1 TSD - 1a 

## Meteorological Modeling using Penn State/NCAR 5 th 

## Generation Mesoscale Model (MM5)

Bureau of Air Quality Analysis and Research

Division of Air Re sources New York State Department of Environmental Conservation

Albany, NY 12233

February 1, 20062

Meteorological Modeling using Penn State/NCAR 5 th Generation Mesoscale Model (MM5) Version 3.6 of MM5 was used to generate annual 2002 meteorology for t he OTC modeling work. Prof. Dalin Zhang of the University of Mary land performed the MM5 simulations in consultation with NYSDEC staff. The model was applied in L ambert conformal map projection and utilized MPP Version developed for clusters. The two

- way ne sted domain consisted of coarse (36km) and fine (12km) mesh corresponding to 149x129 and 175x175 grids, respectivel y, 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 (40 o N, 97 o W) and parallels at 33 o N and 45 o N. 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 (266 4km, 2304km). I n the case of the 12km grid, the southwest corner is at (252km,

- 900km) and the northeast corner at (2340km, 1188km). I n the vertical direction, the terrain following σ

- coordinate s ys tem was used with the pressure at each σ

- level determined from a reference state that is estimated using the hy drostatic equation from a given sea

- level pressure and temperature with a standard lapse rate. There are 30 unevenl y spaced σ levels, giving 29 vertical lay ers, with higher resolution within the planetary boundary

lay er (PBL). The σ levels are: 1.0000, 0.9974, 0.9940, 0.8980, 0.9820, 0.9720, 0.9590, 0.943 0, 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 lay er was set at about 10m, the level at which surface winds were ty picall y 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

- Fritsc h (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 b oundary

lay er (PBL) scheme (Zhang and Anthes 1982; Zhang and Zheng 2004)  Simple radiative cooling scheme (Grell et al. 1994)  Multi

- lay er soil model to predict land surface temperatures using the surface energy budget equation (Dudhia 1996) Note that the Bl ackadar PBL scheme has been modified in order to correct the phase shift of surface wind speed and temperature diurnal cycle, following a stud y that compared five different PBL schemes: the Gay no - Seaman TKE scheme (Shafran et al. 2000), Burk

-3

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 obse rv ations 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 nudg ing 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 U MD’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;  Su rface 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 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 measur ements for CASTNet) were used to compare with the MM5 outputs. The evaluation was performed with METSTAT program developed b y Environ Corporation ( www.camx.com/files/metstat.15feb05.tar.gz. .When comparing to NWS data, the METSTAT interpolates the first lay er 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 calc ulated standard statistical measures

– average, bias, error and index of agreement between the measured and predicted parameters. Figure 2 display s 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 CASTNet4

day time wind speed, respectivel y. It should be pointed out that there are differences in how the meteorological information is collected and reported b y 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, whe reas MM5 predictions are reflective of the last time

- step of the hour of computation. I nterestingl y, 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 humidi ty trend well, but MM5 missed the observed semi

- diurnal cy cles. 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 average s 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. Figure 3 display s such a comparison for temperature and wind speed 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 sy noptic scale variations. Detailed plots of this comparison are available on request from NYSDEC. Vertical Profiler

– Winds The Win d

- Profiler network measurements along the U. S. East Coast ( www.madis

fsl.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 c omparison, the wind

- profiler measurements were interpolated to the MM5 vertical levels. The approach used was simple interpolation between two adjacent wind

- profiler lay ers to the MM5 vertical level, and was limited to that reported by the profiler measure ment. 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 reasonabl y 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/w ebgcip.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 total cloud fraction was estimated b y MCI P based on the MM5’s low cloud, m iddle cloud and high cloud predictions. In general,5

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. Th e detailed plots of this comparison are available on request from NYSDEC or at the following URL Precipitation comparison The monthly

total observed precipitation data were constructed from 1/8

- degree daily precipitation anal ys is data ( http://data.eol.ucar.edu/codiac/dss/id=21.093 produced b y 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 M M5 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 con vective rain activities not captured b y 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 . I t would be useful to asse ss 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 1 2km domain. I t 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 predict ed wind speeds were divided into calm and non - calm according to observed wind speed. Figure 7 display s 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 surfac e 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 measur ed data, while the underprediction of MM5 for the non

- calm conditions may due to the adopted PBL scheme in this simulation. Summary In this stud y, we performed an assessment of the MM5 simulation to real

- world data, both at the surface level as well as i n 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 c omparison. Since NWS data had been used6

through nudging processes in developing the MM5 simulation, 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 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 diag nose if the MM5 simulation is yi elding the right t ype of dynamics in the vertical. The anal ys es 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 i n 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 p rediction model with second

- order closure phy sics. 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. D udhia, J., 1996: A multi

- lay er soil temperature model for MM5. Preprints, 6 th Annual MM5 Users Workshop, Boulder, CO. Dolwick, P. 2005: http://cleanairinfo.com/modeling workshop/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 lay er vertical diffusion in a medium

- range forecast model. Mon. Wea. Rev., 124, 2322

– 2339. Jajic, Z. I., 1990: The step

- mountain coordinate: Phy sical package. Mon. Wea. Rev., 118, 1429
- 1443. Jajic, Z. I., 1994: The step

- mountain Eta coordin ate model: Further development of the convection, viscous sublay er and turbulent closure schemes. Mon. Wea. Rev ., 122, 927
- 945. Johnson, M. 2004: www.ladco.org/tech/photo/photochemical.htm l 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
- 170.7

Mellor, G. L., and T. Yamada, 1974: A hierarchy of turbule nce closure models for planetary boundary

lay ers. J. Atmos. Sci., 31, 1791

– 1806. Shafran, P.C., N.L. Seaman, and G. A. Gay no, 2000: Evaluation of numerical predictions of boundary

lay er structure during the Lake Michigan ozone study . J. Appl. Me teor., 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

lay er. Mon. Wea. Rev., 119, 73 4
- 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 sy noptic

- scale data. Mon. Wea. Rev., 118, 1250
- 1277. Zhang, D.

- L. , 1989: The effect of para meterized ice microphy sics on the simulation of vortex circulation with a mesoscale hy drostatic model. Tellus, 41A, 132

- 147. Zhang, D.

- L, and R. A. Anthes, 1982: A high

- resolution model of the planetary boundary lay er - sensitivity

tests a nd 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

- lay er parameterizations. J. Appl. Meteor., 43, 157

- 169.8

Table 1 Measured calm and non

- calm occurrences over the modeling domain during August 2002 based on NWS data

Hour #Non
Calm #Calm #Total % Calm 00Z 18209 3924 22133 17.7 01Z 16531 6026 22557 26.7 02Z 15604 6929 22533 30.8 03Z 14983 72 45 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 2222 8 7 23Z 20231 2292 22523 10.29

Figure 1: OTC MM5 modeling domain with areal extent of 12km and 36km grids10

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 t e d T e m p e r a tu r e

275 280 285 290 295 300 3058/ 18/ 28/ 38/ 48/ 58/ 68/ 78/ 88/ 98/108/118/128/138/148/158/168/178/188/198/208/218/228/238/248/258/268/278/288/298/308/31K Obs T e m p P r dTem p

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

275 280 285 290 295 300 3058/ 18/ 28/ 38/ 48/ 58/ 68/ 78/ 88/ 98/108/118/128/138/148/158/168/178/188/198/208/218/228/238/248/258/268/278/288/298/308/31K Obs T e m p P r dTem p

Obs er ve d/Pr ed ic te d Wi nds pe ed

1 2 3 4 5 6 78/ 18/ 28/ 38/ 48/ 58/ 68/ 78/ 88/ 98/108/118/128/138/148/158/168/178/188/198/208/218/228/238/248/258/268/278/288/298/308/31m/s Ob sWn dSpd Pr dW ndSpd

Obs er ve d/Pr ed ic te d W inds pe ed

1 2 3 4 5 6 78/ 18/ 28/ 38/ 48/ 58/ 68/ 78/ 88/ 98/108/118/128/138/148/158/168/178/188/198/208/218/228/238/248/258/268/278/288/298/308/31m/s Ob sWn dSpd Prd Wn dSpd11

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

from May to September 200212

Figure 3b: Spatial distribution of correlation coefficients for Temperature between

MM5 and NWS data from May to September 2002.13

Richmond , VA

Concord, NH

Figure 4: MM5 and Wind profiler comparison for August 6 to 17, 2002 at Richmond, VA and Concord, NH. The u pper and lower panes at each station are for MM5 and profiler, respectivel y. The abcissa represents day and the ordinate the height (m).14

Figure 5: MM5 and Satellite cloud images for August 14, 2 002 at 0700 EST15

Figure 6: MM5 predicted and measured precipitation over the domain for the month of August 200216 0 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 7: Comparison of averaged wi nd speed between MM5 and observed under calm (C) and non

- calm (NC) conditions.