基于深度学习的7~15 d温度格点预报偏差订正

Application of Deep Learning Bias Correction Method to Temperature Grid Forecast of 7-15 Days

为了提高模式对于7~15 d温度格点预报准确性,基于U-Net模型以及U-Net残差连接模型,采用2018年12月25日—2022年7月5日多种组合气象数据作为输入数据特征,针对TIGGE数据中心提供的全球集合预报CMA-GEPS 2 m气温控制预报,开展168~360 h时效的格点预报误差订正试验。结果表明:对于240 h预报时效,两种深度学习模型中,U-Net模型表现较好;对于不同输入数据特征,加入起报时刻ERA52 m气温产品的U-Net模型表现最佳,在多个预报时效上有较好的订正效果,均方根误差减小率为10%~25%,可有效改善模式对于15.75°~55.25°N,73°~136.5°E区域北部的蒙古高原、西部的青藏高原及部分山地的预报误差较大的不足;而加入CMA-GEPS控制预报10 m风预报产品后改进不明显。总体上,基于U-Net模型构建的模式格点预报偏差订正模型可有效降低7~15 d温度格点预报误差,进一步提升复杂地形下格点预报的准确性。

The forecast error of numerical weather forecasting is inevitable,and there are still difficulties in temperature forecast of 7-15 days.To improve forecast accuracy and timeliness,the deviation correction technique is often used in operation.In recent years,deep learning methods have shown great potential in statistical post-processing of model forecasts.To improve the accuracy of Global Ensemble Prediction System of China Meteorological Administration(CMA-GEPS)for 7-15 days,error characteristics of 2 m temperature and 10 m wind products of CMA-GEPS control forecast provided by TIGGE data center from 25 December 2018 to 5 July 2022,and ERA5 data provided by ECMWF are analyzed.The U-Net model and residual connection model is used to conduct 2 m temperature lattice forecast error revision experiment for the lead time of 168-360 h in the region 15.75°-55.25°N,73°-136.5°E.The experiments are designed with various data features to explore differences of the deep learning methods for longer lead time with different sample characteristics and model parameters,and performances of models are examined by comparing the bias,mean absolute bias and root mean square error.The results show that 2 m temperature forecast errors of 7-15 days become larger as the lead time increases.The model forecast skill gradually decreases,and in the target area,performance in eastern and southern marine and offshore areas is better than in western and northern the plateaus and mountains.The differences in the spatial distribution of errors are more prominent.Among the revised models,the effect of the U-Net model is better than that of the U-Net residual connection model,and adding the initial 2 m temperature data of ERA5 can greatly improves the performance,but the effect of adding CMA-GEPS control forecast 10 m wind product of CMA-GEPS control forecast is not apparent.For 9 lead times,the revised root mean square errors are reduced by 10%-25%,and the model can effectively reduce the large forecast errors for the northern Mongolian Plateau and the western Tibetan Plateau,and some mountainous areas in the target area.