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Zhou G.-Q.,Yangtze River Delta Center for Prediction and Warning of Environmental Meteorology | Zhou G.-Q.,Shanghai Key Laboratory of Meteorology and Health | Geng F.-H.,Yangtze River Delta Center for Prediction and Warning of Environmental Meteorology | Geng F.-H.,Shanghai Key Laboratory of Meteorology and Health | And 10 more authors.
Zhongguo Huanjing Kexue/China Environmental Science | Year: 2015

A numerical chemical weather forecasting system was established and operationally implemented based on the WRF-Chem Model, an online coupled regional chemical transport model. Performance of the modeling system on daily maximum 1-hour and 8-hour ozone (1 h and 8 h O3) concentrations was evaluated between May 1st and September 30th, 2013. The results showed that the numerical forecasting has generally good performance. There is no substantial systematic bias in 1h and 8h O3 concentrations and correspondent IAQI in forecasts of 24 h, 48 h, and 72 h. The correlation coefficients (R) are ~0.8, and the mean and median biases are around 1×10-9~2×10-9. The forecasted O3 attainment vs. pollution days as well as primary pollutants are also in good agreement with observations. The performance of 48 h forecast is slightly better than that of 24 h and 72 h forecast, and these of the later two are generally close to each other. Meanwhile, further improvement is still needed. For example, model shows substantial biases in O3 concentrations or IAQI forecasts in some cases, and the accuracy of O3 IAQI level forecast is substantially lower than that of concentration and IAQI value forecast. In general, the numerical forecasting system shows relatively good performance in O3 forecasts during May to September, 2013, and it has the capability to support the air quality forecast over Shanghai. ©, 2015, Chinese Society for Environmental Sciences. All right reserved.

Chang L.-Y.,Shanghai Meteorological Service | Chang L.-Y.,Yangtze River Delta Center for Prediction and Warning of Environmental Meteorology | Xu J.-M.,Shanghai Meteorological Service | Xu J.-M.,Yangtze River Delta Center for Prediction and Warning of Environmental Meteorology | And 9 more authors.
Huanjing Kexue/Environmental Science | Year: 2016

To analyze the characteristics and formation mechanism of a heavy air pollution episode in Shanghai City from January 23th to January 24th, 2013, the observed data of PM2.5 concentration and ground meteorological data and the WRF-Chem model were collected. The analysis revealed that the synoptic necessary mechanism of the heavy air pollution episode could be characterized by the following patterns: Accompanied with weak cold front activities, the city experienced weak winds (i.e. stable atmosphere) at first and then northerly winds (i.e. pollutant transport process), causing the continuous increase and maintaining of pollutant concentration. The detailed results are shown as follows: Firstly, the stable atmosphere circulation pattern which lasted for 10 hours was not good for air pollution dispersion, as a result, local PM2.5 concentrations continued to increase and reached severe pollution level and the high concentrations maintained for 7 hours caused by the stable boundary layer (e.g. static surface winds and low level temperature inversion) during nighttime, and the average PM2.5 concentrations during the stable weather process was 172.4 μg·m-3. Secondly, the dispersion condition was slightly improved later on with the arrival of a weak cold front, the upstream pollution transportation occurred at the same time, leading to further increase of PM2.5 concentration (up to 280 μg·m-3), and the average PM2.5 concentration during the upstream transportation process was 213.6 μg·m-3. Numerical simulation with the WRF-Chem model showed that, average contribution of upstream transportation to local PM2.5 concentrations during the episode was 23%. Among them, the contribution during the stable weather and upstream transportation stage was 17.2% and 32.2%. Our results suggested that there were significant differences in the contribution of upstream transportation to the local PM2.5 concentration of Shanghai due to variation of weather conditions. Therefore, the government can design effective emission control strategy in advance taking pollution weather forecasting into account. © 2016, Science Press. All right reserved.

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