A self-attention multisource precipitation fusion model for improving long-sequence precipitation estimation accuracy

Accurate precipitation estimation is essential in agricultural production, water resource management, and flood forecasting. However, high-precision precipitation data remain very hard to obtain due to the complex spatio-temporal distribution of precipitation. Most existing methods considering spatio-temporal correlations in precipitation rely on a convolutional neural network for spatial feature extraction. However, these methods are less efficient in capturing global spatial features due to the local receptive fields of convolutional operators. In this study, we designed a Self-LSTM cell structure capable of effectively capturing temporal and global spatial features. Based on this, a self-attention precipitation fusion model (SAPFM) is proposed. The results demonstrate that SAPFM outperforms basic models and the original precipitation products. SAPFM improves by 28.8% and 21.8% on the Kling-Gupta efficiency (KGE) and Correlation Coefficient (CC) compared to the best-performing precipitation product (GsMap), respectively. Additionally, SAPFM reduces the Root Mean Square Error (RMSE) by 12.5%.