Multi-element deep learning fusion correction method for numerical weather prediction

As the mainstream technology of modern weather forecast, numerical weather prediction (NWP) has been developing in the direction of refinement in recent years, yet the prediction error is still unavoidable. Therefore, it is of great significance to improve the accuracy of numerical weather forecast by revising the results. A traditional method of prediction correction, i.e., the Anomaly Numeral-correction with Observations (ANO), is used to correct the forecast based on statistics of historical data. Results indicate that this method has a good effect. As an emerging method, deep learning has been gradually applied to the field of meteorology in recent years, and has achieved significant results in precipitation prediction and cloud image recognition. Domestic scholars in China used CU-Net, a deep learning model to correct the deviations of the model grid point forecast data of 2 m temperature, 2 m relative humidity and 10 m wind respectively from the European Centre for Medium-Range Weather Forecast (ECMWF), which significantly improved the forecast compared with the ANO method. Based on the above tests, this paper uses dense convolutional structure network model to improve the CU-Net model and forms a new deviation correction model for NWP, which is named as Dense-CUnet, and further develops a deviation correction model named Fuse-CUnet to integrates multiple meteorological elements from NWP and topographic features. Deviation correction tests and comparative analysis of these different models have been carried out. Root mean square error (RMSE) and mean absolute error (MAE) are used as the scoring metrics. By comparing with the original prediction results of ECMWF and the results revised by the ANO and CU-Net methods, it is found that the dense-convolution structure network model Dense-CUnet can be used to effectively modify the positive effect. Moreover, the Fuse-CUnet model that integrates multiple elements can greatly improve the revision effect.