MMPFormer: A Memory-Aware Multiscale Predictive Transformer for Precipitation Nowcasting

In recent years, global climate change has intensified, leading to frequent occurrences of short-term extreme rain-fall events. How to accurately predict short-term precipitation within the next few hours has become a concern for people, especially for meteorologists. Traditional Numerical Weather Prediction (NWP) models rely on solving complex physical equations, resulting in significant computational costs and low temporal resolution, which limit their suitability for nowcasting tasks. Weather radar echo extrapolation, which forecasts future echoes from historical observations, has thus become the predominant approach for precipitation nowcasting. In the realm of deep learning-based methods, convolutional or recurrent neural network-based extrapolation pipelines inherently struggle in processing sequential data and capturing the multi-scale spatial features inherent in radar echo images. Additionally, current models often focus on enhancing dense prediction capabilities while neglecting to mine or utilize the evolutionary patterns of historical echoes, leading to diminished accuracy in long-term sequence forecasting. Moreover, high precipitation areas are critical for public safety, but current models often fail to forecast them accurately due to insufficient emphasis during learning. In this paper, we propose a memory-aware multi-scale predictive transformer (MMPFormer) for precipitation nowcasting. Specifically, we designed the Multi-Scale Spatial-Temporal Transformer (MS-STT), which incorporates multi-scale convolution techniques alongside self-attention mechanisms to effectively extract and model the spatiotemporal correlations within historical radar echoes. Additionally, we have designed a ConvGRU-Instructed Memory Mechanism (CIMM) to alleviate the degradation of radar echo details during extended extrapolation periods, enabling accurate forecasts over the next three hours. Furthermore, the Key Area Attention Module (KAAM), a plug-and-play module, has been introduced. It emphasizes high precipitation areas through attention mechanisms, mitigating the negative impacts of missing high precipitation by previous learning-based methods. Quantitative and qualitative experimental results on radar echo datasets demonstrate the superior performance of our method in modeling spatiotemporal dynamics and long-term extrapolation for precipitation forecasting.