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Zhiwei Han and Meigen Zhang

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. Numerical experiment: RAQMS (A Regional Air Quality Model System)

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Zhiwei Han and Meigen Zhang

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  1. 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

  2. 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 schemes1. 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)

  3. Other Options • The study domain: 90ºE-145ºE, 15º-50ºN • The study period: March 2001 • Horizontal grid resolution: 0.5º • 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: SO2, NOx and O3 • Statistical measures: Correlation coeeficient (R), mean bias error (MBE) • root mean square error (RMSE), normalized mean bias (NMB) • normalized mean error (NME)

  4. Results • Predicted near surface hourly species concentrations • Table 1 Statistics for the predicted hourly species concentrations (ppbv) with the 3 schemes at Hedo site R: SO2 (0.59~0.61), NOx (0.14~0.25), O3(0.63~0.65) MBE: SO2 (-0.07~-0.18), NOx (0.39~0.53), O3(12.0~12.4) NMB: SO2 (-0.12~-0.26), NOx (0.52~0.86), O3(0.27~0.28) All schemes underpredict SO2 and overpredict NOx and O3 MRF largest underprediction of SO2, B&D largest overprediction of NOx GSE less skill for NOx variability

  5. 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

  6. 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 NOx (R 0.01~0.21, NMB -0.2 ~ 0.32) For SO2 and O3, 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 O3 is due to the prescribed top BD SO2 larger positive bias due to volcanic emission

  7. Results • Monthly mean Kz (m2s-1)and species concentrations (ppb) at 150 m at 14:00 LST Kz B&D MRF GSE SO2 O3

  8. Results • 5. Monthly mean Kz (m2s-1)and species concentrations (ppb) at 150 m at 02:00 LST Kz B&D MRF GSE SO2 O3

  9. Results • 6. Monthly mean Kz and species concentrations at 14:00 LST at 120ºE cross section 2500m Kz B&D MRF GSE 2500m SO2 Further investigation is undergoing …

  10. Thank you !

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