A spatio-temporal graph-guided convolutional LSTM for tropical cyclones precipitation nowcasting

Abstract

Real-time and accurate Tropical Cyclone (TC) precipitation nowcasting plays an important role in disaster prevention and mitigation. The strong ability of deep learning has been proved in many precipitation nowcasting studies. However, few deep learning solutions focus on the precipitation caused by TC, which has complex patterns and is potential for disasters. In this paper, we develop a novel framework for TC precipitation nowcasting. To effectively utilize the TC information, we design a multi-source information fusion mechanism between precipitation and TC. More importantly, we propose a Graph-guided Spatio-Temporal Module (GSTM), which designs a construction scheme of dynamic reasoning graphs to capture the spatio-temporal relationship between intra-frame and inter-frame. It is also the first attempt to introduce the graph concept into precipitation nowcasting. We utilize the TC and precipitation data from 2017 to 2019 in the Northwest Pacific Ocean and divide the data into training (3524 sequences), validation (352 sequences), and test set (705 sequences) at a ratio of 10:1:2. Extensive experiments demonstrate that compared with the Convolution LSTM model, our model obtains a significant improvement by 2.74%, 4.24%, 6.29%, 5.78% in F1-score when the precipitation intensity is 5, 10, 20, 30 mm/h, respectively. Meanwhile, we select three typical TC precipitation cases for visual analysis, which proves that our model has excellent perception ability in the details of TC spatial distribution and can deal with different TC scenarios effectively. The effectiveness of our model indicates that the method based on deep learning has strong applicability and development prospects in the TC precipitation nowcasting, providing infinite possibilities and potential for this field.