**MM5-AERMOD Tool Roger W. Brode** **U.S. EPA/OAQPS Air Quality Modeling Group** **9 th** **Conference on Air Quality Modeling October 9, 2008** **Research Triangle Park, NC Outline** **• Problem Statement and Objective • MM5-AERMOD Tool Description** **• Example Test Case – Detroit • Next Steps** **• Summary AERMOD Met Data Issues:** **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, B owen ratio,** **albedo – Upper air data sparsely located, especially in mou** **ntainous areas • Significant gaps in NWS data for calms and variabl** **e winds; frequency of gaps has increased with ASOS and METAR** **• Onsite meteorological data collection is expensive and time** **consuming 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 OBJECTIVE:** **Develop MM5-AERMOD Tool that . . . • Provides spatially consistent AERMOD inputs** **– Grid cell selected based on application/source sit e; overcomes** **sparsity of observed data – Surface and upper-air data located in same grid ce** **ll – 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) Surface Roughness** **Bowen Ratio Albedo** **User Input NWS Obs** **AERMET**To AERMOD**.sfc file .pfl file** **Gridded output MM5** **MM5 – AERMOD Tool** To AERMOD**.sfc file .pfl file** Traditional AERMET ApplicationMM5-AERMOD Tool Application**Example Test Case – Detroit Area • Apply MM5-AERMOD Tool** **– Extract 2002 MM5 12km data for Detroit Metropolitan Airport (DTW) grid cell** **– Sub-domain of 30 x 30 grid cells around Detroit wa s** **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 MM5 12km Grid Cell for DTW** **Detroit 2002 Windroses NWS (DTW) MM5 (12km Grid** **Cell) At anemometer height of 10m At first half-sigma leve** **l of 19m 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 d ownwash (ND)** **** Hb = building height**SourceID *Height(m)Exit Temp.(K)Exit Velocity(m/s)Diameter(m)Downwash**00NBND 0.0 ambient 0.0 0.0 None – no building 10BYND 10.0 300 15.0 0.5 None – no building** **10BYDW 10.0 293 1.0 2.4 Squat; source on NE** **corner 10NBND 10.0 ambient 0.0 0.0 None – no building** **35BYND 35.0 432 11.7 2.4 None – no building 35BYDW 35.0 432 11.7 2.4** **Squat; source on NE corner** **35BYDW2 35.0 432 11.7 2.4** **Squat; source 4 Hb off NE corner ** ****50NBND 50.0 ambient 0.0 0.0 None – no building 100BYND 100.0 425 26.5 5.6 None – no building** **Detroit 2002 MM5-NWS AERMOD Comparisons – Model Results (C/Q)** *** 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 d ownwash (ND)** **** Hb = building height 24-hr Averages – Rural** 1.75**0.13 0.07** 1.59**0.13 0.08** **100BYND**1.71**7.90 4.63** 1.58**10.10 6.39** **50NBND**1.25**1.70 1.36** 1.25**2.02 1.62** **35BYDW2**1.14**25.41 22.37** 1.12**25.93 23.16** **35BYDW**0.84**1.70 2.03** 0.93**2.02 2.17** **35BYND**1.75**250.58 143.31** 1.91**305.53 159.82** **10BYDW**1.21**103.91 86.20** 1.33**135.13 101.30** **10BYND**1.80**312.10 173.23** 1.87**368.20 196.83** **10NBND**7.24**27708.70 3825.26** 13.29**69048.50 5194.18** **00NBND**RatioMM5AERMETRatioMM5AERMETSource IDMM5/NWSH2H (μg m-3/g s-1)MM5/NWSH1H (μg m-3/g s-1)**Meteorology Comparison for MM5 H1H 24-hr Average Day – Rural** ------- MM5 DATA --------YRMNDYHRHFLUXUSTARWSPDWDIR2 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.**Meteorology Comparison for MM5 H1H 24-hr Average Day – Rural** ------- MM5 DATA -------- ------- NWS DATA --------YRMNDYHRHFLUXUSTARWSPDWDIRHFLUXUSTARWSPDWDIR2 12 11 1 -3.5 0.030 1.62 226. -19.50.244 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. 7.90.189 1.50 341.2 12 11 11 -0.9 0.020 0.70 52. 8.0 0.2522.10 360.2 12 11 12 -0.4 0.122 0.54 65. 13.70.196 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. -4.80.086 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. -11.10.192 2.10 244.**Meteorology Comparison for MM5 H1H 24-hr Average Day – Rural** ------- MM5 DATA -------- ------- NWS DATA --------YRMNDYHRHFLUXUSTARWSPDWDIRHFLUXUSTARWSPDWDIR2 12 11 1 -3.5 0.030 1.62 226. -19.50.244 2.60 181.2 12 11 2 -2.1 0.021 1.38 223. -999.0 -9.0000.00 0.2 12 11 3 -0.9 0.011 1.09 221. -999.0 -9.0000.00 0.2 12 11 4 -0.6 0.009 0.77 220. -999.0 -9.0000.00 0.2 12 11 5 -0.6 0.009 0.49 217. -999.0 -9.0000.00 0.2 12 11 6 -0.6 0.009 0.20 216. -999.0 -9.0000.00 0.2 12 11 7 -0.6 0.009 0.12 27. -999.0 -9.0000.00 0.2 12 11 8 -0.7 0.010 0.42 33. -999.0 -9.0000.00 0.2 12 11 9 -0.7 0.011 0.64 39. -999.0 -9.0000.00 0.2 12 11 10 -0.8 0.013 0.73 45. 7.90.189 1.50 341.2 12 11 11 -0.9 0.020 0.70 52. 8.0 0.2522.10 360.2 12 11 12 -0.4 0.122 0.54 65. 13.70.196 1.50 350.2 12 11 13 9.7 0.197 0.44 83. 56.6 -9.0000.00 0.2 12 11 14 8.4 0.185 0.38 116. 35.1 -9.0000.00 0.2 12 11 15 2.2 0.130 0.39 160. 23.7 -9.0000.00 0.2 12 11 16 -0.6 0.038 0.58 187. 6.4 -9.0000.00 0.2 12 11 17 -0.9 0.022 0.76 201. -999.0 -9.0000.00 0.2 12 11 18 -0.8 0.014 0.93 215. -999.0 -9.0000.00 0.2 12 11 19 -1.1 0.015 1.14 226. -999.0 -9.0000.00 0.2 12 11 20 -2.3 0.023 1.42 235. -999.0 -9.0000.00 0.2 12 11 21 -4.3 0.033 1.74 237. -4.80.086 1.50 255.2 12 11 22 -7.3 0.047 2.08 238. -999.0 -9.0000.00 0.2 12 11 23 -11.0 0.063 2.44 239. -999.0 -9.0000.00 0.2 12 11 24 -14.6 0.080 2.77 240. -11.10.192 2.10 244.**MM5-NWS AERMOD Model Results (C/Q) Using MM5** z0**~ 0.3m for AERMET/NWS***** 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 d ownwash (ND)** **** Hb = building height 24-hr Averages – Rural** 1.75**0.13 0.07** 1.59**0.13 0.08** **100BYND**1.71**7.90 4.63** 1.58**10.10 6.39** **50NBND**1.25**1.70 1.36** 1.25**2.02 1.62** **35BYDW2**1.14**25.41 22.37** 1.12**25.93 23.16** **35BYDW**0.84**1.70 2.03** 0.93**2.02 2.17** **35BYND**1.75**250.58 143.31** 1.91**305.53 159.82** **10BYDW**1.21**103.91 86.20** 1.33**135.13 101.30** **10BYND**1.80**312.10 173.23** 1.87**368.20 196.83** **10NBND**7.24**27708.70 3825.26** 13.29**69048.50 5194.18** **00NBND**RatioMM5AERMETRatioMM5AERMETSource IDMM5/NWSH2H (μg m-3/g s-1)MM5/NWSH1H (μg m-3/g s-1)**MM5-NWS AERMOD Model Results (C/Q) Using AERSURFACE** z0**~ 0.06m for AERMET/NWS***** 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 d ownwash (ND)** **** Hb = building height 24-hr Averages – Rural** 2.19**0.13 0.06** 2.01**0.13 0.06** **100BYND**1.70**7.90 4.65** 1.48**10.10 6.85** **50NBND**1.07**1.70 1.59** 1.03**2.02 1.97** **35BYDW2**1.21**25.41 21.03** 1.17**25.93 22.12** **35BYDW**1.91**1.70 0.89** 2.01**2.02 1.01** **35BYND**1.59**250.58 157.35** 1.68**305.53 182.36** **10BYDW**1.85**103.91 56.21** 1.94**135.13 69.61** **10BYND**2.12**312.10 147.02** 2.40**368.20 153.62** **10NBND**2.73**27708.70 10151.59** 6.50**69048.50 10621.38** **00NBND**RatioMM5AERMETRatioMM5AERMETSource IDMM5/NWSH2H (μg m-3/g s-1)MM5/NWSH1H (μg m-3/g s-1)**MM5-NWS AERMOD Model Results (C/Q) Using AERSURFACE** z0**~ 0.06m for AERMET/NWS NWS winds supplemented with 1-minute ASOS data** *** 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 d ownwash (ND)** **** Hb = building height 24-hr Averages – Rural** 1.94**0.13 0.06** 1.80**0.13 0.07** **100BYND**1.15**7.90 6.85** 1.18**10.10 8.54** **50NBND**0.86**1.70 1.97** 0.95**2.02 2.12** **35BYDW2**1.12**25.41 22.59** 1.12**25.93 23.22** **35BYDW**1.69**1.70 1.01** 1.71**2.02 1.19** **35BYND**1.29**250.58 194.79** 1.08**305.53 282.81** **10BYDW**1.52**103.91 68.33** 1.77**135.13 76.34** **10BYND**2.24**312.10 139.35** 1.75**368.20 209.89** **10NBND**1.16**27708.70 23844.40** 2.42**69048.50 28588.16** **00NBND**RatioMM5AERMETRatioMM5AERMETSource IDMM5/NWSH2H (μg m-3/g s-1)MM5/NWSH1H (μg m-3/g s-1)**Summary of MM5-AERMOD Status • Draft MM5-AERMOD Tool has been developed** **• Comparisons of MM5-AERMOD vs. traditional AERMET approach for Detroit 2002** **– Preliminary results encouraging, especially when AERSURFACE and 1-minute ASOS wind data included** **• Basic approach is promising, but considerable work remains before endorsing for regulatory modeling** **MM5-AERMOD Next Steps • Conduct more detailed comparison of MM5-AERMOD Too** **l vs. NWS AERMET results, including meteorology and dispersio** **n • Conduct additional “sensitivity” analyses using met** **eorological inputs from each approach, including:** **– wider range of source types; – different options for interpolation of MM5 grid;** **– use of full profiles of MM5 wind and temperature vs. “degraded” subsets of MM5 data;** **– grid-to-grid variability across a domain; and – sensitivity to MM5 grid resolution** **• Extend comparisons to other geographic areas; tent ative plans include** **Atlanta, GA, Birmingham, AL, and Baltimore, MD • Coordinated collaboration with other researchers (** **e.g., UNC MCIP2AERMOD project with FAA)** **• Assess use of multiple MM5 grids within single AER MOD application** **• Assess interim approach of deriving pseudo-UA data from MM5 for** **processing through AERMET with site-specific or NWS surface data** **• “Validate” use of MM5-AERMOD data against field stu dy data** **MM5-AERMOD Next Steps (cont.) • Consider coupling MM5-AERMOD Tool with MM5-** **CALPUFF Tool • Extension of effort to support WRF model, possibly** **based on an MCIP-to-AERMOD approach (MCIP being the** **meteorological processor for CMAQ) • Host invited workshop on use of gridded prognostic** **met data with dispersion models to guide the best use o** **f science** **• As the range of options for developing met inputs for** **models expands, need to develop criteria for accept ance** **• Questions of how to disseminate this “technology” t o user** **community will eventually need to be addressed Questions?**