Thunderstorm Nowcasting with Recurrent-Convolution Deep Learning: A Case Study in Ho Chi Minh City

Machine learning for precipitation nowcasting using radar reflectivity images is a promising approach in weather forecasting. This paper proposes a new pipeline of using Convolutional Neural Networks (CNNs) integrated with Gated Recurrent Units (LSTM) and visual Attention mechanisms for quantitative precipitation nowcasting (QPN) using radar reflectivity images. We propose an end-to-end nowcasting network that leverages a Convolutional-LSTM with Axial Attention as the backbone to develop an adaptive forecasting system. Our results demonstrate the potential of deep learning techniques to significantly improve short-term weather predictions. The models were trained and validated using real-world Doppler radar data from Nha Be, Ho Chi Minh City, ensuring both practical relevance and applicability. Experimental results highlight that the combination of 3D Convolution, Convolutional-LSTM, and Axial Attention achieves superior performance, underscoring the effectiveness of this approach.