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Sensitivity of Air Quality Model Predictions to Various Parameterizations of Vertical Eddy Diffusivity Zhiwei Han and Meigen Zhang Institute of Atmospheric Physics Chinese Academy of Science Beijing, China
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Numerical experiment: RAQMS (A Regional Air Quality Model System) 3-d Eularian model with a spherical and terrain-following coordinate Advection, Diffusion, Dry deposition, multi-phase chemistry, cloud and scavenging etc. Han et al.(2006) Atmospheric Environment, Environmental Modelling & Software PBL schemes 1. Medium-Range Forecasts (MRF), non-local first-order, countergradient term in Kz profile for the well mixed PBL, Hong and Pan (1996) 2. Gayno-Seaman(GSE), 1.5-order local closure, prognostic equation for TKE Shafran et al.(1998) 3. PBL similarity theory (B&D), (MCIP-CMAQ), Byun(1991), Byun and Dennis (1995)
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Other Options The study domain: 9 0 º E-145 º E, 15 º -50 º N The study period: March 2001 Horizontal grid resolution: 0.5 º Vertical resolution: 16 layers to 10km, with 9 layers <2.5 km Emissions: Anthropogenic and biomass burning from Streets et al (2003) Boundary conditions: monthly means from Mozart II (constant at boundary) Meteorological fields: MM5, FDDA applied (3-d reanalysis nudging) Model validation and sensitivity analysis Observations: ground level monitoring sites of Japan (Hedo) 5 flights of DC-8 and P-3B from the TRACE-P experiment Obs in source regions ? Species: SO 2, NO x and O 3 Statistical measures: Correlation coeeficient (R), mean bias error (MBE) root mean square error (RMSE), normalized mean bias (NMB) normalized mean error (NME)
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Results 1.Predicted near surface hourly species concentrations Table 1 Statistics for the predicted hourly species concentrations (ppbv) with the 3 schemes at Hedo site R: SO 2 (0.59~0.61), NO x (0.14~0.25), O 3 (0.63~0.65) MBE: SO 2 (-0.07~-0.18), NO x (0.39~0.53), O 3 (12.0~12.4) NMB: SO 2 (-0.12~-0.26), NO x (0.52~0.86), O 3 (0.27~0.28) All schemes underpredict SO 2 and overpredict NO x and O 3 MRF largest underprediction of SO 2, B&D largest overprediction of NO x GSE less skill for NO x variability
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Results 2. Predicted hourly species concentrations for upper levels Table 2 Statistics for the predicted concentrations (ppbv) at altitudes <2km in comparison with the TRACE-P data Similar skill for SO2 (R 0.65~0.66, NMB 0.14~0.18) Overprediction of SO2, in contrast to the underprediction in Table 1 (NMB -0.12~-0.26) B&D and MRF underpredict NOx, GSE prediction close to obs, with largest R (0.36) All schemes underpredcit O3(NMB -0.15 ~ -0.17), in contrast to the overprediction for near surface (NMB 0.27~0.28) B&D largerst overpredction for surface NOx in contrast to the largest underprediction MRF largerst underprediction for surface SO2 in contrast to the largest overprediction
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Results 3. Predicted hourly species concentrations for upper levels Table 1 Same as Table 2 but for 2~5 km The difference among schemes increases for NO x (R 0.01~0.21, NMB -0.2 ~ 0.32) For SO 2 and O 3, the consistency among schemes is similar to that in Table 2. The model skill apparently degrades in the region of 2-5 km Positive bias (NMB 0.25~0.27) for O 3 is due to the prescribed top BD SO2 larger positive bias due to volcanic emission
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Results 4.Monthly mean Kz (m 2 s -1 ) and species concentrations (ppb) at 150 m at 14:00 LST Kz SO 2 O3O3 B&DMRFGSE
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Results 5. Monthly mean Kz (m 2 s -1 ) and species concentrations (ppb) at 150 m at 02:00 LST Kz SO 2 O3O3 B&DMRFGSE
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Results 6. Monthly mean Kz and species concentrations at 14:00 LST at 120 º E cross section Kz SO 2 B&DMRFGSE 2500m Further investigation is undergoing …
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