AUTHOR=Qu Yuanhao , Ma Jinghui , Yu Zhongqi TITLE=Extended-Range Forecasting of PM2.5 Based on the S2S: A Case Study in Shanghai, China JOURNAL=Frontiers in Environmental Science VOLUME=10 YEAR=2022 URL=https://www.frontiersin.org/journals/environmental-science/articles/10.3389/fenvs.2022.882741 DOI=10.3389/fenvs.2022.882741 ISSN=2296-665X ABSTRACT=

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, the long short-term memory (LSTM) model is introduced for the application in extended-range forecasting of PM2.5 in Shanghai by incorporating three members of the Subseasonal-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-day PM2.5 prediction model over Shanghai was developed, providing new insights for air pollution extended-range forecasting. The new model not only exhibited much better accuracy but also captured the pollution process more closely than traditional methods, such as multiple regression (MLR). The prediction root-mean-square errors (RMSEs) based on the China Meteorological Administration (CMA), the U.K. model, and the European Centre for Medium-Range Weather Forecasts (ECMWF) were 24.84, 24.35, and 22.27 μg m−3, respectively, and their Heidke Skill Scores (HSSs) 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 dealing with only a single site instead of a two-dimensional prediction, which requires further investigation in future studies.