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dc.contributor.authorZhu, S
dc.contributor.authorXu, J
dc.contributor.authorZeng, J
dc.contributor.authorYu, C
dc.contributor.authorWang, Y
dc.contributor.authorWang, H
dc.contributor.authorShi, J
dc.date.accessioned2023-10-26T12:27:07Z
dc.date.issued2023-10-25
dc.date.updated2023-10-26T11:25:04Z
dc.description.abstractThis 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.sponsorshipOpen Research Fund of the Key Laboratory of Meteorology and Ecological Environment of Hebei Provinceen_GB
dc.description.sponsorshipNational Natural Science Foundation of Chinaen_GB
dc.description.sponsorshipOpen Fund of Innovation Center for FengYun Meteorological Satellite (FYSIC)en_GB
dc.description.sponsorshipFengYun Application Pioneering Projecten_GB
dc.description.sponsorshipChinese Academy of Sciences (CAS) Pioneering Initiative Talents Programen_GB
dc.identifier.citationVol. 10, article 741en_GB
dc.identifier.doihttps://doi.org/10.1038/s41597-023-02656-4
dc.identifier.grantnumberZ202201Hen_GB
dc.identifier.grantnumber42375142en_GB
dc.identifier.grantnumberFY-APP-ZX-2022.0214en_GB
dc.identifier.grantnumberE1RC2WB2en_GB
dc.identifier.urihttp://hdl.handle.net/10871/134332
dc.language.isoenen_GB
dc.publisherNature Researchen_GB
dc.relation.urlhttps://github.com/soonyenju/LESOen_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.titleLESO: A ten-year ensemble of satellite-derived intercontinental hourly surface ozone concentrationsen_GB
dc.typeArticleen_GB
dc.date.available2023-10-26T12:27:07Z
exeter.article-number741
dc.descriptionThis is the final version. Available on open access from Nature Research via the DOI in this recorden_GB
dc.descriptionCode 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.eissn2052-4463
dc.identifier.journalScientific Dataen_GB
dc.relation.ispartofScientific Data, 10(1)
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/en_GB
dcterms.dateAccepted2023-10-17
rioxxterms.versionVoRen_GB
rioxxterms.licenseref.startdate2023-10-25
rioxxterms.typeJournal Article/Reviewen_GB
refterms.dateFCD2023-10-26T12:21:44Z
refterms.versionFCDVoR
refterms.dateFOA2023-10-26T12:27:11Z
refterms.panelCen_GB
refterms.dateFirstOnline2023-10-25


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© The Author(s) 2023. Open Access. This article is licensed under a Creative Commons Attribution 4.0 International 
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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/.