An Operational Numerical Air Quality

Forecasting over Eastern China

Conclusions:

• An operational numerical system (RAEMS) was constructed to forecast the air

quality over eastern China based on WRF-Chem and MEIC inventory

• The performance is consistent in different forecast length of 24h, 48h, and 72h

• About half of cities has R of over 0.6 for PM2.5 and 0.7 for O3-8h

• PM2.5 concentrations agree with observation and so is O3 diurnal variation

• General slight underestimating for PM2.5 and over- for O3

G. Zhou, J. Xu, Y. Xie

Shanghai Meteorological Service

Microphysics (mp_physics） WSM 6-class

Cumulus par.（cu_phy） no

LW radiation（ra_lw） RRTM

SW radiation（ra_sw） Dudhia

Surface layer（sf_sfclay） Monlin_Obukhov

Land surface（sf_surface） Unified Noah

PBL（bl_pbl） YSU

gas chemistry RADM2

Aerosol chemistry ISOROPIA II/SORGAM

Geo/Land-use MODIS

WRF-Chem V3.2

NCAR updated:Aerosol effect on photolysis &

Inorganic aerosol chemistry

Construction of operational RAEMS

Resolution Grids Length

6km 360x40096h 12UTC

60h 00UTC

Anthropogenic emission

Before Aug 2014 After

inventory INTEX-B MEIC

base year 2008 2010

section N.A.

Agriculture, industry,

residence, traffic,

power plant

NH3 N.A. yes

resolution 0.5º 0.25º

monthly N.A. yes

Adjust averagely :

NOx*0.6

SO2*0.4

Diurnal variationShanghai Academy of

Environmental Science (SAES)

Performance over eastern China during 2014-2015, PM2.5 and O3-8h

PM2.5 (N≈84000, 87%) DM8H ozone (N≈89000, 94%)

OBS 24-hr 48-hr 72-hr OBS 24-hr 48-hr 72-hr

Mean Conc. 59.3 47.4 50.0 51.2 41.9 61.0 59.6 59.0

Median Conc. 47.9 40.0 41.8 42.5 38.6 59.2 58.4 57.5

MB -12.0 -9.3 -8.2 18.9 17.7 17.1

ME 24.6 24.6 25.1 21.9 20.9 20.5

RMSE 35.8 35.9 36.2 27.9 26.8 26.4

R 0.67 0.66 0.66 0.63 0.63 0.62

NMB -9% -3% 0% 77% 74% 74%

NME 46% 48% 50% 83% 80% 80%

FAC2 0.71 0.72 0.71 0.75 0.78 0.80

Warmer color

corresponds to

higher data

density

Performance distribution of PM2.5 (131cities) and O3-8h (130 cities)

R R

71 city ≥0.6，34 ≥ =0.7;

better in

north

62 city ≥0.7，18 ≥ =0.8;

Better in

north

Bias Bias

109 city <0;

Worse in

north

Almost >0;

Better near

coast

Acknowledgements

Thanks to

Xuexi Tie (NCAR)

Georg A. Grell (NOAA)

Gregory R. Carmichael (U. Iowa)

Scientific steering Committee of GURME-Shanghai

MEIC group for the EI

Zhou G, J Xu, Y Xie, et al., 2017: Numerical Air Quality Forecasting over Eastern China:

An Operational Application of WRF-Chem. Atmos. Environ., 153, 94–108

Zhou G, Y Xie, J Wu et al., 2016: WRF-Chem based PM2.5 forecast and bias analysis

over the East China Region. China Environmental Science, 36(8): 2251-2259. (in

Chinese with English abstract)

Zhou G, F Geng, J Xu et al., 2015: Numerical ozone forecasting over shanghai. China

Environmental Science, 35(6): 1601-1609. (in Chinese with English abstract)