Partial recovery of meter-scale surface weather

TL;DR

This study demonstrates that a significant component of meter-scale near-surface weather variability can be statistically recovered from existing data, improving weather field accuracy at high resolution across the US.

Contribution

It introduces a method to infer high-resolution near-surface weather fields by conditioning coarse models on surface and Earth observation data, revealing physically meaningful structures.

Findings

Reduced wind error by 29% compared to ERA5

Decreased temperature and dewpoint error by 6%

Captured urban heat islands and land cover effects

Abstract

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.