Atmospheric Measurement Techniques
Atmospheric Measurement Techniques
AMT
Articles | Volume 18, issue 7
Articles | Volume 18, issue 7
https://doi.org/10.5194/amt-18-1689-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/amt-18-1689-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 18, issue 7
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

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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. 
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