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

Viewed

Total article views: 3,038 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
2,324 353 361 3,038 73 105
  • HTML: 2,324
  • PDF: 353
  • XML: 361
  • Total: 3,038
  • BibTeX: 73
  • EndNote: 105
Views and downloads (calculated since 07 Oct 2024)
Cumulative views and downloads (calculated since 07 Oct 2024)

Viewed (geographical distribution)

Total article views: 3,038 (including HTML, PDF, and XML) Thereof 2,984 with geography defined and 54 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 16 Apr 2026
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. 
Read more
Share