谈玲,刘巧,夏景明. 2024. 融合DEM与FY-4A数据的ECMWF预报产品深度学习订正方法. 气象学报,82(X):1-15. DOI: 10.11676/qxxb2024.20230061
引用本文: 谈玲,刘巧,夏景明. 2024. 融合DEM与FY-4A数据的ECMWF预报产品深度学习订正方法. 气象学报,82(X):1-15. DOI: 10.11676/qxxb2024.20230061
Tan Ling, Liu Qiao, Xia Jingming. 2024. A study on the revised deep learning method of ECMWF forecast products based on DEM and FY-4A data. Acta Meteorologica Sinica, 82(X):1-15. DOI: 10.11676/qxxb2024.20230061
Citation: Tan Ling, Liu Qiao, Xia Jingming. 2024. A study on the revised deep learning method of ECMWF forecast products based on DEM and FY-4A data. Acta Meteorologica Sinica, 82(X):1-15. DOI: 10.11676/qxxb2024.20230061

融合DEM与FY-4A数据的ECMWF预报产品深度学习订正方法

A study on the revised deep learning method of ECMWF forecast products based on DEM and FY-4A data

  • 摘要: 精准的数值天气预报是精细化气象公共服务和商业服务的重要前提。欧洲中期天气预报中心(European Center for Medium Weather Forecasting,ECMWF)预报产品在全球被广泛采用,但始终存在系统预报误差。针对数值天气预报中的误差和多源数据融合中的非线性映射等问题,设计了一个ECMWF数值预报产品的深度学习订正模型(Numerical Forecast Correction Network,NFC-Net)。NFC-Net引入了FY-4A卫星观测数据、数字高程模型数据(Digital Elevation Model,DEM)和ERA5历史实况数据订正预报结果,利用多源数据空间分辨率对齐模块、时空特征提取模块解决多源异构数据特征的提取与融合问题,并通过UNet网络实现ECMWF预报产品的订正。为了评估所提算法的性能,利用NFC-Net对ECMWF产品中的2 m气温和10 m风速两个天气要素开展订正试验,并将试验结果与ECMWF预报结果、ANO 方法订正结果、Convlstm方法订正结果、Fuse-CUnet方法订正结果和ERA5实况进行对比。结果显示,NFC-Net模型订正的2 m气温和10 m风速的均方根误差(Root Mean Squared Error,RMSE)分别较ECMWF预报产品下降49.71%和50.86%。表明NFC-Net模型可以充分利用多源数据有效改善复杂地形条件下的订正结果。NFC-Net模型可用于订正ECMWF预报结果,显著提升数值天气预报的精度。

     

    Abstract: Accurate numerical weather prediction is an important prerequisite for refined public and commercial meteorological services. ECMWF forecast products are widely used around the world, where as systematic forecast errors always exist. As a correction of numerical prediction products, multi-source data fusion can effectively reduce prediction errors, which is also a typical high-dimensional nonlinear mapping problem. Due to the heterogeneity of geographic data and ground truth data and satellite data, it is necessary to establish a mechanism to fully extract and utilize effective information from these data while avoid noise and redundancy of the information. In recent years, deep learning methods have been extensively applied to data post-processing in meteorological field. Aiming at errors in numerical weather prediction and the nonlinear mapping problem in multi-source data fusion, this study designs a correction deep learning model NFC-Net for ECMWF numerical prediction products, which mainly includes a multi-source data spatial resolution alignment module, a spatiotemporal feature extraction module, and a UNet correction module. NFC-Net optimizes and corrects the forecast results by integrating multi-source data such as FY-4A satellite data, DEM, and ERA5 historical truth data, and utilizes multi-source data spatial resolution alignment module and spatiotemporal feature extraction module to achieve feature extraction and fusion for multi-source heterogeneous data. At the same time, this paper also proposes a spatial resolution alignment algorithm based on convolutional neural networks (UPS-MSR algorithm) and a dual self-attention mechanism (DSA). The UPS-MSR algorithm uses up-sampling and multi-scale residual networks to achieve grid alignment of meteorological and geographic data with different resolutions, which can effectively avoid information loss. The DSAConvlstm network embedded in DSA module can balance the spatiotemporal correlation and element correlation when extracting features from high-dimensional meteorological information. To evaluate the performance of the proposed method NFC-Net, correction experiments on two weather elements, i.e., 2 m temperature and 10 m wind speed in the ECMWF products, are carried out and the results are compared with the ECMWF forecast results, ANO, Convlstm, Fuse-CUnet and ERA5. The experiments show that the root mean square errors (RMSEs) of 2 m temperature and 10 m wind speed corrected by the NFC-Net model decrease by 49.71% and 50.86%, respectively when compared to that in the ECMWF forecast products. The experimental results indicate that the introduction of high-resolution DEM data in the NFC-Net model can obviously optimize land surface process of the model, and the correction effect is more pronounced under complex terrain condition. The use of FY-4A satellite data enables the model to obtain more three-dimensional information during correction. The application of DSA module can make the model pay more attention to variables that have strong correlations with correction elements, and thereby significantly improves the quality of correction. The proposed method can prospectively be applied in the correction of ECMWF forecast results and promote the accuracy of numerical weather prediction.

     

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