Sensitivity of Air Quality Model Predictions to Various Parameterizations of Vertical

Eddy Diffusivity

Zhiwei Han and Meigen Zhang

Institute of Atmospheric PhysicsChinese Academy of Science

Beijing, China

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)

• 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)

• 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: 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

• 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 underpredictionMRF largerst underprediction for surface SO2 in contrast to the largest overprediction

• 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

• Results4.Monthly mean Kz (m2s-1)and species concentrations (ppb) at 150 m at 14:00 LST

Kz

SO2

O3

B&D MRF GSE

• Results5. Monthly mean Kz (m2s-1)and species concentrations (ppb) at 150 m at 02:00 LST

Kz

SO2

O3

B&D MRF GSE

• Results6. Monthly mean Kz and species concentrations at 14:00 LST at 120ºE cross section

Kz

SO2

B&D MRF GSE

2500m

2500m

Further investigation is undergoing …