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Earth observation embeddings are effective sub-grid descriptors for probabilistic weather downscaling

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Computer Science > Machine Learning

arXiv:2608.12271 (cs)
[Submitted on 12 Aug 2026]

Title:Earth observation embeddings are effective sub-grid descriptors for probabilistic weather downscaling

Authors:Pedro Sousa (1), Will Tebbutt (2), Sadiq Jaffer (1), Robin Young (1), Anil Madhavapeddy (1), Richard E. Turner (2) ((1) Department of Computer Science, University of Cambridge, (2) Department of Engineering, University of Cambridge)
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Abstract: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.
Comments: 39 pages, 12 figures, 6 tables
Subjects: Machine Learning (cs.LG); Atmospheric and Oceanic Physics (physics.ao-ph)
Cite as: arXiv:2608.12271 [cs.LG]
  (or arXiv:2608.12271v1 [cs.LG] for this version)
  https://doi.org/10.48550/arXiv.2608.12271
arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Pedro Miguel Marques Sousa [view email]
[v1] Wed, 12 Aug 2026 17:10:42 UTC (10,246 KB)
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