Connecticut Regional Haze State Implementation Plan November 2009



### ATTACHMENT R

NYSDEC Technical Support Document TSD-1a1 TSD-1a



## Meteorological Modeling 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 Conserva tion

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 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 d eveloped for clusters. The two-way nested domain consisted of coarse (36km) and fine ( 12km) mesh corresponding to 149x129 and 175x175 grids, respectively, in this ap plication (see Figure 1).

The Lambert projection used in this work followed t he 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 pr ojection 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 ea ch σ -level

determined from a reference state that is estimated using the hydrostatic equation from a given sea-level pressure and temperature with a sta ndard lapse rate. There are 30 unevenly spaced σ levels, giving 29 vertical layers, with higher res olution within the planetary boundary layer (PBL). The σ levels are:

1.0000, 0.9974, 0.9940, 0.8980, 0.9820, 0.9720, 0.9 590, 0.9430, 0.9230, 0.8990,

0.8710, 0.8390, 0.8030, 0.7630, 0.7180, 0.6680, 0.6 180, 0.5680, 0.5180, 0.4680,

0.3680, 0.3180, 0.2680, 0.2180, 0.1680, 0.1230, 0.0 800, 0.0400, 0.0000

The surface layer was set at about 10m, the level a t 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 7 5 and 25 seconds, respectively.

The important model physics options used for this M M5 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 boundar y 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 temp eratures using the surface energy budget equation (Dudhia 1996)

Note that the Blackadar PBL scheme has been modifie d 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 sc heme (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 observations f or each domain during the model integration process (Stauffer and Seaman, 1990; Sta uffer 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 experi ments, 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

This assessment covers the period of May through Se ptember 2002.

National Weather Service (NWS) and CASTNet data – S urface temperature, Wind Speed, and Humidity

NWS (TDL) and CASTNet ( www.epa.gov/castnet/ )surface measurements of temperature, wind speed, and humidity (note there w ere no humidity measurements for CASTNet) were used to compare with the MM5 outputs. The evaluation was performed with METSTAT program developed by Environ Corporati on ( www.camx.com/files/metstat.15feb05.tar.gz. .When comparing to NWS data, the METSTAT interpolates the first layer MM5 (at 10m he ight) 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 c hanges were needed as CASTNet data were reported at a height of 10m. In this anal ysis, no exclusion was made for calm conditions. The reported calm winds (zero wind spee d measured) were treated as is in

this evaluation effort. The METSTAT calculated st andard statistical measures – average, bias, error and index of agreement between the measured and predicted

parameters.

Figure 2 displays the temperature and wind speed c omparison 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 co nsidered to be quite good for both NWS and CASTNet data, while MM5 underpredicted NWS and overpredicted CASTNet4

daytime wind speed, respectively. It should be poin ted 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 ho urly 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 com putation. Interestingly, MM5 appears to track quite well the nighttime minimum wind speed f or both networks. In the case of humidity (not shown), MM5 tracks the NWS observed h umidity trend well, but MM5 missed the observed semi-diurnal cycles. Compariso ns for the five months 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 averag es to provide an overall response of the model over the five months. Another way of a ssessing the model is to examine the degree of correlation between the measured and pred icted parameters. Figure 3 displays such a comparison for temperature and wind speed fo r the NWS hourly data covering the period of May through September 2002. For the NWS d ata, the correlations are in the range from 0.7 to 0.8 for wind speed, above 0.96 fo r 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 captu red 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 M M5. There are twelve wind-profiler measurement stations from which data were available for comparison. For convenience of comparison, the wind-profiler measur ements were interpolated to the MM5 vertical levels. The approach used was simple i nterpolation 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 w as to assess if MM5 was able to capture the measured vertical structure, and for th is 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 t he MM5 and wind profiler comparison for the August 2002 episode at Richmond, VA and Con cord, NH. MM5 predicted weaker LLJ winds compared to those based on the wind-profi ler measurements. The detailed plots of this comparison are available on request f rom 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 0.5 o by 0.5 o resolution of the satellite data were interpolated into the 12km MM5 grid for comparison. The MM5 total cloud f raction was estimated by MCIP based on the MM5’s low cloud, middle cloud and high cloud predictions. In general,5

MM5 captured the satellite cloud pattern well but u nderestimates the satellite cloud fraction (see Figure 5 as an example). Part of prob lem may due to the coarse resolution of the satellite cloud data. The detailed plots of thi s 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 analysis data ( http://data.eol.ucar.edu/codiac/dss/id=21.093 produced by Climate Prediction Center, based on 7,000-8,000 hou rly/6-hourly gauge reports and radar). The MM5 monthly total precipitation was est imated from the MM5 predicted

convective and non-convective rainfall and summed u p for each month. In general, MM5 captured the observed spatial patterns in May and S eptember, 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 t his 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 un der observed calm conditions, because of potential pollutant buildup that could o ccur under such conditions. Table 1 lists the summary of the percentage of calm conditi on at each hour for the August 2002 from the NWS data within the 12km domain. It is app arent from the Table that the calm conditions occur primarily during the night and ear ly 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 sp eeds 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 “c alm” group, the average wind speed for MM5 varies from 1 m/s during the night and earl y morning hours and over 1.5 m/s during the day. MM5 is over-predicting during obse rved 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 unde rpredicts by about 0.5 m/s for all hours with noticeable local maximum happening at th e nudging hours. The MM5 nudging process would pull predictions toward the m easured 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 MM 5 simulation to real-world data,

both at the surface level as well as in the vertica l. While there are no specific recommended procedures identified for this assessme nt, similar approaches have been used elsewhere (Dolwick 2005, Baker 2004, and Johns on 2004). Traditionally, the NWS surface measurements are used for such a comparison . Since NWS data had been used6

through nudging processes in developing the MM5 sim ulation, 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 asses sment 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 t o 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 su rface and in the vertical, thereby providing confidence in the use of these data in th e 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 obs erved 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 mo del for MM5. Preprints, 6 th Annual MM5 Users Workshop, Boulder, CO.

Dolwick, P. 2005: http://cleanairinfo.com/modelingworkshop/presentati ons/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.No te 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, 2 322–2339.

Jajic, Z. I., 1990: The step-mountain coordinate: Physical package. Mon. Wea. Rev.,
118, 1429-1443.

Jajic, Z. I., 1994: The step-mountain Eta coordinat e 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 par ameterization 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 o f turbulence closure models for planetary boundary layers. J. Atmos. Sci., 31, 1791 –1806.

Shafran, P.C., N.L. Seaman, and G. A. Gayno, 20 00: 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 1 991: Use of four-dimensional data

assimilation in a limited-area mesoscale model. Par t 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 fo ur-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 parameterize d 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-res olution model of the planetary boundary layer-sensitivity 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 winds and temperatures as simulated by five boundary-layer parameterizatio ns. J. Appl. Meteor., 43, 157-169.8

Table 1 Measured calm and non-calm occurrences ove r 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 10.29

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

Figure 2: Temperature and Wind speed comparisons f or August 2002. In each case the upper panel corresponds to comparison between MM5 a nd 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

2 7 5 2 8 0 2 8 5 2 9 0 2 9 5 3 0 0 3 0 58/ 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 te d T e m p e r a tu r e

2 7 5 2 8 0 2 8 5 2 9 0 2 9 5 3 0 0 3 0 58/ 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

Observed/Predicted Windspeed

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 ObsWndSpd PrdWndSpd

Observed/Predicted Windspeed

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 ObsWndSpd PrdWndSpd11

Figure 3a: Spatial correlation estimates between MM 5 and NWS data for wind speed from May to September 200212

Figure 3b: Spatial distribution of correlation coefficients fo r Temperature between

MM5 and NWS data from May to September 2002.13

Richmond, VA

Concord, NH

Figure 4: MM5 and Wind profiler comparison for Augu st 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 or dinate the height (m).14

Figure 5: MM5 and Satellite cloud images for August 14, 2002 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 wind speed between MM5 and observed under calm (C) and non-calm (NC) conditions.