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

*2026-08-01 — paper*

Authors: Pedro Sousa, Will Tebbutt, Sadiq Jaffer, Robin Young, Anil Madhavapeddy, Richard E. Turner


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 2m temperature and
10m 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 2m temperature and 6.2% for 10m 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.


DOI: 10.48550/arXiv.2608.12271
Classification: preprint
Venue: arXiv
URL: http://arxiv.org/abs/2608.12271

## Related

- [Improving local weather forecasts using Tessera embeddings](https://anil.recoil.org/notes/weather-downscaling-tessera) (note, 2026-08-13)

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Canonical: https://anil.recoil.org/papers/2026-weather-downscaling
Type: paper
Tags: preprint