High resolution nowcasting experiment of severe convections based on deep learning

Nowcasting and early warnings of severe convective weather play an extremely important role in the prevention of meteorological disasters. In meteorological services, it becomes one of the most difficult and hot research topics because of its requirement for high accuracy and fine temporal-spatial resolution. Deep learning method is trying to solve the problem of strong convective nowcasting with high spatial and temporal resolution. First, the strong convective nowcasting is abstracted into a sequence of prediction problems containing both time and space. Then, the long sequence and high temporal resolution weather radar network mosaic data are fed to train the model using the encoder-decoder based on the improved recurrent neural network algorithm. The end-to-end neural network trained by radar echo data is then used to predict evolution characteristics of the radar echoes at 6 min intervals in the next one hour. By comparing the traditional extrapolation method with the deep learning algorithm, it is found that the deep learning method can effectively learn the intrinsic correlation of the data features in the high temporal-spatial resolution sequence, construct the abstract deep features through the multi-layer neural network and effectively capture the motion state of radar echoes. The calculation of prediction hit rate, false alarm rate, critical success index shows that compared with the traditional extrapolation forecast method, deep learning method can improve the nowcasting accuracy of strong convections, and demonstrates a wide application prospect.