Prototype of a Coupled Mesoscale-Microscale 

Modeling System 

by Saba A. Luces and Ronald M. Cionco 

ARL-TR-3706 January 2006 

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Army Research Laboratory 

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ARL-TR-3706 January 2006 

Prototype of a Coupled Mesoscale-Microscale 

Modeling System 

Saba A. Luces and Ronald M. Cionco 

Computational and Information Sciences Directorate 

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Prototype of a Coupled Mesoscale-Microscale Modeling System 

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Luces, Saba A. and Cionco, Ronald M. 

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U.S. Army Research Laboratory 

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14. ABSTRACT 

A prototype system of coupled mesoscale and microscale models was developed to produce an analysis that considered higherresolution terrain. Research was initiated to integrate the microscale code with the mesoscale code to attain higher-resolution 

meteorological forecasts for target areas and other localized areas on the battlefield. A step-wise approach was employed to 

develop a modular prototype that would first show improved winds using high-resolution diagnostic models, but then be capable 

of interchanging various components. The San Francisco Bay area was selected as the common simulation domain, since both 

models had already been run there in previous studies. 

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Lou Luces 

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Contents 

Contents iii

List of Figures iv

List of Tables iv

Summary 1

1. Introduction 2

2. Objective 2

3. Approach 3

4. Model Descriptions 4

4.1 The BFM ........................................................................................................................ 4 

4.2 The HRW ....................................................................................................................... 5 

4.3 Other Models Employed ................................................................................................ 6 

5. Discussion of Simulation Results 7

5.1 Fort Benning MOUT Site............................................................................................... 7 

5.2 Domains for Rodeo, CA................................................................................................. 8 

5.2.1 Rodeo Output, Hour 0 ...................................................................................... 10 

5.2.2 Rodeo Output, Hour 2 ...................................................................................... 11 

5.2.3 Rodeo Output, Hour 4 ...................................................................................... 12 

5.2.4 Rodeo Output, Hour 6 ...................................................................................... 13 

6. Conclusions 15

7. Future Research 15

References 16

Acronyms 18

iii 
Distribution List 19

List of Figures 

Figure 1. The BFM over the MOUT site on 7 August 2000 at 1800Z. ......................................... 8 

Figure 2. The HRW at the MOUT site. ......................................................................................... 8 

Figure 3a. The BFM domain for Rodeo, CA................................................................................. 9 

Figure 3b. The HRW domain for Rodeo, CA................................................................................ 9 

Figure 4a. The BFM at hour 0 (0000Z). ...................................................................................... 11 

Figure 4b. The HRW at hour 0 (0000Z). ..................................................................................... 11 

Figure 5a. The BFM at hour 2 (0200Z). ...................................................................................... 12 

Figure 5b. The HRW at hour 2 (0200Z). ..................................................................................... 12 

Figure 6a. The BFM at hour 4 (0400Z). ...................................................................................... 13 

Figure 6b. The HRW at hour 4 (0400Z). ..................................................................................... 13 

Figure 7a. The BFM at hour 6 (0600Z). ...................................................................................... 14 

Figure 7b. The HRW at hour 6 (0600Z). ..................................................................................... 14 

List of Tables 

Table 1. Simulation meteorological data. .................................................................................... 10 

iv 
Summary 

In order to address the lack of low-level forecast wind response to true high-resolution terrain in 

the Battlescale Forecast Model (BFM), the U.S. Army Research Laboratory (ARL) has 

developed a prototype system of coupled mesoscale and microscale models that considers 

higher-resolution terrain in its analysis. Research was initiated to integrate the microscale HighResolution Wind (HRW) model code (which has long been used to perform diagnostic studies of 

the effects of terrain and vegetation on local winds) with the mesoscale BFM code to attain 

higher-resolution meteorological forecasts for target areas and other localized areas on the 

battlefield. A step-wise approach was employed to develop a modular prototype that would first 

show improved winds using high-resolution diagnostic models, but then be capable of 

interchanging various components. The San Francisco Bay, CA, area was selected as the 

common simulation domain, since both the BFM and the HRW had been run there in previous 

studies. 

To begin, model output was gathered and model input datasets were generated for the 

initialization of both models. Next, the codes were run as separate entities to evaluate model 

output and input compatibilities for several cases. Later, the HRW was incorporated into the 

Artillery BFM run, but with a separate execution for each target area. Level 1 Digital Terrain 

Elevation Data (DTED) were already in hand for the HRW and BFM at the desired resolutions, 

and meteorological input data for the BFM were obtained from the Navy Operational Global 

Atmospheric Prediction System (NOGAPS) at a 1o resolution. Initially, meteorological input 

data for the HRW were generated from the BFM simulations in the form of an equivalent upperair sounding and 10-m meteorological surface station observation, derived from a single BFM 

grid point with its vertical extension “centered” on the HRW domain. Subsequently, surface data 

from the surrounding BFM grid points were added to the HRW run and later formed into an 

objective analysis. 

The new mesoscale-microscale (BFM-HRW) simulations compared well against the solutions 

previously run for the San Francisco, CA, area. More recently, additional runs have been 

performed at other locations with other forecast and diagnostic models. These modifications will 

be presented in a subsequent report. 

 1 
1. Introduction 

The U.S. Army Research Laboratory (ARL) has long used a microscale High-Resolution Wind 

(HRW) model (Cionco, 1985) to perform diagnostic studies of the effects of terrain and 

vegetation on local winds. ARL has also used a hydrostatic mesoscale forecast model, the 

Battlescale Forecast Model (BFM) (Henmi and Dumais, 1998), to produce tailored forecast 

products. Studies of the effectiveness of the BFM to produce low-level target area forecast 

products in complex terrain have demonstrated that low-level mesoscale forecast winds are 

unable to respond to the true high-resolution terrain (Henmi, 2000). 

As a consequence, a prototype system of coupled mesoscale and microscale models has been 

conceived to produce an analysis that considers higher-resolution terrain. Research was initiated 

to integrate the HRW with the BFM to attain higher-resolution meteorological forecasts for 

target areas and other localized areas on the battlefield. Projected changes in forecast model and 

projected three-dimensional requirements for the diagnostic model dictated that the prototype be 

developed with modularity. 

Thus, the resultant prototype system, which consists of interchangeable models, can be applied to 

other military problems, such as the highly important issue of the transport and diffusion of 

chemical and biological materials on the battlefield. The mesoscale-microscale model output 

can provide data showing coarse- and fine-scale winds influenced by detailed surface features 

(such as vegetation or structures), as well as the underlying complex terrain. The scope of this 

report, however, will be restricted to how the models being integrated relate to the field artillery 

problem and its target area. 

2. Objective 

The objectives of the study are to develop a prototype modeling system to accomplish the 

following: 

1. Attain higher-resolution surface layer meteorological (Met) analyses than are available 

from existing mesoscale analyses for the target area and other battlefield areas of interest. 

2. Use the ARL HRW model to attain higher computational resolution at forecast hours. 

3. Complete a transfer technology from basic research to an applied research project. 

4. Retain the capability to change parts of the prototype to cope with changing requirements 

and the evolution of the ARL models. 

This report details examples of the first steps towards developing a prototype (BFM-HRW 

simulations) and contrasts the results of the BFM alone versus the prototype of coupled models. 

 

 2 
3. Approach 

Rather than attempt to build the code for high-resolution modeling into the mesoscale model, a 

more modular, and definitely easier, method was chosen. This simple method utilizes 

intermediate code to capture the proper location within the mesoscale model output and then 

writes out the required data in the format required as input to the microscale model. 

A step-wise approach was used to attain coupling and integration of the models. As a first test of 

the process, the method was applied to our support of the Department of Defense (DoD)–

Massachusetts Institute of Technology (MIT)–Lincoln Lab Smart Sensor Webs Project for the 

Weather Web (WXWEB) demonstration at the Military Operations on Urbanized Terrain 

(MOUT) site at Ft. Benning, GA, during August 2000. During this test, we selected the Met 

message gun and target locations in the mesoscale Computer Aided Artillery Meteorology 

(CAAM) program (the prototype software for the now fielded Meteorological Sensor SystemProfiler (MSS-P) system) as the center of the MOUT site, printed the Met message values at that 

location, and then manually entered this data into the required microscale model input file. 

In the next test, a simulation domain common to both the mesoscale and microscale models was 

identified, so that prior runs of the microscale model could be compared to new runs with the 

models coupled. The domain for the selected mesoscale model, the Artillery version of the BFM 

(Haines et al., 1997), was set up to match the high-resolution target area of previous runs of a 

domain for the microscale model, the HRW. The location selected was the San Francisco Bay, 

CA, area. HRW input files derived from BFM output were manually generated for the same 

days as prior HRW runs. The new and old HRW runs were compared for consistency. 

Qualitatively, the solutions were considered to be consistent with each other. To provide a finer 

analysis, the BFM computational grid was adjusted from 5 km to 2.5 km. Model output 

comparisons proved to be compatible and consistent in the manual mode. 

To begin the integration process, the CAAM program was set up to output the required input file 

for the microscale code at the point in its execution when it was forming the target-area low-level 

Met message. Model results were again compared with similar success. The microscale input 

was changed to accept a terrain file similar to that of the mesoscale input. Microscale output was 

also changed to resemble the mesoscale output file, so that its visualization could be made easier. 

The setup of the Artillery program was altered to form microscale terrain files every time a target 

area was selected. Finally, the operation of the Artillery program was set to run the microscale 

code every time a Met message was required. The microscale output for the lowest level of the 

message line was retrieved for the Met message. 

For this study, several changes and accommodations were made to model input and output, either 

by choice or by dictation from the coupling and integrating process. 

For model input, the following conditions were implemented: 

1. The input file derived from the mesoscale code consists of a single “upper air” profile of 

potential temperature versus height for mandatory levels (850, 700, and 500 mbar); 

however, the mesoscale model surface station Met output was provided as 16 adjacent 

 3 
 mesoscale grids rather than 1 surface station input. These 16 data points were analyzed 

using a Barnes analysis, and slope flow wind calculations were derived from software in 

the Earth Tech Inc. diagnostic Met model called CALMET (Scire et al., 2000). 

2. Digitized terrain elevation was provided from the mesoscale terrain cache originally 

extracted from Digital Terrain Elevation Data (DTED) Level-1 CD-ROMs. 

3. Land feature morphology data was not available at the required resolution; therefore, only 

surface roughness was used. 

For model output, the following conditions were followed: 

• Binary files of wind components, temperature, and the Power-Law Exponent were 

separated and used for Artillery Met messages and display. 

• The microscale output was made to be similar to the mesoscale output file so we could 

adapt that translation software for visualization of the BFM in the University of 

Wisconsin’s Space Science and Engineering Center tool for visualizing five-dimensional 

data, VIS5D (Hibbard et al.,1998). 

• The microscale variables displayed with VIS5D were wind vectors, streamlines, 

temperature, and terrain elevation. 

• For the Artillery Met message, the Power-Law Exponent was used to build a wind profile 

to blend microscale surface wind to an interpolated mesoscale wind profile. 

4. Model Descriptions 

ARL’s mesoscale BFM model and micrometeorological HRW models have been coupled and 

integrated into the prototype simulation system. Navy Operational Global Atmospheric 

Prediction System (NOGAPS) (Bayler and Lewit, 1992) data were used to initialize the BFM. 

Other codes were also used to further analyze BFM output before the HRW input file was 

prepared. ARL’s Atmospheric Sounding Program (ASP) (Passner, 1999) was used to establish 

mandatory levels as a vertical sounding profile. The Environmental Protection Agency (EPA)-

approved CALMET, written by Earth Tech Inc. (Scire et al., 2000), was used to derive slope 

winds to enhance the analysis of BFM output used to initialize the HRW. Brief descriptions of 

the BFM and HRW models are provided, followed by a description of how the input and output 

are formulated. 

4.1 The BFM 

The BFM is a three-dimensional, hydrostatic mesoscale prediction model. It was designed to 

give improved wind data in the boundary layer and improve its forecasting by nudging to a 

larger-scale global forecast model. The model has typically been used with a grid spacing range 

from 2 km to about 20 km. The ARL BFM code (Henmi and Dumais, 1998) was adapted and 

customized from Yamada’s Higher Order Turbulence Model for Atmospheric Circulations 

(HOTMAC) (Yamada and Bunker, 1989). The BFM core uses the Boussinesq approximation, 

an alternating direction implicit numerical scheme, and the Arakawa staggered C-grid. 

Prognostic equations for perturbed quantities of u, v, q; virtual potential temperature, turbulence 

 4 
length scale, and kinetic energy are solved on a timescale determined by wind speed and thermal 

stability. A sigma-Z terrain-following vertical coordinate system is used. Both pressure (Exner 

function) and the vertical wind component, w, are computed diagnostically. Pressure is 

computed from potential temperature and w is set equal to zero at the surface and at the top of 

the domain. 

This project used the version of the BFM developed for Artillery meteorology, as reported in 

Haines et al. (1997). Using this version allowed us to take advantage of the flexibility of the 

surrounding software (CAAM). In CAAM, it was possible to make changes so that every time a 

target area forecast was called for, the HRW could be initiated. A target area was set up at the 

same location of the prior HRW efforts, and forecasts in the target area were made for every 

hour. 

The CAAM BFM was configured for 32 vertical computational levels starting at 2 m and 

extending to 7 km above the highest terrain elevation. A 200 km-by-200 km horizontal domain 

was established using a 5-km grid resolution, and later repeated using a 2.5-km grid resolution 

(because the water regions in the San Francisco, CA, region were not well resolved at a 5-km 

grid spacing). The BFM was run out to a 12-h forecast and produced hourly outputs. 

The input requirements included the following: 

• Nudging data files from the larger-scale NOGAPS data files, updated every 12 h at a 

1o resolution. (NOGAPS data were first acquired at a 2.5o resolution until it was 

determined that a 1o resolution was more appropriate for a 2.5-km BFM grid.). 

• Creating an unsmoothed terrain file at the BFM resolution. 

• Producing two user files for setting the physics and the timing. 

These files are produced in CAAM in response to user inputs. The output is a single file of wind 

components, temperature, and a mixing ratio (called a “UVTQ” file) valid for all forecast hours. 

This output is then post-processed to form the input file required by the microscale model, 

including the required wind, temperature, and stability data at the required pressure levels valid 

at the center of the microscale grid. 

4.2 The HRW 

The HRW is a two-dimensional, diagnostic, time independent model that simulates airflow over 

complex terrain (including the effects of vegetation, buildings, and simple surfaces) with a high 

computational resolution, such as 100 m (40–400 m), and for a very local area, such as 5 km by 

5 km (2 km to 20 km on a side), as described by Cionco (1985). 

Physically, calculations are performed on an array of air parcels in a pressure field such that 

accelerations of these parcels are determined as they negotiate the changing slopes of the terrain 

and the added thermal lift or suppression component imparted by buoyancy. Computations are 

performed for the array of cells as flux boxes, defined by each four adjacent grid points and the 

underlying terrain-morphology surface. These calculations are completed in an iterative manner. 

Simulation values are obtained by direct variational relaxation of the wind field in the layer near 

the surface. 

 5 
Numerically, the variational over-relaxation method is used to obtain a minimum of the 

combined acceleration forces in a pressure field. After the first iteration, the flow computations 

resolve changes of the field of accelerations over variable terrain with thermal lift and 

suppression effects. The solution is established when a minimum is reached, summing the 

change values over all the computational cells in the domain. The “vector of steepest descent” 

method is used during the calculations to approach the minimum. The solution is reached when 

the internal constraints forces imposed by the warped terrain surface, thermal structure, and 

requirements for flow continuity are minimized. 

The procedure uses Gauss’ Principle of Least Constraints (Lanczos, 1962), which requires the 

forces to be minimized in order to satisfy the equations of motion. Mass is conserved during the 

calculations. Empirical wind and temperature vertical (structure) profiles are also used in the 

computational integration through the vertical thickness of the prescribed vertical layer. 

As stand-alone code, the HRW is initialized with single values (at 10 m above ground level) for 

wind speed, wind direction, temperature, pressure, and buoyancy (stability computed from an 

upper-air sounding) derived from field observations or output from a coarser mesoscale model 

analysis. Digitized terrain elevation from DTED Level 1 is also required at each grid point 

within the area. Digitized land morphology datasets of land feature height, type, and footprint 

(Cionco and Ellefsen, 1998 and Ellefsen, 1985) are optional. The pressure and temperature 

fields are adjusted for terrain elevation throughout the domain before the simulation begins. The 

model’s output is composed of the u and v wind components, a vector field, a calculated 

streamline field, temperature, friction velocity, the Richardson Number, the Power-Law 

Exponent, and a partial component of the vertical motion (not always the total w component). 

Each parameter is calculated for the entire horizontal array and is tabulated and viewable with 

the appropriate graphics. 

The HRW has been validated (Cionco and Byers, 1995; Cionco and Chang, 2000) using the 

Meteorology and Diffusion Over Non-Uniform Areas (MADONA) Field Study database (Cionco 

et al., 1999). 

4.3 Other Models Employed 

Additional models were required to perform this research: NOGAPS, ASP, and CALMET. 

NOGAPS (Bayler and Lewit, 1992) is a global spectral prediction model typically run at a 

2.5o resolution (however, at the time of this research, it was available at a 1o resolution). This 

model is capable of providing the nudging input files required by the mesoscale model. At the 

time of this report, there were several other models capable of giving this kind of input: the 

National Oceanic and Atmospheric Administration’s National Centers for Environmental 

Prediction (NCEP) Nested Grid Model (NGM); the North American Mesoscale (NAM) model; 

the Aviation (AVN) model; the Global Forecast System (GFS) model; the Rapid Update Cycle 

(RUC) model; the Air Force Mesoscale Model Version 5 (MM5); and the NCEP Reanalysis 

Program. However, intermediate software to reformat the output from these models is required 

for their use. 

 6 
We decided to use the ASP (Passner, 1999) merely as a target of opportunity. The ASP was 

already required by the CAAM Met message program to provide target area weather from the 

BFM output. However, in the ASP algorithms to compute stability, there was software to extract 

information at mandatory pressure levels. So, the software to form the HRW input files for the 

required pressure level information was hidden in the ASP code. 

The CALMET software (Scire et al., 2000) is a complete analysis and surface-wind diagnostics 

program. Although it could perform many of the tasks required for this prototype, it was only 

used in this early stage to provide a means to further modify the wind fields in complex terrain. 

While the microscale HRW program responds to terrain variations, severe complex terrain can 

overwhelm the vector-of-steepest-descent method. With a surface analysis (modified by the 

slope flow algorithm from CALMET) as the initial field, the HRW was able to more reliably 

reach a solution. 

5. Discussion of Simulation Results 

During the step-wise, manual execution of the codes, analyses were made for the U.S. Army 

MOUT site at Fort Benning, GA, as part of the WXWEB demonstration. An example set of 

solutions is shown in figures 1 and 2. For the San Francisco Bay area simulations, the target area 

was selected at Rodeo on San Pablo Bay, CA. Examples for 4 forecast hours are given for hours 

0, 2, 4, and 6 in figures 3–7. 

5.1 Fort Benning MOUT Site 

The BFM solution is shown in figure 1 for 17 August 2000 at 1800 Zulu (Z) time (during the 

WXWEB demonstrations) for a domain of 200 km by 200 km (which has been zoomed to cover 

only about 58 km) at a 2.5-km grid resolution. The white square represents the 5-km box that is 

the HRW domain. Notice that although the flow is altered by the higher terrain (pink shading to 

the north and east of the site), there is very little flow curvature through the HRW domain. 

Figure 2 shows the high-resolution analysis of the HRW as initialized by the BFM grid point, 

noted in the center of the HRW domain. The BFM’s smoother wind field is further analyzed by 

the HRW with finer resolution to produce a more varied wind speed and direction field, shown 

as vectors and streamlines plotted over terrain contours (in red). The buildings of the MOUT site 

are located at the center of the HRW domain. The HRW clearly resolves more detail in the flow 

field as it interacts with the varying terrain elevation. 

 7 
Figure 1. The BFM over the MOUT site on 7 August 2000 at 1800Z. 

Figure 2. The HRW at the MOUT site. 

5.2 Domains for Rodeo, CA 

Figure 3a shows the BFM terrain configuration and figure 3b shows the HRW detailed terrain 

plot. The intent of figures 3a and 3b is to present only the terrain maps, not the accompanying 

streamline analyses and input data of hour 0. The BFM domain is 200 km by 200 km, while the 

HRW domain is 5 km by 5 km, with computational grids of 2.5 km and 100 m, respectively. 

The white-colored cross denotes the center position of the HRW domain and the input location 

for the BFM output. Met output data for each model (at the location of the cross) are noted in 

the lower-left corner of each figure. After this display, both the BFM and HRW will be enlarged 

and cropped to smaller areas in order to emphasize the details of each simulated solution. 

 8 
 

Figure 3a. The BFM domain for Rodeo, CA. 

NOTE: The box indicates the domain of the HRW and 

the white cross indicates the center of the display. 

Figure 3b. The HRW domain for Rodeo, CA. 

NOTE: The white cross indicates the center of the display. 

 9 
Included in table 1 are values valid at the center point of the microscale model window (denoted 

by the white cross in figures 3a and 3b) derived from the BFM (to be used as input to the HRW), 

as compared to values computed from the HRW. This table shows wind speed, wind direction, 

and stability for the four hours of forecast reported. Note that while wind directions begin to 

conform to each other as time solutions progress, the wind speeds reported by the HRW are 

higher, perhaps reflecting a response to the more complex representation of terrain roughness. 

Table 1. Simulation meteorological data. 

Stability BFM Output to Initialize 

the HRW HRW Output Values 

Time Wind Speed 

(kn) 

Direction 

(deg) 

Wind Speed 

(kn) 

Direction 

(deg) 

Hour 00 Unstable 5.7 258 8.9 265 

Hour 02 Near-neutral (slightly unstable) 2.1 170 2.5 217 

Hour 04 Near-neutral (slightly stable) 3.7 136 4.8 136 

Hour 06 Stable 3.8 129 5.2 129 

5.2.1 Rodeo Output, Hour 0 

The first hour of these simulations is noted as hour 0 (equal to 0000Z). Figures 4a and 4b show 

the enlarged and cropped BFM and HRW plots of vectors (black), streamlines (orange), terrain 

(shades of green), and water (gray) for San Pablo Bay, CA. The BFM’s smooth flow field is for 

a 2.5-km resolution, while the more deformed HRW fields are for 100-m grids. The HRW’s 

flow field during these unstable atmospheric conditions displays steering of the westerly flow 

over the water surface to become a northwesterly flow into the small valleys. There is also a 

notable deformation about the hill, located in the upper right corner, which is not resolved in the 

BFM solution. 

 10 
Figure 4a. The BFM at hour 0 (0000Z). 

Figure 4b. The HRW at hour 0 (0000Z). 

5.2.2 Rodeo Output, Hour 2 

Hour 2 results are given in figures 5a and 5b in the same manner as figures 4a and 4b. The 

BFM’s wind speed and direction have changed from hour 0, while the stability has approached a 

near-neutral condition (slightly unstable). The speed has decreased by a factor of 2 and direction 

has shifted about 90o, changing to a southerly flow. The BFM’s flow field has more deformation 

than was produced during hour 0. The HRW speed field is somewhat enhanced, while 

directionally the valleys continue to affect the high-resolution field. The effects of the hill at 

hour 2 remain as dominant as they were at hour 0. 

 11 
Figure 5a. The BFM at hour 2 (0200Z). 

Figure 5b. The HRW at hour 2 (0200Z). 

5.2.3 Rodeo Output, Hour 4 

The hour 4 results (figures 6a and 6b) show that the BFM flow field has continued to shift 

directionally from previous hours, whereas the speeds have increased a bit. As the BFM flow 

shifts southeasterly (noted at the white cross) and the stability shifts to the slightly stable side of 

neutral, the HRW field now aligns with the valleys and produces minimum directional 

deformation. Some speed increases occur in the main small valley and about the hill area as 

before, due to the symmetry of the hill’s contours. 

 12 
Figure 6a. The BFM at hour 4 (0400Z). 

Figure 6b. The HRW at hour 4 (0400Z). 

5.2.4 Rodeo Output, Hour 6 

Starting at hour 4, as the HRW domain became more stable, the HRW winds tended to track the 

terrain less. At hour 6, although the BFM simulated wind fields shown in figure 7a reveal a little 

change in the streamline shape (the col in the NE corner of the box, denoting the HRW domain, 

has moved slightly to the SE), the HRW fields at hour 4, and at hour 6 (in figure 7b), show an 

almost straight-line flow and less diversity than the BFM. While the lack of 

 13 
turning of the HRW flow in figure 7b with respect to the terrain is due to the stability, the lack of 

mesoscale features compared to the BFM is caused by the lack of diversity of the 16 grid points 

surrounding the white cross in figure 7a. 

Figure 7a. The BFM at hour 6 (0600Z). 

Figure 7b. The HRW at hour 6 (0600Z). 

 14 
6. Conclusions 

The first major step was completed with the BFM/HRW integrated prototype system running 

seamlessly in the Artillery program. The results of this modeling study provided several 

conclusions. Early in the integration study, it was apparent that higher-resolution inputs would 

be required. BFM simulations derived from a 2.5-km computational grid and a 1o NOGAPS 

initialization data field resolved more detail in the mesoscale flow field than was available with a 

5-km grid BFM with a 2.5o NOGAPS initialization. With this higher-resolution mesoscale 

output, the HRW was able to generate a more detailed microscale airflow analysis in the target 

area. In the light wind and gentle terrain of the MOUT scenario, the HRW resolved significant 

terrain effects upon the speed and directional fields. 

For the Rodeo, CA, domain, the output from hour 0 showed significant terrain influences in the 

unstable air and the favorable prevailing wind direction. This influence lessened as the wind 

speed decreased; the directions shifted and aligned more with the terrain; and the air became 

more stable. The HRW also exhibited different speed and direction enhancements as the BFM 

mesoscale winds shifted into different terrain alignment and slowed through the study period. 

7. Future Research 

It is clear that a quantitative evaluation of the coupled system of models is required as a future 

task. Subsequent to the research reported here, it was necessary to leave both the BFM and the 

Artillery modeling behind. The next step of research will focus on a stand-alone product that 

allows study in several directions: 

1. Using the Canopy-Coupled Surface Layer (CCSL) model (Cionco, 1985) as a replacement 

for the HRW in order to capture the wind deviations caused by the surface morphology 

(land cover, land characterization). 

2. Using other mesoscale forecast models that are supported by the research and operational 

communities. 

3. Modularly adding transport and diffusion code to the execution string to better understand 

the effects of terrain and morphology on hazardous releases. 

4. Adding the ARL knowledge gathered from this research into three-dimensional diagnostic 

microscale models. 

 15 
References 

Bayler, G.; Lewit, H. The Navy Operational Global and Regional Atmospheric Prediction 

System at the Fleet Numerical Oceanography Center. Weather and Forecasting 1992, 7 (2). 

Cionco, R.M. Modeling Wind Fields and Surface Layer Profiles over Complex Terrain and 

Within Vegetative Canopies, Forest-Atmosphere Interactions; Hutchinson, B.A., Hicks, B.B. 

Eds.; D. Reidel Publishing Co.: Holland, 1985. 

Cionco, R.M.; Byers, J.H. Initializing the HRW Model with Meteorological Inputs from 

Different Sites to Optimize Model Output, Proceedings of the 11th Symposium on Boundary 

Layers and Turbulence, AMS: Boston, MA, 1995. 

Cionco, R.M.; Chang, S.S. Further Evaluation of the HRW Model Using Conditional Sampling, 

Proceedings of the 14th Symposium on Boundary Layers and Turbulence, American 

Meteorological Society: Boston, MA, 2000. 

Cionco, R.M.; Ellefsen, R. High Resolution Urban Morphology Data for Urban Wind Flow 

Modeling. Atmos. Env. Special Issue on Urban Forest 1998, 32 (1), p 7–17. 

Cionco, R.M. et al. An Overview of MADONA: A Multinational Field Study of High 

Resolution Meteorology and Diffusion over Complex Terrain. Bull. AMS 1999, 80 (1), p 5–

19. 

Ellefsen, R. Urban Terrain Classification System; Quadrapartite Advisory Publication 32; U.S. 

Army TRADOC Systems and Analysis Activity: White Sands Missile Range, NM, 1985. 

Haines, P. et al. Meteorological Data Processing Methods in the Computer-Assisted Artillery 

Meteorology System (Battlescale Forecast Model); ARL-TR-559; U.S. Army Research 

Laboratory: White Sands Missile Range, NM, 1997. 

Henmi, T. Comparison and Evaluation of Operational Mesoscale MM5 and BFM Over WSMR; 

ARL-TR-1476; U.S. Army Research Laboratory: White Sands Missile Range, NM, 2000. 

Henmi, T.; Dumais, Jr., R. Description of the Battlescale Forecast Model; ARL-TR-1032; U.S. 

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

Hibbard, B. et al. Vis5D Documentation, Space Science and Engineering Center; University of 

Wisconsin-Madison: Madison, WI, 1998. 

Lanczos, C. The Variational Principles of Mechanics; The University of Toronto Press: 

Toronto, Canada, 1962. 

Passner, J. Weather Products from a Mesoscale Model, Proceedings of the ITEA Workshop: 

Modeling and Simulation Using Modeling and Simulation for Testing – Are We Ready for 

the Next Millennium?, Las Cruces, NM, 6-9 Dec 1999. 

Scire, J. et al. A User’s Guide for the CALMET Meteorological Model; Earth Tech, Inc.: 

Concord, MA, 2000. 

 16 
Yamada, T.; Bunker, S. A Numerical Model Study of Nocturnal Drainage Flows with Strong 

Wind and Temperature Gradients. J. of Applied Meteorology 1989, 28, pp 545–554. 

 17 
Acronyms 

ARL U.S. Army Research Laboratory 

ASP Atmospheric Sounding Program 

AVN Aviation 

BFM Battlescale Forecast Model 

CAAM Computer Aided Artillery Meteorology 

CCSL Canopy-Coupled Surface Layer 

DoD Department of Defense 

DTED Digital Terrain Elevation Data 

EPA Environmental Protection Agency 

GFS Global Forecast System 

HOTMAC Higher Order Turbulence Model for Atmospheric Circulations 

HRW High-Resolution Wind 

Met meteorological 

MIT Massachusetts Institute of Technology 

MOUT Military Operations on Urbanized Terrain 

MM5 Mesoscale Model Version 5 

MSS-P Meteorological Sensor System- Profiler 

NAM North American Mesoscale 

NCEP National Centers for Environmental Prediction 

NGM Nested Grid Model 

NOGAPS Navy Operational Global Atmospheric Prediction System 

RUC Rapid Update Cycle 

WXWEB Lincoln Lab Smart Sensor Webs Project for the Weather Web 

Z Zulu time 

 18 
Distribution List 

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NASA SPACE FLT CTR 1 CD 

ENVIRONMENTS GROUP 

CODE ED 44 

HUNTSVILLE AL 35812 

US ARMY MISSILE CMND 1 CD 

REDSTONE SCI INFO CTR 

AMSMI RD CS R DOC 

REDSTONE ARSENAL AL 35898-5241 

PACIFIC MISSILE TEST CTR 1 CD 

GEOPHYSICS DIV 

ATTN CODE 3250 

POINT MUGU CA 93042-5000 

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BRANCH 1 CD 

SPAWARSYSCEN SAN DIEGO D858 

49170 PROPAGATION PATH 

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PO BOX 67 

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NCAR LIBRARY SERIALS 1 CD 

NATL CTR FOR ATMOS RSCH 

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BOULDER CO 80307-3000 

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1000 ARMY PENTAGON ROOM 2E383 

WASHINGTON DC 20310-1067 

HQ AFWA/DNX 1 CD 

106 PEACEKEEPER DR STE 2N3 

OFFUTT AFB NE 68113-4039 

AFRL/VSBL 1 CD 

29 RANDOLPH RD 

HANSCOM AFB MA 01731 

ARL CHEMICAL BIOLOGY 1 CD 

NUC EFFECTS DIV 

AMSRD ARL SL CO 

APG MD 21010-5423 

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US ARMY RESEARCH LAB 1 CD 

AMSRD ARL CI OK TL 

2800 POWDER MILL ROAD 

ADELPHI MD 20783-1197 

US ARMY RESEARCH LAB 1 

AMSRD ARL SE EE 

ATTN DR SZTANKAY 

2800 POWDER MILL ROAD 

ADELPHI MD 20783-1145 

US ARMY RESEARCH LAB 1 

AMSRD ARL CI 

ATTN J GOWENS 

2800 POWDER MILL ROAD 

ADELPHI MD 20783-1197 

US ARMY RSRC OFC 1 CD 

ATTN AMSRD ARL RO EN 

PO BOX 12211 

RTP NC 27009 

US ARMY CECRL 1 CD 

CRREL GP 

ATTN DR DETSCH 

HANOVER NH 03755-1290 

ARMY DUGWAY PROVING GRD 1 

STEDP MT M 

ATTN MR BOWERS 

DUGWAY UT 84022-5000 

USAF ROME LAB TECH 1 CD 

CORRIDOR W STE 262 RL SUL 

26 ELECTR PKWY BLD 106 

GRIFFISS AFB ROME NY 13441-4514 

US ARMY FIELD ARTILLERY SCHOOL 1 CD 

ATSF TSM TA 

FT SILL OK 73503-5600 

NAVAL SURFACE WEAPONS CTR 1 CD 

CODE G63 

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19 
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US ARMY OEC 1 CD 

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CETEC ZC 1 

FT BELVOIR VA 22060-5546 

SCI AND TECHNOLOGY 1 CD 

10 BASIL SAWYER DR 

HAMPTON VA 23666-1293 

US ARMY TRADOC 1 CD 

ATCD FA 

FT MONROE VA 23651-5170 

US ARMY TRADOC ANALYSIS CMND 1 CD 

ATRC WSS R 

WSMR NM 88002-5502 

US ARMY RESEARCH LAB 1 CD 

AMSRD ARL CI E 

COMP & INFO SCI DIR 

WSMR NM 88002-5501 

WSMR TECH LIBRARY BR 1 CD 

STEWS IM IT 

WSMR NM 88002 

US ARMY CECOM 1 CD 

INFO & INTELLIGENCE WARFARE DIR 

ATTN AMSEL RD IW IP 

FORT MONMOUTH NJ 07703-5211 

NAVAL RESEARCH LAB 1 CD 

MARINE METEOROLOGY DIV 

7 GRACE HOPPER AVE STOP 2 

MONTEREY CA 93943-5502 

US ARMY RESEARCH LAB 1 

ATTN SFAE C3T IE II 

E BARNES 

WSMR NM 88002 

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DEFENSE TECHL INFO CTR 

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8725 JOHN J KINGMAN RD STE 0944 

FT BELVOIR VA 22060-6218 

US ARMY RESEARCH LAB 1 CD 

ATTN IMNE ALC IMS 

MAIL & RECORDS MGMT 

ADELPHI MD 20783-1197 

US ARMY RESEARCH LAB 2 CD 

AMSRD ARL CI OK TL TECHL LIB 

2800 POWDER MILL ROAD 

ADELPHI MD 20783-1197 

US ARMY RESEARCH LAB 1 CD 

AMSRD CI OK TP TECHL LIB 

APG MD 21005 

US ARMY RESEARCH LAB 3 CD 

AMSRD ARL CI EM 

ATTN S LUCES 

WSMR NM 88002-5002 

US ARMY RESEARCH LAB 3 

AMSRD ARL CI EM 

ATTN R CIONCO 

WSMR NM 88002-5002 

FNMOC 2 

ATTN CE SKUPNIEWICZ 

A GORACH 

7 GRACE HOPPER AVE STOP 1 

MONTEREY CA 93943-5501 

USAF/TMB 1 CD 

WG NORRIE 

1030 S HWY A1A 

PATRICK AFB FL 32925-300 

DR Z BOYBEYI 1 

DIR CAMP 

SCHOOL OF COMPUTATIONAL SCIENCES 

GEORGE MASON UNIVERSITY 

4400 UNIVERSITY DR MS5C3 

FAIRFAX VA22030-4444 

20 
 Copies 

R DAVIS PHD 1 

DIR OF SCIENCE AND ENGINEERING 

PROGRAMS 

OPTIMETRICS INC 

2107 LAUREL BUSH RD STE 209 

BEL AIR MD 21015-5203 

DR J BORIS 1 

NAVAL RESEARCH LAB 

CODE 6400 

4555 OVERLOOK AVE SW 

WASHINGTON DC 20375-5344 

DR C LIND 1 

CORE LETHALITY MODELS PROGRAM 

MISSILE DEFENSE AGENCY 

7100 DEFENSE PENTAGON 

WASHINGTON DC 20301-7100 

DR RJ BABARSKY 1 

NGIC IANG GS CN 

220 SEVENTH STREET NE 

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MR R FRY 1 

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8725 JOHN J KINGMAN RD 

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FORT BELVOIR VA 22060 

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CDR S HAMILTON 1 

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8725 JOHN J KINGMAN RD 

MSC 6201 

FORT BELVOIR VA 22060 

DR J WHITE 1 

MODELING SIMULATION AND ANALYSIS 

TEAM 

EDGEWOOD CB CENTER 

AMSSB 

APG MD 21010-5424 

DR T GILL 1 

DEPT OF GEOLOGICAL SCIENCES & 

ENVIRONMENTAL SCIENCE & ENGINEERING 

UTEP 

500 W UNIVERSITY AVE 

EL PASO TX 79968 

TOTAL 53 (35 CDs & 18 hard copies) 

21 
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22