用地球观测嵌入表示地表特征，提升气象精准预测。
Earth observation embeddings are effective sub-grid descriptors for probabilistic weather downscaling
- 用TESSERA地表嵌入压缩为局部描述符，替代传统地形特征。
- 2米气温和10米风速的预报精度分别提升11.5%和6.2%。
- 适用于新站点和不同输入数据，适合气象精细化建模者。
全球气象再分析和预报通常在粗网格（约25公里）上进行，但具体地点的应用需要任意位置的近地面条件预测，而这些条件还受未解析的地貌和地表特性影响。现有概率性降尺度方法使用手工设计的地形描述符来填补这一空白。本文探讨是否可通过地球观测基础模型提供可迁移的亚网格地表表征用于概率性天气降尺度。我们改进了一个卷积条件神经过程模型，将其与一个由10米分辨率TESSERA嵌入压缩得到的本地表面描述符结合。尽管这些嵌入反映的是年尺度的地表状况，但它们通过编码持续的地表属性，捕捉了位置相对于粗网格大气状态的偏差，从而提升了瞬时2米气温和10米风速的降尺度效果。在五个气候多样的区域中，该方法在空间和时间上均保持外推站点的点预测与概率预测性能，总体使2米气温的CRPS技能提升11.5%，10米风速提升6.2%。分析表明，地形对气温的亚网格结构解释更多，而TESSERA则为风速提供了额外的地表信息。当输入从ERA5改为Aurora AI预测模型，或预测新部署站点且无区域历史时，提升依然有效。据我们所知，这是首个证明长期地球观测嵌入能支持短期天气降尺度的证据，其中亚网格差异系统性地由持久地表属性决定。
原文摘要 · Abstract (English)
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.
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