基于恰当评分规则的扩散概率天气预报

中文摘要 AI 辅助

近期概率天气预报模型使用连续排序概率评分（CRPS）训练随机预测器，以在单次前向传播中生成每个集合成员。这些模型仅从预报上下文学习预测分布，在预报时效较长、不确定性较高时变得困难。为更有效地学习预测分布，我们引入了辅助条件去噪任务，这些任务从上下文及其损坏版本预测相同的未来状态，从而提供可减少预测模糊性的部分未来信息。基于分布扩散模型，我们通过最小化跨噪声水平的恰当评分规则，使用单一随机预测器学习这些任务的条件分布。在推理时，预测器仍可在完全损坏的端点处通过单次前向传播生成每个集合成员。标准CRPS训练可视为我们公式中仅端点情况的特例，因此我们的框架仅通过额外的条件输入即可扩展现有的基于CRPS的预测器。受控实验表明，辅助任务在不同架构上改善了一步预报，且在更长预报时效下增益更大。这些增益扩展到高维全球天气预报，无论是从头训练还是微调，同时改善了校准性，并可能在分布偏移下提升泛化能力。

英文摘要

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.

发表机构

• KAIST（韩国科学技术院）
• Kookmin University（国民大学）
• New York University（纽约大学）

机构由 AI 辅助整理，请以论文原文为准。