California Air Resources Board 1 Model Performance Analysis of Annual Meteorology For Air Quality Modeling Using Mesoscale Model 5 (M M5)

Abstract:

The MM5 model was used to generate meteorological f ields over a 14-month time period between December 1, 1999 and February 1, 200 1 to provide input to air quality models for particulate matter (PM) modeling. The r esults of the analyses show that the model was able to capture the overall observed beha vior of the atmospheric conditions during the entire 14-month time period, although th e model performance varied within some subsets of time periods and at some locations.

Introduction:

Meteorological model setup:

Meteorological conditions leading to elevated ozone and PM levels in the San Joaquin Valley (SJV) over the 14-month period from December 1, 1999 to February 1, 2001 were simulated using the PSU/NCAR Mesoscale Model ( MM5) (version 3.6) (Grell et al, 1995). The model is based on non-hydrostatic, full y compressible motions that allow users to study the atmospheric motions at small sca les by explicitly treating the effects of convective motions on atmospheric circulations. The MM5 model has been improved over more than two decades by contributions from a broad scientific community ( http://www.mmm.ucar.edu/mm5 ) and is recommended for use in air quality studies by the US EPA (2005).

The MM5 model was set up with three nested grids. First, two coarse grids using (70 x 70) and (133 x 133) grid points with 36 and 12 km h orizontal resolutions, respectively, in the (x, y) or (south-north, west-east) directions ( Figure 1, domains D01 and D02) were established to provide the large scale initial and boundary conditions (IC/BCs) for the innermost grid. This innermost grid has (94 x 85) grid points with 4 km horizontal resolution to resolve the fine details of atmospher ic motions within the SJV domain (Figure 1, domain D03). Domain D01 is centered at 120.5 0 W longitude and 37.0 0 N latitude. The inner 12 and 4-km domains are placed within their respective coarse parent grids using certain grid point offsets from the lower left corner of their parent grid in (x, y) direction as provided in Table 1.California Air Resources Board 2 Figure 1 : The location of three nested grids designed to st udy the meteorology and air quality in SJV domain.

Domain name D01 D02 D03 x-dir resolution 36 km 12 km 4 km y-dir resolution 36 km 12 km 4 km # of grids in x-dir 70 133 94 # of grids in y-dir 70 133 85 Lower left corner (N-S) 1 15 43 Lower left corner (W-E) 1 15 54 SJV Domain

Table 1 : The three nested grids used to study meteorology and air quality in the SJV.

The modeling domain has 30 vertical layers, with a 30-m deep first model layer near the surface, extending to 100 mb at the top of the doma in. The thickness of the model layers increase with height. Initially, a higher v ertical resolution with more vertical grid levels was used to better resolve atmospheric proce sses evolving in a stableCalifornia Air Resources Board 3

atmosphere during winter. However, a sensitivity s tudy using a 15 m deep first layer and 50 vertical layers showed that the model result s did not improve appreciably, while the total model run time to finish a simulation was nearly doubled. Therefore, the nominal 30 vertical layers with the 30 m deep first model layer were used here.

MM5 has several options to calculate the components of internal and external forces acting on a volume of air including radiation, conv ection, cloud microphysics, soil fluxes, and boundary layer physics. Many sensitivity studi es were conducted using various model options to gain better agreement with observa tions. Once an optimal configuration was found, it was kept constant for s ubsequent simulations, although sensitivity studies were still carried out to see i f the model results were significantly different from each other. The optimal configurati on used here included the Grell (1995) cumulus parameterization scheme (for coarse grids o nly), the Blackadar boundary layer scheme for calculation of fluxes (Blackadar, 1979, Grell, 1995), the Dudhia simple ice scheme for the treatment of excess moisture (Dudhia , 1989), the Dudhia cloud radiation scheme (Dudhia, 1993) for radiational heating and c ooling of the atmosphere, and the Blackadar multi-layer, force-restore method soil mo del (Blackadar, 1976) for soil physics.

Initial and boundary conditions (IC/BCs) were prepa red using the analyses from the National Center for Environmental Prediction (NCEP) archived at NCAR. The 14-month period was first simulated using IC/BCs with the an alysis nudging option on the coarse 36 and 12 km grids using two-way nested options. O ur modeling experience using various combinations of model options and domain co nfigurations have shown that using the analysis nudging option together with a t wo-way nested grid option for the first two coarse grids improves the model performance com pared to no analysis nudging. Furthermore, the two-way nesting option resolved at mospheric processes within the 12- km grid that the one-way nested option could not. IC/BCs for the 4-km innermost grid were then prepared from the 12 km grid model output , and the.4-km model was run independently from the outer grids.

The year 2000 was chosen for the air quality and me teorology simulations since there were two intensive observational data collection pr ograms carried out that year: 1) Central California Ozone Study (CCOS) between May 1 – September 30, 2000 to study the formation of ozone, and 2) California Regional Particulate Matter Air Quality Study (CRPAQS) between December 1, 1999 and January 31, 2 001 to study the formation of PM.

The 14-month period was simulated using one-month t ime blocks. Each model run was started from the end of a previous numerical simula tion using the corresponding history files to create a continuous annual simulation. Th is method was used since sensitivity simulations showed that the model needed a longer s pin-up time of about 12-hr to reach the observed conditions when the model was started without using the history restart files of the previous run. Furthermore, since the analysis nudging option used for the coarse grids forced the model to stay within the bo undaries of observed conditions, theCalifornia Air Resources Board 4

model performance did not degrade compared to sensi tivity simulations done with shorter time scale such as 1-week long simulations.

Performance Evaluation of 4-km SJV Simulation:

Observational data from 13 surface meteorological s tations located within the SJV were analyzed and compared against model estimates to st udy the temporal and threedimensional spatial evolution of atmospheric motion s as well as to evaluate the model performance. The choice of stations was based on d ata availability and location within the domain. Figure 2 and Table 2 show the location of the monitoring stations located within the SJV. Since there are a large number of f igures analyzed, only selected figures are shown here to demonstrate the model per formance. The figures generated for all 13 stations can be found in Attachments A, B, and C.

There are various analysis methods used in the scie ntific community to study the results and evaluate the performance of a meteorological mo del. The most commonly used methods are summarized in Attachment D. Three meth ods are used here:

(1) Direct comparisons of all hourly values and st atistical analyses using mean, standard deviation, correlation coefficient, root m ean square error (RMSE), mean bias, and index of agreement of temperature, relative hum idity, u and v-component of the wind over the entire year and 3-month seasonal peri ods (January-March, April-June, July-September, and October-December, 2000) at Arvi n, Bakersfield, Fresno, Hanford, Madera, Meadows Field Airport in Bakersfield, Merce d, Modesto, Parlier, San Luis Obispo, Santa Rita, Visalia, and Yosemite (Attachme nt A);

(2) Histograms for the frequency of occurrence of temperature, wind speed, and relative humidity over a 24-hour time period for a one-month period every other month (January, March, May, July, September, November) 20 00 and January 2001) at sites in the vicinity of Fresno and Bakersfield (Attachment B); and

(3) Hourly comparisons of wind speed, direction, a nd temperature over two five-day periods within each month (days 1 through 6, and 11 through 16 for 14 months) at Arvin, Bakersfield, Fresno, Merced, Modesto, Parlie r, and Visalia (Attachment C).California Air Resources Board 5 Figure 2 : The location of observation stations used in the model performance analysis of MM5.

Site name Site location Site county Lat(degree) Lon (de gree) ARV Arvin-Edison Kern 35.21 -118.78 BMF Bakersfield Meadows Field Kern 35.39 -119.01 FAT Fresno Air Terminal Fresno 36.78 -119.72 PLR Parlier Fresno 36.6 -119.5 MCD Merced Merced 37.31 -120.39 MOD Modesto #3 Stanislaus 37.65 -121.19 MFA Meadow Field Airport Kern 35.43 -119.06 VSA Visailia Airport Tulare 36.31 -119.39 YOS Yosemite National Park Mariposa 37.71 -119.71 SRT Santa Rita San Benito 36.35 -120.6 HMA Hanford Municipal Airport Kings 36.32 -119.63 MAD Madera Madera 37.02 -120.19 SLO San Luis Obispo San Luis Obispo 35.306 -120.66

Table 2 :The observation stations used in the model perform ance analysis of MM5.California Air Resources Board 6

The results of method 1:

Figures 3a-d compare observed air temperature, rela tive humidity, u-component, and vcomponent of the wind near the surface against mode l estimates at Merced (MCD), for the entire year 2000. Similarly, Tables 3a-d show the statistical calculations performed for the same variables at the same station for the same time period. Merced was chosen as a representative site; figures and statis tical comparisons for other sites can be found in Attachment A.

Both figures and tables indicate relatively high co rrelation between model results and observations for temperature (0.89) and relative hu midity (0.71). However, the correlation decreases for the u-component of the wi nd (0.53) and the v-component (0.27). Moreover, although the correlation for rel ative humidity is higher than those for the u and v-components of the wind, the model over- predicts the relative humidity most of the time, especially at higher relative humiditi es. Similar degrees of agreement between model results and observations are also see n at other stations (see Attachment A) for all meteorological variables used . The smaller correlation for the wind components is partly due to the fact that anal ysis or observational nudging was not used for the 4-km grid.

The index of agreement between model results and ob servations for each model variable do not show large seasonal variations. Fo r example, relative humidity at Merced is 0.79 for the entire year while it is 0.82 , 0.78, 0.69, and 0.75 in winter, spring, summer, and fall, respectively. The correlation be tween model results and observations is the highest for temperature for the entire year and different seasons, but lower for the u and v-component of the wind for each of these tim e periods. For example, while the correlation is relatively high for the u-component (0.79) for the entire year, it is below 0.4 in other seasons, and it is less than 0.29 for the v-component for all seasons.

(a) (b)California Air Resources Board 7 (c) (d)
Figures 3a-d : Comparisons of model results against observations for a) temperature, b) relative humidity, c) u-component, and d) v-component of the wind near the surface at Merced (MCD) for th e entire year.

Performance stats. Obs. Mod. R-squared value 0.89 Average value 15.664 14.784
Mean Abs. error 2.34 Standard deviation 8.605 7.889
Mean bias -0.88 Maximum value 39.56 35.3
RMSE 3 Minimum value -3.17 -1.83 Agreement index 0.97 Data points 8751 8751 (a)

Performance stats. Obs. Mod. R-squared value 0.71 Average value 69.689 82.937
Mean Abs. error 14.46 Standard deviation 23.769 15.336
Mean bias 13.25 Maximum value 100 100
RMSE 19.01 Minimum value 12 42.76 Agreement index 0.79 Data points 8751 8751 (b)

Performance stats. Obs. Mod. R-squared value 0.53 Average value 0.697 0.453
Mean Abs. error 1.06 Standard deviation 1.7 1.926
Mean bias -0.24 Maximum value 7.51 6.69
RMSE 1.38 Minimum value -6.79 -8.24 Agreement index 0.84 Data points 8752 8752 (c)

Performance stats. Obs. Mod. R-squared value 0.27 Average value -0.519 -0.099
Mean Abs. error 1.27 Standard deviation 0.926 1.962
Mean bias 0.42 Maximum value 4.43 8.99
RMSE 1.73 Minimum value -5.04 -7.21 Agreement index 0.6 Data points 8752 8752 (d) Tables 3a-d : Statistical comparisons for a) temperature, b) re lative humidity, c) u-component, and d) v-component of the wind near the surface at Merced (MCD) for the entire year.California Air Resources Board 8

The results of method 2:

In these comparisons, four representative monitorin g sites in the vicinities of Fresno and Bakersfield were used and grouped for the analyses performed (Table 1, Attachment B). Only a sample set of figures for Fresno for Ma rch 2000 are shown here as samples of the results, while all results from Fresno can b e found in Attachment B in greater detail. The results for Bakersfield are similar to those of Fresno and are not shown.

Figures 4a-d and 5a-d show the comparisons of measu red and simulated hourly averaged values of air temperature, wind speed, rel ative humidity, and mixing ratio, respectively, near the surface for March 2000 using histograms. The frequency distribution of air temperature, relative humidity, and mixing ratio for observations and model results show reasonably good agreement. There are only small differences in observed and simulated diurnal wind speed patterns.

Figures 4a-d : Hourly averaged wind speed (a) measured and (b) simulated, and the frequency of occurrence of air t emperatures (c) measured and (d) simulated for sites in the vicinit y of Fresno during March, 2000. 23 22 21 20 19 18 17 16 15 14 13 12 11 10 9 8 7 6 5 4 3 2 1 5

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Figures 5a-d: Frequency of occurrence of (a) measur ed and (b) simulated relative humidity, and of (c) measured and (d) simulated wat er mixing ratio for sites in the vicinity of Fresno during March, 2000.

Figures 5a-d : Frequency of occurrence of (a) measured and (b) s imulated relative humidity, and of (c) measured and (d) simu lated water mixing ratio for sites in the vicinity of Fresno during March, 2 000.

The results of method 3:

A direct comparison of the model results against ob servations was also carried out by plotting observed and simulated values for each sta tion over two 5-day periods that were randomly selected within each month (days 1-6 and 11-16). Only a sample figure is given here while all other figures can be found in Attachment C. Temporal comparisons of wind speed, wind direction, and temp erature for the Fresno station for December 1-6, 1999 period (Figures 6a-c) show that the observed conditions appear to be captured by the model well. Figures 3-5 a-c and 3-6 a-c in Attachment C show that the model can capture the diurnal evolution of obse rved wind speed, wind direction and temperature variations reasonably well. While the simulated wind speed and direction are in good agreement with the observations, the si mulated temperature also shows variations in capturing the observed maxima and min ima. For example, Figures 3-8 a-c indicate that the model overestimates somewhat the observed daily maxima and minima of temperature during March 1-6, 2000 period at Fresno, and the estimates for the evolution of the wind speed and direction diffe r somewhat. However, the examination of all figures given in Attachment C in dicates that the model does capture 95 85 75 65 55 45 35 25 15 50

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Simulated Mixing Ratio (gm/gm)Relative Frequency (d) Fresno -- March 2000California Air Resources Board 10

the overall evolution. Furthermore, the model’s abi lity in recreating observed conditions also shows variations from one station to another ( Attachment C).

Figures 6a-c : Temporal comparison of simulated (a) wind speed, (b) wind direction, and (c) temperature near the surfac e against observations at Fresno Station during December 1-6, 1999 period.California Air Resources Board 11

Summary and conclusions:

The model performance analyses described above indi cate that MM5 is able to reproduce the overall statistical characteristics o f observed meteorological conditions. The model can recreate the mean, standard deviation , and minimum and maximum values of wind speed and direction over the entire year and each season. However, the model estimates of maxima and minima for near surfa ce temperature can show variations in time and location. The correlation c oefficient of temperature is high for most stations, although the model underestimates th e maximum surface temperature. The model generally captures the observed condition s that show large variations in maxima and minima from one day to the next over som e 5-day periods well.

It should be noted that the simulated time period i s 14 months, and no analysis or observational nudging was performed in the 4-km dom ain. It has been shown by many numerical modelers that observational nudging impro ves the overall model performance. However, the use of observational nud ging can result in the distortion of overall wind flow patterns, or can induce instabili ties. Observational nudging was not used in this study, however the model was able to c apture expected and observed conditions well.California Air Resources Board 12

References:

Blackadar, A. K., 1976: Modeling the Nocturnal Boun dary Layer. Preprints of Third Symposium on Atmospheric Turbulence and Air Quality , Raleigh, NC. 19-22, October 1976, Amer. Meteor. Soc., Boston, 46-49.

Blackadar, A. K., 1979: Advances in Environmental S cience and Engineering, 1. No. 1. Pfaflin and Ziegler, Eds. Gordon and Breach Publish ers, 50-85.

Dudhia, J., 1989: Numerical Study of Convection Obs erved During the Winter Monsoon Experiment Using a Two-Dimensional Model. J. Atmos. Sci., 46, 3077-3107.

Dudhia, J., 1993: Radiation Studies with a High-Res olution Mesoscale Model. Proceedings of the Third Atmospheric Radiation Meas urement (ARM) Science Team Meeting. March 1-4, 1993, Norman, OK. pp. 363-366.

Grell, G. A., J. Dudhia and D. R. Stauffer, 1995: A description of the fifth-generation Penn State/NCAR mesoscale model (MM5). NCAR Technical Note , NCAR/TN398+STR.

US EPA, 2005: Guidance on the Use of Models and Oth er Analyses in Attainment Demonstrations for the 8-hour Ozone NAAQS. EPA-454/ R-05-002. OAQPS/EMAD/AQMG, RTP, NC, October 2005.