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LESO:十年卫星衍生洲际逐小时地面臭氧浓度数据集。

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

机构信息

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.

出版信息

Sci Data. 2023 Oct 25;10(1):741. doi: 10.1038/s41597-023-02656-4.

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.

摘要

本研究提出了一个新的地表臭氧(O)集合,由学习地表臭氧(LESO)框架生成。本研究的目的是调查地表 O 的时空变化。LESO 集合以 0.1°×0.1°的精细空间分辨率提供了独特且准确的每小时(每日/每月/每年根据需要)中国、欧洲和美国的 O 地表浓度,时间跨度为 10 年(2012-2021 年)。LESO 集合通过建立地表 O 与卫星衍生的 O 总量柱之间的关系,并结合高分辨率气象再分析数据来生成。这一突破克服了从卫星信号中反演低层大气中 O 的挑战。全面验证表明,LESO 数据集解释了 O 每小时变化的约 80%,均方根误差为 19.63μg/m。数据集令人信服地捕捉到了日变化、周末效应、季节性和年际变化,这对于与大气和气候科学相关的研究和应用非常有价值。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/aaa0/10600137/651b953b3adc/41597_2023_2656_Fig1_HTML.jpg

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