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Publications

Dataset Journal Article Epublish 10 citations

PubMed: 37880252 DOI: 10.1038/s41597-023-02656-4

LESO: A ten-year ensemble of satellite-derived intercontinental hourly surface ozone concentrations.

Sci Data 10(1):741

Authors: Songyan Zhu, Jian Xu, Jingya Zeng, Chao Yu, Yapeng Wang, Haolin Wang, Jiancheng Shi

Affiliations

  • National Space Science Center, Chinese Academy of Sciences, Beijing, 100190, China.
  • School of GeoSciences, National Center for Earth Observations, University of Edinburgh, Edinburgh, EH9 3FF, UK.
  • National Space Science Center, Chinese Academy of Sciences, Beijing, 100190, China.
  • Department of Economics, Business School, University of Exeter, Exeter, EX4 4PU, UK.
  • Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, 100094, China.
  • Key Laboratory of Radiometric Calibration and Validation for Environmental Satellites, National Satellite Meteorological Center, China Meteorological Administration, Beijing, 100081, China.
  • School of GeoSciences, National Center for Earth Observations, University of Edinburgh, Edinburgh, EH9 3FF, UK.
  • National Space Science Center, Chinese Academy of Sciences, Beijing, 100190, China.

Abstract

This study presents a novel ensemble of surface ozone (O) generated by the LEarning Surface Ozone (LESO) framework. The aim of this study is to investigate the spatial and temporal variation of surface O. The LESO ensemble provides unique and accurate hourly (daily/monthly/yearly as needed) O 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 and satellite-derived O total columns together with high-resolution meteorological reanalysis data. This breakthrough overcomes the challenge of retrieving O in the lower atmosphere from satellite signals. A comprehensive validation indicated that the LESO datasets explained approximately 80% of the hourly variability of O, with a root mean squared error of 19.63 μg/m. 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.

MeSH

Information Storage And Retrieval