Proper scoring rules improve weather forecasts under uncertainty
Proper Scoring Rule-based Diffusion for Probabilistic Weather Forecasting
Machine Learning
Summary
Making accurate weather predictions is hard, especially for times far in the future when there's a lot of uncertainty. The authors present a new way to help weather models learn by giving them extra clues about what the future might look like, even if those clues are a bit noisy. This helps the model know what to expect better and reduces confusion when making predictions. Their method builds on existing approaches and can generate multiple possible weather outcomes quickly, improving forecast accuracy and reliability.
What this means in practice
- •For weather forecast engineers: Improve ensemble weather forecast accuracy and reliability by integrating auxiliary conditional denoising tasks into existing probabilistic prediction models.
- •For climate model developers: Enhance generalization and calibration of high-dimensional earth system models under changing climate conditions using multi-noise-level training methods.
Authors
Joonhyeong Park, Giung Nam, Hyungi Lee, Kyunghyun Cho, Byoungwoo Park, Juho Lee
Abstract
Recent probabilistic weather forecasters train stochastic predictors with the continuous ranked probability score (CRPS) to generate each ensemble member in a single forward pass. These models learn the predictive distribution from the forecast context alone, which becomes difficult at longer forecast horizons where uncertainty is high. To learn the predictive distribution more effectively, we introduce auxiliary conditional denoising tasks that predict the same future state from the context and its corrupted version, which provides partial future information that can reduce prediction ambiguity. Building on distributional diffusion models, we learn the conditional distributions of these tasks with a single stochastic predictor by minimizing a proper scoring rule across noise levels. At inference, the predictor can still generate each ensemble member in a single forward pass at the fully corrupted endpoint. Standard CRPS training is recovered as the endpoint-only special case of our formulation, so our framework extends existing CRPS-based forecasters with only additional conditioning inputs. Controlled experiments show that the auxiliary tasks improve one-step forecasting across architectures, with larger gains at longer forecast horizons. The gains extend to high-dimensional global weather forecasting under both training from scratch and fine-tuning, along with improved calibration and potential benefits for generalization under distribution shift.