基于扩散模型的千米级概率临近降水预报精细化方法
Diffusion-Based Refinement for Kilometer-Scale Probabilistic Precipitation Nowcasting
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中文总结 AI 辅助
本文提出exPreCast-ENS扩散框架，将exPreCast确定性雷达预报转为1km概率集成预报，在朝鲜半岛及法国数据集上提升强降雨预报精度，单GPU生成1h预报仅需3.4秒。
中文摘要 AI 辅助
局地极端降水是引发城市山洪和山体滑坡的主要诱因，但制作兼具精细空间细节与概率不确定性的临近预报仍具挑战性。本文提出exPreCast-ENS，一种条件残差扩散框架，将确定性4公里分辨率雷达临近预报模型exPreCast转换为1公里分辨率概率集成预报模型，同时修正系统性预报误差。该框架以预报结果和前期雷达观测为条件，使集成均值修正基准模型而非对其进行扰动，集成成员则代表未解析的精细尺度变异性。在朝鲜半岛，预报技能随集成规模增大而提升；在2023年两次高影响事件中，30成员集成模型可恢复exPreCast遗漏的38%-47%的强降雨像素，同时保留约95%的正确检测结果，且仅在不到1%的正确未降雨像素上发出警报。该方法在单GPU上生成1小时预报仅需3.4秒，在法国区域MeteoNet雷达数据集上也取得了一致的性能提升。
英文摘要
Localized extreme precipitation is a major trigger of urban flash floods and landslides, yet producing nowcasts that combine fine spatial detail with probabilistic uncertainty remains challenging. Here we introduce exPreCast-ENS, a conditional residual diffusion framework that transforms the deterministic 4 km radar nowcaster exPreCast into a 1 km probabilistic ensemble while correcting systematic forecast errors. Conditioning on both the forecast and preceding radar observations lets the ensemble-mean correct the baseline rather than perturb it, while members represent unresolved fine-scale variability. Over the Korean Peninsula, skill improves with ensemble size. In two high-impact events in 2023, a 30-member ensemble recovers 38-47% of heavy-rain pixels missed by exPreCast while retaining approximately 95% of its correct detections and alarming on under 1% of the pixels it correctly left clear. The method generates a 1-h forecast in 3.4 s on a single GPU and yields consistent improvements on the French regional MeteoNet radar dataset.
发表机构
- Seoul National University（首尔大学）
- Research Institute of Mathematics, Seoul National University（首尔大学数学研究所）
机构由 AI 辅助整理，请以论文原文为准。
