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

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).