TSD-1a



## Meteorological Modeling of 20 02 using Penn State/NCAR

5 th 

## 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

January 10, 2008Meteorological Modeling using Penn State/NCAR 5 th Generation Mesoscale Model (MM5)

Version 3.6 of MM5 was used to generate annual 2002 meteorology for the Mid-Atlantic Northeast Visibility Union (MANE-VU) through the Modeling Committee of Ozone Transport Commission (OTC). Prof. Dalin Zh ang of the University of Maryland (UMD) performed the MM5 simulations in consul tation with NYSDEC staffs. The model was applied in Lambert conformal map projecti on and utilized MPP Version developed for clusters. The two-way nested domain consisted of a coarse (36km) and fine (12km) mesh corresponding to 149x129 and 175x175 grids, respect ively, in this app lication (see Figure 1).

The Lambert projection used in this work followed the Regional Planning Organization (RPO) national domain setup with the center at (40 o N, 97 o W) and parallels at 33 o N and

45 o N. Map projection parameters in referen ce 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 (2 340km, 1188km). In th e 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 50hP a 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) conv ective scheme for both 36- and 12-km domains

• Explicit moisture scheme (without the mixed phase) containing prognostic equations for cloud water (ice) and ra inwater (snow) (Dudhia 1989; Zhang 1989)

• Modified version of the Blackadar pl anetary 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), Burk2Thompson (1989), Blackadar (Zhang and Anthes 1982), MRF (Hong and Pan 1996), and Mellor-Yamada-Jajic (M ellor 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 -1 for Domain 1 (36km), and 2. 5E-4s -1 for Domain 2 (12km);

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

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

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

ASSESSMENT

National Weather Service (NWS) and CAST Net 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 th e MM5 outputs. The evaluation was performed with METSTAT program devel oped 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, but no interpolation was made fo r wind speed and direction. In the case of CASTNet surface measurements, no interpolations were made as CASTNet data were reported at 10m height. In this analysis, no exclusion was made for calm conditions. The reported calm winds (zero wind sp eed measured) were treated as is in this evaluation

effort. The METSTAT calculated standard statis tical measures – average, bias, error and index of agreement between the measured and predicted parameters. Table 1 summarizes the MM5 average bias for each month for wi nd speed, wind direction, temperature, and humidity by comparing data from NWS and CASTNet networks. The humidity data is only available for NWS network. In general, th ere is no systematic bias between winter

and summer seasons for MM5 in terms of wind speed, wind direction and temperature. However, MM5 showed dry bias in the su mmer and wet bias in the winter when compared with humidity data from NWS.

3Figure 2a and 2b display the time series comparison of wind speed between MM5 and

measured data from NWS and CASTNet networ ks for winter months (January, February and December) and summer months (June, July and August), respectively. MM5 underpredicted NWS and overpredicted CASTNet daytime peak wind speed, while MM5 appears to track quite well the nightti me wind speed minimum for CASTNet and overpredicted nighttime wind speed minimu m for the NWS data. MM5 performed quite well in capturing magnitude and diurnal timing for temperature from both NWS and CASTNet data (Figures 3a and 3b). It should be pointed out that there are differences in how the meteorological information is collect ed and reported by the two networks and as computed in MM5. The CASTNet measuremen ts are based on hourly averaged wind speed while NWS reports 2min average at 10min before the hour, whereas MM5 predictions are reflective of th e last time-step of the hour of computation. In the case of humidity (Figure 4), MM5 tracked the NWS observed humidity trend well, but exhibits dry bias for summer season and wet bias for winter season and misses the observed semidiurnal cycles. Comparisons for the whole year of 2002 including bias and root mean square error from both NWS and CASTNe t are available on request from NYSDEC.

The above assessment is based on domain-wide averages to provide an overall response of the model. Another way of assessing the mode l is to examine the spatial distribution of correlation between the measured and predicte d parameters at each monitor. Figures 5a and 5b display such a comparison for wind sp eed and temperature over winter months and summer months, respectively. For the wind speed (Figure 5a), the correlation is in the range of 0.8 to 0.9 for winter months and 0.7 to 0.8 for summer months. For the temperature (Figure 5b), the co rrelation is above 0.95 for summ er months, slightly higher

than winter months. The correla tion for humidity (Figure 5c) is in the range of 0.8 to 0.9

for both winter and summer months. These correlations indicate that MM5 simulation has captured both the diurnal and synoptic s cale 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 whic h 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 cap tures the profiler measured vertical wind field structure reasonably well. Figure 6 disp lays 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 availa ble on request from NYSDEC.

4Cloud Cover – Satellite cloud image

Cloud information derived from satellite image data ( w ww.atmos.umd.edu/~srb/gcip/webgcip.htm ) were used to assess the MM5 prediction

of cloud cover. The 0.5 o by 0.5 o resolution of the satellite data were interpolated into the 12km MM5 grid for comparison. The MM5 to tal cloud fraction was estimated by MCIP based on the MM5’s low cloud, middle cloud an d high cloud predictions. In general, MM5 seems to capture the satellite cloud pa ttern well but underestimates the satellite cloud fraction (see Figure 7a and 7b as examples), which may in part be due to the coarse

resolution of the satel lite 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 precipitati on was estimated from the MM5 predicted

convective and non-convective ra infall and summed up for each month. In general, MM5 captured the observed spatial patterns (see exam ples of Figures 8a and 8b). For winter months, MM5 performed well for February (Figure 8a) but underpredicted for November. For the summer months, MM5 performed well for May and September, but no so well for June, July and August (See Fi gure 8b), that may reflect the summertime convective rain activities are not captured by MM5.

Calm Conditions

Calm conditions are defined as observed wi nd 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 2a and 2b list the summary of the percentage of calm condition at each hour for the February and August 2002, respectively from the NWS data within the 12km domai n. It is apparent

from the Table that the calm conditions o ccur primarily during the night and early morning hours, from 23Z (7 p.m. EDT) to 15Z (11 a.m. EDT) with a peak at around 10Z (6 a.m. EDT). August had much higher percen tage of calm condition than February. To assess MM5 performance, the observed and MM5 predicted wind speeds were divided into calm and non-calm according to observed wind speed. In general MM5 underpredicted the observed non-calm conditions for both February and August (Table 2a and 2b). Figure 9 displays such a comparis on of the MM5 predicted wind speed to the observed wind speed under the calm and non-ca lm 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 ove r 1.5 m/s during the day. MM5 is overpredicting during observed calm wind conditions. There are local minima every 3 hours, due to the surface observed wind speed nudgi ng 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

5predictions toward the measured data, wh ile the underprediction of MM5 for the noncalm conditions may due to the adopted PBL scheme in this simulation.

Summary

In this study, we performed an assessment of the MM5 simulation to measured data, both

with the surface measurement networks as we ll as with information from the vertical wind profilers and satellite cloud images . While there are no specific recommended procedures identified for this assessment, si milar approaches have been used elsewhere (Dolwick 2005, Baker 2004, and Johnson 2004) . Traditionally, the NWS surface measurements are used for such a comparis on. Since NWS data had been used through nudging processes in developing the MM5 simu lation, the comparisons should not be far removed from each other. In this study, we extended the evaluation by using CASTNet

measurements that were not used in the nudging of MM5 simulation. Thus comparison with CASTNet data provides for an indepe ndent 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 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 th ese data in the CMAQ simulations.

References

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

Burk, S. D. and W. T. Thompson, 1989: A vert ically nested region al 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-dimensio nal model. J. Atmos. Sci., 46, 3077–3107.

Dudhia, J., 1996: A multi-layer soil te mperature model for MM5. Preprints, 6 th 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. Stauffe r 1994: A descri ption 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, 199 6: 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.

6Jajic, Z. I., 1994: The step-mountain Eta c oordinate model: Further development of the convection, viscous sublayer a nd turbulent closure schemes. Mon. Wea. Rev ., 122, 927-

945.

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

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

The Kain-Fritsch scheme Cumulus Para meterization. Meteor. Monogr., 46, Amer. Meteor. Soc., 165-170.

Mellor, G.L., and T. Yamada, 1974: A hier archy of turbulence closure models for planetary boundary layers. J. Atmos. Sci., 31, 1791–1806.

Shafran, P.C., N.L. Seaman, and G.A. Gayno, 2000: Evaluation of numerical predictions of boundary layer structure during the Lake Mi chigan 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 m odel. Part II: Effects of data assimilation within the planetary boundary layer. Mon. W ea. Rev., 119, 734-754.

Stauffer, D.R. and N.L. Seaman, 1990: Use of four-dimensional da ta 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 effe ct of parameterized i ce microphysics on the

simulation of vortex circul ation with a mesoscale hydrost atic model. Tellus, 41A, 132-

147.

Zhang, D.-L, and R. A. Anthes, 1982: A hi gh-resolution model of the planetary boundary layer-sensit ivity tests and comparisons w ith SESAME-79 data. J. Appl. Meteor., 21, 1594–1609.

Zhang, D.-L, and W.-Z. Zheng, 2004: Diurnal cycles of surface wi nds and temperatures as simulated by five boundary -layer parameteriza tions. J. Appl. Me teor., 43, 157-169.

7Table 1: Average bias of wind speed, wind di rection, temperature, and humidity of MM5 in comparing with observed data from TDL and CASTNet networks for each month in

2002

Month Wind Speed
(TDL / CASTNet) Wind Direction
(TDL / CASTNet) Temperature
(TDL / CASTNet) Humidity

TDL January -0.53 / 0.34 3.12 / 2.54 -1.18 / -1.25 0.45 February -0.56 / 0.31 3.31 / 0.88 -1.00 / -0.65 0.48

March -0.59 / 0.31 3.48 / 1.93 -0.72 / -0.35 0.52 April -0.55 / 0.38 3.61 / 2.49 -0.48 / -0.52 0.52 May -0.52 / 0.44 3.53 / 2.33 -0.18 / 0.67 -0.02

June -0.56 / 0.28 3.89 / 3.33 -0.12 / 1.03 -0.33 July -0.58 / 0.31 3.62 / 1.44 -0.34 / 0.34 -0.55 August -0.61 / 0.24 2.74 / 2.34 -0.42 / 0.32 -0.23 September -0.54 / 0.30 3.31 / 3.01 -0.54 / 0.76 0.03

October -0.56 / 0.32 2.81 / 1.39 -0.79 / -0.56 0.15

November -0.57 / 0.37 2.28 / 2.35 -1.35 / -1.25 0.34

December -0.59 / 0.39 3.41 / 2.69 -1.20 / -1.17 0.34

8Table 2a: Measured calm and non-calm occu rrences over the modeling domain during February 2002 based on NWS data

Hour (UTC) Obs

Not

Calm Obs

Calm Obs

Total Percent

Calm (%) TDL Avg WinSpd

Not

Calm MM5 Avg WinSpd

Not

Calm

0 17266 2711 19977 13.6 4.28 3.84

1 17270 3324 20594 16.1 4.30 3.82

2 17051 3421 20472 16.7 4.30 3.75

3 16878 3499 20377 17.2 4.32 3.79

4 16401 3513 19914 17.6 4.33 3.78

5 16127 3532 19659 18.0 4.28 3.75

6 15914 3645 19559 18.6 4.26 3.81

7 15841 3703 19544 18.9 4.23 3.75

8 15784 3783 19567 19.3 4.20 3.71

9 15752 3857 19609 19.7 4.19 3.73

10 15630 3932 19562 20.1 4.18 3.70

11 15911 4020 19931 20.2 4.16 3.72

12 16451 4104 20555 20.0 4.21 3.82

13 16844 3891 20735 18.8 4.28 3.86

14 17779 2945 20724 14.2 4.62 4.00

15 18741 1822 20563 8.9 4.98 4.37

16 18740 1337 20077 6.7 5.21 4.66

17 19079 1106 20185 5.5 5.38 4.83

18 19158 954 20112 4.7 5.46 4.93

19 19380 880 20260 4.3 5.49 4.91

20 19545 883 20428 4.3 5.47 4.75

21 19648 859 20507 4.2 5.33 4.46

22 19576 1027 20603 5.0 5.03 4.02

23 18941 1772 20713 8.6 4.57 3.79

9Table 2b: Measured calm and non-calm occu rrences over the modeling domain during August 2002 based on NWS data

Hour (UTC) Obs

Not

Calm Obs

Calm Obs

Total Percent

Calm (%) TDL Avg WinSpd

Not

Calm MM5 Avg WinSpd

Not

Calm

0 18209 3924 22133 17.7 3.14 2.56

1 16531 6026 22557 26.7 2.85 2.45

2 15604 6929 22533 30.8 2.79 2.33

3 14983 7245 22228 32.6 2.81 2.33

4 14309 7540 21849 34.5 2.80 2.28

5 14073 7735 21808 35.5 2.79 2.24

6 13934 7949 21883 36.3 2.78 2.29

7 13792 8040 21832 36.8 2.76 2.23

8 13542 8273 21815 37.9 2.75 2.22

9 13542 8385 21927 38.2 2.74 2.28

10 13708 8591 22299 38.5 2.72 2.25

11 14139 8693 22832 38.1 2.74 2.25

12 15297 7690 22987 33.5 2.89 2.33

13 17336 5192 22528 23.0 3.14 2.41

14 18522 3439 21961 15.7 3.39 2.63

15 18755 2617 21372 12.2 3.60 2.98

16 19169 2015 21184 9.5 3.79 3.15

17 19555 1617 21172 7.6 3.97 3.22

18 19982 1430 21412 6.7 4.08 3.38

19 20149 1389 21538 6.4 4.16 3.43

20 20565 1288 21853 5.9 4.14 3.41

21 20518 1383 21901 6.3 4.06 3.41

22 20672 1556 22228 7.0 3.88 3.12

23 20231 2292 22523 10.2 3.56 2.74

10Figure 1: OTC MM5 modeling domain with areal extent of 12km and 36km grids

11W in d S p eed - T D L

0 1 2 3 4 5 6 7 8

1 2 3 4 5 6 7 8 9 10

11 1

2 1

3 14 1

5 1 6 1 7 1 8 1

9 2 0 2 1 2

2 2 3 2 4 2 5 2

6 2 7 2 8 2 9 3 0 3 1 3

2 3

3 3 4 3 5 3

6 3 7 3 8 3 9 4 0 4 1 4

2 4

3 4 4 4 54

6 4 7 4 8 4 9 50

5 1 5

2 53

54

55

56

5 7 5 8 59 33 5 33 6 33 7 33 8 33 9 34 0 34 1 34 2 34 3 34 4 34 5 34 6 34 7 34 8 34 9 35 0 35 1 35 2 35 3 35 4 35 5 3 5 6 3 5 7 35 8 35 9 3 6 0 3 6 1 3 6 2 363

364

365 Ju lian D aym/s

Obs P rd

W ind S p eed - C A S T N et

0 1 2 3 4 5 6 7

1 2 3 4 5 6 7 8 9 1 0 1

1 1

2 1

3 1

4 1

5 1

6 17 18 19 2 0 21 22 23 24

25

26 27

28

29 30

31 32

33 34

35

36 37

38

39 40

41 42 4 3 4 4 4 5 4

6 4 7 4 8 4 9 5

0 5 1 5

2 53

5 4 5 5 56

57

58 59

3 3 5 3 36 3 37 3 3 8 3 3 9 3 4 0 341

3 4 2 3 4 3 344

345

346

347

348

349

350

351

352

353

3 54 355

356

3 57 3 58 3 59 3 60 3 61 3 62 3 63 3 64 3 65 Julian D aym/s

Obs P rd

Figure 2a: Wind speed comparison for wint er months - January, February, and December, 2002. The upper panel is the comparison between MM5 and NWS data, and the lower panel is the comparison between MM5 and CASTNet data.

12W ind Speed - TD L

0 1 2 3 4 5 6 7

152 156 160 164 168 172 176 180 184 188 192 196 200 204 208 212 216 220 224 228 232 236 240 244 Julian D aym/s Obs Prd

W ind Speed - CASTNet

0 1 2 3 4 5 6 7

152 156 160 164 168 172 176 180 184 188 192 196 200 204 208 212 216 220 224 228 232 236 240 244 Julian Daym/s Obs Prd

Figure 2b: Wind speed comparison for summer months - June, July, and August, 2002. The upper panel is the comparison between MM 5 and NWS data, and the lower panel is the comparison between MM5 and CASTNet data.

13Surface Tem perature - TDL

250 255 260 265 270 275 280 285 290

1 2 3 4 5 6 7 8 9 1 0 1 1 1 2 1 3 1 4 1 5 1 6 1 7 18

1 9 2 0 2 1 22

2 3 2 4 25

2 6 2 7 28

29

3 0 31

32

3 3 3 4 35

3 6 3 7 38

3 9 4 0 41

4 2 4 3 4 4 45

4 6 4 7 48

4 9 5 0 5 1 52

5 3 5 4 55

5 6 5 7 58

5 9 3 35 3 36 3 3 7 3 38 3 39 3 4 0 3 41 3 42 343

3 44 3 45 346

347

3 48 3 49 350

3 51 3 52 3 5 3 354

3 55 3 5 6 357

3 58 3 59 360

3 61 3 62 363

3 64 3 65 Julian D ayK

Obs Prd

Surface Tem perature - C ASTN et

245 250 255 260 265 270 275 280 285 290

1 2 3 4 5 6 7 8 9 10

11

12

1 3 14

15

16

17

1 8 19

20

21

22

23

24

25

26

27

28

29

30

3 1 3 2 33

34

35

3 6 3 7 38

39

40

41

4 2 43

44

4 5 46

47

48

49

5 0 5 1 52

53

54

5 5 5 6 57

58

59 335

336

3 37 3 38 3 39 34 0 34 1

3 42 3 4 3 3 44 3 4 5 346

3 47 3 48 3 49 3 5 0 351

3 52 3 53 35 4 3 5 5 356

3 5 7 3 58 35 9 3 6 0 3 61 3 62 3 63 3 64 3 6 5 J u lian D ayK

Obs Prd

Figure 3a: Temperature comparison for wi nter months - January, February, and December, 2002. Upper panel is the comparison between MM5 and NWS data, and the lower panel is the comparison between MM5 and CASTNet data.

14Surface Tem perature - TDL

280 285 290 295 300 305 310

152 156 160 164 168 172 176 180 184 188 192 196 200 204 208 212 216 220 224 228 232 236 240 244 Julian DayK Obs Prd

Surface Tem perature - CASTNet

280 285 290 295 300 305 310

152 156 160 164 168 172 176 180 184 188 192 196 200 204 208 212 216 220 224 228 232 236 240 244 Julian DayK Obs Prd

Figure 3b: Temperature comparison for summ er months - June, July, and August, 2002. The upper panel is the comparison between MM 5 and NWS data, and the lower panel is thew comparison between MM5 and CASTNet data.

15H u m id ity - T D L

0 1 2 3 4 5 6 7 8

1 2 3 4 5 6 7 8 9 10 1

1 12 13 1 4 15 1

6 17 1

8 19 20 2 1 22 2

3 24 25 2 6 27 2

8 29 3

0 31 32 3 3 34 3

5 36 37 3 8 39 4

0 41 4

2 43 44 4 5 46 4

7 48 49 5 0 51 5

2 53 5

4 55 56 5 7 58 5

9 335

336

3 3 7 338

3 3 9 340

341

3 4 2 343

3 4 4 3 4 5 346

347

348

3 4 9 350

3 5 1 3 5 2 353

3 5 4 355

3 5 6 3 5 7 358

359

360

3 6 1 362

3 6 3 3 6 4 365 J u lia n D a yg/kg Obs P rd

H u m id ity - T D L

0 2 4 6 8 1 0 1 2 1 4 1 6 1 8

152 156 160 164 168 172 176 180 184 188 192 196 200 204 208 212 216 220 224 228 232 236 240 244 J u lia n D a yg/kg

Obs P rd

F igure 4: Humidity comparison for winter months - January, February, and December, 2002, (top panel), and summer months - June , July, and August, 2002 (bottom panel).

16Figure 5a: Spatial correlation estimates between MM5 and NWS data for wind speed for winter months – January to March, 2002 (top panel) and summer months - May to September, 2002 (bottom panel).

17Figure 5b: Spatial distribution of correlati on coefficients for Temperature between

MM5 and NWS data for winter months – January to March, 2002 (top panel), and summer months - May to September, 2002 (bottom panel).

18F igure 5c: Spatial distribution of correlat ion coefficients for Humidity between panel), MM5 and NWS data for winter months – January to March, 2002 (top and summer months - May to September, 2002 (bottom panel).

19R ichmond, VA

igure 6: MM5 and Wind profiler comparison for August 6 to 17, 2002 at Richmond, VA nd Concord, NH. The upper and lower panes at each station are for MM5 and profiler, respectively. The abscissa represents day and the ordinate the height (m). Concord, NH

F

a

20Figure 7a: Observed Satellite and MM5 cloud im ages for August 14, 2002 at 0700 EST

21igure 7b: Observed Satellite and MM5 cloud images for January 24, 2002 at 1200 EST F

22Figure 8a: Measured and MM5 predicted prec ipitation over the domain for the month of ebruary 2002. F

23Figure 8b: Measured and MM5 predicted precipitation over the domain for the month of August 2002

240 0.5

1 1.5

2 2.5

3 3.5

4 4.5

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

TIMEWIND SPEED (m/s)

TDLAvgNC

MM5AvgNC

MM5AvgC

Figure 9: Comparison of averaged wind speed between MM5 and observed under calm (C) and non-calm (NC) conditions.

25