TSD-1a

Meteorological Modeling using Penn State/NCAR 5th

Generation Mesoscale Model (MM5)

Bureau of Air Quality Analysis and Research

Division of Air Resources

New York State Department of Environmental Conservation

Albany, NY 12233

March 19, 2006
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Meteorological Modeling using Penn State/NCAR 5th Generation Mesoscale Model 

(MM5)

Version 3.6 of MM5 was used to generate annual 2002 meteorology for the OTC 

modeling work. Prof. Dalin Zhang of the University of Maryland performed the MM5 

simulations in consultation with NYSDEC staff. The model was applied in Lambert 

conformal map projection and utilized MPP Version developed for clusters. The two-way

nested domain consisted of coarse (36km) and fine (12km) mesh corresponding to 

149x129 and 175x175 grids, respectively, in this application (see Figure 1). 

The Lambert projection used in this work followed the Regional Planning Organization 

(RPO) national domain setup with the center at (40oN, 97oW) and parallels at 33oN and 

45oN. Map projection parameters in reference to the projection center point are as 

follows: Southwest corner for the 36 km grid is at (-2664km, -2304km) and the northeast 

corner at (2664km, 2304km). In the case of the 12km grid, the southwest corner is at 

(252km, -900km) and the northeast corner at (2340km, 1188km). In the vertical direction, 

the terrain following σ-coordinate system was used with the pressure at each σ-level 

determined from a reference state that is estimated using the hydrostatic equation from a 

given sea-level pressure and temperature with a standard lapse rate. There are 30 

unevenly spaced σ levels, giving 29 vertical layers, with higher resolution within the 

planetary boundary layer (PBL). The σ levels are:

1.0000, 0.9974, 0.9940, 0.8980, 0.9820, 0.9720, 0.9590, 0.9430, 0.9230, 0.8990,

0.8710, 0.8390, 0.8030, 0.7630, 0.7180, 0.6680, 0.6180, 0.5680, 0.5180, 0.4680,

0.3680, 0.3180, 0.2680, 0.2180, 0.1680, 0.1230, 0.0800, 0.0400, 0.0000

The surface layer was set at about 10m, the level at which surface winds were typically 

observed, and the model top was set at 50hPa with a radiative top boundary condition. 

The time steps for the 36km and 12km domains were 75 and 25 seconds, respectively.

The important model physics options used for this MM5 simulation include:

 Kain-Fritsch (1993) convective scheme for both 36- and 12-km domains 

 Explicit moisture scheme (without the mixed phase) containing prognostic 

equations for cloud water (ice) and rainwater (snow) (Dudhia 1989; Zhang 1989)

 Modified version of the Blackadar planetary boundary layer (PBL) scheme 

(Zhang and Anthes 1982; Zhang and Zheng 2004)

 Simple radiative cooling scheme (Grell et al. 1994)

 Multi-layer soil model to predict land surface temperatures using the surface 

energy budget equation (Dudhia 1996)

Note that the Blackadar PBL scheme has been modified in order to correct the phase shift 

of surface wind speed and temperature diurnal cycle, following a study that compared 

five different PBL schemes: the Gayno-Seaman TKE scheme (Shafran et al. 2000), Burk-
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Thompson (1989), Blackadar (Zhang and Anthes 1982), MRF (Hong and Pan 1996), and 

Mellor-Yamada-Jajic (Mellor and Yamada 1974; Jajic 1990, 1994). The details of the 

study can be found at Zhang and Zheng (2004).

Nudging Processes 

The MM5 provides options for nudging observations for each domain during the model 

integration process (Stauffer and Seaman, 1990; Stauffer et al. 1991). The Eta analyses of 

upper-air winds, temperature and water-vapor mixing ratio as well as their associated surface 

fields were used for nudging every 6 hours, and the Eta surface wind fields blended with 

surface wind observations were used to nudge every 3 hours. While only the surface winds 

were nudged, their influences could extend into the PBL as well (see Stauffer et al. 1991). 

Based on UMD’s prior experience in numerical experiments, the following nudging 

coefficients have been used: 

 Upper-air wind fields: 5. 0E-4s-1for Domain 1 (36km), and 2. 5E-4s-1for Domain 2 

(12km); 

 Upper-air temperature fields: 1.0E-5s-1 for both Domains; 

 Surface winds: 5. 0s-1E-4s-1for Domain 1, and 2.5E-4s-1for Domain 2; and 

 Surface temperature and moisture: not nudged due to instability consideration. 

ASSESSMENT

This assessment covers the period of May through September 2002.

National Weather Service (NWS) and CASTNet data – Surface temperature, Wind 

Speed, and Humidity

NWS (TDL) and CASTNet (www.epa.gov/castnet/) surface measurements of 

temperature, wind speed, and humidity (note there were no humidity measurements for 

CASTNet) were used to compare with the MM5 outputs. The evaluation was performed 

with METSTAT program developed by Environ Corporation 

(www.camx.com/files/metstat.15feb05.tar.gz) When comparing to NWS data, the 

METSTAT interpolates the first layer MM5 (at 10m height) temperature and humidity

data to a height of 2m, the level that corresponds to the NWS measurement of these 

parameters. However, no such interpolation was made for wind speed and direction. In 

the case of CASTNet surface measurements, no such changes were needed as CASTNet 

data were reported at a height of 10m. In this analysis, no exclusion was made for calm 

conditions. The reported calm winds (zero wind speed measured) were treated as is in 

this evaluation effort. The METSTAT calculated standard statistical measures –

average, bias, error and index of agreement between the measured and predicted 

parameters.

Figure 2 displays the temperature and wind speed comparison of MM5 and measured 

data from NWS and CASTNet networks for August 2002. MM5 performance for both in 

magnitude and diurnal timing, temperature can be considered to be quite good for both 

NWS and CASTNet data, while MM5 underpredicted NWS and overpredicted CASTNet 
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daytime wind speed, respectively. It should be pointed out that there are differences in 

how the meteorological information is collected and reported by the two networks as well 

as in MM5. The CASTNet measurements are based on hourly averaged wind speed while 

NWS reports 2min average at 10min before the hour, whereas MM5 predictions are 

reflective of the last time-step of the hour of computation. Interestingly, MM5 appears to 

track quite well the nighttime minimum wind speed for both networks. In the case of 

humidity (not shown), MM5 tracks the NWS observed humidity trend well, but MM5 

missed the observed semi-diurnal cycles. Comparisons for the five months including bias 

and root mean square error from both NWS and CASTNet are available on request from 

NYSDEC.

The above assessment is based on domain-wide averages to provide an overall response 

of the model over the five months. Another way of assessing the model is to examine the 

degree of correlation between the measured and predicted parameters. Figures 3a and 3b 

displays such a comparison for wind speed and temperature, respectively, for the NWS 

hourly data covering the period of May through September 2002. For the NWS data, the 

correlations are in the range from 0.7 to 0.8 for wind speed, above 0.96 for temperature, 

and in the range of 0.8 to 0.9 for humidity. CASTNet data (not shown) also exhibit 

similar correlation. These correlations indicate that MM5 simulation has captured both 

the diurnal and synoptic scale variations. Detailed plots of this comparison are available 

on request from NYSDEC. 

Vertical Profiler – Winds 

The Wind-Profiler network measurements along the U. S. East Coast (www.madisfsl.org/cap) were used to evaluate the vertical profiles from MM5. There are twelve 

wind-profiler measurement stations from which data were available for comparison. For 

convenience of comparison, the wind-profiler measurements were interpolated to the 

MM5 vertical levels. The approach used was simple interpolation between two adjacent 

wind-profiler layers to the MM5 vertical level, and was limited to that reported by the 

profiler measurement. The focus of the comparison was to assess if MM5 was able to 

capture the measured vertical structure, and for this we used the observed Low Level Jet 

(LLJ) as an indicator. The comparison was performed for June, July and August 2002. In 

general it is found that MM5 captures the profiler measured vertical wind field structure 

reasonably well. Figure 4 displays an example of the MM5 and wind profiler comparison

for the August 2002 episode at Richmond, VA and Concord, NH. MM5 predicted weaker 

LLJ winds compared to those based on the wind-profiler measurements. The detailed 

plots of this comparison are available on request from NYSDEC. 

Cloud Cover – Satellite cloud image

Cloud information derived from satellite image data 

(www.atmos.umd.edu/~srb/gcip/webgcip.htm )were used to assess the MM5 prediction 

of cloud cover. The 0.5oby 0.5oresolution of the satellite data were interpolated into the 

12km MM5 grid for comparison. The MM5 total cloud fraction was estimated by MCIP 

based on the MM5’s low cloud, middle cloud and high cloud predictions. In general, 
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MM5 captured the satellite cloud pattern well but underestimates the satellite cloud 

fraction (see Figure 5 as an example). Part of problem may due to the coarse resolution of 

the satellite cloud data. 

Precipitation comparison

The monthly total observed precipitation data were constructed from 1/8-degree daily

precipitation analysis data (http://data.eol.ucar.edu/codiac/dss/id=21.093 produced by 

Climate Prediction Center, based on 7,000-8,000 hourly/6-hourly gauge reports and 

radar). The MM5 monthly total precipitation was estimated from the MM5 predicted 

convective and non-convective rainfall and summed up for each month. In general, MM5 

captured the observed spatial patterns in May and September, but no so well for June, 

July and August (See Figure 6), perhaps reflective of the summertime convective rain 

activities not captured by MM5. Detailed plots of this comparison are available on 

request from NYSDEC. 

Calm Conditions

Calm conditions are defined as observed wind speed of zero knots and wind direction as 

0

o

. It would be useful to assess how MM5 performs under observed calm conditions, 

because of potential pollutant buildup that could occur under such conditions. Table 1 

lists the summary of the percentage of calm condition at each hour for the August 2002 

from the NWS data within the 12km domain. It is apparent from the Table that the calm 

conditions occur primarily during the night and early morning hours, from 23Z (7 p.m. 

EDT) to 15Z (11 a.m. EDT) with a peak at 10Z (6 a.m. EDT). To assess MM5 

performance, the observed and MM5 predicted wind speeds were divided into calm and 

non-calm according to observed wind speed. Figure 7 displays such a comparison of the 

MM5 predicted wind speed to the observed wind speed under the calm and non-calm 

conditions for the month of August 2002. For the “calm” group, the average wind speed 

for MM5 varies from 1 m/s during the night and early morning hours and over 1.5 m/s 

during the day. MM5 is over-predicting during observed calm wind conditions. There 

are local minima every 3 hours, due to the surface observed wind speed nudging in MM5. 

In contrast under the non-calm conditions, MM5 underpredicts by about 0.5 m/s for all 

hours with noticeable local maximum happening at the nudging hours. The MM5 

nudging process would pull predictions toward the measured data, while the 

underprediction of MM5 for the non-calm conditions may due to the adopted PBL

scheme in this simulation.

Summary

In this study, we performed an assessment of the MM5 simulation to real-world data, 

both at the surface level as well as in the vertical. While there are no specific 

recommended procedures identified for this assessment, similar approaches have been 

used elsewhere (Dolwick 2005, Baker 2004, and Johnson 2004). Traditionally, the NWS 

surface measurements are used for such a comparison. Since NWS data had been used 

through nudging processes in developing the MM5 simulation, the comparisons should 
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not be far removed from each other. In this study, we extended the evaluation by using 

CASTNet measurements that were not used in the MM5 simulations. Thus comparison 

with CASTNet data provides for an independent assessment and should complement the 

comparison with NWS data. We also compared the MM5 results with the wind profiler 

data and cloud data derived from satellite images to diagnose if the MM5 simulation is 

yielding the right type of dynamics in the vertical. The analyses shows that in general, the 

performance of the MM5 is reasonable both at the surface and in the vertical, thereby 

providing confidence in the use of these data in the CMAQ simulations.

 

References 

Baker, K. 2004: www.ladco.org/tech/photo/photochemical.html

Burk, S. D. and W. T. Thompson, 1989: A vertically nested regional numerical weather 

prediction model with second-order closure physics. Mon. Wea. Rev., 117, 2305–2324. 

Dudhia, J., 1989: Numerical study of convection observed during the winter monsoon 

experiments using a mesoscale two-dimensional model. J. Atmos. Sci., 46, 3077–3107. 

Dudhia, J., 1996: A multi-layer soil temperature model for MM5. Preprints, 6th Annual 

MM5 Users Workshop, Boulder, CO.

Dolwick, P. 2005: 

http://cleanairinfo.com/modelingworkshop/presentations/MPE_Dolwick.pdf

Grell, G. A., J. Dudhia , and D. R. Stauffer 1994: A description of the fifth-generation 

Penn State/NCAR Mesoscale Model (MM5). NCAR Tech.Note NCAR/TN-398 1 STR, 

122 pp. 

Hong, S.-H., and H.-L. Pan, 1996: Nonlocal boundary layer vertical diffusion in a 

medium-range forecast model. Mon. Wea. Rev., 124, 2322–2339. 

Jajic, Z. I., 1990: The step-mountain coordinate: Physical package. Mon. Wea. Rev., 

118, 1429-1443. 

Jajic, Z. I., 1994: The step-mountain Eta coordinate model: Further development of the 

convection, viscous sublayer and turbulent closure schemes. Mon. Wea. Rev., 122, 927-

945.

Johnson, M. 2004: www.ladco.org/tech/photo/photochemical.html

Kain, J.S., and J.M. Fritsch, 1993: Convective parameterization for mesoscale models: 

The Kain-Fritsch scheme. Cumulus Parameterization. Meteor. Monogr., 46, Amer. 

Meteor. Soc., 165-170. 

Mellor, G. L., and T. Yamada, 1974: A hierarchy of turbulence closure models for 

planetary boundary layers. J. Atmos. Sci., 31, 1791–1806. 
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Shafran, P.C., N.L. Seaman, and G. A. Gayno, 2000: Evaluation of numerical 

predictions of boundary layer structure during the Lake Michigan ozone study. J. Appl. 

Meteor., 39, 412-426. 

Stauffer, D. R., N. L. Seaman and F. S. Binkowski 1991: Use of four-dimensional data 

assimilation in a limited-area mesoscale model. Part II: Effects of data assimilation 

within the planetary boundary layer. Mon. Wea. Rev., 119, 734-754.

Stauffer, D. R. and N. L. Seaman, 1990: Use of four-dimensional data assimilation in a 

limited-area mesoscale model. Part I: Experiments with synoptic-scale data. Mon. 

Wea. Rev., 118, 1250-1277. 

Zhang, D.-L., 1989: The effect of parameterized ice microphysics on the 

simulation of vortex circulation with a mesoscale hydrostatic model. Tellus, 41A, 132-

147. 

Zhang, D.-L, and R. A. Anthes, 1982: A high-resolution model of the planetary 

boundary layer-sensitivity tests and comparisons with SESAME-79 data. J. Appl. 

Meteor., 21, 1594–1609. 

Zhang, D.-L, and W.-Z. Zheng, 2004: Diurnal cycles of surface winds and temperatures 

as simulated by five boundary-layer parameterizations. J. Appl. Meteor., 43, 157-169. 
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Table 1 Measured calm and non-calm occurrences over the modeling domain 

during August 2002 based on NWS data

Hour

#NonCalm #Calm #Total % Calm

00Z 18209 3924 22133 17.7

01Z 16531 6026 22557 26.7

02Z 15604 6929 22533 30.8

03Z 14983 7245 22228 32.6

04Z 14309 7540 21849 34.5

05z 14073 7735 21808 35.5

06Z 13934 7949 21883 36.3

07Z 13792 8040 21832 36.8

08Z 13542 8273 21815 37.9

09Z 13542 8385 21927 38.2

10Z 13708 8591 22299 38.5

11Z 14139 8693 22832 38.1

12Z 15297 7690 22987 33.5

13Z 17336 5192 22528 23

14Z 18522 3439 21961 15.7

15Z 18755 2617 21372 12.2

16Z 19169 2015 21184 9.5

17Z 19555 1617 21172 7.6

18Z 19982 1430 21412 6.7

19Z 20149 1389 21538 6.4

20Z 20565 1288 21853 5.9

21Z 20518 1383 21901 6.3

22Z 20672 1556 22228 7

23Z 20231 2292 22523 10.2
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Figure 1: OTC MM5 modeling domain with areal extent of 12km and 36km grids
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Figure 2: Temperature and Wind speed comparisons for August 2002. In each case the 

upper panel corresponds to comparison between MM5 and NWS data and the lower 

panel between MM5 and CASTNet data.

O b s e r v e d / P r e d i c te d T e m p e r a tu r e

275

280

285

290

295

300

305

8/ 18/ 28/ 38/ 48/ 58/ 68/ 78/ 88/ 9

8/108/118/128/138/148/158/168/178/188/198/208/218/228/238/248/258/268/278/288/298/308/31

K

Obs T e m p P r dTem p 

O b s e r v e d / P r e d i c te d T e m p e r a tu r e

275

280

285

290

295

300

305

8/ 18/ 28/ 38/ 48/ 58/ 68/ 78/ 88/ 9

8/108/118/128/138/148/158/168/178/188/198/208/218/228/238/248/258/268/278/288/298/308/31

K

Obs T e m p P r dTem p 

Observed/Predicted Windspeed

1

2

3

4

5

6

7

8/ 18/ 28/ 38/ 48/ 58/ 68/ 78/ 88/ 9

8/108/118/128/138/148/158/168/178/188/198/208/218/228/238/248/258/268/278/288/298/308/31

m/s

ObsWndSpd PrdWndSpd 

Observed/Predicted Windspeed

1

2

3

4

5

6

7

8/ 18/ 28/ 38/ 48/ 58/ 68/ 78/ 88/ 9

8/108/118/128/138/148/158/168/178/188/198/208/218/228/238/248/258/268/278/288/298/308/31

m/s

ObsWndSpd PrdWndSpd 
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Figure 3a: Spatial correlation estimates between MM5 and NWS data for wind speed

 from May to September 2002
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Figure 3b: Spatial distribution of correlation coefficients for Temperature between 

MM5 and NWS data from May to September 2002.
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Richmond, VA

Concord, NH

Figure 4: MM5 and Wind profiler comparison for August 6 to 17, 2002 at Richmond, VA 

and Concord, NH. The upper and lower panes at each station are for MM5 and profiler, 

respectively. The abcissa represents day and the ordinate the height (m).
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Figure 5: MM5 and Satellite cloud images for August 14, 2002 at 0700 EST
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Figure 6: MM5 predicted and measured precipitation over the domain for the month of 

August 2002 
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0

0.5

1

1.5

2

2.5

3

3.5

4

4.5

00Z 03Z 06Z 09Z 12Z 15Z 18Z 21Z

TIME

WIN

D

S

P

E

E

D (m/s)

TDLAvgNC

MM5AvgNC

MM5AvgC

Figure 7: Comparison of averaged wind speed between MM5 and observed under calm 

(C) and non-calm (NC) conditions.