用掩码建模提升天气预测长期准确性
Masked Autoregressive Model for Weather Forecasting
- 通过遮蔽输入数据训练，让模型重建缺失信息以学习时空关系
- 在5个数据集上优于传统方法，长期预测误差显著降低
- 适合需要高精度长时序预测的气象与气候研究者
全球气候变化加剧了对精准可靠天气预报的需求。传统自回归方法虽擅长时间建模，但在长期预测中存在误差累积问题。尽管引入领先时间嵌入可缓解此问题，却难以维持大气事件间的关键关联。为此，我们提出面向天气预报的掩码自回归模型（MAM4WF）。该模型利用掩码建模，在训练时遮蔽部分输入数据，使模型通过重建缺失信息来学习鲁棒的时空关系。MAM4WF融合自回归与领先时间嵌入的优势，兼具灵活的预测时长建模能力与迭代预测集成机制。我们在天气、气候预测及视频帧生成数据集上评估该模型，结果表明其在五个测试数据集上均表现更优。
原文摘要 · Abstract (English)
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.
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