Recent advancements in deep learning have propelled the field of weather prediction models to new heights. Despite their progress, these models often struggle with real-world application due to their sensitivity to spatial-temporal shifts, a vulnerability particularly pronounced in weather prediction tasks where overfitting to local and temporal variations is common. This paper presents an investigation into the development of a robust precipitation forecasting model that stands resilient to such shifts. We introduce Temporal Frame Interpolation (TFI), an innovative technique designed to fortify forecasting models against spatial-temporal discrepancies. TFI operates by generating synthetic samples through the interpolation of adjacent frames from satellite imagery and ground radar data, thereby enriching the training dataset and bolstering the model's defense against noise on frames. Additionally, we integrate a novel multi-level dice loss, which exploits the ordinal nature of rainfall intensities to further refine model performance. These methodologies have collectively advanced our model's forecasting precision, achieving \textit{1st place} on the transfer learning leaderboard in the \textit{Weather4Cast'23 competition}.It not only demonstrates the efficacy of our approaches but also sets a new benchmark for deep learning applications in meteorological forecasting. Our code and weights have been public on \url{https://github.com/Secilia-Cxy/UNetTFI}.

通过引入Temporal Frame Interpolation (TFI)和multi-level dice loss等创新技术，该研究提出了一种抵御时空差异的强韧降水预测模型，取得了气象预测领域的突破性成果，并在Weather4Cast'23竞赛中获得第一名，为深度学习在气象预测中设立新的基准。

学习鲁棒降水预测的时间帧插值