The growing impact of global climate change amplifies the need for accurate and reliable weather forecasting. Traditional autoregressive approaches, while effective for temporal modeling, suffer from error accumulation in long-term prediction tasks. The lead time embedding method has been suggested to address this issue, but it struggles to maintain crucial correlations in atmospheric events. To overcome these challenges, we propose the Masked Autoregressive Model for Weather Forecasting (MAM4WF). This model leverages masked modeling, where portions of the input data are masked during training, allowing the model to learn robust spatiotemporal relationships by reconstructing the missing information. MAM4WF combines the advantages of both autoregressive and lead time embedding methods, offering flexibility in lead time modeling while iteratively integrating predictions. We evaluate MAM4WF across weather, climate forecasting, and video frame prediction datasets, demonstrating superior performance on five test datasets.

本研究解决了传统自回归方法在长期天气预报中面临的误差积累问题，尤其是对大气事件相关性的保持不足。作者提出的掩蔽自回归模型（MAM4WF）通过数据掩蔽训练，增强了时空关系的学习能力。研究结果显示，MAM4WF在多个天气和气候预报数据集上表现优越，具备实质性的预报能力提升。 

用于天气预报的掩蔽自回归模型