Evaluation of precipitation forecasting methods and an advanced lightweight model

Abstract

Abstract Precipitation forecasting is crucial for warning systems and disaster management. This study focuses on deep learning-based methods and categorizes them into three categories: Recurrent Neural Network (RNN-RNN-RNN), Convolutional Neural Network (CNN-CNN-CNN), and CNN-RNN-CNN methods. Then, we conduct a comprehensive evaluation of typical methods in these three categories using the SEVIR precipitation dataset. The results show that RNN-RNN-RNN suffers from instability in long-term forecasts due to error accumulation, CNN-CNN-CNN struggles to capture temporal signals but produces relatively stable forecasts, and CNN-RNN-CNN significantly increases model complexity and inherits the drawbacks of RNN, leading to worse forecasts. Here, we propose an advanced lightweight precipitation forecasting model (ALPF) based on CNN. Experimental results demonstrate that ALPF can effectively forecast spatial-temporal features, maintaining CNN’s feature extraction capabilities while avoiding error accumulation in RNN’s propagation. ALPF achieves long-term stable precipitation forecasts and can better capture large precipitation amounts.