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深度学习方法在上海市PM2.5浓度预报中的应用
The application of deep learning method in Shanghai PM2.5 prediction
为提升PM2.5浓度预报能力，尤其是对PM2.5重污染的预报能力，以中尺度气象-化学耦合模式系统（WRF-Chem）为基础，结合中尺度WRF气象预报数据、地面及高空气象观测数据、PM2.5浓度观测数据，基于人工智能深度学习序列到序列的算法建立了上海市PM2.5统计预报模型.结果表明，人工智能深度学习算法（Seq2seq）明显修正了WRF-Chem模式由于模型非客观性造成的偏差，提高了上海市PM2.5浓度的预报能力；该算法优化和修正了WRF-Chem模式结果，并通过检验发现可以使PM2.5浓度预报值与实况值间的相关系数由0.51上升至0.79，均方根误差由25.9μg/m3下降至15.01μg/m3.而单独使用套索法（Lasso）线性回归算法对WRF-Chem模式优化效果不理想.基于Seq2seq的PM2.5浓度预报修正模型能够有效提升预报精度.
In order to improve the forecasting ability of PM2.5 concentration, especially the forecasting ability of PM2.5 heavy pollution, this study was based on the mesoscale meteorological-chemical coupled modeling system (WRF-Chem) forecast data in combination with weather and PM2.5 (fine particulate matter) observational data, a machine learning model of PM2.5 prediction in Shanghai, China was established. The results showed that the machine learning algorithm combined with WRF-Chem prediction obviously corrected the deviation of model prediction due to the non-objectivity of the model, and improved the prediction effect. The linear regression method (Lasso) could not obtain a suitable optimization effect, and the deep learning sequence to sequence algorithm was selected to improve the model and verified by experiments. The overall correlation coefficient of the method for the PM2.5 prediction increased from 0.51 to 0.79. The root mean square error was reduced from 25.9μg/m3 to 15.0μg/m3. The Seq2seq modified PM2.5 forecasting model based on WRF-Chem can effectively improve the prediction accuracy. It took a useful application prospect in air quality forecast.
PM2.5浓度预报 / Sequence to sequence模型 / WRF-Chem / 上海市
PM2.5 forecast / sequence to sequence model / Shanghai / WRF-Chem
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国家重点研发计划项目（2016YFC0201900）；国家自然科学基金资助项目（41475040）；上海市科委自然科学基金资助项目（19ZR1462100）；上海气象局面上科研项目（MS201808）；上海气象局启明星科研项目（QM201715）
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