that follows 

the terrain at low levels and constant pressure surfaces at upper levels. The dynamics 

formulation uses vorticity and divergence, virtual potential temperature, specific humidity, and 

terrain pressure as the dynamic variables. The model is central in time with a semi-implicit 

treatment of gravity-wave propagation (9). 

The current physics package includes the bulk-Richardson number dependent, vertical mixing 

scheme; a time-implicit, Louis surface parameterization; gravity wave drag; shallow cumulus 

mixing of moisture, temperature, and winds; the Emanuel cumulus parameterization; convection 

and stratiform cloud parameterization; and solar and long-wave radiation (10-16). 

The NOGAPS forecast calculations are made at the base times of 0000 and 1200 universal time 

coordinated (UTC) each day, with NOGAPS points spaced at a 0.5° latitudinal distance apart on 

the mandatory pressure surfaces. The forecast product is available to IMETS as much as 7 h 

after base time. For example, if the BFM forecast calculation is made at a base time of 

1200 UTC, the 1200 UTC NOGAPS data may not be available until about 1900 UTC, thus the 

previous NOGAPS dataset is used instead (0000 UTC from earlier that day). The NOGAPS data 

used are the 0, 6, 12, 18, 24, and 36-h forecasts for the BFM runs to 24 h from base time. 

The NOGAPS data are post-processed at AFWA to reduce the size of the files such that only the 

mandatory data are received. An example of the NOGAPS data is displayed in table 1. 
5 

Table 1. A sample of NOGAPS data received from AFWA. 

Pressure level 

(mbar) 

Height 

(mean sea level) 

Temperature 

(°K) 

U Component of 

Wind 

(m/sec) 

V Component of 

Wind 

(m/sec) 

Mixing 

Ratio 

(g/kg) 

100.0 16580.1 199.8 1.90 4.00 -2.52 

150.0 14155.2 208.9 3.80 18.0 1.67 

200.0 12358.0 218.9 2.90 19.9 2.63 

250.0 10900.0 228.1 3.0 15.2 2.49 

300.0 9657.5 237.6 5.80 8.7 1.82 

400.0 7588.2 253.7 12.9 0.10 1.14 

500.0 5892.7 265.4 14.0 -2.1 2.27 

700.0 3183.9 282.9 9.5 -4.0 5.02 

850.0 1534.3 293.8 13.0 -3.2 5.65 

925.0 800.1 296.5 7.4 -3.9 7.0 

1000.0 111.1 299.3 2.5 -5.0 6.02 

As seen in table 1, pressure, geopotential height, temperature, wind, and mixing ratio data are 

received for each NOGAPS point. These data must be horizontally interpolated from the 

NOGAPS to the BFM grid points. Eventually, the goal is to run the BFM and produce the 

following parameters on the BFM’s z* coordinate system: 

Θv = virtual potential temperature (°K) 

Qv = water vapor mixing ratio (g/kg) 

U = east-west component of wind (m/s) 

V = north-south component of wind (m/s) 

Pgr = surface pressure distribution (mbar) 

where 

 z* = vertical coordinate used in BFM, and defined as 
6 

g

g

H z

z z

z H −

− =

*

(1)

where 

z = the Cartesian vertical coordinate 

zg = the ground elevation 

H = the material surface top of the BFM in the z* coordinate 

and 

H = the corresponding height in the z coordinate defined by H=h +zgmax, 

where 

zgmax = the maximum value of the terrain elevation in the BFM domain. 

To analyze the NOGAPS data, the water vapor mixing ratio is calculated from the dew-point 

temperature, and U, V, T, Q, and Φ on pressure levels are horizontally interpolated to the BFM 

horizontal grid points, using the following method: 

A first-guess value of the a parameter Ψ at grid point (i,j) is calculated as 

( )

∑

∑















 −

















Ψ −

Ψ =

4 k

r N

exp

4 k

r N

exp

i , j 2

ij ,

N

2

ij ,

N N

 (2) 

where 

Ψ = a parameter 

N = the NOGAPS point 

Exponential function = the Barnes’ weighting function 

rij, N = the normalized distance between a grid point (i,j) and the Nth NOGAPS point 

and 

k = an empirical parameter to determine the shape of the weighting function. 
7 

By using the first-guess values of the four grid points surrounding the Nth NOGAPS point, an 

interpolated value at this point, Ψ’

N, is bilinearly calculated as 

 t1 = Ψ(x,y)+ (x’-x)*[Ψ(x+1,y)-Ψ(x,y)] (3)

 t2 = Ψ(x,y+1)+ (x’

-x)*[Ψ(x+1,y+1)-Ψ(x,y+1)] (4)

Ψ’

N = t1 + (y’

-y)*[t2 – t1] (5)

Here, 

(x,y) = the southwest grid of four grid points surrounding the Nth NOGAPS point located at 

(x’

,y’). 

The difference between the NOGAPS value and the ΨN and Ψ’

N is, ∆N = ΨN – Ψ’N is now 

distributed to the BFM grid points as 

∑

∑

















γ

















γ

∆ −

∆ =

4

r

exp

4

r

exp

(i, j)

ij , N

2

N

ij , N

N N

2

(6)

where 

γ = an empirical weight reduction factor (0.2). 

A final interpolated value of Ψ at (i,j) is 

Ψf (i,j) = Ψ(i,j) + ∆ (i,j) (7) 

Once these NOGAPS data have been horizontally interpolated to the BFM grid points, they must 

be interpolated in the vertical so that parameter Ψ on pressure surfaces is linearly interpolated to 

z

*

 levels of the BFM as 

( ) ( st k

k 1 k

k 1 k

k

* z z − φ φ − φ ) Ψ − Ψ Ψ = Ψ +

+

+ (8)

where 
8 

zst = the Cartesian height above sea level of z*

, calculated from eq 1 as 

H

H z z

z z z

* gmax g

st g

+ − = + (9)

The radiosonde data is interpolated to BFM grid-point locations using an inverse-distance 

weighting function, then the data is reinterpolated vertically from the Cartesian surfaces to the 

model, the terrain-following coordinate surfaces. Prior to the three-dimensional (3-D) objective 

analysis, a quality control program checks the upper-air data for errors in the geopotential height 

values, the extreme temperature inversions, the extreme superadiabatic lapse rates, and the wind 

field. At each sounding location, the wind vector components, potential temperature, and water 

vapor mixing ratio are vertically interpolated to 30 different levels using a linear interpolation 

method followed by the horizontal interpolation to the BFM grid using the weighting factor, 1/r2

, 

as 

∑

∑ Ψ

Ψ =

N

ij , N

2

N

ij , N

2

N

r

1

r ( i , j) (10)

Linear vertical interpolation from zi to zst, given by eq 9, is performed for each parameter. The 

final step to initializing the model is to do a composite of the NOGAPS data and the upper-air 

data. The 3-D fields of all the BFM parameters are obtained from the NOGAPS data on 11 

levels, zi. The upper-air data are interpolated to 30 vertical levels, zi. The 3-D fields created 

from the NOGAPS data are now used as background fields. The mean difference between the 

upper-air data and NOGAPS data is defined as 

( )

N

d

N

N 1

∑ G,N U,N

=

Ψ −Ψ

= (11)

where 

d = mean difference. 
9 

The value of ΨG,N is the interpolated value of the Nth upper-air location by using the four 

surrounding grid points and ΨU,N Nth upper-air data. A bilinear interpolation method is then 

used to calculate ΨG,N. 

By replacing ΨG (i,j), by 

(i,j) (i,j) d G G

* Ψ =Ψ + (12)

the mean error is removed, but the values of Ψ*

M,N interpolated from Ψ*G (i,j) for the Nth upper 

air location are not generally equal to ΨU,N. 

The difference 

(13) G,N

*

N U,N

* d =Ψ −Ψ

is not zero, but the mean d* is 

0

N

d

d

N

N 1

*

N

* = =

∑=

(14)

The final step is to distribute d*

 to the entire field using a weighting function of 1/r2, thus getting 

an adjusted value for each parameter. This procedure is applied to all vertical levels, zi, for all 

the parameters, and vertical linear interpolation from zi to z*

 are performed. Linear interpolation 

from zi levels to zst are performed for all parameters and dew-point fields are converted to water 

vapor. 

3.2 Initialization Using the MM5 Data 

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

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

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

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

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

rawinsonde stations, using either a Cressman or multiquadric scheme. In the MM5, there is also 
10 

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

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

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

nesting capability; advanced physical parameterization; a 3-D data assimilation system via 

nudging; and the ability to be run on various platforms (17). 

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

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

continental United States twice daily, initialized at 0600 UTC and 1800 UTC, respectively. Due 

to computational and processing constraints, there is a 6-h stagger between the initialization 

valid time of the 15-km mesh and the first forecast output, thus the first MM5 forecast is a 6-h 

forecast. The frequency of the model output is every 3 h for a time period of 48 h. 

To generate complete data at the standard observation heights of 10 magl and 2 magl, ARL uses 

similarity theory to extrapolate to these lower levels from the lowest MM5 sigma level. In this 

fashion, ARL can produce temperature, dew-point, and wind data at levels 2 magl and 10 magl, 

in addition to the 41 MM5 sigma levels of data. 

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

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

parameterization accounts for sub-gridscale convection and compensating subsidence. 

• MRF planetary boundary-layer model: This model parameterizes the mixture of heat, 

moisture, and momentum in the boundary layer. 

• Reisner mixed phase explicit moisture microphysics: Cloud and rainwater fields and ice 

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

• Dudhia cloud radiation: This parameter provides solar and infrared fluxes at the ground 

and atmospheric tendencies resulting from the radiative processes. 

• MM5 five-layer soil model: Temperature is predicted in 1, 2, 4, 8, 16-cm layers with a 

fixed substrate below, using vertical diffusion equations. 

The 15-km MM5 data used to initialize the BFM looks the same as the NOGAPS data, with the 

exception of the addition of the heights in meters along with the heights above mean sea level for 

each vertical level for each of the 43 levels. An example of just a few of the levels is shown in 

table 2. 
11 

Table 2. A sample of MM5 data used to initialize the BFM. 

Pressure 

(mbar) 

HT 

(m) 

HT MSL 

(m) 

Temperature 

(°K) 

U component 

(knots) 

V component 

(knots) 

Mixing Ratio 

(g/kg) 

981.4 2.0 246.0 311.4 18.8 -2.1 2.4

980.5 10.0 254.0 310.7 18.8 -3.0 3.4

979.3 20.0 264.1 310.1 18.8 -3.5 4.0

975.9 52.2 296.2 309.5 18.4 -3.8 4.4

970.7 100.7 344.7 308.9 18.1 -4.0 4.7

963.4 169.8 413.8 308.2 17.7 -4.1 5.0

NOTE: HT = height and HT MSL = height above mean sea level. 

4. Statistical Evaluation of Mesoscale Models 

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

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

(CC). The equations for these are 

m n

x x

AD

m

j

n

i

o i j p i j

*

| |

1 1

∑ ∑ , , , ,

= =

−

= (15) 

where 

x = meteorological variable 

o = observation 

p = prediction of variable 

i = i

th surface station 

j = j

th forecast day 

n = number of stations 

m = total number of forecast days. 
12 

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

( )

m n

RMSE

m

j

n

i

x x o i j p i j

*

1 1

2 , , , ,

∑ ∑ = =

−

= (16) 

The values of RMSE are proportional to those of the AD. The CC is displayed in eq 17. The CC 

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

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

observed and predicted values of a meteorological forecast. 

∑ ∑ ∑ ∑

∑ ∑

= = = =

= =

∗

= m

j 1

n

i 1

m

j 1

n

i 1

2

o ,i , j

2

m

j 1

n

i 1

o ,i , j p , i , j

x * x p , i , j

x x

CC

 (17) 

5. Evaluation of the Different BFM Initialization Methods 

There were 53 model runs done in this study in a variety of locations in the United States; 

however, there was an emphasis on typical wintertime weather cases during the cold seasons of 

2002-2003 and 2003-2004. There were many grid locations used in this study, although about 

40 percent of all model runs were conducted on the New York grid due to the complex weather 

patterns and cloud forecasts during the cold season. The main focus in this research effort was to 

investigate the temperature, dew-point, wind, and cloud output of the BFM. To validate these 

data, hourly surface observations were used at a variety of locations on the grid. Since the 

BFM-MM5 only has a 12-h model run time, the statistics for this study were calculated at 0 h, 

3 h, 6 h, and 12 h after base time. In these discussions BFM-NOGAPS will refer to the BFM 

version run with NOGAPS data as initial data and BFM-MM5 will refer to the version of the 

model using the MM5 data as input. 
13 

The results in table 3 are very similar to the results using the BFM-NOGAPS that Passner 

presented in an earlier study, which showed a CC of 0.96 at 0 h, 0.94 at 6 h, and 0.95 at 12 h 

after base time (18). 

While the statistical work of Henmi in many evaluations of the BFM shows different results and 

biases, it should be noted that Henmi did much of his evaluation using a BFM with 1° NOGAPS 

data rather than 0.5° data (which was used in this study). While it is uncertain how much of an 

influence the input data differences are responsible for, it is unfair to compare them directly. 

Additionally, Henmi tested the BFM at limited locations rather than combining many grids into 

one study (19). 

Table 3. Surface temperature error for the BFM-NOGAPS and the BFM-MM5. 

Model Hour Samples AD RMSE CC 

BFM-NOGAPS 

00-h 188 2.31 3.13 0.94

03-h 189 1.89 2.47 0.97

06-h 190 2.29 2.96 0.96

12-h 180 2.57 3.26 0.96

BFM-MM5 

00-h 189 2.19 2.84 0.95

03-h 189 2.39 3.14 0.95

06-h 190 2.72 3.42 0.95

12-h 180 3.27 4.24 0.93

One of the intriguing trends in table 3 is that the BFM-NOGAPS shows better skill than the 

BFM-MM5 starting with the 3-h time and continuing through the 12-h forecast. In theory, these 

results might be unexpected since the MM5 runs are conducted on a 15-km grid; however, 

earlier investigation of the MM5 shows larger errors in early time frames (≤ 12-h forecasts) than 

in later ones (>12 h). Passner’s 2003 study indicated the MM5 has a CC of 0.85 at 6 h, 

compared to 0.95 at 12 h and 0.97 at 36 h. These errors in the early forecast periods may have 

contributed to the larger errors in the BFM-MM5 (18). 

The dew-point skills follow the same overall trends, as seen in table 4. These data illustrate the 

same pattern as seen in table 3, except for at the 0-h time, when the BFM-MM5 data 
14 

demonstrates a higher CC, although the RMSE and AD remain comparable. Beginning with the 

3-h forecast, the BFM-NOGAPS exhibits higher skill in all categories. In the 2003 study of 

MM5 forecast data, Passner showed that the MM5 had a higher AD and lower CC at 6 h after 

base time, but showed an improvement in skill at 12 h and beyond. In table 4, this trend is not 

observed, instead the BFM-MM5 shows an increase in error through the 12-h forecast period. 

Additionally, the same pattern is demonstrated using the BFM-NOGAPS (18). 

Table 4. Surface dew-point error for the BFM-NOGAPS and the BFM-MM5. 

Model Hour Samples AD RMSE CC

BFM-NOGAPS

00-h 185 1.16 1.95 0.95

03-h 185 2.02 2.76 0.96

06-h 186 2.04 2.78 0.96

12-h 176 2.72 3.55 0.90

BFM-MM5

00-h 189 1.28 1.93 0.98

03-h 189 2.35 3.27 0.94

06-h 190 2.71 3.65 0.92

12-h 180 3.50 4.55 0.88

The wind speed and wind direction were also studied for each BFM. Table 5 shows the AD, 

RMSE, and CC for wind direction. 

The results in table 5 are inconclusive through the 12-h forecast period. The most noticeable 

difference in model performance is at the initial time, where the BFM-NOGAPS has a higher 

CC, but conversely has a higher AD and RMSE. In other studies by Henmi (such as the one 

utilizing the Ft. Irwin, CA, grid in 1998), Henmi found a CC of approximately 0.50 for the wind 

direction; however, again in that study, he used 1° NOGAPS data rather than the 0.5° NOGAPS 

data used in this study (20). 
15 

Table 5. Surface wind-direction errors for BFM-NOGAPS and the BFM-MM5. 

Model Hour Samples AD RMSE CC 

BFM-NOGAPS

00-h 161 34.8 56.5 0.73

03-h 168 43.6 67.5 0.57

06-h 167 40.1 63.4 0.47

12-h 165 44.3 63.0 0.62

BFM-MM5

00-h 158 24.9 46.6 0.64

03-h 164 41.3 68.2 0.65

06-h 163 39.9 60.8 0.60

12-h 163 44.7 64.4 0.58

In table 6, the results for the wind speed errors are shown for each method of the BFM. 

Table 6. Surface wind-speed errors for the BFM-NOGAPS and the BFM-MM5. 

Model Hour Samples AD RMSE CC 

BFM-NOGAPS

00-h 177 2.7 3.4 0.74

03-h 177 4.1 5.3 0.62

06-h 178 3.9 4.8 0.70

12-h 169 3.9 5.0 0.52

BFM-MM5

00-h 185 2.4 3.4 0.76

03-h 185 4.2 5.2 0.54

06-h 186 3.2 4.0 0.73

12-h 177 4.2 5.3 0.41
16 

The results in table 6 indicate nearly identical errors for each model-initialization method and 

this agrees with results in Passner’s 2003 BFM study (not published), which yielded a CC of 

0.76 at 0 h and 0.42 at 12 h after model initiation. Henmi’s 2000 study at White Sands Missile 

Range indicated a CC of 0.53 for the BFM, which is somewhat lower than the results of this 

current study; however, his model evaluation occurred in the windy months of April and May 

and thus were most likely subjected to higher error and lower CC (19).

While all the parameters discussed in tables 3-6 are direct model output of the BFM, it is also 

necessary to explore how the post-processed variables are influenced by initial model data. The 

post-processed variables are meteorological parameters that can not be derived until all basic 

model information is available after the completion of the model forecast hour. It is often 

advantageous to derive variables after the model run, since they are not dependent upon time 

derivatives and add additional run time for the model. Some of the parameters derived using this 

technique include clear-air turbulence, icing, thunderstorm probability, clouds, and fog. Since it 

is impossible to show the output of all the elements derived by the post-processor, only the shortterm cloud forecasts are discussed in this report. The cloud forecasts are based on the modelderived relative humidity, time of day, and season. 

To evaluate the cloud amounts and heights, the cloud forecasts were compared to Meteorological 

Aviation Routine Weather Reports, which are coded weather observations from selected airports 

across the world. For a forecast to be “correct,” the height of the observed cloud had to be 

within the following specifications: 

• 1000 ft of the forecasted cloud height below 5000 ft above ground level (AGL) 

• 1500 ft between 5000 to 10000 ft AGL 

• 2000 ft above 10000 ft AGL 

Since the error was not considered significant, scattered clouds were not considered as “wrong” 

forecasts in instances when there was no ceiling forecasted. However, if a ceiling was forecasted 

and only scattered clouds were observed, the forecast was considered wrong. When a broken 

layer was forecasted and an overcast layer was observed, the forecast was still considered 

correct, as was a forecast for overcast conditions where broken clouds were reported. Once an 

overcast layer was reported, it was impossible to verify any layers above that layer. In this 

study, the clouds were verified only at the hour of the observation. Table 7 shows the results of 

the BFM-NOGAPS and BFM–MM5 in this study, along with an explanation of the error. 
17 

Table 7. Accuracy of cloud forecasts using the BFM-NOGAPS and the BFM-MM5. 

Model Hour Samples Percent 

Right 

Percent 

Wrong 

Missed 

Ceiling 

Forecasted 

Ceiling 

Forecasted 

Ceiling 

Too Low 

Forecasted 

Ceiling 

Too High 

BFM-NOGAPS 

00-h 179 61 39 28 10 17 15 

03-h 175 58 42 31 25 11 6 

06-h 177 56 44 31 15 20 12 

12-h 176 56 44 22 30 21 5 

Total 707 58 42 112 80 69 38 

BFM-MM5 

00-h 189 59 41 21 21 23 15 

03-h 184 53 47 20 33 27 7 

06-h 191 57 43 16 36 25 6 

12-h 187 51 49 11 41 34 6 

Total 751 55 45 68 131 99 34 

In table 7, there is no significant difference in the accuracy of the post-processed cloud forecasts; 

both BFM methods show only a slight decrease in skill over the course of the 12-h forecast. In a 

study done in 1998-1999, the BFM-NOGAPS followed these trends yielding forecasts that were 

72 percent correct at 0 h, 58 percent correct at 3 h, 56 percent correct at 6 h, and 64 percent 

correct at 12 h. It is uncertain as to why the earlier study had a higher percentage of correct 

forecasts at 0 h and 12 h, although it may be attributed to the 1998-1999 sample being a 

combination of 0000- and 1200-UTC forecasts, while the current study involves only 1200-UTC 

forecasts. Additionally, the data shown in table 7 uses a larger sample size than was used the 

previous study (18). 

One similarity to earlier studies of the BFM-NOGAPS is the cloud errors trend. The BFMNOGAPS “missed” a ceiling about 37 percent of the time in the wrong forecasts—in other 

words, a ceiling was observed but had not been forecasted. In 27 percent of the wrong cases, the 

BFM-NOGAPS forecasted a ceiling, although none was observed. In 23 percent of the wrong 

samples, the post-processor forecasted a ceiling, but the actual ceiling was observed lower than 

the forecast predicted, and in 13 percent of the cases, the observed ceiling was higher than what 

had been forecasted. These findings agree with the 1998-1999 study; however, the BFM in this 

study missed fewer ceilings than the BFM in the previous study. Again, it should be noted the 
18 

earlier study contained models run at both 1200 and 0000 UTC, as well as covered a smaller 

sample size, so some of the difference may be due to this combination. 

The most prominent difference in this study is the cloud-forecast errors generated using the 

BFM-MM5, as displayed in table 7. There is a greater error in forecasting a ceiling and having 

none observed, than in missing a ceiling forecast (forecasting no ceiling where a ceiling is 

observed). In 39 percent of the wrong forecasts, a ceiling was forecasted but did not occur, 

while in 20 percent of the wrong cases, no ceiling was forecasted, but one was actually observed. 

 There was a slight trend in forecasting a ceiling too low in the BFM-MM5 (30 percent of wrong 

forecasts) as compared the BFM-NOGAPS (23 percent of the wrong forecasts). Additionally, 

these trends became more pronounced through the 12-h forecast cycle. There was no bias in the 

forecast error at 0 h; however, at 12 h, 45 percent of the error was due to forecasting a ceiling 

and observed none, whereas 82 percent of the error was from forecasting a ceiling too low or 

forecasting a ceiling and having none observed. The BFM-MM5 rarely misses a ceiling by 

forecasting it too high. 

6. Conclusions and Discussion 

Two different methods to initialize the BFM were used: one using the 11-level, 0.5 ° NOGAPS 

data and one using the 43-level, 15-km MM5 data delivered by AFWA. The main conclusion in 

this report is that neither of the two dissimilar models used as initial data (NOGAPS versus 

MM5) provided much statistical differences to the skills of the BFM, though in many of the 

parameters, the results were slightly improved using NOGAPS data as opposed to MM5 data, 

which had a smaller grid resolution and a higher vertical resolution. 

In this work, the only significant revisions of the BFM for the MM5 data were in interpolating to 

height levels rather than pressure levels, and in making the nudging uniform since there was no 

significant scale difference between the 10- and 15-km grids. Otherwise, there were no 

adjustments made in the BFM for the two data sources. 

However, while there were no significant differences in the BFM performance between the two 

initialization sources, it is interesting to note that BFM-MM5 did demonstrate a slightly higher 

CC for temperature and dew-point data and a better AD for wind direction and wind speed data 

at the initial time (0 h). By the 3-h forecast period, the BFM-NOGAPS displayed better skill in 

the temperature and dew-point fields, but showed no differences in the wind fields. Thus, if 

there is any advantage to using the BFM-MM5, it appears to be most useful at the initial time, 

which might infer that a smaller grid resolution (15 km) and a higher vertical resolution might be 

most useful in the short term for the BFM. As expected, overall model performance decreased 

with time for the temperature and dew-point fields, although the model’s wind fields degraded 
19 

most significantly at the 6-h forecast mark. It is uncertain why the BFM would show the highest 

error at 6 h, but it may be due to the maximum influence of radiation (typically at 1800 UTC in 

this study). 

In their work over the Pacific Northwest, Mass et al. (21), noted that MM5 model performance 

was significantly improved when changing from a 36-km grid to a 12-km grid, but there was not 

as much improvement from a 12-km grid to a 4-km grid. This raised the question as to how 

much model improvement could be expected by changing the initial data from 0.5° to 15 km, as 

was done here. Based on these results of this study, not much is gained by doing so with the 

BFM. 

Additionally, there are other issues in this study that should be examined. The results were 

based on surface observations only, and while there was no noticeable difference in skill 

between the methods, the upper-air levels have not been investigated. By increasing vertical 

resolution, there might have been great improvement in the upper levels, which was not found in 

this study since only surface data were verified. It is possible that there may have been poor 

analysis of initial MM5 data, since a detailed evaluation was never studied. Another possible 

influence might have been the number of cloudy-weather cases in this study; if too many cloudyweather cases were part of this study, as seems likely, there would not have been enough 

emphasis placed on the radiation scheme and nudging. And finally, there might have been too 

many synoptically driven cases, which again would deemphasize the influence of the mesoscale 

models (22). 

Still, a study of models using different initialization schemes is an interesting one. It does appear 

that in the case of the BFM, not much is gained by using MM5 data over NOGAPS data to 

initialize the model. It is uncertain if this trend would be seen with other models or how 

different models would integrate different large-scale data. 
20 

References 

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Parameterization at ECMWF. ECMWF Workshop on Planetary Boundary 

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13. Tiedtke, M. The Sensitivity of the Time-Scale Flow to Cumulus Convection in the ECMWF 

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22. Dumais, R.E., Jr. Private communication. (2004). 
22 

List of Acronyms 

3-D three-dimensional 

AD mean absolute difference 

AFWA Air Force Weather Agency 

AGL above ground level 

ARL U.S. Army Research Laboratory 

BFM Battlescale Forecast Model 

CC correlation coefficient 

HOTMAC Higher Order Turbulence Model for Atmospheric Circulations 

IMETS Integrated Meteorological System 

mbar millibar 

MM5 Mesoscale Model Version 5 

NOGAPS Naval Operational Global Atmospheric Prediction System 

RMSE root mean square error 

UTC universal time coordinated 
23 

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