Extrapolation method of precipitation nowcasting radar echo based on GCA-ConvLSTM prediction network

Precipitation nowcasting is of great significance for severe convective weather warning. Radar echo extrapolation is a commonly used precipitation nowcasting method. However, the traditional radar echo extrapolation methods are encountered with the dilemma of such as low data utilization and of the extrapolation ambiguity. To solve the above problems, weutilize the radar data of Shaanxi Province and chooses the Encoder-Decoder as the overall structure of the prediction model. Besides, we choose the ConvLSTM (Convolutional Long Short-Term Memory) as the unit of the prediction model, and designs aglobal channel attention mechanism integrated into the model tobuild a prediction network called GCA-ConvLSTM (Global Channel Attention based ConvLSTM). In addition, to further improve the fitting ability of our prediction model, we use the ensemble learning method to achieve this goal. The algorithm first samples the dataset through the Bagging algorithm, and then we use these sampled data to train three GCA-ConvLSTM networks as the base learner. In the end, we obtain a better performance model which effectively combines the three base learners by using a weighted voting strategy. The experimental results show that the improved GCA-ConvLSTM radar echo extrapolation method based on the ensemble learning algorithm improves the accuracy and timeliness of the precipitation nowcasting compared with the existing deep learning methods.In the evaluation experiments under the reflectivity thresholds of 25 dBz, 35 dBz and 45 dBz, this method is 0.149, 0.192, and 0.085 higher than the average CSI values of the mainstream deep learning models.The extrapolation results of this method have clearer edges and detailed textures, which alleviates the blurring problem in the later stage of extrapolation.