Atmospheric Measurement Techniques
Atmospheric Measurement Techniques
AMT
Articles | Volume 17, issue 17
Articles | Volume 17, issue 17
https://doi.org/10.5194/amt-17-5147-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
Special issue:
https://doi.org/10.5194/amt-17-5147-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 17, issue 17
Research article
 | 
05 Sep 2024
Research article |  | 05 Sep 2024

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

Yujin J. Oak, Daniel J. Jacob, Nicholas Balasus, Laura H. Yang, Heesung Chong, Junsung Park, Hanlim Lee, Gitaek T. Lee, Eunjo S. Ha, Rokjin J. Park, Hyeong-Ahn Kwon, and Jhoon Kim

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Short summary
We present an improved NO2 product from GEMS by calibrating it to TROPOMI using machine learning and by reprocessing both satellite products to adopt common NO2 profiles. Our corrected GEMS product combines the high data density of GEMS with the accuracy of TROPOMI, supporting the combined use for analyses of East Asia air quality including emissions and chemistry. This method can be extended to other species and geostationary satellites including TEMPO and Sentinel-4.
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Special issue
GEMS: first year in operation (AMT/ACP inter-journal SI)