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

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

Houston, TX Courtesy: University of Texas

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)

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)

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)

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

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 Simulation LSM PBL N_Y NOAH YSU S_Y SLAB R_Y RUC N_M MYJ Simulation Grid Size Nesting Option 1W12 12-km None 2W12 Two-way 1W4 4-km One-way 2W4

Normalized Mean Biases (NMBs), % Sensitivity to PBL and LSM Schemes Time Series and Statistics for Meteorology Normalized Mean Biases (NMBs), % T2 RH WSP WDR PBLH N_Y -0.3 -27.4 12.6 6.6 54.4 N_M -1.2 -21.7 27.1 6.4 22.7 S_Y -4.1 2.5 1.7 7.5 24.3 R_Y -30.5 24.2 5.7 53.7

Sensitivity to PBL and LSM Schemes Spatial Distributions of O3 and PM2 N_Y S_Y N_M R_Y O3 PM2.5

Sensitivity to PBL and LSM Schemes Temporal Distributions of O3 and PM2.5

Normalized Mean Biases (NMBs), % Sensitivity to PBL and LSM Schemes Vertical Distributions of O3 and Chemistry Statistics Normalized Mean Biases (NMBs), % O3 NO NO2 CO PM2.5 N_Y 26.1 -80.2 54.9 -37.3 -1.0 N_M 10.5 -75.9 83.2 -24.1 6.1 S_Y 9.7 -74.7 110 -14.9 14.6 R_Y 13.4 -76.4 60.4 -33.2 0.5

Normalized Mean Biases (NMBs), % Sensitivity to HGS and Nesting Time Series and Statistics for Meteorology Normalized Mean Biases (NMBs), % T2 RH WSP WDR PBLH 1W12 -2.7 -22.1 3.0 14.8 54.8 1W4 -7.4 -9.3 -4.8 12.1 2W12 -1.8 -18.4 22.8 10.6 59.3 2W4 -2.8 -21.6 6.4 13.1 53.8

Sensitivity to HGS and Nesting Spatial Distributions of O3 and PM2.5 1W12 1W4 2W12 2W4 O3 PM2.5

Sensitivity to HGS and Nesting Temporal Distribution of O3 and PM2.5

Normalized Mean Biases (NMBs), % Sensitivity to HGS and Nesting Vertical Distribution of O3 and Chemistry Statistics Normalized Mean Biases (NMBs), % O3 NO NO2 CO PM2.5 1W12 29.0 -80.1 105 -38.3 -32.1 1W4 19.1 -59.2 242 65.2 -22.3 2W12 27.7 -79.8 100 -32.8 2W4 25.8 -71.0 154 140 -35.1

Statistical Summary - Meteorology RH WSP WDR PBLH N_Y U UU O OOO N_M OO S_Y R_Y 1W12 1W4 2W12 2W4 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 %

Statistical Summary - Chemistry O3 NO NO2 CO PM2.5 N_Y OO UUU OOO UU U N_M O S_Y R_Y 1W12 1W4 2W12 2W4 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 %

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.

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