TSD-1a



## Met e orologi cal Modelin g us ing Penn State/NCAR 5 th



## Gen erati o n Mes o scale Model (MM5)

Bureau of Air Quality Analysis and Research

Division of Air Resources

Ne w York State Department of Environmenta l Conservation

Albany, NY 12233

February 1, 2006

1Meteorological Modeling using Penn State/NCAR 5 th Generation Mesoscale Mo del (MM5)

Version 3.6 of MM5 was used to generate annual 2002 m e teorology for the OTC modeling work. Prof. Dalin Zhang of the Un iversity of Maryland perform e d the MM5

sim u lations in consultation with NYSDEC st aff

. The m odel was applied in Lam b ert
confor m a l m a p projection and utilized MPP Ve rsion developed for clusters. The two-way

nested dom ain consisted of coarse (36k m ) and fine (12km ) m e sh corresponding to 149x129 and 175x175 grids, respectively, in this application (see Figure 1).

The Lam b er t projection used in this work followed the Regional Planning Organization (RPO) natio nal dom a in setup with th e cente r at ( 4 0 o N, 97 o W ) and parallels at 33 o N and

45 o N. Map projection param e ters in referen ce to the pro j ection center po int are as

follows: Southwest corner for the 36 k m grid is at (-2664km , -2304k m ) and the northeast corner at (26 64km

, 2304km ). In the c a se of the 12 km grid, the southwest corner is at (252km , -90 0 k m ) and the northeast corner at (23 40k m , 1188 k m ). In the vertical direction,

the te rra in f o llowing σ -coordinate s y stem was used with th e pressure at each σ -level

determ ined from a reference state that is estim ated using the hydrostatic equation from a given sea-level pressure and tem p erature with a s t andard laps e rate. There are 30 unevenly sp aced σ levels, giving 29 vertical layers, w ith high er reso lution within th e planetary boundary layer (PBL). The σ leve ls ar e :

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 s e t at abou t 10 m, the level at which surface winds were typically observed, and the m odel top was set at 50hP a with a radiative top boundary condition.

The tim e steps for the 36k m and 12km do m a ins were 75 and 25 seconds, respectively.

The im portant m odel physics options used for this MM5 simulation include:

• Kain-Fritsch (1993) conv ective schem e for both 36- and 12-km do m a ins

• Explicit m o isture scheme (without the m i xed phase) containing prognostic equations for cloud water (ice) and ra inwater (snow) (Dudhia 1989; Zhang 1989)

• Modified version of the Blackadar p l anetary boundary layer (PBL) scheme (Zhang and Anthes 1982; Zhang and Zheng 2004)

• Sim p le radiative cooling schem e (Grell et al. 1994)

• Multi-layer soil m odel to predict lan d surface temperatures using the su rface energy budget equation (Dudhia 1996)

Note that th e Blackadar PBL schem e has been modified in order to correct the phase shift of surface wind speed an d tem p erature diurnal cy cle, followin g a study th at com p ared

five different PBL schem e s: the Gayno-Seam an TKE sche me (Shafran et al. 2000), B u rk2Tho m pson (1989), Blackadar (Zhang and Anthes 1982), MRF (Hong and Pan 1996), and Mellor-Yamada-Jajic (M ellor and Yam a da 1974; Jajic 1990, 1994). The details of the study can be found at Zhang and Zheng (2004).

Nud g ing Pr ocesses

The MM5 provides options for nudging observations for each dom a in durin g the m odel integration process ( S tauf fer and Seam an, 1990; Sta uffer et al. 1 991). The Et a analy s e s of upper-air wi nds, tem p erature and wat e r-vapor m i xing ra tio as well as their associated surface fields were used for nudgi ng every 6 hours, a nd the Eta surface wind fields blended with surface wind observations were used to nudge every 3 hours. While only the surface winds were nudge d

, their influences could e x tend into the PBL as well (see Stauffe r et al. 1991)

. Based on UMD’ s prior e xperience in num erical ex perim e nts, th e following n udging

coefficients have been used:

• Upper-air wi nd fields: 5. 0E-4s -1 for Dom a in 1 (36km ), and 2. 5E-4s -1 for Do m a in 2 (12km );

• Upper-air te m p erature fields: 1.0E-5s -1 for both Dom a ins;

• Surface winds: 5. 0s -1 E-4s -1 for Dom a in 1, and 2.5E -4s -1 for Do m a in 2; and

• Surface tem p erature and m o isture: not nudged due to instability consideration.

ASSESSMENT

This assessm ent covers the period of May through Septem ber 2002.

National W e ather Serv ice (NWS) a nd CAST Net data – Surface tempe r ature, Win d Speed, and Humidity

NW S (TDL) and CASTNet ( www.epa.gov/castnet/ )surface m easurem ents of

tem p erature, wind speed, and hum idity (note there were no hum idity m easurem ents for CASTNet) were used to com p are with th e MM5 outputs. The evaluation was performed with METSTAT program devel oped by Environ Corporation ( www.ca m x .com /files/metstat.15feb05.tar.gz. .When com p aring to NW S data, the

METSTAT inte rpola t es the f i rs t laye r MM5 (at 10 m height) tem p erature and hum idity data to a height of 2 m , the level that corresponds to the NW S m easurem e n t of these

param e ters. However, no such interpolation was m a de for wind speed and direction. In

the cas e of CASTNet surface m easurem ents, no such chan ges 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 wind s (zero wind speed m easured) were treated as is in

this ev aluation effort. The METSTAT cal culated standard stat istical measures – average, b i as, erro r and index of agreem ent between the m easured and predicted

param e ters.

Figure 2 displays the temperature and wi nd speed com p arison of MM5 and m easured

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

m a gnitude and diurnal tim i ng, te m p e r ature can be considered to be quite good for both

NW S and CASTNet data, while MM5 underpre dicted NWS and overpredicted CASTNet

3daytim e wind speed, respectively. It should be pointed out that there are differences in

how the m e t e orological infor m ation is collect ed and reported by the two networks as well

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

NW S reports 2m in average at 10m in before the h o ur, whereas MM5 predictions are

reflective of the last tim e -step of the hour of co mputation. Interestingly, MM5 appears to track qu ite well the n i g h ttim e m i nimum wind sp eed f o r both networks. In the c a se o f

hu m i dity (not shown), MM5 tracks the NW S observed hu m i dity trend well, but M M 5

m i ssed the observed sem i -diu rnal cycles. Com p arisons for the five m onths including bias

and root m ean square error from bot h NW S and CASTNet are available o n request fro m NYS DEC.

The above assessm ent is based on dom a in-wide averages to provide an overall response

of the m odel over the five m onths. Another wa y of assessing the m odel is to exam ine the degree of co rrelation between the m e asured a nd predicted param e ters. Figure 3 displays

such a com p arison for temperature and wind sp eed for the NWS hourly data covering the period of May through S e ptem ber 2002. For the NW S data, the correlations are in the range from 0.7 to 0.8 for wind speed, above 0. 96 for tem p erature, and in the range of 0.8

to 0.9 for hum idity. CASTNet data (not show n) also exhibit sim ilar correlation. These

correlations indicate that MM5 sim u lation has captured both the diurnal and synoptic

scale v a riations. Detailed plots of this com p arison are availab l e on requ est from

NYS DEC.

Vertica l Profiler – Win d s

The W i nd-Profiler netw ork m easurem ents along the U. S. East Coast ( www. m a disfsl.org/cap) were used to evaluate the vertical p r o f iles from M M 5. There are twelv e wind-prof ile r m easurement station s f r om which data were ava ilab l e f o r co mparison. For

convenience of co m p arison, the wind-profiler m e asurem ents were interpolated to the

MM5 vertic al lev e ls. Th e approach u s ed was simple in terpo l a tion be twee n two adjace nt wind-prof ile r laye rs to th e MM5 ver t ical level, an d was lim ited to tha t rep o rted by the profiler m easurem ent. The focus of the com p arison was to as sess if MM5 was able to capture the m easured vertical s t ructu r e, and f o r this we used the observ e d Low Level Jet (LLJ) as an indicator. The com p arison was pe rform e d for June, July and August 2002. In

genera l it is f o und that MM5 captu r e s the prof iler m easured vertical win d field structure reasonably well. Figure 4 displays an exam ple of the MM5 and wind profiler com p arison for the August 2002 episode at Richmond, VA a nd Concord, NH. MM5 predicted w eaker

LLJ winds co m p ared to those based on the wind-profiler m easurem ents. The detailed plots of this com p arison are availa ble on request f r om NYSDEC.

Cloud Cove r – Satellite cloud image

Cloud inform ation derived fro m satellite im age data ( www.at m o s.um d.edu/~ s rb/gcip/webgcip.htm )were used to assess the MM5 prediction

of cloud cover. The 0.5 o by 0.5 o reso lution of the sate llite da ta were interp olated into the

12k m MM5 grid for comparison. The MM5 to tal cloud fraction was estim ated by MCIP

based on the MM5’s low cloud, m i ddle cloud and high cloud predictions. In general,

4MM5 captured the satellite cloud pattern we ll but underestim ates the satellite cloud fraction (see Figure 5 as an exam ple). Part of pro b lem m a y due to the coa r se re solutio n of

the satellite cloud data. The de tailed plots of this com p arison are available on request

fro m NYSDEC or at the following URL

Precipitatio n comparis on

The m onthl y total observed precipitation data w e re constructed from 1/8

- degree daily precip ita tion analys is data ( http://data

. eol.uca r .ed u /codia c /dss /id=21.093 produced by

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

radar ) . The MM5 m onthly total pr ecipita tion was estim ated f r om the MM5 predic ted

convective and non-convective ra infall and summed up for each m onth. In general, MM5 captured the observed spatial patterns in Ma y and Septem ber, but no so well for June, July and August (See Figure 6), perhaps refl ective of the summer tim e c onvective rain

activ ities no t captu red b y MM5. Detailed p l ots o f this com p arison a r e ava ilab l e on request from NYSDEC.

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 perform s under ob served calm conditions, because of p o tential po llutant bu ildu p that cou l d occur under such conditions. Table 1

lists the summ ary of the percen tage o f calm condi tion at each hour for the August 2002

f r om the NWS data within the 12k m dom a in. It is app a ren t f

r om the Table tha t the calm conditions occur prim arily during the night a nd early m o rning 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 perform a nce, the observed and MM5 predicte d wind speeds were divided into calm and

non-calm according to o b served win d speed. Fig u re 7 disp lays such a co mparison of the MM5 predicted wind speed to the observed wind speed under the calm and non-calm

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

for MM5 varies from 1 m / s during the night and early m o rning hours and over 1.5 m/s during the day. MM5 is over-predicting dur ing observed calm wind conditions. There

are lo cal m i nim a every 3 hours, due to the su rface observ e d 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 m a xi m u m happening at the nudging hours. The MM5 nudging process would pull pr edictions toward the m easured data, while the underprediction of MM5 for the non-calm conditions m a y due to the adopted PBL

schem e in this sim u latio n.

Summary

In this study, we perform e d an assessm ent of the MM5 sim u lation to r e al-world d a ta,
both at the s u rface level as well as inth e vertical. W h ile there are no specific

recomm ended procedu r es iden tified for this ass e ssm ent, sim i lar app r oach es have been used elsewhere (Dolwick 2005, Baker 2004, and Johnson 2004). Traditionally, the NWS

surface m easurem ents are used for su ch a com p arison. Since NW S data had been used

5through nudging processes in developing the MM5 sim u lation, the comparisons should

not be far removed from each other. In th is study , we extended the evaluation by using

CASTNet measurem ents that were n o t used in th e MM5 sim u lations. Th us com p arison with CASTNet data provides for an indepe ndent assessm ent and should co m p lem e nt the

com p arison with NW S data. W e also com p ared the MM5 res u lts with the wind prof iler

data and cloud data derived fro m satellite im ages to diagnose if the MM5 sim u lation is yielding the right type of dynam i cs in the vertic al. The analyses shows that in general, the perform a nce of the MM5 is reasonable both at the surface and in the vertical, thereby providing confidence in the use of th e s e data in th e CMAQ simulations.

References

Baker, K. 2004: www.ladco.org/tech/photo/photochem ical.htm l

Burk, S. D. and W

. T. Thom pson, 1989: A ve rtically n e sted reg i on al nu m e rical weather pred iction m odel with secon d -o rd er closu r e ph ysics. Mon. W ea. Rev., 117, 230 5–2 324.

Dudhia, J., 1989: Num e rical study o f convect ion observed d u ring the w i nter m onsoo n experim e nts usin g a m e soscale two

- dim e nsion a l m odel. J. A t m o s. Sci., 4 6 , 3077– 310 7.

Dudhia, J., 1996: A m u lti-layer soil temp erature m odel for MM5. Preprints, 6 th Annual MM5 Users Workshop, Boulder, CO.

Dolwick

, P. 2005: http://cleanairinfo.com / modelingwor kshop/presentations/MP E _Dolwick.pdf

Grell, G. A., J. Dudhia , and D. R. Stauffer 199 4: A description of th e fifth-gen e ration

Penn State/NCAR Mesoscale Mod e l (MM5). NCAR Tech.Note NCAR/TN-398 1 STR, 122 pp.

Hong, S.-H.

, and H. -L. Pan, 199 6: Nonlocal bo undary layer vertical d i ffusion in a

m e dium -range forecast m odel. Mon

. W ea. Rev., 124, 23 22– 2339.

Jajic, Z. I., 1990: T h e step -m ountain coordin a te: Physical package. Mon. W ea. Rev., 118, 142 9-1 443.

Jajic, Z. I., 1994: The step-m ountain Eta c oordinate m odel: Further develop m ent of the convection, viscous sublayer a nd turbulent closure schem e s. Mon. Wea. Rev ., 122, 927-

945.

Johnson, M. 2004: www.ladco.org/tech/photo/photochem i cal.htm l

Kain, J.S., and J.M. Fritsch, 1 993 : Convectiv e param e teri zation for m e soscale m odels:

The Kain-F ritsch sch e me. Cum u lus Param e terization. Meteo r . Mo nogr., 46, Am er. Meteor. Soc., 165 -170.

6Mellor, G. L., and T

. Yam a da, 197 4: A h i erarchy of tu rbu l ence closu r e m odels for plan etary bo undary layers. J. A t m o s. Sci., 31, 17 9 1–1806.

Shafran, P. C., N.L. Seam an, and G. A. Gayn o, 2000 : Ev alu a tion of n u m e rical pred ictions of boundary lay e r stru cture d u ring the Lak e Michigan ozo n e stud y. J. A ppl. Meteor., 39 , 412 -426.

Stauffer, D. R., N. L. Seam an and F. S. Binkowski 199 1: Use of four-dim ensional d a ta

ass i m ilation in a lim ited

- area m e soscale m odel

. Part II: Effect s of data assim ilation

with in the planetary bou ndary layer. Mon. W ea. Rev., 119 , 7 34-754

.

Stauffer, D. R. and N. L

. Seam an, 1 990: U s e of four-d im ension al data assim ilation in a

lim ited-area m e sos cale m odel. Part I: Experiment s with sy noptic-scale data. Mon.

W ea. Rev., 118, 12 50-1277.

Zhang, D. -L ., 1989 : Th e effect of pa ram e terized ice m i crophysics on th e sim u lation of vortex circu l ation with a m e soscale hyd rost atic m odel. T e llus, 41A, 132-

147.

Zhang, D.-L , and R. A. Anthes, 1982: A h i gh -reso l u t ion m odel of the p l an etary boundary layer-sensit iv ity tests and com p ariso n s w ith SESA ME-79 data. J. App l

. Meteor., 21, 1594–16 09.

Zhang, D. -L , and W

. -Z. Zheng, 200 4: Diu r n a l cycles of su rf ace w i nds and tem p eratures
as sim u lated by five bou ndary -lay er param e te rizations. J. Ap pl. Meteo r ., 43, 157 -169

.

7Table 1 Measured calm and non-calm occurrences over the m odeling do m a in during August 2002 based on NW S d a ta

Hou r #No n

C a l m # C a l m # T o t a l % C a l m

0 0 Z 1820 9 3 9 2 4 2 2 1 3 3 1 7

. 7

0 1 Z 1653 1 6 0 2 6 2 2 5 5 7 2 6

. 7

0 2 Z 1560 4 6 9 2 9 2 2 5 3 3 3 0

. 8

0 3 Z 1498 3 7 2 4 5 2 2 2 2 8 3 2

. 6

0 4 Z 1430 9 7 5 4 0 2 1 8 4 9 3 4

. 5

0 5 z 1407 3 7 7 3 5 2 1 8 0 8 3 5

. 5

0 6 Z 1393 4 7 9 4 9 2 1 8 8 3 3 6

. 3

0 7 Z 1379 2 8 0 4 0 2 1 8 3 2 3 6

. 8

0 8 Z 1354 2 8 2 7 3 2 1 8 1 5 3 7

. 9

0 9 Z 1354 2 8 3 8 5 2 1 9 2 7 3 8

. 2

1 0 Z 1370 8 8 5 9 1 2 2 2 9 9 38.5

1 1 Z 1413 9 8 6 9 3 2 2 8 3 2 3 8

. 1

1 2 Z 1529 7 7 6 9 0 2 2 9 8 7 3 3

. 5

1 3 Z 1733 6 5 1 9 2 2 2 5 2 8 2 3

1 4 Z 1852 2 3 4 3 9 2 1 9 6 1 1 5

. 7

1 5 Z 1875 5 2 6 1 7 2 1 3 7 2 1 2

. 2

1 6 Z 1916 9 2 0 1 5 2 1 1 8 4 9

. 5

1 7 Z 1955 5 1 6 1 7 2 1 1 7 2 7

. 6

1 8 Z 1998 2 1 4 3 0 2 1 4 1 2 6

. 7

1 9 Z 2014 9 1 3 8 9 2 1 5 3 8 6

. 4

2 0 Z 2056 5 1 2 8 8 2 1 8 5 3 5

. 9

2 1 Z 2051 8 1 3 8 3 2 1 9 0 1 6

. 3

2 2 Z 2067 2 1 5 5 6 2 2 2 2 8 7

2 3 Z 2023 1 2 2 9 2 2 2 5 2 3 1 0

. 2

8Figure 1: O T C MM5 modeling dom ain with areal extent of 12k m and 36k m grids

9O b s e r v e d / P r e d i c t e d T e m p e r a t u 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 O b s T e m p P r d T e m 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 t u 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 O b s T e m p P r d T e m p

O b s e r v e d / P r e di c t e d W i nds pe e d

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 O b s W

ndS pd P r dW n d S pd

Ob s e r v e d / P r e di c t e d W i nds pe e d

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 s W n d S p d P r dW n d S pd

Figure 2: T e m p erature and W i nd speed com p arisons for August 2002. In each case the upper panel corresponds to com p arison betw een MM5 and NWS data and the lower panel between MM5 and CASTNet data.

10Figure 3a: Spatial correlation estimates between MM5 and NW S data for wind speed

fro m May to Septem ber 2002

11Figure 3b: Spatial distribution of correlation coe fficients for Tem p erature between MM5 and NWS data from May to Septem ber 2002.

12Richm ond, VA

Concord, NH

Figure 4: MM5 and W i n d profiler comparison for August 6 to 17, 2002 at Richm ond, VA and Concord, NH. The upper and lo wer panes at each station are for MM5 and profiler, respectively. The abciss a repres ents d a y and the o r dinate the h e ight (m ).

13Figure 5: MM5 and Satellite cloud im ages for August 14, 2002 at 0700 E S T

14Figure 6: MM5 predicted and m easu r ed precipitation over the do m a in for the m onth of August 2002

150 0.5

1 1.5

2 2.5

3 3.5

4 4.5

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

TI M EWIND SPEED (m/s)

TD L A v g N C

MM5AvgNC

MM5AvgC

Figure 7: Comparison of averaged wind sp eed between MM5 and observed under calm (C) and non-calm (NC) conditions.

16