Tropical cyclone ensemble forecast framework based on spatiotemporal model

To explore tropical cyclone prediction methods that integrate multimodal meteorological data, this study proposes a novel approach. The proposed model employs an LSTM-based temporal branch to extract temporal sequence features from the CMA dataset and a U-Net-based spatial branch to extract three-dimensional spatial features from the ERA5 dataset. These features are then fused through an encoder-decoder structure to integrate high-dimensional spatiotemporal characteristics. Experimental results demonstrate that the spatiotemporal model significantly improves the prediction accuracy for 24-hour lead times. Subsequently, to further optimize the experimental results, the study introduces an ensemble forecasting framework. This framework enhances prediction accuracy by adjusting the outputs of multiple spatiotemporal model prediction members. The optimization is achieved by solving the objective function that reflects the forecast geographical error, thereby optimizing the weighted coefficients. The experimental results indicate that the ensemble forecasting framework can further optimize prediction outcomes.