LESO: A ten-year ensemble of satellite-derived intercontinental hourly surface ozone concentrations
dc.contributor.author | Zhu, S | |
dc.contributor.author | Xu, J | |
dc.contributor.author | Zeng, J | |
dc.contributor.author | Yu, C | |
dc.contributor.author | Wang, Y | |
dc.contributor.author | Wang, H | |
dc.contributor.author | Shi, J | |
dc.date.accessioned | 2023-10-26T12:27:07Z | |
dc.date.issued | 2023-10-25 | |
dc.date.updated | 2023-10-26T11:25:04Z | |
dc.description.abstract | This study presents a novel ensemble of surface ozone (O3) generated by the LEarning Surface Ozone (LESO) framework. The aim of this study is to investigate the spatial and temporal variation of surface O3. The LESO ensemble provides unique and accurate hourly (daily/monthly/yearly as needed) O3 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 O3 and satellite-derived O3 total columns together with high-resolution meteorological reanalysis data. This breakthrough overcomes the challenge of retrieving O3 in the lower atmosphere from satellite signals. A comprehensive validation indicated that the LESO datasets explained approximately 80% of the hourly variability of O3, with a root mean squared error of 19.63 μg/m3. 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. | en_GB |
dc.description.sponsorship | Open Research Fund of the Key Laboratory of Meteorology and Ecological Environment of Hebei Province | en_GB |
dc.description.sponsorship | National Natural Science Foundation of China | en_GB |
dc.description.sponsorship | Open Fund of Innovation Center for FengYun Meteorological Satellite (FYSIC) | en_GB |
dc.description.sponsorship | FengYun Application Pioneering Project | en_GB |
dc.description.sponsorship | Chinese Academy of Sciences (CAS) Pioneering Initiative Talents Program | en_GB |
dc.identifier.citation | Vol. 10, article 741 | en_GB |
dc.identifier.doi | https://doi.org/10.1038/s41597-023-02656-4 | |
dc.identifier.grantnumber | Z202201H | en_GB |
dc.identifier.grantnumber | 42375142 | en_GB |
dc.identifier.grantnumber | FY-APP-ZX-2022.0214 | en_GB |
dc.identifier.grantnumber | E1RC2WB2 | en_GB |
dc.identifier.uri | http://hdl.handle.net/10871/134332 | |
dc.language.iso | en | en_GB |
dc.publisher | Nature Research | en_GB |
dc.relation.url | https://github.com/soonyenju/LESO | en_GB |
dc.rights | © The Author(s) 2023. 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/. | en_GB |
dc.title | LESO: A ten-year ensemble of satellite-derived intercontinental hourly surface ozone concentrations | en_GB |
dc.type | Article | en_GB |
dc.date.available | 2023-10-26T12:27:07Z | |
exeter.article-number | 741 | |
dc.description | This is the final version. Available on open access from Nature Research via the DOI in this record | en_GB |
dc.description | Code availability: The scripts for processing and reading the LESO datasets are accessible on Github (https://github.com/soonyenju/LESO) under the MIT license. The tools and libraries, including Python v3.9, Numpy v1.20.3, Xarray v0.19.0, Pandas v1.3.3, Deep Forest v2021.2.1 (DF21), scigeo v0.0.13, and sciml v0.0.5, were used to build the LESO framework for generating datasets of surface O3 concentrations. The validation of LESO datasets was processed using scitbx v0.0.42 and scikit-learn v0.24.2. | en_GB |
dc.identifier.eissn | 2052-4463 | |
dc.identifier.journal | Scientific Data | en_GB |
dc.relation.ispartof | Scientific Data, 10(1) | |
dc.rights.uri | https://creativecommons.org/licenses/by/4.0/ | en_GB |
dcterms.dateAccepted | 2023-10-17 | |
rioxxterms.version | VoR | en_GB |
rioxxterms.licenseref.startdate | 2023-10-25 | |
rioxxterms.type | Journal Article/Review | en_GB |
refterms.dateFCD | 2023-10-26T12:21:44Z | |
refterms.versionFCD | VoR | |
refterms.dateFOA | 2023-10-26T12:27:11Z | |
refterms.panel | C | en_GB |
refterms.dateFirstOnline | 2023-10-25 |
Files in this item
This item appears in the following Collection(s)
Except where otherwise noted, this item's licence is described as © The Author(s) 2023. 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/.