A bias-corrected GEMS geostationary satellite product for nitrogen dioxide using machine learning to enforce consistency with the TROPOMI satellite instrument

Name: A bias-corrected GEMS geostationary satellite product for nitrogen dioxide using machine learning to enforce consistency with the TROPOMI satellite instrument
Creator: Harvard University
Published: 2024-01-01T00:00:00+08:00

Harvard Dataverse2024-09-25 更新2026-04-09 收录

大气污染遥感监测

卫星数据校正

数据链接：

https://dataverse.harvard.edu/citation?persistentId=doi:10.7910/DVN/ZQQJRO 数据链接链接失效反馈

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资源简介：

We present an improved NO2 vertical column density (VCD) product from the Geostationary Environment Monitoring Spectrometer (GEMS) by calibrating it to TROPOspheric Monitoring Instrument (TROPOMI) with a machine learning (ML) algorithm. A first step was to reprocess both GEMS and TROPOMI datasets to adopt common NO2 vertical profiles and resulting air mass factors (AMFs) from the GEOS-Chem model. The second step was to correct the residual difference, Δ(GEMS-TROPOMI), with the ML model. The corrected GEMS product preserves the data density of GEMS, providing hourly daytime data over East/South Asia and neighboring oceans, and is consistent with TROPOMI. It is available for the duration of the GEMS record (November 2020 to present).

提供机构：

Harvard University

创建时间：

2024-01-01