用生成模型快速生成高分辨率降雪分布，精度远超传统方法。
Conditional Flow Matching for Probabilistic Downscaling of Maximum 3-day Snowfall in Alaska
- 基于流匹配的条件生成模型，将粗分辨率气候数据与地形信息转为精细降水场
- 谱保真度提升87.8%，连续概率评分显著降低，50成员集合生成仅需数秒
- 结果空间结构合理，反映地形驱动的物理不确定性，适合气候风险评估
复杂地形中的降水受几公里尺度的地形过程控制，但气候模型通常以50–100公里分辨率运行，无法体现这些细节。利用高分辨率区域模型如WRF进行动力降尺度虽可解析过程，但每种情景需数月计算时间，难以支持不确定性量化所需的大量集合。本文提出WxFlow，一种基于流匹配的条件生成模型，学习从粗分辨率气候输出和高分辨率地形映射到校准的细粒度降水场概率集合。应用于阿拉斯加东南部4公里分辨率的WRF模拟最大3日积雪量时，WxFlow在谱保真度上实现87.8%的提升，连续概率评分显著低于传统的层结修正双线性插值法，且在笔记本电脑上可在数秒内生成50成员集合。集合发散具有空间一致性，由地形决定，体现合理的物理不确定性结构。所有代码已公开于https://github.com/glide-ism/wrf-flow。
原文摘要 · Abstract (English)
Precipitation in complex terrain is governed by orographic processes operating at scales of a few kilometers, yet climate models typically run at resolutions of 50--100~km where this topographic detail is absent. Dynamical downscaling with high-resolution regional models such as WRF can resolve these processes, but the computational cost -- months of wall-clock time per scenario -- precludes the large ensembles needed for uncertainty quantification. We present WxFlow, a conditional generative model based on flow matching that learns to map coarse-resolution climate model output and high-resolution topography to calibrated probabilistic ensembles of fine-scale precipitation fields. Applied to 4~km WRF simulations of maximum 3-day snowfall over southeast Alaska, WxFlow achieves 87.8\% improvement in spectral fidelity and dramatically lower Continuous Ranked Probability Scores relative to conventional lapse-rate-corrected bicubic downscaling, while generating 50-member ensembles in seconds on a laptop. Ensemble spread is spatially coherent and governed by topography, reflecting physically plausible uncertainty structure. All code is available at https://github.com/glide-ism/wrf-flow.
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