Air Mass Forecasts Using the 

Mesoscale Model Version 5 (MM5) 

by Jeff Passner 

ARL-TR-4032 January 2007 

Approved for public release; distribution is unlimited. 
NOTICES 

Disclaimers 

The findings in this report are not to be construed as an official Department of the Army position 

unless so designated by other authorized documents. 

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

approval of the use thereof. 

Destroy this report when it is no longer needed. Do not return it to the originator. 
Army Research Laboratory 

White Sands Missile Range, NM 88002-5501 

ARL-TR-4032 January 2007 

Air Mass Forecasts Using the 

Mesoscale Model Version 5 (MM5) 

Jeff Passner 

Computational and Information Sciences Directorate 

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

OMB No. 0704-0188 

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

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

collection of information, including suggestions for reducing the burden, to Department of Defense, Washington Headquarters Services, Directorate for Information Operations and 

Reports (0704-0188), 1215 Jefferson Davis Highway, Suite 1204, Arlington, VA 22202-4302. Respondents should be aware that notwithstanding any other provision of law, no person 

shall be subject to any penalty for failing to comply with a collection of information if it does not display a currently valid OMB control number. 

PLEASE DO NOT RETURN YOUR FORM TO THE ABOVE ADDRESS. 

1. REPORT DATE (DD-MM-YYYY)

January 2007 

2. REPORT TYPE 

Final 

3. DATES COVERED (From - To) 

5a. CONTRACT NUMBER 

5b. GRANT NUMBER 

4. TITLE AND SUBTITLE 

Air Mass Forecasts Using the Mesoscale Model Version 5 (MM5) 

5c. PROGRAM ELEMENT NUMBER 

5d. PROJECT NUMBER 

5e. TASK NUMBER 

6. AUTHOR(S) 

Jeff Passner 

5f. WORK UNIT NUMBER 

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

U.S. Army Research Laboratory 

Computational and Information Sciences Directorate 

Battlefield Environment Division (ATTN: AMSRD-ARL-CI-EM) 

White Sands Missile Range, NM 88002-5501

8. PERFORMING ORGANIZATION 

 REPORT NUMBER 

ARL-TR-4032 

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

U.S. Army Research Laboratory 

2800 Powder Mill Road 

Adelphi, MD 20783-1197

11. SPONSOR/MONITOR'S REPORT 

 NUMBER(S) 

ARL-TR-4032 

12. DISTRIBUTION/AVAILABILITY STATEMENT 

Approved for public release; distribution is unlimited. 

13. SUPPLEMENTARY NOTES 

14. ABSTRACT 

Weather can play a significant role in military planning and operations; thus, the U.S. Army Research Laboratory has developed a number of 

decision aids to optimize the use of weather conditions and forecasts in the battlefield. These weather variables are most often derived from 

mesoscale weather models, which provide weather forecast information from 0 to 48 h. The most basic variables, such as temperature, 

moisture, and wind data, are output from the model directly, but many of the decision aids need more sophisticated information for their 

systems, such as icing, turbulence, and air mass type. This study will investigate the relationship between model output and air mass forecasts. 

The model used is the Fifth-Generation National Center for Atmospheric Research (NCAR)/Penn State Mesoscale Model (MM5), which was 

centered on the urban, complex terrain of Salt Lake City, UT. 

15. SUBJECT TERMS

Mesoscale, air mass, MM5, complex terrain 

16. SECURITY CLASSIFICATION OF:

19a. NAME OF RESPONSIBLE PERSON

Jeffrey E. Passner 

a. REPORT 

U 

b. ABSTRACT 

 U 

c. THIS PAGE 

U 

17. LIMITATION 

 OF 

 ABSTRACT 

SAR 

18. NUMBER 

 OF 

 PAGES 

28 19b. TELEPHONE NUMBER (Include area code) 

505-678-3193 

Standard Form 298 (Rev. 8/98) 

Prescribed by ANSI Std. Z39.18 

ii
Contents 

List of Figures iv

List of Tables iv

Summary 1

1. Introduction 2

2. Research of Dust and Aerosol Models 2

2.1 Navy Aerosol Analysis and Prediction System (NAAPS)............................................. 3 

2.2 AFWA’s Dust Transport Application ............................................................................ 3 

3. ARL’s Method 3

3.1 The MM5........................................................................................................................ 4 

3.2 Air Mass Routine Used at ARL ..................................................................................... 5 

4. Verification 6

4.1 The Utah AMC............................................................................................................... 6 

4.2 SLC Terrain.................................................................................................................... 7 

4.3 MM5 Performance and Biases....................................................................................... 7 

4.4 Discussion of Errors in the MM5................................................................................... 9 

4.5 SLC Air Mass Forecasts and Performance..................................................................... 9 

5. Conclusions 16

References 17

Acronyms 18

Distribution List 20

iii
List of Figures 

Figure 1. The plot of the wind direction in degrees from the Utah AMC program........................ 6 

Figure 2. The complex, urban terrain of the Salt Lake Valley in Utah........................................... 7 

Figure 3. The trend in PM2.5 concentrations during a cold front event at SLC........................... 10 

Figure 4. The trend in PM10 concentrations during a cold front event at SLC............................ 11 

Figure 5. A look at how both the PM10 and PM2.5 change as the air mass changes. ................. 11 

Figure 6. The wind shifts gradually from the south to northwest during the morning hours of 

14 February 2005. ................................................................................................................... 12 

Figure 7. The wind speeds during the 13-14 February 2005 timeframe....................................... 13 

List of Tables 

Table 1. MM5 forecast skill at SLC from January 2004 to May 2005........................................... 8 

Table 2. MM5 wind speed error at Hawthorne site (m/s)............................................................... 9 

Table 3. The change in main weather parameters in the SLC area between 0300 and 1000 

UTC, 14 February 2005. ......................................................................................................... 14 

Table 4. MM5 forecast and observations for temperature, wind, and air mass type at 

Hawthorne, UT, 14 February 2005......................................................................................... 14 

Table 5. Percent of correct forecast (rural or urban) using MM5 output at Hawthorne, UT........ 15 

iv
Summary 

Weather can play a significant role in military planning and operations; thus, the U.S. Army 

Research Laboratory has developed a number of decision aids to optimize the use of weather 

conditions and forecasts in the battlefield. These decision aids, such as the Integrated Weather 

Effects Decision Aid (IWEDA) and the Target Acquisition Weapons Software (TAWS), utilize 

many different weather forecast elements for either rule-based systems or physics-based systems. 

These weather variables are most often derived from mesoscale weather models, which provide 

weather forecast information from 0 to 48 h. The most basic variables, such as temperature, 

moisture, and wind data, are output from the model directly, but many of the decision aids need 

more sophisticated information for their systems, such as icing, turbulence, and air mass type. 

This study will investigate the relationship between model output and air mass forecasts. The 

model used is the Fifth-Generation National Center for Atmospheric Research (NCAR)/ 

Penn State Mesoscale Model (MM5), which was centered on the urban, complex terrain of 

Salt Lake City, UT. 

1
1. Introduction 

Weather can play a significant role in military planning and operations; thus, the U.S. Army 

Research Laboratory (ARL) has developed a number of decision aids to optimize the use of 

weather. These decision aids, such as the Integrated Weather Effects Decision Aid (IWEDA) and 

the Target Acquisition Weapons Software (TAWS), utilize many different weather parameters 

for either rule-based systems or physics-based systems. Weather variables, such as temperature, 

moisture, and wind data, are derived from the Fifth-Generation NCAR/Penn State Mesoscale 

Model (MM5); however, many of the decision aids need more detailed weather information for 

their systems, such as icing, turbulence, and air mass type. This study will investigate the 

relationship between model output and air mass forecasts. The model was centered on the urban, 

complex terrain of Salt Lake City, UT, to validate the model and air mass forecasts and compare 

them to output provided by the Utah Air Monitoring Center. 

2. Research of Dust and Aerosol Models 

There have been numerous attempts to model aerosols and dust production from many groups. 

Most of these efforts come from Government researchers who are particularly motivated to study 

and display air-quality or air-pollution conditions and forecasts. The National Oceanic and 

Atmospheric Administration (NOAA) has developed the Real-Time Environmental Applications 

and Display System, which accesses and displays meteorological data and runs a trajectory and 

dispersion model (1). 

One of the more recent efforts in the prediction of air quality has been using a real-time 

simulation of the Weather Research and Forecasting (WRF) model, which is called WRF-Chem 

and runs a 36-km air quality forecast out to 72 h. The chemistry package consists of many 

options of both biogenic emissions and anthrogenic sources of pollutants, as well as several 

choices of aerosols schemes. The model uses dry deposition coupled with a soil and 

vegetation scheme. (2) 

The effort to run weather models and forecast air quality is a worldwide problem and many 

Government and university scientists have made an effort to run models to forecast dust and 

aerosols. The Dust Regional Atmospheric Model (DREAM), produced by the Earth Sciences 

Division of the Barcelona Supercomputing Center, predicts the life cycle of the eroded desert 

dust using the National Centers of Environmental Prediction’s Eta model. Its main components 

are dust production with an introduced viscous sub-layer; particle size distribution effects; soil 

wetness effects on dust production; dry and wet deposition; and horizontal and vertical advection 

of turbulent and lateral diffusion (3). 

However, of most concern to the ARL are the effects of aerosols and dust on military operations. 

Both the Navy Research Laboratory (NRL) and the Air Force Weather Agency (AFWA) have 

approached this problem. 

2
2.1 Navy Aerosol Analysis and Prediction System (NAAPS) 

NRL has developed the NAAPS, which is a global aerosol model. The model uses 

meteorological fields from the Navy Operational Global Atmospheric Prediction System 

(NOGAPS) analysis and forecast on a 1×1° grid at 6-h intervals using 24 vertical levels to 

100 hPa. The main emphasis of the NAAPS is on sulfur, dust, and smoke emissions. (4) 

The sulfur dioxide emission is based on the Global Emissions Inventory Activity (version 1A) 

for the year 1985 with a seasonal variation and two-level vertical distribution. Smoke emissions 

are based on geostationary satellite and moderate resolution imaging fire products. Dust emission 

occurs whenever the friction velocity exceeds a threshold value, snow depth is less than a critical 

value, and the surface moisture is less than a critical value. The flux is taken from Westphal’s 

work and includes only particles with radii smaller than 5 microns. (5) 

2.2 AFWA’s Dust Transport Application 

Toon and Colarco developed the Community Aerosol Research Model for the National 

Aeronautics and Space Administration, Ames Research Center, which the Johns Hopkins 

University Applied Physics Laboratory modified to incorporate MM5 data from AFWA. AFWA 

renamed this model the Dust Transport Application (DTA), which is used operationally as it 

ingests 45-km MM5 data. The surface dust flux, which uses the MM5 surface wind output, is 

based on wind threshold velocity, which depends on particle diameter; air and particle density; 

and soil moisture. The soil moisture is determined by using the Agricultural Meteorological 

Model soil moisture output to determine soil wetness. When the MM5 indicates that precipitation 

is falling, it is assumed that there is no surface dust flux at a grid point. The DTA removes dust 

through wet deposition and accounts for atmospheric stability using a vertical wind parameter 

and vertical diffusion coefficient. The DTA uses a dust source region database developed by 

Ginoux. (6, 7, 8) 

AFWA implemented the DTA onto the Joint Air Force and Army Weather Information Network 

in February 2003. In evaluations of the DTS, Wesley et al. (9) note that the DTA excels at 

forecasting synoptic-scale dust storms and dust storms that are generated by strong pressure 

gradients. The DTA model has difficulties with small-scale events and some mesoscale events, 

such as those generated by thunderstorms. Wind speed, atmospheric stability, and source region 

are the three main environmental factors in dust forecasting and the dust forecasts are highly 

dependent on season, soil type, vegetation, and surface heating. 

3. ARL’s Method 

Unlike the requirements at NRL and AFWA, currently ARL Tactical Decision Aids (TDAs) do 

not require aerosol or dust forecasts. The main focus at ARL is on the air mass type; thus, there 

is no need for satellite data and some of the complex information needed by NAAPS and the 

DTA. This simplifies the problem greatly and permits a rudimentary solution in the initial effort 

to determine the air mass type. The three main aerosols of interest for this project are the rural, 

urban, and maritime aerosols (10): 

3
• The rural aerosol is typically found in continental air masses. This aerosol is composed 

principally of sub-micrometer soil particles and products of certain natural and manmade 

processes occurring in continental regions. 

• The urban model describes aerosols that include significant concentrations of particles 

produced in urban and industrial complexes. The chemical makeup of the aerosol is that of 

the rural aerosol plus an added component representing soot-like aerosols 

 • The maritime model characterizes aerosols that include sea-salt particles. This model 

should be used when the air mass is maritime and the target area is more than a few 

kilometers inland. 

To determine which aerosol model is the dominating one at the initial time or some future 

forecast period, the output from the MM5 is used. 

3.1 The MM5 

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

to simulate or predict mesoscale and regional-scale atmospheric circulation (11). 

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

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

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

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

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

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

tend to approximate isobaric surfaces. 

Other features of MM5 are that it is globally relocatable; has flexible, multiple nesting 

capabilities, advanced physical parameterization, and a three-dimensional data assimilation 

system via nudging; and can be run on various platforms. 

The version of the MM5 being used in this study is version 3 from AFWA with a resolution of 

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

continental United States twice each day, which are initialized at 0600 universal time coordinates 

(UTC) and 1800 UTC, respectively. Due to computational and processing constraints, there is a 

6-h stagger between the initialization valid time of the 15-km mesh and the first forecast output; 

thus, the first MM5 forecast is a 6-h forecast. The frequency of the model output is every 3 h for 

a time period of 48 h. 

The AFWA version of the MM5 places the lowest model vertical level at 20 magl. To generate 

data at the standard observation heights of 10 and 2 magl, similarity theory is being used at ARL 

to extrapolate to these lower levels from the lowest MM5 sigma level. In this fashion, 

temperature, dew point, and wind data at levels 2 and 10 magl are produced at ARL in addition 

to the 41 MM5 sigma levels of data. 

4
The parameterizations selected by AFWA with this version of the MM5 are as follows: 

1. Grell cumulus parameterization—Designed for grid sizes of 10 to 30 km, this 

parameterization accounts for sub-gridscale convection and compensating subsidence. 

2. Medium Range Forecast (MRF) planetary boundary-layer (PBL) model—This model 

parameterizes the mixture of heat, moisture, and momentum in the boundary layer. 

3. Reisner mixed phase explicit moisture microphysics—Cloud and rainwater fields and ice 

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

4. Dudhia cloud radiation—This parameter provides solar and infrared fluxes at the ground 

and atmospheric tendencies resulting from the radiative processes. 

5. MM5 five-layer soil model. 

3.2 Air Mass Routine Used at ARL 

The original method for determining the aerosol model selection process used the Low 

Transmission Atmospheric Propagation code (LOWTRAN), which asked the user to answer a 

series of questions, such as how long the air mass had been over land or how far from an urban 

area a location was. One difficult feature of this software and program was that it involved 

human interface and it was often difficult to judge how long air parcels had been over land or 

water. Also, the definition of rural or urban was vague, given that the 8-km distance from the 

central business district was arbitrary from city to city. Additional complexities were that the 

MM5 did not indicate where the air mass originated and it was assumed that the boundary layer 

was considered 1.5 to 2.0 km above ground level (AGL) at all times. (12) 

The method for air mass type in this project was based on the theories applied in the LOWTRAN 

method, but it was modified significantly for output from a mesoscale model. As a first test, the 

software determined if the air mass was marine or continental by checking the elevation and 

proximity to the ocean. In addition, if the rainfall rate was over 12.5 mm/h, it was assumed that 

this rainfall would effectively wash out the salt and it was considered a “continental” air mass. 

If an air mass was determined to be continental, then the ARL software made a determination if 

the air mass was either rural or urban. This was a more complex assessment since the computer 

software had to estimate the inversion height and lapse rate below the inversion. To determine 

the inversion height, the program investigated the vertical structure of the atmosphere for 

significant layers of warming and determined if the height of the inversion level was not too 

close to ground or too high above the ground to trap significant amounts of aerosols. If there 

was no inversion, it was assumed that mixing was sufficient enough to disperse the aerosols 

and that the environment was considered as rural. The program then checked the vertical lapse 

and surface wind speed. The following three cases helped to determine if the air mass was 

urban or rural: 

• Stable boundary-layer: Light winds (urban air mass), strong winds (rural air mass) 

• Neutral boundary-layer: Light winds (urban air mass), strong winds (rural air mass) 

• Unstable boundary-layer: All wind speeds, considered rural air mass 

5
The final factor to determine if air mass types were urban or rural was the accumulated 

precipitation over the last three hours. If the precipitation from the model forecast was over 

6.0 mm, then it was assumed that wet deposition was efficient and the air mass is rural. All 

terrain points over 2.0 km were considered rural. 

4. Verification 

Verification of the air mass routine was conducted by setting the MM5 grid over the Salt Lake 

City (SLC), UT, area and using data from the Utah Air Monitoring Center (AMC) to compare 

the particle matter (PM) readings to the air mass forecasts. Data were collected at Lindon, 

Ogden, and Logan, as well as in the Hawthorne area of SLC. 

4.1 The Utah AMC 

The Utah AMC is responsible for operating and maintaining an ambient air monitoring network 

that provides air pollution information for the daily Air Quality Index (AQI), health advisories, 

seasonal wood burn conditions, and summer ozone alerts. AMC data is used to determine the 

relationship of existing pollutant concentrations to the national air quality standards and to 

assist in reducing pollution levels. The pollutants of most interest in this study are particulate 

matter smaller than 10 microns (PM10, 1-h averaging time) and particulate matter smaller than 

2.5 microns (PM2.5, 1-h averaging time). These data are available at a number of locations in 

and around the SLC area. In addition to air pollutants, many of the Utah sites provide 

information in wind direction, wind speed, relative humidity, temperature, and short-wave 

radiation in w/m2. These data are available in plots that output data over a two-day period as 

displayed in figure 1. (13) 

Figure 1. The plot of the wind direction in degrees from the Utah AMC program. 

6
4.2 SLC Terrain 

The Salt Lake Valley is a unique place to study meteorological models and meteorological 

conditions. The city is surrounded by higher terrain in all directions except the north where the 

Great Salt Lake is located. Additionally, the valley with a large population has become an 

intriguing region to study air pollution and aerosols, since the valley is often characterized by a 

stable boundary layer with a strong morning inversion. The complex terrain is also a challenge 

because of a variety of wind flows influenced by the mountains and the lake. The terrain is seen 

in figure 2. 

Figure 2. The complex, urban terrain of the Salt Lake Valley in Utah. 

NOTE: Source: http://www.visitsaltlake.com/getting_around/maps.html. 

4.3 MM5 Performance and Biases 

As noted, the MM5 data from AFWA was used to model the meteorological conditions and 

provide forecast data for this study. The 0600 UTC models were used with the grid centered over 

urban SLC and forecast results were compared only for the 6- to 24-h forecast periods. Data was 

collected from January 2004 to May 2005. 

7
In table 1, the results of the MM5 temperature, wind, temperature inversions, and radiation 

forecasts are shown for the Hawthorne area of SLC. The mean absolute difference (MAD) is the 

sum of the errors divided by the number of data points. The root mean square error (RMSE) is 

the square root of the mean square error and the correlation coefficient (CC) shows the strength 

of a linear relationship between two random variables. 

Table 1. MM5 forecast skill at SLC from January 2004 to May 2005. 

Forecast 

Average 

Observed 

Average MAD RMSE CC 

Temperature (°C) 2.7 5.9 3.9 5.0 0.88 

Wind direction (°) 216 222 54 44 0.49 

Wind speed (m/s) 4.3 3.1 2.2 3.1 0.38 

Inversion height (m) 335 318 243 410 0.65 

Short-wave radiation (w/m2) 355 449 146 211 0.86 

As might be expected in such a difficult forecasting domain, results of the MM5 forecasts 

through 18 h are not particularly good. The mean absolute difference in the temperature forecast 

were almost 4 °C, while the correlation coefficient was only 0.88. On average, the wind direction 

was in error by 54° in each forecast and wind speeds were about 2.2 m/s-1 in error. The incoming 

short-wave radiation forecast were generally underforecasted and inversion heights were on 

average 243 m different from the actual observations off the 1200 or 0000 UTC soundings 

from SLC. 

The AFWA MM5 biases at Hawthorne were as follows: 

• Temperature: The model underforecasts strongly between 1200 and 0000 UTC, with no 

bias noted at 0300 and 0600 UTC. Overall, the model underforecasts the temperature 78% 

of time (1 °C or more) from 1200 to 0000 UTC. 

• Wind Speed: The model overforecasts (0.8 m/s or more) 70% of the cases at 1200 and 1500 

UTC. No significant bias was observed at 1800, 2100, and 0000 UTC, but bias to 

overforecast wind speed starts again at 0300 UTC. 

• Wind Direction: At 1200 UTC, the forecasts were 5° in error in the veering (clockwise) 

direction and had 26° veering of wind at 1500 UTC. In 65% of the cases, the forecasts 

displayed veering at 1500 UTC. 

• Inversions: No high or low bias noted on inversion height. The “YES/NO” forecast 

displayed a probability of detection of an inversion 0.84, had a false alarm rate of 0.04, and 

correctly forecasting the non-event of 0.95. 

• Solar Radiation: The MM5 underforecasts incoming short-wave radiation 71% of the 

time at 1500 and 2100 UTC. There was no model bias noted in late in the afternoon 

at 0000 UTC. 

Of most concern for aerosol forecasts and air mass type is the forecasted wind speed. In table 2, 

the MM5 wind speed errors are shown for the 6- to 18-h forecast periods. The largest errors 

appear to be in the early morning hours when the downslope winds are dominating at 1200 and 

1500 UTC. The error becomes smaller during the afternoon hours when the wind shifts to the 

8
lake breeze (from the northwest or north) and then the error increases again late in day as the 

solar angle decreases. The CC of the wind is very low in the morning and late afternoon but very 

good during the late morning hours and mid-afternoon. 

Table 2. MM5 wind speed error at Hawthorne site (m/s). 

Forecast (time) MAD 

(m/s) 

RMSE 

(m/s) 

CC 

(m/s) 

6-h (1200 UTC) 2.7 3.0 0.16 

9-h (1500 UTC) 2.7 3.4 0.28 

12-h (1800 UTC) 1.6 2.1 0.76 

15-h (2100 UTC) 1.4 1.7 0.81 

18-h (0000 UTC) 2.4 2.8 -0.03 

4.4 Discussion of Errors in the MM5 

There are many reasons the forecasts may be in error, especially the temperature error. The first 

reason is, of course, related to using a 15-km grid, which can lead to significant errors in the 

model elevation due to the very steep gradient in the terrain just east of the location studied. 

There is evidence that the difference between the model terrain height and actual terrain height is 

400 m. Another factor in the temperature error is the MRF PBL scheme. Long-term studies, such 

as Mass et al., show surface winds in the MRF PBL are too strong especially at night, low-level 

winds are too geostrophic, and there is too much vertical mixing. Traditionally, the MM5 is poor 

at forecasting temperatures in shallow cold air, which occurs in 85% of the 1200 UTC SLC 

soundings. Another factor that can cause model error is that the model spin-up can still cause 

forecasting problems at 1200 and 1500 UTC. (14) 

4.5 SLC Air Mass Forecasts and Performance 

An example of how the model and air mass forecasts interact is shown using the case of 

13–14 February 2005. 

A series of plots from the SLC AMC site shows how the PM10.0 and PM2.5 change during an 

interesting weather event in the Salt Lake Valley. As seen in figure 3, the concentration of 

PM2.5 drops steadily during this weather sequence. The values of PM2.5 for most of the day on 

13 February 2005 are considered to be in the “moderate” range according to the AQI, although 

the PM2.5 values drop from about 35 μg/m3 at midnight 13 February 2005 to near 10 μg/m3 at

midnight 14 February 2005 when the AQI category is considered “good.” Similarly, in figure 4, 

the readings of PM10 drop from about 30 μg/m3 to near 10 μg/m3 by midnight 14 February 2005. 

The PM10 values are considered to be “good” air quality. Thus, on this day, it appears that there 

is a larger amount of the smaller PM2.5, which is often associated with aerosols produced by 

local traffic, burning, and industry. These trends are also seen in figure 5, where the hourly plots 

of PM2.5 and PM10 are shown from 0300 to 1000 UTC on 14 February 2005. Based on these 

data, it appears that the decrease of the smaller PM2.5 pollutants decreases more than the larger 

PM10-sized particles. (15) 

9
Figure 3. The trend in PM2.5 concentrations during a cold front event at SLC. 

10
Figure 4. The trend in PM10 concentrations during a cold front event at SLC. 

0

5

10

15

20

25

0300 

UTC

0400

UTC

0500

UTC

0600

UTC

0700

UTC

0800

UTC

0900

UTC

1000

UTC

PM2.5

PM10.0

Figure 5. A look at how both the PM10 and PM2.5 change as the air mass changes. 

11
It is interesting to try to relate the trends in the particulate matter to the observed weather. Figure 

6 displays the wind direction during the two days studied. The timeframe in the plot is in local 

standard time (LST). The wind direction is generally from the south during the day on 

13 February 2005, but shifts to the northwest or from the Great Salt Lake for a two-hour period 

between 1900 and 2100 LST (0200 and 0400 UTC 14 February 2005). 

Figure 6. The wind shifts gradually from the south to northwest during the morning hours of 14 February 2005. 

During time 1900 to 2100 LST timeframe, the wind speeds drop to 5 mph (2.2 m/s) as displayed 

in figure 7, but increase to 10 mph (4.5 m/s) when the wind shifts back to the south, a direction 

associated with the valley component of the wind. During the night, the dominating wind 

direction is from the south, but the wind shifts gradually to the southwest at 0800 LST 

(1500 UTC) and to the northwest at 0900 LST (1600 UTC), 14 February 2005. The surface 

pressure field and observed weather indicate that a synoptic-scale cold front passes through the 

region during the morning hours between 0600 and 0800 LST (1300 to 1500 UTC). This is 

seen by the wind shift as well as a pressure rise from 1013.6 hPa at 1100 UTC to 1015.6 hPa 

at 1700 UTC. 

12
Figure 7. The wind speeds during the 13-14 February 2005 timeframe. 

It is also intriguing to note that even with the cold frontal passage at about 0700 LST (1400 

UTC), the reduction of particulate matter begins as much as 12 h before the cold front reaches 

the SLC area as seen in table 3. The temperature rises dramatically, from 2.8 °C to 8.9 °C in 2 h, 

as the winds shift from the cold water to the slight downslope direction from the south. This is 

also reflected in the relative humidity values, which drop from 89% to approximately 60% 

during the overnight hours. During the time period shown in table 3, the cloud height lowers and 

light rain is observed at the SLC airport at 0300 LST (1000 UTC), 14 February 2005. It is 

uncertain as to the exact reason the PM values decrease so quickly, but it can be assumed that 

there is significant vertical mixing even in the overnight hours due to the approaching weather 

system and perhaps some associated upper dynamical energy. 

13
Table 3. The change in main weather parameters in the SLC area between 0300 and 1000 UTC, 14 February 2005. 

Hour 

(UTC) 

PM 2.5 

(μg/m3

) 

PM 10.0 

(μg/m3) 

Visibility and 

Precipitation 

(mi) 

Clouds 

(ft AGL) 

Temperature 

(°C) 

Relative 

Humidity 

(%) 

Wind 

Direction 

Wind 

Speed 

(m/s) 

0300 24 25 10 110 BKN 2.8 89 340 2.2 

0400 24 24 10 110 BKN 7.2 60 190 3.1 

0500 19 24 10 100 BKN 8.9 56 200 4.5 

0600 12 16 10 200 OVC 8.9 58 205 4.5 

0700 9.5 13 10 100 BKN 8.9 60 200 4.5 

0800 8 13 10 85 BKN 8.3 60 200 3.6 

0900 7.1 12 10 65 BKN 8.3 60 200 5.4 

1000 6.3 12 10R- 60 OVC 8.3 61 200 4.0 

NOTE: BKN = broken ceiling and OVC = overcast ceiling. 

As a final part of this case study, the MM5 forecast during this event was compared to the actual 

surface weather observations. Table 4 displays the model forecast for temperature, wind 

direction, wind speed, and air mass type. 

Table 4. MM5 forecast and observations for temperature, wind, and air mass type at Hawthorne, UT, 14 February 2005. 

Hour 

(UTC) 

Temp 

Forecast 

(°C) 

Temp 

Observed 

(°C) 

Wind 

Direction 

Forecast 

(°) 

Wind 

Direction 

Observed 

(°) 

Wind 

Speed 

Forecast 

(m/s) 

Wind 

Speed 

Observed 

(m/s) 

Air Mass 

Forecast 

Type 

Air Mass 

Observed 

(PM2.5) 

0000 

6-h 

forecast 

6.5 9.4 208 190 4.5 5.9 Rural 25 

0300 

9-h 

forecast 

4.5 2.8 173 340 8.6 3.0 Rural 23 

0600 

12-h 

forecast 

3.7 8.9 195 205 7.6 5.9 Rural 16 

0900 

15-h 

forecast 

2.8 8.3 216 200 5.6 7.1 Rural 7 

As seen in table 4, this is a complex case where the wind direction forecast is significantly in 

error at the 9-h forecast (0300 UTC) as the forecast is from the south and the observed is from 

the northwest, or off the lake. This leads to cooler temperatures in the observation and the lower 

wind speed. By the 12-h forecast (0600 UTC), the forecast is correct again with the higher wind 

speeds, a slight downslope component in the valley, and an error to underforecast the 

temperatures. The air mass type is forecasted to be a rural type during the entire period due to the 

strong surface winds providing the mechanism to remove the PM2.5 out of the region. 

14
Based on this study, the inversion “YES/NO” forecasts are very accurate. However, wind 

forecast bias at 1200 and 1500 UTC mean that air mass forecasts may show the least skill at that 

time and the most skill from 1800 to 2100 UTC. 

In table 5, for PM2.5 any forecast or observation of less than 30 μg/m3 was considered to be a 

rural forecast, while any forecast or observation of less than 55 μg/m3

 was considered rural for 

PM10. The results are what might be expected based on the MM5 forecast bias; the largest errors 

in the wind speed, as seen in table 2, are in the early morning hours from 1200 to 1500 UTC, 

where the absolute error is 2.7 m/s. Once the wind error decreases to an absolute error of about 

1.5 m/s, as they do at 1800 and 2100 UTC, the accuracy of the PM2.5 and PM10 forecast 

increases dramatically before decreasing again at 0000 UTC, which is 18 h after model 

initialization. Still, the problem is not so simplistic. Typically the 1200 and 1500 UTC hours 

feature low-level inversions, especially in the cold season at SLC. While the MM5 provides an 

accurate vertical profile of the temperature profile, it does not always have an accurate forecast 

for when the inversion will break and the more “urban” air mass will mix out to become a “rural” 

air mass. However, an even more difficult task for this air mass program is that there is no 

current way of initializing the air mass at the start of the forecast. Obviously, the amount of 

particular matter at the start of the forecast period plays a major role in how the air mass type 

changes over the entire forecast cycle. Depending on just the forecasted weather parameters is an 

idealized solution to a difficult problem, but it does not provide a complete picture of how the air 

mass responds to the environmental conditions. Still, once the inversion does break, the air mass 

program does has a good feel for the transition from an “urban” or stable boundary layer to a 

more well-mixed or “rural” environment. 

Table 5. Percent of correct forecast (rural or urban) using MM5 output at Hawthorne, UT. 

Time/Size 

(UTC) 

PM2.5 

(%) 

PM10 

(%) 

1200 29 32 

1500 44 48 

1800 78 74 

2100 95 78 

0000+ 63 61 

Total 63 61 

NOTE: Randomly used values of <30 ug/m3 as rural for PM2.5 and <55 ug/m3 PM10 for 127 samples. 

15
5. Conclusions 

ARL has developed a number of decision aids to optimize the use of weather. These decision 

aids, such as the IWEDA and the TAWS, utilize many different weather parameters. In an effort 

to provide more detailed weather information for the TDAs, an air mass type program has been 

formulated using the output from the MM5. The MM5 was centered on the urban, complex 

terrain of SLC to validate the model and air mass forecasts and comparing them to output 

provided by the Utah AMC. This project approached two problems; the accuracy of the MM5 in 

the SLC area and the relationship between the model output and the air mass forecast. 

The original approach to forecasting the air mass type was the LOWTRAN model, which 

determined if an air mass was urban, rural, or marine based on questions posed to the user. The 

current air mass forecast is an automated process, which is based on elevation, proximity to the 

ocean, rainfall rates, inversion heights, lapse rates, and wind speed at a model grid point. These 

factors then provided an air mass type as either marine, rural, or urban. 

The MM5 results at Hawthorne, UT, were not encouraging, although this was expected given the 

15-km resolution in a very complicated grid area that includes mountain ranges and a body of 

water. The mean absolute difference in wind speed was 2.2 m/s, the wind direction was 54° in 

error, the temperature forecast was 3.9 °C in error, and the inversion heights were 243 m 

different than the observation. It was found the largest wind speed errors were in the early 

morning hours and again near sunset (18-h forecast), while the wind speed forecast was most 

accurate at the 12- and 15-h forecast periods. Given the importance of the wind speed in the air 

mass forecast, it was found the air mass program had it highest verification at the same time that 

the wind speed forecasts were most accurate. 

The method used in this effort was quite simplistic, and while it does provide accurate results 

after the morning inversion breaks, there is still much work needed in this research effort. A way 

to determine the air mass type at the model initialization would provide much-needed 

information that would help improve results. Data such as land-use, vegetation type, and soil 

moisture would enhance the model, especially for the PM10 output and dust forecasts. 

Improvement in the mixing height forecasts and the time that inversion breaks or forms would be 

useful additional input to the program. Additionally, wind gust information could help with the 

forecast for dust particles and its influence on the air mass. 

A long-term goal would be to use the air mass type program and use the results in the visibility 

routine that are post processed. One other improvement would be to use the 2-km WRF model as 

the forecast output data. Using the smaller resolution would give additional terrain information, 

which should help the model in providing more accurate wind information and give significantly 

better forecast information for the air mass forecast. 

Overall, the air mass program did provide good, basic guidance. The simple structure of the 

program can definitely be upgraded with more land-use information, better model resolution, and 

more information about the initial state of the atmosphere. These improvements can be added to 

future generations of the program. 

16
References 

1. NOAA Air Resources Laboratory. http://www.arl.noaa.gov/research/waq/ (accessed 18 Oct 

2006). 

2. Grell, G.A. et al. Fully Coupled Online Chemistry Within the WRF Model. Atmos. Environ. 

2005, 39, 6957–6975. 

3. Pérez, C. et al. A Long Saharan Dust Event Over the Western Mediterranean: LIDAR, Sun 

Photometer and Regional Dust Modeling. J. Geophy Res. 2006, 111, 6579–6591. 

4. Navy Research Laboratory. http://www.nrlmrl.navy.mil/aerosol/Docs/globaer_model.htm

(accessed 15 Sept 2006). 

5. Westphal, D.L.; Toon, O.B.; Carlson, T.N. A Case Study of Mobilization and Transport of 

Saharan Dust. J. Atmos. Sci. 1988, 45, 2145–2175. 

6. Barnum B.H. et al. Forecasting Dust Storms Using CARMA – Dust Model and MM5 

Weather Data. Preprints of the 5th Conference on Atmospheric Chemistry: Gases, Aerosols, 

Clouds, Long Beach, CA, 2003, Paper 7.8. 

7. Hasselbarth, L.M.; Barnum, B.H.; Toth, B.A. Surface Flux Dust Model Version Description; 

UPOS-BC3-01; University Partners for Operational Support, Johns Hopkins University 

Applied Physics Laboratory: Laurel, MD, 2001. 

8. Ginoux, P. et al. Sources and Distributions of Dust Aerosols Simulated with the GOCART 

Model. J. Geophys. Res. 2001, 106, 20255–20273. 

9. Wesley, J. J. et al. Dust Transport Application (DTA) Tutorial, Air Force Weather Agency. 

https://weather.afwa.af.mil/about-info/DTA/DTA_Tutorial.html (accessed 25 Oct 2006). 

10. Gouveia, M.J. et al. Electro-Optical Tactical Decision AID (EOTDA) User’s Manual, 

Version 3.1; PL-TR-94-2174 (I); Phillips Laboratory: Hanscom AFB, MA, 1994. 

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

Validation Tests and Simulation of an Atlantic Cyclone and Cold Front. Monthly Weather 

Rev. 1993, 121, 1493–1513. 

12. Kneizys, F.X. et al. Atmopsheric Transmittance/Radiance: Computer Code LOWTRAN 5; 

AFGL-TR-80-0067; Air Force Geophysical Laboratory, Phillips Laboratory: Hanscom, MA, 

1980. 

13. Utah Department of Environmental Quality, Division of Air Quality. 

http://www.airmonitoring.utah.gov/ (accessed 19 October 2006). 

14. Mass C. Mesoscale Predictability and Process Issues: Conclusions from Five Years of RealTime Regional Forecasts. Preprints of the 10th Conference on Mesoscale Processes: Portland, 

OR, 2003, Paper 2.4. 

15. Dabberdt, W.F. et al. Meteorological Research Needs for Improved Air Quality Forecast. 

Bull. Amer. Meteor. Soc. 2004, 85, 563–586. 

17
Acronyms 

AFWA Air Force Weather Agency 

AGL above ground level 

AMC Air Monitoring Center 

AQI Air Quality Index 

ARL U.S. Army Research Laboratory 

BKN broken ceiling 

CC correlation coefficient 

DREAM Dust Regional Atmospheric Model 

DTA Dust Transport Application 

hPa hecto-pascals 

IWEDA Integrated Weather Effects Decisions Aids 

LOWTRAN Low Transmission Atmospheric Propagation 

LST local standard time 

MAD mean absolute difference 

MM5 Mesoscale Model Version 5 

MRF Medium Range Forecast 

NAAPS Navy Aerosol Analysis and prediction System 

NCAR National Center for Atmospheric Research 

NOAA National Oceanic and Atmospheric Administration 

NOGAPS Naval Operational Global Atmospheric Prediction System 

NRL Navy Research Laboratory 

OVC overcast ceiling 

PBL planetary boundary layer 

PM particulate matter 

PM10 particulate matter 10 microns or less 

PM2.5 particulate matter 2.5 microns or less 

RMSE root mean square error 

SLC Salt Lake City 

TAWS Target Acquisition Weather Software 

18
TDA Tactical Decision Aid 

UTC universal time coordinate 

WRF Weather Research and Forecasting 

19
Distribution List 

 Copies 

US ARMY MISSILE CMND 1 

REDSTONE SCI INFO CTR 

AMSMI RD CS R DOC 

BLDG 4484 

REDSTONE ARSENAL AL 35898 

ATMOSPHERIC PROPAGATION BRANCH 1 

SPAWARSYSCEN SAN DIEGO D858 

49170 PROPAGATION PATH 

SAN DIEGO CA 92152-7385 

NCAR LIBRARY SERIALS 1 

NATL CTR FOR ATMOS RSCH 

PO BOX 3000 

BOULDER CO 80307-3000 

HEADQUARTERS DEPT OF ARMY 1 

DAMI POB 

WEATHER TEAM 

1000 ARMY PENTAGON ROOM 2E383 

WASHINGTON DC 20310-1067 

HQ AFWA/DNX 1 

106 PEACEKEEPER DR STE 2N3 

OFFUTT AFB NE 68113-4039 

AFRL/VSBL 1 

29 RANDOLPH RD 

HANSCOM AFB MA 01731 

ARL CHEMICAL BIOLOGY 1 

NUC EFFECTS DIV 

AMSRL SL CO 

APG MD 21010-5423 

US ARMY RESEARCH LAB 1 

AMSRL D 

2800 POWDER MILL ROAD 

ADELPHI MD 20783-1145 

US ARMY RESEARCH LAB 1 

AMSRD ARL SE EE 

ATTN DR SZTANKAY 

2800 POWDER MILL ROAD 

ADELPHI MD 20783-1145 

 Copies 

US ARMY RESEARCH LAB 1 

AMSRD ARL CI 

ATTN J GOWENS 

2800 POWDER MILL ROAD 

ADELPHI MD 20783-1197 

US ARMY CECRL 1 

CECRL GP 

ATTN DR DETSCH 

HANOVER NH 03755-1290 

DEPT OF THE AIR FORCE 1 

OL A 2D WEATHER SQUAD MAC 

HOLLOMAN AFB NM 88330-5000 

PL WE 1 

KIRTLAND AFB NM 87118-6008 

USAF ROME LAB TECH 1 

CORRIDOR W STE 262 RL SUL 

26 ELECTR PKWY BLD 106 

GRIFFISS AFB ROME NY 13441-4514 

AFMC DOW 1 

WRIGHT PATTERSON AFB OH 45433-5000 

US ARMY FIELD ARTILLERY SCHOOL 1 

ATSF TSM TA 

FT SILL OK 73503-5600 

US ARMY OEC 1 

CSTE EFS 

PARK CENTER IV 

4501 FORD AVE 

ALEXANDRIA VA 22302-1458 

US ARMY RESEARCH LAB 1 

AMSRL CI E 

COMP & INFO SCI DIR 

WSMR NM 88002-5501 

US ARMY MISSILE CMND 1 

AMSMI 

REDSTONE ARSENAL AL 35898-5243 

USTRADOC 1 

ATCD FA 

FT MONROE VA 23651-5170 

20
 Copies Copies 

WSMR TECH LIBRARY BR 1 US ARMY RESEARCH LAB 1 

STEWS IM IT ATTN IMNE AD IM DR

WSMR NM 88002 MAIL & RECORDS MGMT 

ADELPHI MD 20783-1197 

US ARMY CECOM 1 

INFORMATION & INTELLIGENCE US ARMY RESEARCH LAB 2 

WARFARE DIRECTORATE AMSRD ARL CI OK TL 

ATTN AMSEL RD IW IP 2800 POWDER MILL ROAD 

FORT MONMOUTH NJ 07703-5211 ADELPHI MD 20783-1197 

NAVAL RESEARCH LABORATORY 1 US ARMY RESEARCH LAB 2 

MARINE METEOROLOGY DIVISION AMSRD CI OK TP TECHL LIB 

7 GRACE HOPPER AVENUE STOP 2 ATTN T LANDFRIED 

MONTEREY CA 93943-5502 APG MD 21005 

DTIC 1 elec US ARMY RESEARCH LAB 3 hard copies, 2 CDs 

DEFENSE TECHNICAL CISD BED 

ATTN DTIC OCP W SMITH ATTN J PASSNER 

8725 JOHN J KINGMAN RD STE 0944 AMSRD ARL CI EM 

FT BELVOIR VA 22060-6218 WSMR NM 88002-5501 

TOTAL 34 (30 CDs, 3 hard copies & 1 electronic) 

21
INTENTIONALLY LEFT BLANK. 

22