Data-Efficient Ensemble Weather Forecasting with Diffusion Models
This work addresses data efficiency in weather forecasting for climate scientists, offering an incremental improvement over existing methods.
The paper tackled the problem of computational expense in autoregressive diffusion models for ensemble weather forecasting by exploring curated data selection strategies, showing that a simple time stratified sampling approach achieves similar or better performance than full-data training while using only 20% of the training data.
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.