Weather forecasting is a long-standing computational challenge with direct societal and economic impacts. This task involves a large amount of continuous data collection and exhibits rich spatiotemporal dependencies over long periods, making it highly suitable for deep learning models. In this paper, we apply pre-training techniques to weather forecasting and propose W-MAE, a Weather model with Masked AutoEncoder pre-training for weather forecasting. W-MAE is pre-trained in a self-supervised manner to reconstruct spatial correlations within meteorological variables. On the temporal scale, we fine-tune the pre-trained W-MAE to predict the future states of meteorological variables, thereby modeling the temporal dependencies present in weather data. We conduct our experiments using the fifth-generation ECMWF Reanalysis (ERA5) data, with samples selected every six hours. Experimental results show that our W-MAE framework offers three key benefits: 1) when predicting the future state of meteorological variables, the utilization of our pre-trained W-MAE can effectively alleviate the problem of cumulative errors in prediction, maintaining stable performance in the short-to-medium term; 2) when predicting diagnostic variables (e.g., total precipitation), our model exhibits significant performance advantages over FourCastNet; 3) Our task-agnostic pre-training schema can be easily integrated with various task-specific models. When our pre-training framework is applied to FourCastNet, it yields an average 20% performance improvement in Anomaly Correlation Coefficient (ACC).

翻译：天气预报是一项具有直接社会和经济影响的长期计算挑战。该任务涉及大量连续数据采集，并展现出长周期内的丰富时空依赖性，使其非常适用于深度学习模型。本文我们将预训练技术应用于天气预报，并提出W-MAE——一种采用掩码自编码器预训练的天气预测模型。W-MAE以自监督方式进行预训练，旨在重构气象变量内部的空间相关性。在时间尺度上，我们通过对预训练后的W-MAE进行微调，预测气象变量的未来状态，从而对天气数据中存在的时序依赖性进行建模。我们使用第五代ECMWF再分析数据（ERA5）进行实验，每六小时选取一次样本。实验结果表明，W-MAE框架具有三大优势：1）在预测气象变量未来状态时，使用预训练的W-MAE能有效缓解预测中的累积误差问题，在中短期内保持稳定性能；2）在预测诊断变量（如总降水量）时，我们的模型相较FourCastNet展现出显著性能优势；3）我们的任务无关预训练方案可简便地集成到各类特定任务模型中。将该预训练框架应用于FourCastNet时，异常相关系数（ACC）平均提升20%。