Improving Precipitation Forecasting through Early Fusion and Spatiotemporal Prediction: A Case Study Using the MultiPred Model

Abstract

Due to the complexity and uncertainty of meteorological systems, traditional precipitation forecasting methods have certain limitations. Therefore, based on the common characteristics of meteorological data, a precipitation forecasting model named MultiPred is proposed, with the goal of continuously predicting precipitation for 4 h in a specific region. This model combines the multimodal fusion method with recursive spatiotemporal prediction models. The training and testing process of the model roughly involves using spatial feature extraction networks and temporal feature extraction networks to generate preliminary predictions for multimodal data. Subsequently, a modal fusion layer is employed to further extract and fuse the spatial features of the preliminary predictions from the previous step, outputting the predicted precipitation values for the target area. Experimental tests and training were conducted using ERA5 multi-meteorological modal data and GPM satellite precipitation data from 2017 to 2020, covering longitudes from 110° to 122° and latitudes from 20° to 32°. The training set used data from the first three years, while the validation set and test set each comprised 50% of the data from the fourth year. The initial learning rate for the experiment was set to 1 × 10−4, and training was performed for 1000 epochs. Additionally, the training process utilized a loss function composed of Mean Absolute Error (MAE), Mean Squared Error (MSE), and Structural Similarity Index (SSIM). The model was evaluated using the Critical Success Index (CSI), Probability of Detection (POD), and the Heidke Skill Score (HSS). Experimental results demonstrate that MultiPred excels in precipitation forecasting, particularly for light precipitation events with amounts greater than or equal to 0.1 mm and less than 2 mm. It achieves optimal performance in both light and heavy precipitation forecasting tasks.