Partial recovery of meter-scale surface weather
This work provides a computationally feasible approach to infer meter-resolution weather conditions at a continental scale, improving the accuracy of near-surface weather data for various applications and users who rely on precise local weather information.
This paper demonstrates that meter-scale near-surface weather variability, previously absent from weather analyses, can be statistically recovered. By conditioning coarse atmospheric data with surface measurements and high-resolution Earth observation data, they inferred 10m resolution fields for wind, temperature, and humidity across the contiguous United States, reducing wind error by 29% and temperature/dewpoint error by 6% compared to ERA5.
Near-surface atmospheric conditions can differ sharply over tens to hundreds of meters due to land cover and topography, yet this variability is absent from current weather analyses and forecasts. It is unclear whether such meter-scale variability reflects irreducibly chaotic dynamics or contains a component predictable from surface characteristics and large-scale atmospheric forcing. Here we show that a substantial, physically coherent component of meter-scale near-surface weather is statistically recoverable from existing observations. By conditioning coarse atmospheric state on sparse surface station measurements and high-resolution Earth observation data, we infer spatially continuous fields of near-surface wind, temperature, and humidity at 10 m resolution across the contiguous United States. Relative to ERA5, the inferred fields reduce wind error by 29% and temperature and dewpoint error by 6%, while explaining substantially more spatial variance at fixed time steps. They also exhibit physically interpretable structure, including urban heat islands, evapotranspiration-driven humidity contrasts, and wind speed differences across land cover types. Our findings expand the frontier of weather modeling by demonstrating a computationally feasible approach to continental-scale meter-resolution inference. More broadly, they illustrate how conditioning coarse dynamical models on static fine-scale features can reveal previously unresolved components of the Earth system.