In this study, ensemble forecast combined with linear regression method is used to reduce the uncertainty in air quality models. Firstly, the PM10 forecasts by three models (NAQPMS, CAMx and CMAQ) in EMS-Beijing are evaluated over Beijing areas. In order to improve the forecast performance, the linear regression method (REG) is used to combine the forecast results of the three models and is compared with the ensemble mean method. The results show that for single model forecast, great difference exists among different models and no model performs much better for all statistic indexes than the other two models. Overall, CMAQ performs better in tendency prediction, while NAQPMS has smaller root mean square errors than the other two models. Ensemble mean method presents poor performance in improving the PM10 forecasts from the three models. On the other hand, REG brings significant improvement of the PM10 forecast. When an appropriate training length (36 days) is applied, the root mean square errors of PM10 forecast over 28 stations of Beijing is reduced by 32%~43% when using REG and the bias decreased considerably to 5.8 μg·m-3. This result implies that REG can greatly improve forecast performance than single model and ensemble mean forecast. Furthermore, the REG also greatly improve capturing of pollution episode forecast.
|Number of pages
|Huanjing Kexue Xuebao/Acta Scientiae Circumstantiae
|Published - Jan 6 2015
All Science Journal Classification (ASJC) codes
- Environmental Engineering
- Environmental Chemistry
- Environmental Science(all)