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MM5-AERMOD Tool

MM5-AERMOD Tool. Roger W. Brode U.S. EPA, OAQPS, AQMG Research Triangle Park, NC. EPA Region 10 and State Meeting Seattle, Washington October 22, 2007. Acknowledgments. Brian Orndorff – NOAA/EPA Joe Touma – NOAA/EPA Mark Evangelista – NOAA/EPA Dennis Atkinson – NOAA/EPA

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MM5-AERMOD Tool

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  1. MM5-AERMOD Tool Roger W. Brode U.S. EPA, OAQPS, AQMG Research Triangle Park, NC EPA Region 10 and State Meeting Seattle, Washington October 22, 2007

  2. Acknowledgments • Brian Orndorff – NOAA/EPA • Joe Touma – NOAA/EPA • Mark Evangelista – NOAA/EPA • Dennis Atkinson – NOAA/EPA • George Duggan – SEE/EPA • James Thurman – CSC • Karen Wesson – EPA/OAQPS • Al Cimorelli – EPA/Region 3 • Bret Anderson – EPA/Region 7 • Tanya Otte – EPA/ORD Paper in A&WMA Journal, May 2007: “Using Prognostic Model-Generated Meteorological Output in the AERMOD Dispersion Model: An Illustrative Application in Philadelphia, PA” Touma, Isakov, Cimorelli, Brode, and Anderson

  3. Overview of Presentation • Problem Statement and Objective • MM5-AERMOD Tool Description • Example Test Case - Detroit • Next Steps • Summary

  4. Problem Statement • Meteorological data are key inputs to air quality models such as AERMOD • NWS met data currently used in most cases, however: • Met observation sites may not be representative of source locations (10’s-100’s km away) • Representativeness issue has new dimensions given AERMOD’s dependence on surface characteristics: roughness, Bowen ratio, albedo • Upper air data are sparsely located, especially in mountainous areas • Significant gaps in NWS data for calms and variable winds; frequency of gaps has increased with ASOS and METAR • Onsite meteorological data collection is expensive and time consuming

  5. Potential Solution • These problems may be alleviated by using outputs from prognostic gridded meteorological models • Gridded meteorological models routinely generate datasets that could be beneficial for use in dispersion models like AERMOD • Gridded met data already used for regulatory modeling with CALMET/CALPUFF for long range transport applications

  6. OBJECTIVE:Develop MM5-AERMOD Tool that . . . • Provides spatially consistent AERMOD inputs • Grid cell selected based on application/source site; overcomes sparsity of observed data • Surface and upper-air data located in same grid cell • Hourly values available for every grid cell • Allows AERMOD to use parameters calculated by MM5’s advanced atmospheric physics (e.g., heat flux, friction velocity, PBL height) • Uses MM5 data to calculate other parameters previously calculated from NWS observations (e.g., convective velocity scale)

  7. MM5-AERMOD Tool and Functions • Tool is a Fortran program that operates in a Windows or Linux environment with simple NAMELIST input (no GUI) • Extracts and processes MM5 gridded meteorological data for user-specified location • Some variables are directly passed from MM5 to AERMOD • Some variables are interpolated from four surrounding grid cells to the grid node nearest the area of interest • Some variables are calculated based on the available MM5 data • Outputs a “surface” file and a “profile” file formatted for input to AERMOD

  8. User Input MM5 NWS Obs Surface Roughness Bowen Ratio Albedo Gridded output AERMET MM5 – AERMOD Tool .sfc file .sfc file .pfl file .pfl file To AERMOD To AERMOD Traditional AERMET Application MM5-AERMOD Tool Application

  9. AERMOD Parameters Input Directly from MM5 • Sensible heat flux • Surface friction velocity • Surface roughness length • PBL height (assigned to CBL-ht during day and mechanical mixing ht at night) • Wind speed & direction at half-sigma levels • Temperature at half-sigma levels • 2m temperature • Precipitation type and amount

  10. AERMOD Parameters Computed from MM5 Data • Convective velocity scale • Mechanical mixing height • Lapse rate above mixing height • Monin-Obukhov length • Bowen ratio (only used for deposition) • Relative humidity (only used for deposition) • Surface pressure (only used for deposition) • Cloud cover (only used for deposition)

  11. MM5 Staggered Grid Structure Schematic Representation Showing the Horizontal Staggering of the Dot (●) and Cross (x) Grid Points Area of interest

  12. x x A x x T Interpolation of MM5 Gridded Data • Letter “A” represents the area of interest. • Vector quantities (e.g. U and V) are available at the “dot point” nearest to “A” • Scalar quantities (e.g. T) from each of the four “cross points” are interpolated to the center “dot point”

  13. MM5-AERMOD Tool Output Files • AERMOD-Ready Input Files • Surface file -- contains reference wind direction/speed, temperature, relative humidity, pressure, cloud cover and precipitation. Also contains boundary layer variables. • Profile file -- contains one or more levels of winds, temperature, and if available, horizontal wind direction fluctuations and vertical wind speed fluctuations • Log file -- contains user inputs, MM5 grid cells from which data are extracted, informational messages for cases where a meteorological variable may have been altered (e.g., out-of-range)

  14. Example Test Case – Detroit Area • Apply MM5-AERMOD Tool • Extract 2002 MM5 12km data for Detroit Metropolitan Airport (DTW) grid cell • Subdomain of 30 x 30 grid cells around Detroit was extracted from MM5 data for input to tool • Apply traditional NWS-AERMET approach • Use 2002 DTW Integrated Surface Data (TD-3505) and DTX upper air data • Analyze, compare, document results

  15. MM5 12km Grid Cell for DTW

  16. Detroit 2002 WindrosesNWS (DTW)MM5 (12km Grid Cell) At anemometer height of 10m At first half-sigma level of 19m

  17. Detroit 2002 WS Frequency DistributionsNWS (DTW) MM5 (12km Grid Cell)

  18. Detroit 2002 MM5 Evaluations – 1st Q

  19. Detroit Sensible Heat Flux Comparisons

  20. MM5 Sensible Heat Flux Comparisons

  21. Detroit 2002 MM5-NWS AERMOD Comparisons – Source Descriptions * Source ID is a combination of the source release height (00, 10, 35, 50, or 100), buoyant (BY) or non-buoyant (NB) release, and downwash (DW) or no downwash (ND) ** Hb = building height

  22. Detroit 2002 MM5-NWS AERMOD Comparisons – Model Results (C/Q) 24-hr Averages – Rural * Source ID is a combination of the source release height (00, 10, 35, 50, or 100), buoyant (BY) or non-buoyant (NB) release, and downwash (DW) or no downwash (ND) ** Hb = building height

  23. Detroit 2002 MM5-NWS AERMOD Comparisons – Model Results (C/Q) 24-hr Averages – Urban * Source ID is a combination of the source release height (00, 10, 35, 50, or 100), buoyant (BY) or non-buoyant (NB) release, and downwash (DW) or no downwash (ND) ** Hb = building height

  24. Meteorology Comparison for MM5 H1H 24-hr Average Day – Rural ------- MM5 DATA -------- YRMNDYHRHFLUXUSTARWSPDWDIR 2 12 11 1 -3.5 0.030 1.62 226. 2 12 11 2 -2.1 0.021 1.38 223. 2 12 11 3 -0.9 0.011 1.09 221. 2 12 11 4 -0.6 0.009 0.77 220. 2 12 11 5 -0.6 0.009 0.49 217. 2 12 11 6 -0.6 0.009 0.20 216. 2 12 11 7 -0.6 0.009 0.12 27. 2 12 11 8 -0.7 0.010 0.42 33. 2 12 11 9 -0.7 0.011 0.64 39. 2 12 11 10 -0.8 0.013 0.73 45. 2 12 11 11 -0.9 0.020 0.70 52. 2 12 11 12 -0.4 0.122 0.54 65. 2 12 11 13 9.7 0.197 0.44 83. 2 12 11 14 8.4 0.185 0.38 116. 2 12 11 15 2.2 0.130 0.39 160. 2 12 11 16 -0.6 0.038 0.58 187. 2 12 11 17 -0.9 0.022 0.76 201. 2 12 11 18 -0.8 0.014 0.93 215. 2 12 11 19 -1.1 0.015 1.14 226. 2 12 11 20 -2.3 0.023 1.42 235. 2 12 11 21 -4.3 0.033 1.74 237. 2 12 11 22 -7.3 0.047 2.08 238. 2 12 11 23 -11.0 0.063 2.44 239. 2 12 11 24 -14.6 0.080 2.77 240.

  25. Meteorology Comparison for MM5 H1H 24-hr Average Day – Rural ------- MM5 DATA -------- ------- NWS DATA -------- YRMNDYHRHFLUXUSTARWSPDWDIRHFLUXUSTARWSPDWDIR 2 12 11 1 -3.5 0.030 1.62 226. -19.5 0.244 2.60 181. 2 12 11 2 -2.1 0.021 1.38 223. -999.0 -9.000 0.00 0. 2 12 11 3 -0.9 0.011 1.09 221. -999.0 -9.000 0.00 0. 2 12 11 4 -0.6 0.009 0.77 220. -999.0 -9.000 0.00 0. 2 12 11 5 -0.6 0.009 0.49 217. -999.0 -9.000 0.00 0. 2 12 11 6 -0.6 0.009 0.20 216. -999.0 -9.000 0.00 0. 2 12 11 7 -0.6 0.009 0.12 27. -999.0 -9.000 0.00 0. 2 12 11 8 -0.7 0.010 0.42 33. -999.0 -9.000 0.00 0. 2 12 11 9 -0.7 0.011 0.64 39. -999.0 -9.000 0.00 0. 2 12 11 10 -0.8 0.013 0.73 45. 7.9 0.189 1.50 341. 2 12 11 11 -0.9 0.020 0.70 52. 8.0 0.252 2.10 360. 2 12 11 12 -0.4 0.122 0.54 65. 13.7 0.196 1.50 350. 2 12 11 13 9.7 0.197 0.44 83. 56.6 -9.000 0.00 0. 2 12 11 14 8.4 0.185 0.38 116. 35.1 -9.000 0.00 0. 2 12 11 15 2.2 0.130 0.39 160. 23.7 -9.000 0.00 0. 2 12 11 16 -0.6 0.038 0.58 187. 6.4 -9.000 0.00 0. 2 12 11 17 -0.9 0.022 0.76 201. -999.0 -9.000 0.00 0. 2 12 11 18 -0.8 0.014 0.93 215. -999.0 -9.000 0.00 0. 2 12 11 19 -1.1 0.015 1.14 226. -999.0 -9.000 0.00 0. 2 12 11 20 -2.3 0.023 1.42 235. -999.0 -9.000 0.00 0. 2 12 11 21 -4.3 0.033 1.74 237. -4.8 0.086 1.50 255. 2 12 11 22 -7.3 0.047 2.08 238. -999.0 -9.000 0.00 0. 2 12 11 23 -11.0 0.063 2.44 239. -999.0 -9.000 0.00 0. 2 12 11 24 -14.6 0.080 2.77 240. -11.1 0.192 2.10 244.

  26. Meteorology Comparison for MM5 H1H 24-hr Average Day – Rural ------- MM5 DATA -------- ------- NWS DATA -------- YRMNDYHRHFLUXUSTARWSPDWDIRHFLUXUSTARWSPDWDIR 2 12 11 1 -3.5 0.030 1.62 226. -19.5 0.244 2.60 181. 2 12 11 2 -2.1 0.021 1.38 223. -999.0 -9.000 0.00 0. 2 12 11 3 -0.9 0.011 1.09 221. -999.0 -9.000 0.00 0. 2 12 11 4 -0.6 0.009 0.77 220. -999.0 -9.000 0.00 0. 2 12 11 5 -0.6 0.009 0.49 217. -999.0 -9.000 0.00 0. 2 12 11 6 -0.6 0.009 0.20 216. -999.0 -9.000 0.00 0. 2 12 11 7 -0.6 0.009 0.12 27. -999.0 -9.000 0.00 0. 2 12 11 8 -0.7 0.010 0.42 33. -999.0 -9.000 0.00 0. 2 12 11 9 -0.7 0.011 0.64 39. -999.0 -9.000 0.00 0. 2 12 11 10 -0.8 0.013 0.73 45. 7.9 0.189 1.50 341. 2 12 11 11 -0.9 0.020 0.70 52. 8.0 0.252 2.10 360. 2 12 11 12 -0.4 0.122 0.54 65. 13.7 0.196 1.50 350. 2 12 11 13 9.7 0.197 0.44 83. 56.6 -9.000 0.00 0. 2 12 11 14 8.4 0.185 0.38 116. 35.1 -9.000 0.00 0. 2 12 11 15 2.2 0.130 0.39 160. 23.7 -9.000 0.00 0. 2 12 11 16 -0.6 0.038 0.58 187. 6.4 -9.000 0.00 0. 2 12 11 17 -0.9 0.022 0.76 201. -999.0 -9.000 0.00 0. 2 12 11 18 -0.8 0.014 0.93 215. -999.0 -9.000 0.00 0. 2 12 11 19 -1.1 0.015 1.14 226. -999.0 -9.000 0.00 0. 2 12 11 20 -2.3 0.023 1.42 235. -999.0 -9.000 0.00 0. 2 12 11 21 -4.3 0.033 1.74 237. -4.8 0.086 1.50 255. 2 12 11 22 -7.3 0.047 2.08 238. -999.0 -9.000 0.00 0. 2 12 11 23 -11.0 0.063 2.44 239. -999.0 -9.000 0.00 0. 2 12 11 24 -14.6 0.080 2.77 240. -11.1 0.192 2.10 244.

  27. MM5-NWS AERMOD Model Results (C/Q)Using MM5 z0~ 0.3m for AERMET/NWS 24-hr Averages – Rural * Source ID is a combination of the source release height (00, 10, 35, 50, or 100), buoyant (BY) or non-buoyant (NB) release, and downwash (DW) or no downwash (ND) ** Hb = building height

  28. MM5-NWS AERMOD Model Results (C/Q)Using AERSURFACE z0~ 0.06m for AERMET/NWS 24-hr Averages – Rural * Source ID is a combination of the source release height (00, 10, 35, 50, or 100), buoyant (BY) or non-buoyant (NB) release, and downwash (DW) or no downwash (ND) ** Hb = building height

  29. MM5-NWS AERMOD Model Results (C/Q)Using AERSURFACE z0~ 0.06m for AERMET/NWSWith NWS winds based on 1-minute ASOS data 24-hr Averages – Rural * Source ID is a combination of the source release height (00, 10, 35, 50, or 100), buoyant (BY) or non-buoyant (NB) release, and downwash (DW) or no downwash (ND) ** Hb = building height

  30. Next Steps • Conduct more detailed comparison of MM5-AERMOD Tool vs. NWS-AERMET results, including meteorology and dispersion • Conduct “sensitivity” analyses with additional source types using meteorological inputs from each approach • Extend comparisons to other geographic areas • Eventually need to “validate” use of MM5-AERMOD data against field study data

  31. Summary • Tool allows for MM5 data to be extracted and formatted for input to the AERMOD model • Preliminary comparisons show many similarities, but some significant differences between two approaches (MM5-AERMOD and NWS-AERMET) for Detroit area • Use of gridded met data holds some promise, but additional study is needed to more fully understand and assess the methodology before acceptance of MM5 data for routine AERMOD applications

  32. Questions or Suggestions? Contact Information: Roger W. Brode U.S. EPA/OAQPS/AQAD Air Quality Modeling Group Research Triangle Park, NC 27711 brode.roger@epa.gov (919) 541-3518

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