Articles | Volume 18, issue 7
https://doi.org/10.5194/amt-18-1689-2025
https://doi.org/10.5194/amt-18-1689-2025
Research article
 | 
16 Apr 2025
Research article |  | 16 Apr 2025

Predictions of failed satellite retrieval of air quality using machine learning

Edward Malina, Jure Brence, Jennifer Adams, Jovan Tanevski, Sašo Džeroski, Valentin Kantchev, and Kevin W. Bowman

Related authors

Joint spectral retrievals of ozone with Suomi NPP CrIS augmented by S5P/TROPOMI
Edward Malina, Kevin W. Bowman, Valentin Kantchev, Le Kuai, Thomas P. Kurosu, Kazuyuki Miyazaki, Vijay Natraj, Gregory B. Osterman, Fabiano Oyafuso, and Matthew D. Thill
Atmos. Meas. Tech., 17, 5341–5371, https://doi.org/10.5194/amt-17-5341-2024,https://doi.org/10.5194/amt-17-5341-2024, 2024
Short summary

Related subject area

Subject: Gases | Technique: Remote Sensing | Topic: Data Processing and Information Retrieval
Assimilation of volcanic sulfur dioxide products from IASI and TROPOMI into the chemical transport model MOCAGE: case study of the 2021 La Soufrière Saint Vincent eruption with the March 2022 version of MOCAGE
Mickaël Bacles, Jonathan Améric, and Vincent Guidard
Atmos. Meas. Tech., 18, 2659–2680, https://doi.org/10.5194/amt-18-2659-2025,https://doi.org/10.5194/amt-18-2659-2025, 2025
Short summary
In-flight estimation of instrument spectral response functions using sparse representations
Jihanne El Haouari, Jean-Michel Gaucel, Christelle Pittet, Jean-Yves Tourneret, and Herwig Wendt
Atmos. Meas. Tech., 18, 2573–2590, https://doi.org/10.5194/amt-18-2573-2025,https://doi.org/10.5194/amt-18-2573-2025, 2025
Short summary
Robustness of atmospheric trace gas retrievals obtained from low-spectral-resolution Fourier transform infrared absorption spectra under variations of interferogram length
Bavo Langerock, Martine De Mazière, Filip Desmet, Pauli Heikkinen, Rigel Kivi, Mahesh Kumar Sha, Corinne Vigouroux, Minqiang Zhou, Gopala Krishna Darbha, and Mohmmed Talib
Atmos. Meas. Tech., 18, 2439–2446, https://doi.org/10.5194/amt-18-2439-2025,https://doi.org/10.5194/amt-18-2439-2025, 2025
Short summary
Retrieval of NO2 profiles from 3 years of Pandora MAX-DOAS measurements in Toronto, Canada
Ramina Alwarda, Kristof Bognar, Xiaoyi Zhao, Vitali Fioletov, Jonathan Davies, Sum Chi Lee, Debora Griffin, Alexandru Lupu, Udo Frieß, Alexander Cede, Yushan Su, and Kimberly Strong
Atmos. Meas. Tech., 18, 2397–2423, https://doi.org/10.5194/amt-18-2397-2025,https://doi.org/10.5194/amt-18-2397-2025, 2025
Short summary
A channel selection methodology for enhancing volcanic SO2 monitoring using FY-3E/HIRAS-II hyperspectral data
Xinyu Li, Lin Zhu, Hongfu Sun, Jun Li, Ximing Lv, Chengli Qi, and Huanhuan Yan
Atmos. Meas. Tech., 18, 2333–2352, https://doi.org/10.5194/amt-18-2333-2025,https://doi.org/10.5194/amt-18-2333-2025, 2025
Short summary

Cited articles

Aumann, H. H., Chahine, M. T., Gautier, C., Goldberg, M. D., Kalnay, E., McMillin, L. M., Revercomb, H., Rosenkranz, P. W., Smith, W. L., Staelin, D. H., Strow, L. L., and Susskind, J.: AIRS/AMSU/HSB on the aqua mission: Design, science objectives, data products, and processing systems, IEEE T. Geosci. Remote Sens., 41, 253–263, https://doi.org/10.1109/TGRS.2002.808356, 2003. a, b
Bowman, K. W., Steck, T., Worden, H. M., Worden, J., Clough, S., and Rodgers, C.: Capturing time and vertical variability of tropospheric ozone: A study using TES nadir retrievals, J. Geophys. Res.-Atmos., 107, ACH21–1–ACH21–11, https://doi.org/10.1029/2002JD002150, 2002. a
Bowman, K. W., Rodgers, C. D., Kulawik, S. S., Worden, J., Sarkissian, E., Osterman, G., Steck, T., Lou, M., Eldering, A., Shephard, M., Worden, H., Lampel, M., Clough, S., Brown, P., Rinsland, C., Gunson, M., and Beer, R.: Tropospheric Emission Spectrometer: Retrieval method and error analysis, IEEE T. Geosci. Remote Sens., 44, 1297–1306, https://doi.org/10.1109/TGRS.2006.871234, 2006. a
Brence, J.: brencej/RetrievalFailure: v1.0.0, Zenodo [code], https://doi.org/10.5281/zenodo.15189518, 2025. a
Brence, J., Tanevski, J., Adams, J., Malina, E., and Džeroski, S.: Surrogate models of radiative transfer codes for atmospheric trace gas retrievals from satellite observations, Mach. Learn., 112, 1337–1363, https://doi.org/10.1007/s10994-022-06155-2, 2023. a
Download
Short summary
The large fleet of Earth observation satellites in orbit currently generate huge volumes of data, requiring significant computational resources to process these data in a timely manner. We present a method for predicting poor-quality measurements using machine learning. We find that machine learning methods can accurately predict poor-quality measurements and remove them from the processing chain, saving time and computational resources. 
Share