Precipitation Nowcasting Based on Radar Echo Images via Multiscale Spatiotemporal LSTM

Accurate precipitation nowcasting up to two hours in advance is crucial for weather-sensitive decision-making, such as flood disaster warnings. Although deep learning-based radar echo extrapolation methods show advantages, they still face challenges, particularly in predicting local spatial variations and mitigating performance degradation with increasing prediction time horizon. This study proposes a new multi-scale spatiotemporal prediction LSTM model (MS<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup>T-LSTM) to enhance precipitation nowcasting capabilities by fusing multi-scale spatial features through parallel convolutional structures and improving spatiotemporal reasoning through stacked LSTMs. Notably, multi-scale spatiotemporal feature gate is introduced into LSTM unit to transmit multi-scale spatiotemporal features of radar echo image sequences, mitigating the issue of time-dependent performance degradation. The loss function for model optimization is carefully selected via series of comparative experiments. The experiments show that MS<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup>T-LSTM outperforms benchmark models in predicting precipitation events with different intensities. Specifically, in comparison to PredRNN++, the F1 score, Critical Success Index, Heidke Skill Score, and Probability of Detection for low-intensity precipitation threshold (5mm/h) improved by 2.3%, 3.5%, 13%, and 10.6%. This study demonstrates the effectiveness of integrating multi-scale spatiotemporal features to enhance precipitation forecasting, providing valuable insights for spatiotemporal prediction modeling and optimization.