用扩散模型将天气预报从低分辨率转为高分辨率概率预测，无需调参。
Universal Diffusion-Based Probabilistic Downscaling
- 用一个通用扩散模型，直接从低分辨率预报生成高分辨率概率结果。
- 90小时内对观测站数据的评估显示，概率技能（CRPS）显著提升。
- 适用于不同气象模型，适合想提升预报精度和不确定性的从业者。
我们提出一种通用的基于扩散的降尺度框架，将确定性低分辨率天气预报转化为无需任何模型特化微调的概率高分辨率预测。单一条件扩散模型在粗分辨率输入（约25公里）与高分辨率区域再分析目标（约5公里）配对数据上训练，并以完全零样本方式应用于来自异构上游气象模型的确定性预报。聚焦近地面变量，我们在长达90小时的预报时效内，基于独立地面站点观测评估概率预报性能。在多种人工智能与数值天气预报系统中，降尺度后集合平均预报始终优于各模型自身原始确定性预报，且概率技能（以CRPS衡量）提升显著。结果表明，基于扩散的降尺度可为业务天气预报流程提供一种可扩展、模型无关的概率接口，有效提升空间分辨率与不确定性表征能力。
原文摘要 · Abstract (English)
We introduce a universal diffusion-based downscaling framework that lifts deterministic low-resolution weather forecasts into probabilistic high-resolution predictions without any model-specific fine-tuning. A single conditional diffusion model is trained on paired coarse-resolution inputs (~25 km resolution) and high-resolution regional reanalysis targets (~5 km resolution), and is applied in a fully zero-shot manner to deterministic forecasts from heterogeneous upstream weather models. Focusing on near-surface variables, we evaluate probabilistic forecasts against independent in situ station observations over lead times up to 90 h. Across a diverse set of AI-based and numerical weather prediction (NWP) systems, the ensemble mean of the downscaled forecasts consistently improves upon each model's own raw deterministic forecast, and substantially larger gains are observed in probabilistic skill as measured by CRPS. These results demonstrate that diffusion-based downscaling provides a scalable, model-agnostic probabilistic interface for enhancing spatial resolution and uncertainty representation in operational weather forecasting pipelines.
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