Articles | Volume 15, issue 18
https://doi.org/10.5194/amt-15-5497-2022
https://doi.org/10.5194/amt-15-5497-2022
Research article
 | 
27 Sep 2022
Research article |  | 27 Sep 2022

A new machine-learning-based analysis for improving satellite-retrieved atmospheric composition data: OMI SO2 as an example

Can Li, Joanna Joiner, Fei Liu, Nickolay A. Krotkov, Vitali Fioletov, and Chris McLinden

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Cited articles

Bhartia, P. K.: OMI/Aura Ozone (O3) Total Column 1-Orbit L2 Swath 13x24 km V003, Goddard Earth Sciences Data and Information Services Center (GES DISC) [data set], Greenbelt, MD, USA, https://doi.org/10.5067/Aura/OMI/DATA2024, 2005. 
Castellanos, P. and da Silva, A.: A neural network correction to the scalar approximation in radiative transfer, J. Atmos. Ocean. Tech., 36, 819–832, https://doi.org/10.1175/JTECH-D-18-0003.1, 2019. 
Chan, K. L., Khorsandi, E., Liu, S., Baier, F., and Valks, P.: Estimation of surface NO2 concentrations over Germany from TROPOMI satellite observations using a machine learning method, Remote Sens., 13, 969, https://doi.org/10.3390/rs13050969, 2021. 
Chimot, J., Veefkind, J. P., Vlemmix, T., de Haan, J. F., Amiridis, V., Proestakis, E., Marinou, E., and Levelt, P. F.: An exploratory study on the aerosol height retrieval from OMI measurements of the 477 nm O2 – O2 spectral band using a neural network approach, Atmos. Meas. Tech., 10, 783–809, https://doi.org/10.5194/amt-10-783-2017, 2017. 
De Santis, D., Petracca, I., Corradini, S., Guerrieri, L., Picchiani, M., Merucci, L., Stelitano, D., Del Frate, F., Prata, F., and Schiavon, G.: Volcanic SO2 near-real time retrieval using TROPOMI data and neural networks: The December 2018 Etna test case, 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS, Brussels, Belgium, 12–16 July 2021, 8480–8483, https://doi.org/10.1109/IGARSS47720.2021.9554915, 2021. 
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Short summary
Satellite observations provide information on the sources of SO2, an important pollutant that affects both air quality and climate. However, these observations suffer from relatively poor data quality due to weak signals of SO2. Here, we use a machine learning technique to analyze satellite SO2 observations in order to reduce the noise and artifacts over relatively clean areas while keeping the signals near pollution sources. This leads to significant improvement in satellite SO2 data.