这篇论文把卫星嵌入加到天气降尺度里，温度风速预测都更准了，CRPS提升两位数，做天气AI的值得看看。
该研究将TESSERA地球观测基础模型的嵌入引入概率天气降尺度，压缩10米分辨率的嵌入补丁作为局部表面描述符。在25公里ERA5再分析场基础上，该方法使2米温度CRPS技能提升11.5%，10米风速提升6.2%。改善在五个气候区域的时空外站点上均成立，且换用Aurora预报模型作为输入时依然有效。这是首次证明长期尺度地球观测嵌入能支持短期天气降尺度任务。
Earth observation embeddings are effective sub-grid descriptors for probabilistic weather downscaling
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 descriptors. We ask instead whether Earth observation foundation models can provide transferable sub-grid surface representations for probabilistic weather downscaling. We augment a convolutional conditional neural process 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 summarise surface conditions over annual timescales, they improve downscaling of instantaneous 2 m temperature and 10 m wind speed 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. We further analyse how its contribution differs by variable, finding that topography explains more of temperature's sub-grid structure, while TESSERA provides additional surface information for wind speed. These improvements persist when the coarse input is changed from ERA5 to forecasts from the Aurora AI forecasting model, and when predicting at newly deployed stations with no regional history. To our knowledge, this is the first evidence that long-timescale Earth-observation embeddings can support short-timescale weather downscaling where sub-grid departures are systematically structured by persistent surface properties.