Extended-Range Forecasting of PM2.5 Based on the S2S: A Case Study in Shanghai, China - Qu, Yuanhao; Ma, Jinghui; Yu, Zhongqi (Frontiers in Environmental Science, 2022)
Air pollution has become one of the most challenging problems in China, especially in economically developed and densely populated regions such as Shanghai. In this study, long short-term memory (LSTM) model is introduced for application in extended-range forecasting of PM2.5 in Shanghai by incorporating three members of the sub-seasonal-to-seasonal prediction project (S2S) forecasting, moderate resolution imaging spectroradiometer (MODIS) aerosol optical depth (AOD) data, and large-scale circulation factors derived from ERA-5 reanalysis. Therefore, an accurate 40 d PM2.5 prediction model over Shanghai was developed, providing new insights for air pollution extended-range forecasting. The new model exhibited not only much better accuracy but also captured the pollution process more closely compared to traditional methods, such as multiple regression (MLR). The prediction root mean square errors (RMSE) based on the China Meteorological Administration (CMA), United Kingdom (UK), and the European Center for Medium-Range Weather Forecasts (ECMWF) were 24.84, 24.35, and 22.27 µg•m-3, respectively, and their Heidke Skill Scores (HSS) were between 0.1 and 0.5. As a result, the S2S-LSTM model for extension period pollution prediction with higher accuracy developed in this study could further burst the hot spots of pollution extended-range prediction research. However, limitations of the prediction model are still in existence, especially in deals with only a single site instead of a two-dimensional prediction, which requires further investigation in future studies.