A Novel Method for Precipitation Nowcasting Based on ST-LSTM

Abstract

Precipitation nowcasting is of great significance for severe convective weather warnings. Radar echo extrapolation is a commonly used precipitation nowcasting method. However, the traditional radar echo extrapolation methods are encountered with the dilemma of low prediction accuracy and extrapolation ambiguity. The reason is that those methods cannot retain important long-term information and fail to capture short-term motion information from the long-range data stream. In order to solve the above problems, we select the spatiotemporal long short-term memory (ST-LSTM) as the recurrent unit of the model and integrate the 3D convolution operation in it to strengthen the model's ability to capture short-term motion information which plays a vital role in the prediction of radar echo motion trends. For the purpose of enhancing the model's ability to retain long-term important information, we also introduce the channel attention mechanism to achieve this goal. In the experiment, the training and testing datasets are constructed using radar data of Shanghai, we compare our model with three benchmark models under the reflectance thresholds of 15 and 25. Experimental results demonstrate that the proposed model outperforms the three benchmark models in radar echo extrapolation task, which obtains a higher accuracy rate and improves the clarity of the extrapolated image.