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

Related authors

Solar Backscatter Ultraviolet (BUV) Retrievals of Mid-Stratospheric Aerosols from the 2022 Hunga Eruption
Robert James Duncan Spurr, Matt Christi, Nickolay Anatoly Krotkov, Won-Ei Choi, Simon Carn, Can Li, Natalya Kramarova, David Haffner, Eun-Su Yang, Nick Gorkavyi, Alexander Vasilkov, Krzysztof Wargan, Omar Torres, Diego Loyola, Serena Di Pede, Joris Pepijn Veefkind, and Pawan Kumar Bhartia
EGUsphere, https://doi.org/10.5194/egusphere-2025-2938,https://doi.org/10.5194/egusphere-2025-2938, 2025
This preprint is open for discussion and under review for Atmospheric Measurement Techniques (AMT).
Short summary
Estimating surface sulfur dioxide concentrations from satellite data: Using chemical transport models vs. machine learning
Zachary Watson, Can Li, Fei Liu, Sean W. Freeman, Huanxin Zhang, Jun Wang, and Shan-Hu Lee
EGUsphere, https://doi.org/10.5194/egusphere-2025-1735,https://doi.org/10.5194/egusphere-2025-1735, 2025
Short summary
Opinion: Beyond global means – novel space-based approaches to indirectly constrain the concentrations of and trends and variations in the tropospheric hydroxyl radical (OH)
Bryan N. Duncan, Daniel C. Anderson, Arlene M. Fiore, Joanna Joiner, Nickolay A. Krotkov, Can Li, Dylan B. Millet, Julie M. Nicely, Luke D. Oman, Jason M. St. Clair, Joshua D. Shutter, Amir H. Souri, Sarah A. Strode, Brad Weir, Glenn M. Wolfe, Helen M. Worden, and Qindan Zhu
Atmos. Chem. Phys., 24, 13001–13023, https://doi.org/10.5194/acp-24-13001-2024,https://doi.org/10.5194/acp-24-13001-2024, 2024
Short summary
Version 1 NOAA-20/OMPS Nadir Mapper total column SO2 product: continuation of NASA long-term global data record
Can Li, Nickolay A. Krotkov, Joanna Joiner, Vitali Fioletov, Chris McLinden, Debora Griffin, Peter J. T. Leonard, Simon Carn, Colin Seftor, and Alexander Vasilkov
Earth Syst. Sci. Data, 16, 4291–4309, https://doi.org/10.5194/essd-16-4291-2024,https://doi.org/10.5194/essd-16-4291-2024, 2024
Short summary
Estimation of anthropogenic and volcanic SO2 emissions from satellite data in the presence of snow/ice on the ground
Vitali E. Fioletov, Chris A. McLinden, Debora Griffin, Nickolay A. Krotkov, Can Li, Joanna Joiner, Nicolas Theys, and Simon Carn
Atmos. Meas. Tech., 16, 5575–5592, https://doi.org/10.5194/amt-16-5575-2023,https://doi.org/10.5194/amt-16-5575-2023, 2023
Short summary

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. 
Download
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.
Share