Sensitivity Modeling to Investigate Modeling Improvements for the June July 2006 Denver Ozone Episode

Transcription

1 Sensitivity Modeling to Investigate Modeling Improvements for the June July 26 Denver Ozone Episode Prepared for: Regional Air Quality Council 1445 Market Street, Suite 26 Denver, Colorado 822 Prepared by: Ralph Morris, Edward Tai and Bonyoung Koo ENVIRON International Corporation 773 San Marin Drive, Suite 2115 Novato, California, Dennis McNally and Cyndi Loomis Alpine Geophysics, LLC 7341 Poppy Way Arvada, Colorado 87 June 3, 211 Project Number: A

2 Contents Page 1. INTRODUCTION BACKGROUND Modeling since the 28 Denver Ozone SIP OVERVIEW OF THE 28 DENVER OZONE SIP MODELING DATABASE NEW DENVER OZONE SIP MODELING REPORT OBJECTIVES AND ORGANIZATION OF THE REPORT 6 2. METEOROLOGICAL MODELING SENSITIVITY RESULTS INTRODUCTION MM5 SENSITIVITY MODELING WRF SENSITIVITY MODELING MODEL PERFORMANCE EVALUATION Temperature Bias and Error Water Mixing Ratio Bias and Error Wind Index of Agreement Precipitation SELECTION OF ALTERNATIVE METEOROLOGICAL FIELDS FOR CAMX BASE CASE MODELING REVISED CAMX 26 BASE CASE SIMULATION AND MODEL PERFORMANCE EVALUATION INTRODUCTION OZONE MODEL PERFORMANCE STATISTICS Hourly Ozone Model Performance Metrics Hour Ozone Model Performance Metrics ANALYSIS OF OZONE MODEL PERFORMANCE FOR FIVE 3 DAY EPISODES June 17 19, June 26 28, June 29 July 1, July 13 15, July 27 29, PRECIPITATION AND CLOUDS SENSITIVITY TEST CAMX MM5/WRF MODEL EVALUATON CONCLUSIONS 77 s:\denver_o3_211\reports\revised_26_base_case\draft#2\ denver_sens_met_26_draft_jun3_211.doc i

3 4. CMAQ SENSITIVITY TEST AND PROCESS ANALYSIS MODELING DEVELOPMENT OF CMAQ MODELING DATABASE OZONE MODEL PERFORMANCE STATISTICS Hourly Ozone Model Performance Metrics Hour Ozone Model Performance Metrics ANALYSIS OF OZONE MODEL PERFORMANCE FOR FIVE 3 DAY EPISODES June 17 19, June 26 28, June 29 July 1, July 13 15, July 27 29, Discussion of Model Performance Evaluation PROCESS ANALYSIS MODELING Process Analysis in CAMx Integrated Process Rate Analysis Integrated Reaction Rate Analysis Chemical Process Analysis Process Analysis Application to Denver REFERENCES 19 APPENDICES Appendix A: MM5 and WRF 36/12km Performance Metrics Appendix B: WRF Run 15 5 Day Accumulated Precipitation Comparison Appendix C: Time Series of Predicted and Observed Hourly Ozone Concentrations for Five Three Day Ozone Episodes and Three Model Configurations TABLES Table 1 1. Fourth highest daily maximum 8 hour ozone concentrations during and ozone Design Values (ppb) for monitoring sites in the DMA/NFR region. 2 Table 2 1. MM5 Model Configurations. 1 Table 2 2. MM5 and WRF 34 Level Vertical Structure. 11 Table 2 3. MM5 and WRF 46 Level Vertical Structure. 12 Table 2 4. WRF Model Configurations. Highlighted cells differ from the configuration used in the previous simulation. 15 s:\denver_o3_211\reports\revised_26_base_case\draft#2\ denver_sens_met_26_draft_jun3_211.doc ii

4 Table 2 5. Table 2 6. Table 2 7. Table 2 8. Table 2 9. Table 3 1. Table 3 2. Table 3 3. Table 3 4. Temperature bias (K) for the 4 km Colorado domain by month (benchmark <±.5 K). 18 Temperature error (K) for the 4 km Colorado domain by month (benchmark < 2. K). 19 Water mixing ratio bias (g/kg) for the 4 km Colorado domain by month (benchmark < 1. g/kg). 2 Water mixing ratio error (g/kg) for the 4 km Colorado domain by month (benchmark < 2. g/kg). 21 Wind index of agreement (IOA) for the 4 km Colorado domain by month (benchmark >.6). 22 Summary ozone model performance statistical; metrics for CAMx/MM5, CAMx/WRF1 and CAMx/WRF2 and the June 17 19, 26 episode. 51 Summary ozone model performance statistical; metrics for CAMx/MM5, CAMx/WRF1 and CAMx/WRF2 and the June 26 28, 26 episode. 56 Summary ozone model performance statistical metrics for CAMx/MM5, CAMx/WRF1 and CAMx/WRF2 and the June 29 July 1, 26 episode. 61 Summary ozone model performance statistical metrics for CAMx/MM5, CAMx/WRF1 and CAMx/WRF2 and the July 13 15, 26 episode. 66 Table 3 6. Summary ozone model performance statistical metrics. 77 FIGURES Figure 1 1a. Nested 36/12/4 km modeling domains for the 28 Denver 8 hour ozone SIP modeling study. Blue line domains are for CAMx/SMOKE domains that are nested in the MM5 red line domains. 5 Figure 1 1b. Nested 12/4 km modeling domains for the 28 Denver ozone SIP SMOKE emissions and CAMx air quality modeling. 5 Figure /12/4km MM5 Domain. 13 Figure 2 2. Figure 2 3. Figure 2 4. June 26 observed and model estimated total accumulated precipitation (mm). 23 June 26 observed and model estimated total accumulated precipitation (mm). 24 June 26 observed and model estimated total accumulated precipitation (mm). 25 s:\denver_o3_211\reports\revised_26_base_case\draft#2\ denver_sens_met_26_draft_jun3_211.doc iii

5 Figure 2 5. Figure 2 6. Figure 2 7. Figure 2 8. Figure 2 9. June 26 observed and model estimated total accumulated precipitation (mm). 26 June 26 observed and model estimated total accumulated precipitation (mm). 27 June 26 observed and model estimated total accumulated precipitation (mm). 28 July 26 observed and model estimated total accumulated precipitation (mm). 29 July 26 observed and model estimated total accumulated precipitation (mm). 3 Figure 2 1. July 26 observed and model estimated total accumulated precipitation (mm). 31 Figure July 26 observed and model estimated total accumulated precipitation (mm). 32 Figure July 26 observed and model estimated total accumulated precipitation (mm). 33 Figure July 26 observed and model estimated total accumulated precipitation (mm). 34 Figure 3 1a. Hourly ozone statistical perform metrics for CAMx/MM5 (Run22), CAMx/WRF1 (CMAQ like Kz) and CAMx/WRF2 (ACM2) for June 1 15, 26 and the Denver NAA. 39 Figure 3 1b. Hourly ozone statistical perform metrics for CAMx/MM5 (Run22), CAMx/WRF1 (CMAQ like Kz) and CAMx/WRF2 (ACM2) for June 16 3, 26 and the Denver NAA. 4 Figure 3 2a. Hourly ozone statistical perform metrics for CAMx/MM5 (Run22), CAMx/WRF1 (CMAQ like Kz) and CAMx/WRF2 (ACM2) for July 1 15, 26 and the Denver NAA. 41 Figure 3 2b. Hourly ozone statistical perform metrics for CAMx/MM5 (Run22), CAMx/WRF1 (CMAQ like Kz) and CAMx/WRF2 (ACM2) for July 16 3, 26 and the Denver NAA. 42 Figure 3 3a. 8 Hour ozone statistical perform metrics for CAMx/MM5 (Run22), CAMx/WRF1 (CMAQ like Kz) and CAMx/WRF2 (ACM2) for June 1 15, 26 and the Denver NAA. 44 Figure 3 3b. 8 Hour ozone statistical perform metrics for CAMx/MM5 (Run22), CAMx/WRF1 (CMAQ like Kz) and CAMx/WRF2 (ACM2) for June 16 3, 26 and the Denver NAA. 45 Figure 3 4a. 8 Hour ozone statistical perform metrics for CAMx/MM5 (Run22), CAMx/WRF1 (CMAQ like Kz) and CAMx/WRF2 (ACM2) for July 1 15, 26 and the Denver NAA. 46 s:\denver_o3_211\reports\revised_26_base_case\draft#2\ denver_sens_met_26_draft_jun3_211.doc iv

6 Figure 3 4b. 8 Hour ozone statistical perform metrics for CAMx/MM5 (Run22), CAMx/WRF1 (CMAQ like Kz) and CAMx/WRF2 (ACM2) for July 16 3, 26 and the Denver NAA. 47 Figure 3 5. Location of ozone monitoring sites within and near the Denver Metropolitan Area (DMA) (triangle symbols point to monitor identifier). 49 Figure 3 6a. Time series of predicted and observed hourly ozone concentrations for June 17 19, 26, CAMx/MM5 (run22.124), CAMx/WRF1 (wrfcmaq) and CAMx/WRF2 (wrfacm2). 52 Figure 3 6b. Time series of predicted and observed hourly ozone concentrations for June 17 19, 26, CAMx/MM5 (run22.124), CAMx/WRF1 (wrfcmaq) and CAMx/WRF2 (wrfacm2). 53 Figure 3 7. Spatial distribution of predicted and observed daily maximum 8 hour ozone concentrations on June 17 (left) and 19 (right) 26 for CAMx/MM5 (top), CAMx/WRF1 (middle) and CAMx/WRF2 (bottom). 54 Figure 3 8a. Time series of predicted and observed hourly ozone concentrations for June 26 28, 26, CAMx/MM5 (run22.124), CAMx/WRF1 (wrfcmaq) and CAMx/WRF2 (wrfacm2). 57 Figure 3 8b. Time series of predicted and observed hourly ozone concentrations for June 26 28, 26, CAMx/MM5 (run22.124), CAMx/WRF1 (wrfcmaq) and CAMx/WRF2 (wrfacm2). 58 Figure 3 9. Spatial distribution of predicted and observed daily maximum 8 hour ozone concentrations on June 26 (left) and 27 (right) 26 for CAMx/MM5 (top), CAMx/WRF1 (middle) and CAMx/WRF2 (bottom). 59 Figure 3 1a.Time series of predicted and observed hourly ozone concentrations for June 29 July 1, 26, CAMx/MM5 (run22.124), CAMx/WRF1 (wrfcmaq) and CAMx/WRF2 (wrfacm2). 62 Figure 3 1b.Time series of predicted and observed hourly ozone concentrations for June 29 July 1, 26, CAMx/MM5 (run22.124), CAMx/WRF1 (wrfcmaq) and CAMx/WRF2 (wrfacm2). 63 Figure Spatial distribution of predicted and observed daily maximum 8 hour ozone concentrations on June 3 (left) and July 1 (right) 26 for CAMx/MM5 (top), CAMx/WRF1 (middle) and CAMx/WRF2 (bottom). 64 Figure 3 12a.Time series of predicted and observed hourly ozone concentrations for July 13 15, 26, CAMx/MM5 (run22.124), CAMx/WRF1 (wrfcmaq) and CAMx/WRF2 (wrfacm2). 67 Figure 3 12b.Time series of predicted and observed hourly ozone concentrations for July 13 15, 26, CAMx/MM5 (run22.124), CAMx/WRF1 (wrfcmaq) and CAMx/WRF2 (wrfacm2). 68 s:\denver_o3_211\reports\revised_26_base_case\draft#2\ denver_sens_met_26_draft_jun3_211.doc v

7 Figure Spatial distribution of predicted and observed daily maximum 8 hour ozone concentrations on July 13 (left) and 14 (right) 26 for CAMx/MM5 (top), CAMx/WRF1 (middle) and CAMx/WRF2 (bottom). 69 Figure 3 14a.Time series of predicted and observed hourly ozone concentrations for July 27 29, 26, CAMx/MM5 (run22.124), CAMx/WRF1 (wrfcmaq) and CAMx/WRF2 (wrfacm2). 72 Figure 3 14b.Time series of predicted and observed hourly ozone concentrations for July 27 29, 26, CAMx/MM5 (run22.124), CAMx/WRF1 (wrfcmaq) and CAMx/WRF2 (wrfacm2). 73 Figure Spatial distribution of predicted and observed daily maximum 8 hour ozone concentrations on July 28 (left) and 29 (right) 26 for CAMx/MM5 (top), CAMx/WRF1 (middle) and CAMx/WRF2 (bottom). 74 Figure 4 1. Figure 4 2. Figure 4 3. Figure 4 4. Hourly ozone statistical perform metrics for CMAQ and CAMx/WRF1 for June, 26 and the Denver NAA. 8 Hourly ozone statistical perform metrics for CMAQ and CAMx/WRF1 for July, 26 and the Denver NAA Hour ozone statistical perform metrics for CMAQ and CAMx/WRF1 for June, 26 and the Denver NAA Hour ozone statistical perform metrics for CMAQ and CAMx/WRF1 for July, 26 and the Denver NAA. 84 Figure 4 5a. Time series of predicted and observed hourly ozone concentrations for June 17 19, 26, CMAQ and CAMx/WRF. 86 Figure 4 5b. Time series of predicted and observed hourly ozone concentrations for June 17 19, 26, CMAQ and CAMx/WRF1. 87 Figure 4 6. Spatial distribution of predicted and observed daily maximum 8 hour ozone concentrations on June 17 (top), 18 (middle) and 19 (bottom) 26 for CMAQ (left) and CAMx/WRF1 (right). 88 Figure 4 7a. Time series of predicted and observed hourly ozone concentrations for June 26 28, 26, CMAQ and CAMx/WRF1. 9 Figure 4 7b. Time series of predicted and observed hourly ozone concentrations for June 26 28, 26, CMAQ and CAMx/WRF1. 91 Figure 4 8. Spatial distribution of predicted and observed daily maximum 8 hour ozone concentrations on June 26 (top), 27 (middle) and 28 (bottom) 26 for CMAQ (left) and CAMx/WRF1 (right). 92 Figure 4 9a. Time series of predicted and observed hourly ozone concentrations for June 29 July 1, 26, CMAQ and CAMx/WRF1. 94 Figure 4 9b. Time series of predicted and observed hourly ozone concentrations for June 29 July 1, 26, CMAQ and CAMx/WRF1. 95 s:\denver_o3_211\reports\revised_26_base_case\draft#2\ denver_sens_met_26_draft_jun3_211.doc vi

8 Figure 4 1. Spatial distribution of predicted and observed daily maximum 8 hour ozone concentrations on June 29 (top), June 3 (middle) and July 1 (bottom) 26 for CMAQ (left) and CAMx/WRF1 (right). 96 Figure 4 11a.Time series of predicted and observed hourly ozone concentrations for July 13 15, 26, CMAQ and CAMx/WRF1. 98 Figure 4 11b.Time series of predicted and observed hourly ozone concentrations for July 13 15, 26, CMAQ and CAMx/WRF1. 99 Figure Spatial distribution of predicted and observed daily maximum 8 hour ozone concentrations on July 13 (top), 14 (middle) and 15 (bottom) 26 for CMAQ (left) and CAMx/WRF1 (right). 1 Figure 4 13a.Time series of predicted and observed hourly ozone concentrations for July 27 29, 26, CMAQ and CAMx/WRF1. 12 Figure 4 13b.Time series of predicted and observed hourly ozone concentrations for July 27 29, 26, CMAQ and CAMx/WRF1. 13 Figure Spatial distribution of predicted and observed daily maximum 8 hour ozone concentrations on July 27 (top), 28 (middle) and 29 (bottom) 26 for CMAQ (left) and CAMx/WRF1 (right). 14 s:\denver_o3_211\reports\revised_26_base_case\draft#2\ denver_sens_met_26_draft_jun3_211.doc vii

9 1. INTRODUCTION 1.1 BACKGROUND In November 27, the Denver Metropolitan Area and North Front Range (DMA/NFR) region was designated as an ozone nonattainment area (NAA) based on measured ozone data during that violated the ppm 8 hour ozone NAAQS. This resulted in a requirement to prepare an 8 hour ozone State Implementation Plan (SIP) that demonstrates ozone attainment by 21. The Denver Regional Air Quality Council (RAQC), in conjunction with the Colorado Department of Health and Environment Air Pollution Control Division (CDPHE/APCD), contracted with ENVIRON International Corporation and Alpine Geophysics, LLC to develop a June July 26 photochemical modeling database and conduct ozone attainment demonstration modeling and other analysis that demonstrated that the DMA/NFR NAA would achieve the hour ozone NAAQS by 21. The documentation of the ozone attainment demonstration modeling and other technical analysis for the 28 Denver 8 hour ozone SIP is available on the CDPHE/APCD website. 1 On March 12, 28, EPA promulgated a new primary ozone NAAQS that has the same form as the 1997 ozone NAAQS, but lowers the threshold from.8 ppm (85 ppb) to.75 ppm (76 ppb). Of the ~18 ozone monitors in the DMA/NFR NAA with sufficient data during 28 21, two have hour ozone Design Values that are.75 ppm or higher (i.e., 76 ppb). These two monitors are the Rocky Flats North (RFNO) and the Chatfield (CHAT) monitoring sites with ozone Design Values of 78 and 76 ppb, respectively. The CDPHE has recommended that the Denver 8 hour ozone nonattainment area (NAA) for the March 28 ozone NAAQS have the same boundaries as the 1997 ozone NAAQS NAA. 2 In January 21, EPA proposed a reconsideration of the March 28 ozone NAAQS to lower the 8 hour ozone NAAQS to somewhere in the.6.7 ppm range. The current schedule is to finalize the reconsideration of the ozone NAAQS by July 211. Table 1 1 lists the hour ozone Design Values for the 14 monitoring sites in the DMA/NFR region. Most (1) monitoring sites would exceed a.7 ppm (71 ppb or higher) ozone NAAQS with another two also above a.65 ppm (66 ppb or higher) threshold s:\denver_o3_211\reports\revised_26_base_case\draf t#2\denver_sens_met_26_draft_jun3_211.doc 1

10 Table 1 1. Fourth highest daily maximum 8 hour ozone concentrations during and ozone Design Values (ppb) for monitoring sites in the DMA/NFR region. Monitoring Site 4 th Highest 8 Hour Ozone Design Value Welby Highland NA South Boulder Creek Carriage Chatfield Arvada Welch Rocky Flats North NREL Greeley Tower Fort Collins RMNP Fort Collins West DMAS Modeling since the 28 Denver Ozone SIP Since the preparation of the 28 Denver ozone SIP, the RAQC and CDPHE have performed additional ozone modeling that was split into two phases: Phase I focused on making future year ozone projections for the farther out 215 and 22 future years to estimate the potential level of ozone reductions needed to attain possible new ozone NAAQS levels; and The objective of the Phase II work effort was to make improvements in the ability to simulate ozone formation in the DMA/NFR region. The Phase I 215/22 modeling performed preliminary 215/22 ozone projections and 22 ozone source apportionment modeling that was documented in three reports as follows: 215 and 22 Ozone Projections for the Denver Area (Morris et al., 29) _Ozone_Projections_Jul15_29.pdf 22 Ozone Source Apportionment Modeling for the Denver Area (McNally et al., 29) Final%222_OSAT_Report.pdf Updated Model Performance Evaluation and Future Year Ozone Projections using Improved Ozone Modeling Techniques for the Denver Region (Morris et al., 211a). The Phase II modeling examined potential updates to the photochemical modeling system that would improve its ozone predictive capability in the Denver region. These improvements included an update to the CAMx vertical velocity algorithm to improve its ozone modeling capability and eliminate overstated ozone concentrations over high terrain in the spring due to excessive vertical transport of ozone of stratospheric origin to the surface (Morris et al., 211a). s:\denver_o3_211\reports\revised_26_base_case\draf t#2\denver_sens_met_26_draft_jun3_211.doc 2

11 The emissions inventories in the Denver region were also examined to determine whether improvements could be made. VOC source apportionment modeling was conducted using the Chemical Mass Balance (CMB) and Positive Matrix Factorization (PMF) receptor models using VOC observations as well as the CAMx emissions based chemical transport model that suggested that VOC emissions in the oil and gas production area in Weld County are understated (Morris, Tai and Sturtz, 211). The Phase II model improvements also performed meteorological model sensitivity simulations to investigate whether improvements in meteorological conditions can lead to improved ozone model performance, which is the subject of this report. 1.2 OVERVIEW OF THE 28 DENVER OZONE SIP MODELING DATABASE The June July 26 modeling database used in the 28 Denver 8 hour ozone SIP attainment demonstration modeling was developed using the fifth generation Mesocale Model (MM5) meteorological model (Anthes and Warner, 1978; Dudhia, 1993); the Sparse Matrix Operating Kernel Emissions (SMOKE) modeling system (Coats, 1996); and the Comprehensive Air quality Model with extensions (CAMx) photochemical grid model (ENVIRON, 28). Figure 1 1a displays the 36/12/4 km modeling domains used for the MM5 and SMOKE/CAMx modeling. CAMx simulations were first performed for the 36 km continental U.S. (CONUS) modeling domain and the results processed to generate boundary conditions (BCs) for the 12 km modeling domain (i.e., one way grid nesting between the 36 km and 12 km CAMx domains). CAMx was then used to simulate ozone formation within the 12/4 km modeling domain using two way interactive grid nesting (Figure 1 1b). Once the 12 km BCs were defined from the 26 and km CAMx base case simulations, sensitivity and control strategy evaluations runs were made on the 12/4 km modeling domain. The Denver 8 hour ozone SIP modeling work was performed mostly during the 28 calendar year and produced the following reports: Development of a Denver 8 hour ozone SIP attainment demonstration Modeling Protocol (Morris et al., 27). Denver_8 Hour_Ozone_Nov28_27.pdf MM5 meteorological modeling and model performance evaluation (McNally et al., 28a). eb25_28.pdf Development of a preliminary 36/12/4 km photochemical modeling database for the June July 26 episode, the DMA, and initial model performance evaluation, sensitivity test modeling and identification of the optimal model configuration (Morris et al., 28a). 7_28.pdf Final base case modeling and model performance evaluation for the June July 26 DMA episode (Morris et al., 28b). aftfinal_aug29_28.pdf s:\denver_o3_211\reports\revised_26_base_case\draf t#2\denver_sens_met_26_draft_jun3_211.doc 3

12 21 base case modeling, emission sensitivity tests and ozone source apportionment modeling (McNally et al., 28b). Draft1_Sep8_28.pdf 21 control strategy and attainment demonstration modeling (Morris et al., 28c). trat_draft_sep22_28.pdf Final 21 control strategy attainment demonstrations modeling (Morris et al., 29a) ontrol_jan12_29.pdf s:\denver_o3_211\reports\revised_26_base_case\draf t#2\denver_sens_met_26_draft_jun3_211.doc 4

13 CAMx 4 MM5 4km CAMx 12km MM5 12km CAMx 36 km MM5 36 km Figure 1 1a. Nested 36/12/4 km modeling domains for the 28 Denver 8 hour ozone SIP modeling study. Blue line domains are for CAMx/SMOKE domains that are nested in the MM5 red line domains CAMx 4km CAMx 12km Figure 1 1b. Nested 12/4 km modeling domains for the 28 Denver ozone SIP SMOKE emissions and CAMx air quality modeling. s:\denver_o3_211\reports\revised_26_base_case\draf t#2\denver_sens_met_26_draft_jun3_211.doc 5

14 1.3 NEW DENVER OZONE SIP MODELING To address the next round of ozone SIP modeling for the Denver area to address the new (July 211) ozone NAAQS that is expected to be in the.6.7 ppm range, a new Modeling Protocol was been prepared (Morris et al., 211b). Elements of the new Denver SIP modeling are as follows: Use of the latest version of the CAMx model (currently V5.3) as well as corroborative modeling using the Community Multiscale Air Quality (CMAQ) modeling system (Byun and Ching, 1999). 36/12/4 km nested grid modeling domains with two way nesting across all three grid nests so that ozone source apportionment can be used to address upwind and downwind transport issues. Modeling of a May through August 28 period during which many days of elevated ozone concentrations occurred in the Denver region. Use of the CONCEPT MV emissions model with the new Motor Vehicle Emissions Simulator (MOVES 21 3 ) along with vehicle activity data from the Denver Regional Council of Government (DRCOG) and North Front Range Metropolitan Planning Organization (NFRMPO) Traffic Demand Model (TDM) to generate highly resolved day specific hourly gridded emission for the Denver area. Use of the SMOKE emissions model to generate emissions for the other anthropogenic emissions source categories outside on road mobile sources in the Denver area. Develop meteorological inputs using the Weather Research Forecast (WRF 4 ) meteorological model. 1.4 REPORT OBJECTIVES AND ORGANIZATION OF THE REPORT This report documents the final activities of the Phase II model improvements portion of the post 28 Denver ozone SIP modeling analysis. It is forward looking toward the next Denver ozone SIP modeling analysis that will use new modeling tools, such as the WRF meteorological model. Specifically, this report addresses the following activities: Meteorological model sensitivity modeling of the JuneJuly 26 episode using the MM5 and WRF meteorological models that is described in Chapter 2. Chapter 3 describes revised CAMx base case modeling using the best performing meteorological model sensitivity test and model performance evaluation for ozone. The develop of the CMAQ modeling database for the June July 26 episode is described in Chapter 4, along with performing Process Analysis modeling using both the CAMx and CMAQ models. References are provided in Chapter model.org/index.php 6 s:\denver_o3_211\reports\revised_26_base_case\draf t#2\denver_sens_met_26_draft_jun3_211.doc

15 2. METEOROLOGICAL MODELING SENSITIVITY RESULTS 2.1 INTRODUCTION In order to select the most appropriate meteorological model and model options for the 28 modeling platform, extensive testing was performed using the 26 SIP modeling episode. This section summarizes the modeling configurations and the results for 11 MM5 model configurations and 18 WRF configurations. 2.2 MM5 SENSITIVITY MODELING The MM5 modeling for the 26 episode (McNally et al., 28a) was conducted several years ago and since that time several ideas have been proposed on how the meteorological fields may be improved. This testing compared the original 26 MM5 simulation against simulations using alternative vertical structures, initialization datasets, and observational nudging datasets. Run 1 is the MM5 simulation used in the 28 Denver SIP. Below we give a brief summary of the MM5 input data preparation procedure used for this two monthly meteorological modeling exercise. Model Selection: The publicly available non hydrostatic version of MM5 (version 3.7.4) was used for this modeling study. Preprocessor programs of the MM5 modeling system including TERRAIN, REGRID, LITTLE_R, and INTERPF were used to develop model inputs. Horizontal Domain Definition: The computational grid is presented in Figure 2 1. The outer 36 km domain (D1) has 165 x 129 grid cells, selected to maximize the coverage of the ETA analysis region. The mid scale 12 km domain (D2) has 187 x 157 grid cells, selected to maximize the coverage of the western region around Colorado. The inner 4 km domain (D3) has 151 x 136 grid cells, selected to maximize the coverage of the Denver Metropolitan area as well as the mountain regions to the west and high plains areas to the east of Denver. The projection was in Lambert Conformal Coordinates (LCC) with the national RPO grid projection pole of 4 o, 97 o with true latitudes of 33 o and 45 o. The datum set was NWS 84. Topographic Inputs: Topographic information for the MM5 was developed using the NCAR and the United States Geological Survey (USGS) terrain databases. The grid was based on the 2 min (~4 km) Geophysical Data Center global data. Terrain data was interpolated to the model grid using a Cressman type objective analysis scheme. To avoid interpolating elevated terrain over water bodies, after the terrain databases were interpolated onto the MM5 grid, the NCAR graphic water body database was used to correct elevations over water bodies. Vegetation Type and Land Use Inputs: Vegetation type and land use information was developed using the most recently released PSU/NCAR databases provided with the MM5 distribution. Standard MM5 surface characteristics corresponding to each land use category were employed. Atmospheric Data Inputs: The first guess meteorological fields were taken from the NCAR ETA archives. Surface and upper air observations used in the objective analyses, following the procedures outlined by Stauffer and Seaman at PSU, were quality inspected by MM5 pre s:\denver_o3_211\reports\revised_26_base_case\draf t#2\denver_sens_met_26_draft_jun3_211.doc 7

16 processors using automated gross error checks and "buddy" checks. In addition, rawinsonde soundings were subject to vertical consistency checks. The synoptic scale data used for this initialization (and in the analysis nudging discussed below) were obtained from the conventional National Weather Service (NWS) twice daily radiosondes and 3 hourly NWS surface observations. Water Temperature Inputs: The ETA database contains a skin temperature field. This can be and was used as the water temperature input to these MM5 simulations. Past studies have shown that these skin temperatures, used as the water temperature surrogates, can lead to temperature errors along coastlines. However, for this analysis which focuses on bulk continental scale transport across the central United States, this issue is likely not important and the skin temperatures were used. FDDA Data Assimilation: This simulation used analysis based nudging Four Dimensional Data Assimilation (FDDA). For these simulations analysis nudging coefficients of 2.5x1 4 and 1.x1 4 were used for winds and temperature, respectively. Physics Options: The MM5 model physics options were employed in this analysis as follows: Kain Fritsch 2 Cumulus Parameterization on 36/12 km Domain; No Cumulus Parameterization on 4 km Domain; Pleim Xiu Land Surface Model (LSM) and Asymmetric Convective Mixing (ACM) Planetary Boundary Layer (PBL) Schemes; Reisner 2 Mixed Phase Moisture Scheme; and RRTM Atmospheric Radiation Scheme Application Methodology: The MM5 model was executed in 5 day blocks initialized at 12 GMT every 4 days with a 9 second time step. Model results were output every 6 minutes and output files were split at 24 hour intervals. Twelve (12) hours of spin up was included in each 4 day block before the data was used in this atmospheric simulation and subsequent evaluation. The model was run with 36 km and 12 km resolution nests with light smoothing feedback and the 4 km grid as a one way nest. The other simulations are various permutations on the configuration altering vertical structure (34 versus 46 layers), Initialization datasets (4km ETA versus 12km NAM), and observation dataset (ADP versus MADIS). The specific configurations are presented in Table 2 1. The 34 Level vertical structure is presented in Table 2 2. This grid has nominally 18 meter thick layers near the surface and extends up to 1 mb. The 46 Level vertical structure is presented in Table 2 3. The 46 level structure has nominally 8 meter thick layers near the surface and extends to 5 mb. The 46 level structure was developed to explore whether thinner surface layers may better capture surface flow patterns. The model top was extended from 1 mb to 5 mb to better capture potential intercontinental pollutant transport in the air quality model. s:\denver_o3_211\reports\revised_26_base_case\draf t#2\denver_sens_met_26_draft_jun3_211.doc 8

17 For the past many years, the 4 km ETA initialization (UCAR, 28a) has been a popular choice for initializing MM5. However, recently the 12 km North American Model (NAM) 5 has been increasingly used to initialize MM5. The advantage of the NAM fields is the higher spatial resolution (12 km versus 4 km). The downside of the NAM dataset is the lower temporal resolution. The NAM model archives are only available every 6 hours, while the ETA fields are archived every 3 hours. A set of very popular observation datasets, which are blended with the ETA or NAM initialization datasets with the LITTLE_R program, are the UCAR Automated Data Processing (ADP) surface 6 and upper air 7 datasets. A newer dataset that contains many more observational data sources is the Meteorological Data Ingest System (MADIS) 8. In addition to the standard National Weather Service data contained in the ADP system, MADIS also contains an extensive collection of Mesonet and other data sources s:\denver_o3_211\reports\revised_26_base_case\draf t#2\denver_sens_met_26_draft_jun3_211.doc 9

18 Table 2 1. MM5 Model Configurations. Run ID Run Vertical Layers Radiation Mixed Phase Water PBL Scheme Cumulus Scheme 36/12 NudgeInitialization Little_r Data Obs. Nudging Run 1 Original 26 SIP MM5 34 RRTM Reisner-2 ACM2 Pleim Kain-Fritsch 2 (36/12) Modest 4km ETA ADP DS 472 Run 2 adp.34.noobs 34 RRTM Reisner-2 ACM2 Pleim Kain-Fritsch 2 (36/12) Modest 4km ETA ADP Upper None Run 3 nam.adp.34.noobs 34 RRTM Reisner-2 ACM2 Pleim Kain-Fritsch 2 (36/12) Modest 12km NAM ADP Upper None Run 4 nam.adp.46.noobs 46 RRTM Reisner-2 ACM2 Pleim Kain-Fritsch 2 (36/12) Modest 12km NAM ADP Upper None Run 5 nam.adp.34.madis.nomesonet.noobs 34 RRTM Reisner-2 ACM2 Pleim Kain-Fritsch 2 (36/12) Modest 12km NAM MADIS No Mesonet None Run 6 nam.adp.34.madis.nomesonet 34 RRTM Reisner-2 ACM2 Pleim Kain-Fritsch 2 (36/12) Modest 12km NAM MADIS No Mesonet MADIS Run 7 nam.adp.34.madis.mesonet 34 RRTM Reisner-2 ACM2 Pleim Kain-Fritsch 2 (36/12) Modest 12km NAM MADIS Mesonet MADIS Run 8 nam.adp.46.madis.mesonet 46 RRTM Reisner-2 ACM2 Pleim Kain-Fritsch 2 (36/12) Modest 12km NAM MADIS Mesonet MADIS Run 9 nam.adp.46.madis.nomesonet 46 RRTM Reisner-2 ACM2 Pleim Kain-Fritsch 2 (36/12) Modest 12km NAM MADIS No Mesonet MADIS Run 1 nam.adpsfc.46.noobs 46 RRTM Reisner-2 ACM2 Pleim Kain-Fritsch 2 (36/12) Modest 12km NAM ADP None Run 11 nam.adpsfc.46.ds RRTM Reisner-2 ACM2 Pleim Kain-Fritsch 2 (36/12) Modest 12km NAM ADP DS 472 s:\denver_o3_211\reports\revised_26_base_case\draft#2\denver_sens_met_2 6_draft_jun3_211.doc 1

19 Table 2 2. MM5 and WRF 34 Level Vertical Structure. k(mm5) Sigma Press.(mb) Height(m) Depth(m) s:\denver_o3_211\reports\revised_26_base_case\draf t#2\denver_sens_met_26_draft_jun3_211.doc 11

20 Table 2 3. MM5 and WRF 46 Level Vertical Structure. k(mm5) Sigma Press.(mb) Height(m) Depth(m) s:\denver_o3_211\reports\revised_26_base_case\draf t#2\denver_sens_met_26_draft_jun3_211.doc 12

21 MM5 4km MM5 12km MM5 36 km Figure /12/4km MM5 Domain. Denver MM5 Domain 36 km: 165 x 129 dot points (-2952, -234) to (2952, 234) 12 km: 187 x 157 dot points (-1836, -936) to ( 396, 936) 4 km: 151 x 136 dot points ( -984, -324) to ( -384, 216) 2.3 WRF SENSITIVITY MODELING The WRF model has only fairly recently been used for air quality modeling. As such, the modeling community has less experience in applying the model and determining which of the myriad model options are most appropriate for a specific application. For that reason the WRF sensitivity modeling undertaken in this study explores a more broad set of model configurations than the MM5 modeling previously described. To simplify the intercomparison of the MM5 and WRF modeling results, where possible the same modeling configuration options were used. The WRF modeling used as close as possibly the same horizontal grid definitions, vertical domain definition and application methodology as MM5. However, the while certain model physics options are available in both MM5 and WRF, s:\denver_o3_211\reports\revised_26_base_case\draf t#2\denver_sens_met_26_draft_jun3_211.doc 13

22 the WRF model physics were based on judgment of the most appropriate for this application, rather than to be consistent with the now quite out of date MM5 model physics. The WRF application used the most recently released model at the time the modeling was performed, namely WRF A total of 18 WRF simulations were attempted in this study. The various options are presented in Table 2 4. Several of the simulations what were initialized were not able to be run to completion without error and results are not presented in this report. Namely, Run 2ad could not complete because the dynamic time step algorithm did not output at the top of the hour, and so was not able to be analyzed, and Runs 1, 11 and 12 were unstable when the 6 th order diffusion weights were set to.5 or larger. Runs 1 through 4 were based on other group s experiences in setting up the WRF modeling for air quality applications. Run 1 was based on the suggestion of modeling options to be used in a WRF application for Wyoming. Run 2 was based on an application in Texas looking at PBL schemes (Hu et al., 21). Run 3 was based on the joint Midwestern/North Eastern application (David Brown, Iowa DNR, Personal Communication) and Run 4 was based on the NCAR Convective Forecast suggestion 9. Of these initial simulations Run 3 was the best performing so the subsequent simulations were perturbations on this basic configuration s:\denver_o3_211\reports\revised_26_base_case\draf t#2\denver_sens_met_26_draft_jun3_211.doc

23 Table 2 4. WRF Model Configurations. Highlighted cells differ from the configuration used in the previous simulation. Run ID Run Long Wave RadShort-Wave RaMixed Phase Water Surface Layer Physics Surface Physics PBL Scheme Cumulus Scheme 36/12 Nudge6th Order Dif Timestep Initialization Vertica Description ra_lw_physics ra_sw_physics mp_physics sf_sfclay_physics sf_surface_physics bl_pbl_physics cu_physics Run 1 WY DEQ/EPA OAQPS RRTMG (4) RRTMG (4) Morrison 2-moment (1 Monin-Obukhov (1) Noah LSM (2) YSU (1) Kain-Fritsch (1) (36/12) Weak.12 Run 2 Run 2a 4km ETA 12 4km ETA Hu and Nelson Gammon 21 Weak Analysis Nudging RRTM (1) Dudhia (1) WSM 6-Class (6) Monin-Obukhov (1) Noah LSM (2) YSU (1) Grell-Devenyi (3) Weak Hu and Neilson Gammon 21 4km ETA Modest Analysis Nudging RRTM (1) Dudhia (1) WSM 6-Class (6) Monin-Obukhov (1) Noah LSM (2) YSU (1) Grell-Devenyi (3) Modest Hu and Neilson Gammon 21 4km ETA Modest Analysis Nudging RRTM (1) Dudhia (1) WSM 6-Class (6) Monin-Obukhov (1) Noah LSM (2) YSU (1) Grell-Devenyi (3) Modest.12 dynamic Run 2ad 4km ETA Run 4 NCAR Convective Forecast RRTM (1) Goddard (2) Thompson Graupel (8) Janjic-Eta (2) Noah LSM (2) Mellor-Yamada-Jajic TKGrell-3 (5) Modest km ETA Run 3 MW/NE Configuration RRTMG (4) RRTMG (4) Morrison 2-moment (1 Pleim-Xiu (7) Pleim-Xiu (7) ACM2 Pleim (7) Kain-Fritsch (1) (36/12) Modest km ETA Run 5 MW/NE Configuration w/ WDM6 RRTMG (4) RRTMG (4) WDM-6 (16) Pleim-Xiu (7) Pleim-Xiu (7) ACM2 Pleim (7) Kain-Fritsch (1) (36/12) Modest km ETA Run 9 MW/NE Configuration w/ WDM6 RRTMG (4) RRTMG (4) WDM-6 (16) Pleim-Xiu (7) Pleim-Xiu (7) ACM2 Pleim (7) Kain-Fritsch (1) (36/12) Modest km ETA Run 1 MW/NE Configuration w/ WDM6 RRTMG (4) RRTMG (4) WDM-6 (16) Pleim-Xiu (7) Pleim-Xiu (7) ACM2 Pleim (7) Kain-Fritsch (1) (36/12) Modest km ETA Run 11 MW/NE Configuration w/ WDM6 RRTMG (4) RRTMG (4) WDM-6 (16) Pleim-Xiu (7) Pleim-Xiu (7) ACM2 Pleim (7) Kain-Fritsch (1) (36/12) Modest 1 6 4km ETA Run 12 MW/NE Configuration w/ WDM6 RRTMG (4) RRTMG (4) WDM-6 (16) Pleim-Xiu (7) Pleim-Xiu (7) ACM2 Pleim (7) Kain-Fritsch (1) (36/12) Modest km ETA Run 13 MW/NE Configuration w/ WDM6 RRTMG (4) RRTMG (4) WDM-6 (16) Pleim-Xiu (7) Pleim-Xiu (7) ACM2 Pleim (7) Kain-Fritsch (1) (36/12) Weak km ETA Run 14 MW/NE Configuration w/ WDM6 RRTMG (4) RRTMG (4) WDM-6 (16) Pleim-Xiu (7) Pleim-Xiu (7) ACM2 Pleim (7) Kain-Fritsch (1) (36/12) None km ETA Run 6 MW/NE Configuration w/ WSM-3 RRTMG (4) RRTMG (4) WSM-3 (3) Pleim-Xiu (7) Pleim-Xiu (7) ACM2 Pleim (7) Kain-Fritsch (1) (36/12) Modest km ETA Run 8 MW/NE Configuration w/ WSM-3 RRTMG (4) RRTMG (4) WSM-3 (3) Pleim-Xiu (7) Pleim-Xiu (7) ACM2 Pleim (7) Kain-Fritsch (1) (36/12) Modest km NAM Run 15 MW/NE Configuration w/ WSM-3 RRTMG (4) RRTMG (4) WSM-3 (3) Pleim-Xiu (7) Pleim-Xiu (7) ACM2 Pleim (7) Kain-Fritsch (1) (36/12) Modest km NAM Run 16 MW/NE Configuration w/ WSM-3 RRTMG (4) RRTMG (4) WSM-3 (3) Pleim-Xiu (7) Pleim-Xiu (7) ACM2 Pleim (7) Kain-Fritsch (1) (36/12) Modest km ETA Run 7 MW/NE Configuration w/ WSM-6 RRTMG (4) RRTMG (4) WSM-6 (6) Pleim-Xiu (7) Pleim-Xiu (7) ACM2 Pleim (7) Kain-Fritsch (1) (36/12) Modest s:\denver_o3_211\reports\revised_26_base_case\draft#2\denver_sens_met_2 6_draft_jun3_211.doc 15

24 2.4 MODEL PERFORMANCE EVALUATION The model performance evaluation follows the same approach as was used for the 28 Denver ozone SIP MM5 evaluation (McNally et al., 28a). While the previous MM5 evaluation included analysis of the 36/12/4 km grids, since the primary focus of this project is on Colorado, the evaluation in the body of the report is restricted to just the 4km domain which covers the majority of Colorado. Results for the 12 km and 36 km grids on a state by state and regional basis are presented in Appendix A. The model evaluation approach was based on a combination of qualitative and quantitative analyses. The qualitative approach was to compare the model estimated monthly total precipitation with the monthly Center for Prediction of Climate (CPC) precipitation analysis using graphical outputs. The quantitative approach was to examine tabulations of the model bias and error for temperature, and mixing ratio and the index of agreement for the winds. Interpretation of bulk statistics over a continental or regional scale domain is problematic. For the 4 km domain, the statistics were generated for the area covered by the domain which is most of Colorado. The interpretation of the statistics may be problematic given the significant differences across the State in elevation and microclimates. Nonetheless, these statistics are offered as some measure of the quality of the data set generated. The observed database for winds, temperature, and water mixing ratio used in this analysis was the NOAA Forecast Systems Lab (FSL) MADIS surface observations. The rain observations are taken from the CPC retrospective rainfall archives 1. To evaluate the performance of the 26 MM5 and WRF simulations, a number of performance benchmarks for comparison were used. Emery and co workers (21), have derived and proposed a set of daily performance benchmarks for typical meteorological model performance. These standards were based upon the evaluation of about 3 MM5 and RAMS meteorological simulations in support of air quality applications performed over several years and reported by Tesche et al. (21). The purpose of these benchmarks was not to give a passing or failing grade to any one particular meteorological model application, but rather to put its results into the proper context of other models and meteorological data sets. The key to the benchmarks is to understand how good or poor the results are relative to other model applications run for the U.S. Thus, this section compares the calculated statistical measures against these benchmarks to assess the MM5 performance in terms of its viability for use in modeling and meteorological assessments. These benchmarks include bias and error in temperature and mixing ratio as well as the Wind Speed Index of Agreement (IA) between the models and data bases. The benchmark for each variable is: Temperature bias: <±.5 K Temperature error: <2. K Mixing ratio bias: <±1. g/kg Mixing ratio error: <2. g/kg Wind Speed Index of Agreement (IA): = worst, 1 = best,.6 = acceptable s:\denver_o3_211\reports\revised_26_base_case\draf t#2\denver_sens_met_26_draft_jun3_211.doc

25 2.4.1 Temperature Bias and Error Temperature bias for the 4 km Colorado domain is presented in Table 2 5. The bulk of the WRF simulations tended to produce lower temperature bias than the MM5 simulations. But the majority of the simulations, both WRF and MM5, did a very good job on temperature bias with the majority being less that the ±.5 K benchmark. Temperature error is presented in Table 2 6. The original 26 MM5 simulation, as used in the 28 Denver/Northern Front Range SIP (Run 1), produced the lowest overall temperature error (1.78 K) that achieves the performance benchmark (<2. K). For the WRF simulations the temperature error was largely insensitive to changes in model configuration, with the values being near 2.3 K. The best performing WRF simulation was Run 16, where a very shallow (~8m) was used. In virtually all cases the 2 K benchmark is exceeded. This is quite often the case in the elevated terrain of the intermountain west Water Mixing Ratio Bias and Error Water mixing ratio bias model performance statistics are presented in Table 2 7. Once again, the best performing simulation averaged over the two month period was MM5 Run 1, the 28 Denver ozone SIP simulation. However, this MM5 simulation showed a temperature overestimation in June (.81 g/kg) and an underestimation in July (.45 g/kg), which led to this average. A model configuration with lower bias in both months is preferred. In general the WRF model did better than MM5, with perhaps the best performing WRF simulation being Run 15. Interestingly, going to the higher vertical resolution in WRF (Run 15 to Run 16) degraded the mixing ratio bias from.36 to.6 g/kg. All the WRF configurations, and the majority of the MM5 simulations were well within the ±1. g/kg benchmark. Table 2 8 presents the mixing ratio error results. As with mixing ratio bias, the WRF model generally does a better job than MM5. The mixing ratio error results are largely insensitive to the WRF modeling configurations chosen with all the results ranging from 1.2 to 1.5 g/kg. All simulations, both WRF and MM5 are within the 2. g/kg benchmark Wind Index of Agreement Wind index of agreement is presented in Table 2 9. The WRF model tends to have a higher index of agreement (better agreement with observations) than the MM5 model. The WRF options that were varied only had a small impact on the index of agreement with the majority of the simulations having an index of.81, which is above the.6 acceptable level. Again, interestingly, increasing the vertical resolution (Run 15 to Run 16) tended to decrease the index of agreement from.81 to Precipitation This section compares the monthly accumulated CPC and model estimated precipitation for June and July. On all figures the top left pane presents the CPC observations. The WRF simulations for July are presented in Figures 2 2 through 2 4. The CPC observed precipitation shows that for June the state was quite dry with significant precipitation only over the eastern border of the state. All the WRF simulations tended to overestimate both the s:\denver_o3_211\reports\revised_26_base_case\draf t#2\denver_sens_met_26_draft_jun3_211.doc 17

26 spatial coverage and the absolute amount of precipitation. Perhaps the best performing simulations were runs 15 and 16 (Figure 2 4), although these simulations still tended to overestimate the precipitations. The June MM5 comparisons are presented in Figures 2 5 through 2 7. The MM5 model predicted less precipitation than WRF and subsequently better comparison with the observations. July WRF precipitation comparisons are presented in Figures 2 8 through 2 1. July 26 was wetter than June 26 across the state with a maximum precipitation estimated near Colorado Springs. The WRF model systematically and drastically overestimated precipitation during July. The best performing simulations were run 15 and 16 (Figure 2 1), although they still overestimated precipitation in July 26. Figures 2 11 through 2 13 present the July MM5 comparisons. MM5 did a much better job estimated July rainfall than WRF. The 28 Denver/Northern Front Range SIP simulation (run 1) tended to overestimate precipitation in the western portion of the domain (Figure 2 11). Runs 1 and 11 tended to underestimate precipitation throughout the domain. The other simulations did reasonable jobs predicting precipitation rates and spatial coverage. Table 2 5. Temperature bias (K) for the 4 km Colorado domain by month (benchmark <±.5 K). Run June '6 July '6 Average WRF run WRF run WRF run2a WRF run WRF run WRF run WRF run WRF run WRF run WRF run WRF run WRF run WRF run WRF run MM5 run MM5 run MM5 run MM5 run MM5 run MM5 run MM5 run MM5 run MM5 run MM5 run MM5 run s:\denver_o3_211\reports\revised_26_base_case\draf t#2\denver_sens_met_26_draft_jun3_211.doc 18

27 Table 2 6. Temperature error (K) for the 4 km Colorado domain by month (benchmark < 2. K). Run June '6 July '6 Average WRF run WRF un WRF run2a WRF un WRF un WRF run WRF run WRF run WRF run WRF run WRF run WRF run WRF run WRF run MM5 run MM5 run MM5 run MM5 run MM5 run MM5 run MM5 run MM5 run MM5 run MM5 run MM5 run s:\denver_o3_211\reports\revised_26_base_case\draf t#2\denver_sens_met_26_draft_jun3_211.doc 19