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An Examination of WRF/Chem: Physical Parameterizations, Nesting Options, and Grid Resolution

An Examination of WRF/Chem: Physical Parameterizations, Nesting Options, and Grid Resolution. Chris Misenis * , Xiaoming Hu, and Yang Zhang North Carolina State University Jerome Fast Pacific Northwest National Laboratory Georg Grell and Steven Peckham NOAA Earth System Research Laboratory.

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An Examination of WRF/Chem: Physical Parameterizations, Nesting Options, and Grid Resolution

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  1. An Examination of WRF/Chem: Physical Parameterizations,Nesting Options, and Grid Resolution Chris Misenis*, Xiaoming Hu, and Yang Zhang North Carolina State University Jerome Fast Pacific Northwest National Laboratory Georg Grell and Steven Peckham NOAA Earth System Research Laboratory *Now with N.C. DENR – Division of Air Quality

  2. Outline • Background and Motivation • Data Description and Model Configurations • Sensitivity to PBL and Land Surface Schemes • Sensitivity to Horizontal Grid Spacing and Nesting • Conclusions

  3. Houston, TX Courtesy: University of Texas

  4. Process Interactions in WRF/Chem • Non-hydrostatic (with hydrostatic option) and fully mass-conserving. • Simulates trace gases and particulates “online” with meteorology. • Developed by the NOAA with contributions from NCAR, PNNL, NCSU, and BAMS. • Surface fluxes (sensible, latent heat) derived from LSM affect PBL scheme. • Surface meteorology from PBL affects LSM. • Both have direct impact on formation and transport of atmospheric pollutants. For more information: http://ruc.fsl.noaa.gov/wrf/WG11/ Grell et al., 2005, Atmos. Environ., 39 Fast et al., 2006, J. Geophys. Res., 111 Courtesy: UCAR (http://www.mmm.ucar.edu/mm5/documents/MM5_tut_Web_notes/MM5/mm5.htm)

  5. Data Description – TexAQS-2000 • Intensive field campaign in the Houston-Galveston area of East Texas during 8 to 9/2000. • Measured gaseous, particulate, and hazardous air pollutants at approximately 20 ground sites. • Measured vertical profiles by aircraft from several organizations. • Complex meteorological and geographical characteristics challenged capabilities of air quality models. Courtesy: University of Texas (http://uts.cc.utexas.edu/~gcarch/HoustonSuperSite/site_listmainpage1.htm)

  6. WRF/Chem Configurations • Horizontal Grid Spacing: 12- and 4-km • Vertical Grid Spacing: 57 layers • Simulation Period: 28 August – 2 September, 2000 from TexAQS-2000 • WRF (v.2.1.1) Options: • PBL: MYJ, YSU • LSM: RUC, Slab, NOAH • Surface Layer: Monin-Obukhov • Microphysics: Turned Off • Shortwave Radiation: Goddard • Longwave Radiation: RRTM (rapid radiative transfer model)

  7. WRF/Chem Configurations (cont.) • Chemistry Options: • Gas-Phase Mechanism: RADM2 • Aerosol Module: MADE/SORGAM • Ini. Cond.: Horizontally homogeneous • Emissions: TCEQ for gases NEI ’99 v.3 for PM

  8. WRF/Chem Simulation Design 12-km Sensitivity Simulations Nesting/Grid Option Simulations • Baseline: N_Y, 1W12 • Physics Sensitivity: N_M, S_Y, R_Y • HGS/Nesting Sensitivity: 2W12, 1W4, 2W4

  9. Sensitivity to PBL and LSM SchemesTime Series and Statistics for Meteorology Normalized Mean Biases (NMBs), %

  10. Sensitivity to PBL and LSM SchemesSpatial Distributions of O3 and PM2.5 N_M N_Y S_Y R_Y O3 PM2.5

  11. Sensitivity to PBL and LSM SchemesTemporal Distributions of O3 and PM2.5

  12. Sensitivity to PBL and LSM SchemesVertical Distributions of O3 and Chemistry Statistics Normalized Mean Biases (NMBs), %

  13. Sensitivity to HGS and NestingTime Series and Statistics for Meteorology Normalized Mean Biases (NMBs), %

  14. Sensitivity to HGS and NestingSpatial Distributions of O3 and PM2.5 1W12 1W4 2W12 2W4 O3 PM2.5

  15. Sensitivity to HGS and NestingTemporal Distribution of O3 and PM2.5

  16. Sensitivity to HGS and NestingVertical Distribution of O3 and Chemistry Statistics Normalized Mean Biases (NMBs), %

  17. Statistical Summary - Meteorology OOO: > 40% OO: 15 to 40% O: 0 to 15% U: 0 to -15% UU: -15 to -40% UUU: < -40% Normalized Mean Biases (NMB) in %

  18. Statistical Summary - Chemistry OOO: > 40% OO: 15 to 40% O: 0 to 15% U: 0 to -15% UU: -15 to -40% UUU: < -40% Normalized Mean Biases (NMB) in %

  19. Summary • No one simulation seems to greatly outperform the others for this particular episode. • Statistically, S_Y performs better for O3, NO, and CO, while N_Y performs better for NO2, and R_Y for PM2.5 (in terms of NMB). 1W4 performs better for O3, NO, and PM2.5, while 2W12 performs better for NO2 and CO. • Temporal variability of O3 is fairly well-captured, while PM2.5 is worse, though not as poor as CO or NOx species. • Computational efficiency is a major factor only for nesting options. • Two-way significantly slower than one-way. • Further understanding of model parameterizations and atmospheric processes is needed. • Large biases in PBLH, NOx, and, CO. How well current model parameterizations handle processes that influence meteorology and chemistry should be further examined.

  20. Acknowledgements • Pacific Northwest National Laboratory: Drs. William Gustafson and Rahul Zaveri • Group Members: Air Quality Forecasting Lab (NCSU) • Funding: NSF Award No. Atm-0348819 NOAA # DW13921548

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