Accelerating Ground‐Based Aerosol Retrieval From Shortwave Radiation Using Tabular Foundation Models

Abstract Retrieving aerosol optical properties from broadband surface solar radiation measurements presents a challenging inverse problem that has yet to be fully explored. This study presents a novel framework using optimal estimation (OE) to retrieve aerosol optical depth (AOD) and Ångström exponent, which subsequently serve as regression targets for a tabular foundation model—TabPFN. Leveraging its in‐context learning capability, TabPFN is primed using only 500 OE‐retrieved samples, enabling the transformation of computationally intensive radiative‐transfer inversions into an instantaneous inference process. Results from independent test samples demonstrate exceptional performance, with TabPFN achieving a 66.88% reduction in the fractional gross error of AOD relative to the MERRA‐2 prior. This approach provides a scalable, high‐precision solution for high‐frequency aerosol monitoring using existing pyranometer networks.