Abstract
This study presents a novel ensemble of surface ozone (O(3)) generated by the LEarning Surface Ozone (LESO) framework. The aim of this study is to investigate the spatial and temporal variation of surface O(3). The LESO ensemble provides unique and accurate hourly (daily/monthly/yearly as needed) O(3) surface concentrations on a fine spatial resolution of 0.1◦ × 0.1◦ across China, Europe, and the United States over a period of 10 years (2012-2021). The LESO ensemble was generated by establishing the relationship between surface O(3) and satellite-derived O(3) total columns together with high-resolution meteorological reanalysis data. This breakthrough overcomes the challenge of retrieving O(3) in the lower atmosphere from satellite signals. A comprehensive validation indicated that the LESO datasets explained approximately 80% of the hourly variability of O(3), with a root mean squared error of 19.63 μg/m(3). The datasets convincingly captured the diurnal cycles, weekend effects, seasonality, and interannual variability, which can be valuable for research and applications related to atmospheric and climate sciences.