Spatiotemporal Meteorological Prediction Based on Fully Convolutional Neural Network

Accurate prediction of meteorological data is critical for enhancing the capacity to respond to climate change, reducing disaster risks, and ensuring the sustainable development of human society. However, existing data-driven methods fail to meet the forecasting demand regarding accuracy and efficiency. Therefore, this study introduced spatiotemporal meteorological prediction UNet (STMP-UNet), a UNet-based spatiotemporal meteorological prediction network. The network used an attention-optimized spatial UNet to extract global-local spatial connections. Additionally, it utilized a multiscale temporal pyramid gating module (TPGM) to capture the evolution patterns of meteorological spatial features. Meanwhile, we constructed a spatiotemporal composite loss function based on mean square error and temporal differentiation to better fit the complex nonlinear evolutionary patterns in the meteorological domain. Comparison and ablation experiments demonstrated that the proposed model and its modules in this study can better predict meteorological data for the next three and five days. The meteorological model trained on data from Northeast China is applied to predict conditions in the Pacific region, confirming the transportability of STMP-UNet and the robustness of the multiscale prediction approach. Finally, STMP-UNet achieves weather data prediction for the next five days in just 38 ms, demonstrating its superior efficiency compared to other models. Therefore, the STMP-UNet model can be used as an effective tool for predicting meteorological data, aiding decision-making in meteorological-related fields.