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LESO: A ten-year ensemble of satellite-derived intercontinental hourly surface ozone concentrations
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(...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10600137/ https://www.ncbi.nlm.nih.gov/pubmed/37880252 http://dx.doi.org/10.1038/s41597-023-02656-4 |
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author | Zhu, Songyan Xu, Jian Zeng, Jingya Yu, Chao Wang, Yapeng Wang, Haolin Shi, Jiancheng |
author_facet | Zhu, Songyan Xu, Jian Zeng, Jingya Yu, Chao Wang, Yapeng Wang, Haolin Shi, Jiancheng |
author_sort | Zhu, Songyan |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-10600137 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-106001372023-10-27 LESO: A ten-year ensemble of satellite-derived intercontinental hourly surface ozone concentrations Zhu, Songyan Xu, Jian Zeng, Jingya Yu, Chao Wang, Yapeng Wang, Haolin Shi, Jiancheng Sci Data Data Descriptor 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. Nature Publishing Group UK 2023-10-25 /pmc/articles/PMC10600137/ /pubmed/37880252 http://dx.doi.org/10.1038/s41597-023-02656-4 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Data Descriptor Zhu, Songyan Xu, Jian Zeng, Jingya Yu, Chao Wang, Yapeng Wang, Haolin Shi, Jiancheng LESO: A ten-year ensemble of satellite-derived intercontinental hourly surface ozone concentrations |
title | LESO: A ten-year ensemble of satellite-derived intercontinental hourly surface ozone concentrations |
title_full | LESO: A ten-year ensemble of satellite-derived intercontinental hourly surface ozone concentrations |
title_fullStr | LESO: A ten-year ensemble of satellite-derived intercontinental hourly surface ozone concentrations |
title_full_unstemmed | LESO: A ten-year ensemble of satellite-derived intercontinental hourly surface ozone concentrations |
title_short | LESO: A ten-year ensemble of satellite-derived intercontinental hourly surface ozone concentrations |
title_sort | leso: a ten-year ensemble of satellite-derived intercontinental hourly surface ozone concentrations |
topic | Data Descriptor |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10600137/ https://www.ncbi.nlm.nih.gov/pubmed/37880252 http://dx.doi.org/10.1038/s41597-023-02656-4 |
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