用20%数据训练扩散模型，天气预报精度接近全量数据。
Data-Efficient Ensemble Weather Forecasting with Diffusion Models
- 采用时间分层采样策略，仅用20%数据训练。
- 在部分指标上优于全数据模型，整体性能略降。
- 适合数据稀缺的气象建模场景，推动高效训练研究。
尽管数值天气预报方法占据主导地位，但近年来基于扩散模型等深度学习方法在集合天气预报中展现出潜力。然而，这类模型通常为自回归结构，计算成本高，而气候科学中的数据常受限于数量少、获取成本高或难以处理。本文研究了精心筛选的数据对自回归扩散模型的影响，评估多种数据采样策略，发现简单的时间分层采样在性能上可媲美甚至超越全数据训练。值得注意的是，该方法仅使用20%训练数据，在某些指标上表现更优，其余指标仅略有下降。结果证明了数据高效扩散训练的可行性，尤其适用于天气预报，推动未来基于模型感知或自适应采样的研究。
原文摘要 · Abstract (English)
Although numerical weather forecasting methods have dominated the field, recent advances in deep learning methods, such as diffusion models, have shown promise in ensemble weather forecasting. However, such models are typically autoregressive and are thus computationally expensive. This is a challenge in climate science, where data can be limited, costly, or difficult to work with. In this work, we explore the impact of curated data selection on these autoregressive diffusion models. We evaluate several data sampling strategies and show that a simple time stratified sampling approach achieves performance similar to or better than full-data training. Notably, it outperforms the full-data model on certain metrics and performs only slightly worse on others while using only 20% of the training data. Our results demonstrate the feasibility of data-efficient diffusion training, especially for weather forecasting, and motivates future work on adaptive or model-aware sampling methods that go beyond random or purely temporal sampling.
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