发表机构
University of Cambridge（剑桥大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究提出用TESSERA嵌入增强卷积条件神经过程，用于概率性天气降尺度，在五个气候区提升了2米温度和10米风速的降尺度技能，且对不同变量和新站点均有效。
AI 中文摘要
全球天气再分析和预报在粗网格上解析大气状态的演变，但特定站点的应用需要任意位置的预测，近地面条件还取决于未解析的地形和陆表属性。现有概率降尺度器使用手工制作的地形描述符来解决这一差距，而我们则探究地球观测基础模型是否能为概率性天气降尺度提供可迁移的亚网格地表表示。我们对卷积条件神经过程进行了增强，该过程用于对约25公里分辨率的粗网格ERA5再分析场进行降尺度，增强方式是添加一个学习到的局地地表描述符，该描述符通过压缩10米分辨率的TESSERA嵌入块获得。尽管这些嵌入总结了年时间尺度的地表条件，但它们通过编码持久的地表属性来捕获某一位置与粗网格大气状态的偏差，从而改进了瞬时2米温度和10米风速的降尺度效果。在五个气候多样的区域，该嵌入在空间和时间上留出的站点上提高了点预测和概率技能，总体上使2米温度的CRPS技能提升了11.5%，10米风速的CRPS技能提升了6.2%。我们进一步分析了其贡献因变量的差异，发现地形解释了温度的更多亚网格结构，而TESSERA为风速提供了额外的地表信息。当粗输入从ERA5变为Aurora AI预报模型的预报，以及在没有区域历史的新部署站点进行预测时，这些改进仍然存在。据我们所知，这是首个证据表明，长时间尺度的地球观测嵌入可支持短时间尺度的天气降尺度，其中亚网格偏差由持久地表属性系统地构成。
英文摘要
Global weather reanalyses and forecasts resolve the evolving atmospheric state on coarse grids, but site-specific applications require predictions at arbitrary locations where near-surface conditions also depend on unresolved terrain and land-surface properties. Existing probabilistic downscalers address this gap using hand-crafted topographic and surface descriptors. We ask instead whether Earth observation foundation models can provide transferable subgrid surface representations for probabilistic weather downscaling. We augment a convolutional conditional neural process (ConvCNP) that downscales coarse ERA5 reanalysis fields at ~25 km resolution with a learned local surface descriptor, obtained by compressing a patch of TESSERA embeddings at 10 m resolution. Although these embeddings summarize annual surface conditions, they improve downscaling by encoding persistent surface properties that capture a location's departure from the coarse-grid atmospheric state. Across five climatically diverse regions, the embedding improves point and probabilistic skill at stations held out in both space and time, overall improving CRPS skill by 11.5% for 2 m temperature and 6.2% for 10 m wind speed relative to a topography-only ConvCNP baseline. A hand-crafted descriptor incorporating richer surface information than topography alone captures comparable persistent subgrid signal but yields far smaller predictive gains than the learned embedding representation. These improvements persist when forecasts from the Aurora AI model replace ERA5 reanalysis fields and when predicting at newly deployed weather station networks. To our knowledge, this is the first evidence that long-timescale Earth observation embeddings can support short-timescale weather downscaling where subgrid departures are systematically structured by persistent surface properties.
Comments46 pages, 13 figures, 9 tables
