Articles | Volume 17, issue 17
https://doi.org/10.5194/amt-17-5147-2024
https://doi.org/10.5194/amt-17-5147-2024
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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Interpreting Geostationary Environment Monitoring Spectrometer (GEMS) geostationary satellite observations of the diurnal variation in nitrogen dioxide (NO2) over East Asia
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Cited articles

Balasus, N., Jacob, D. J., Lorente, A., Maasakkers, J. D., Parker, R. J., Boesch, H., Chen, Z., Kelp, M. M., Nesser, H., and Varon, D. J.: A blended TROPOMI+GOSAT satellite data product for atmospheric methane using machine learning to correct retrieval biases, Atmos. Meas. Tech., 16, 3787–3807, https://doi.org/10.5194/amt-16-3787-2023, 2023. 
Beirle, S. and Wagner, T.: A new method for estimating megacity NOx emissions and lifetimes from satellite observations, Atmos. Meas. Tech., 17, 3439–3453, https://doi.org/10.5194/amt-17-3439-2024, 2024. 
Belitz, K. and Stackelberg, P. E.: Evaluation of six methods for correcting bias in estimates from ensemble tree machine learning regression models, Environ. Modell. Softw., 139, 105006, https://doi.org/10.1016/j.envsoft.2021.105006, 2021. 
Bey, I., Jacob, D. J., Yantosca, R. M., Logan, J. A., Field, B. D., Fiore, A. M., Li, Q., Liu, H. Y., Mickley, L. J., and Schultz, M. G.: Global modeling of tropospheric chemistry with assimilated meteorology: Model description and evaluation, J. Geophys. Res.-Atmos., 106, 23073–23095, https://doi.org/10.1029/2001JD000807, 2001. 
Boersma, K. F., Jacob, D. J., Eskes, H. J., Pinder, R. W., Wang, J., and van der A, R. J.: Intercomparison of SCIAMACHY and OMI tropospheric NO2 columns: Observing the diurnal evolution of chemistry and emissions from space, J. Geophys. Res.-Atmos., 113, D16S26, https://doi.org/10.1029/2007JD008816, 2008. 
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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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