GraphAT Net: A Deep Learning Approach Combining TrajGRU and Graph Attention for Accurate Cumulonimbus Distribution Prediction

Abstract

In subtropical regions, heavy rains from cumulonimbus clouds can cause disasters such as flash floods and mudslides. The accurate prediction of cumulonimbus cloud distribution is crucial for mitigating such losses. Traditional machine learning approaches have been used on radar echo data generated by constant altitude plan position indicator (CAPPI) radar systems for predicting cumulonimbus cloud distribution. However, the results are often too foggy and fuzzy. This paper proposes a novel approach that integrates graph convolutional networks (GCN) and trajectory gated recurrent units (TrajGRU) with an attention mechanism to predict cumulonimbus cloud distribution from radar echo data. Experiments were conducted using the moving modified National Institute of Standards and Technology (moving MNIST) dataset and real-world radar echo data, and the proposed model showed a 59.12% improvement in mean square error (MSE) and a 16.26% improvement in structure similarity index measure (SSIM) on average in the moving MNIST dataset, a 65.40% improvement in MSE, and an 10.29% improvement in SSIM on average in the radar echo dataset. These results demonstrate the effectiveness of the proposed approach for improving the prediction accuracy of cumulonimbus cloud distribution.