Atmospheric Measurement Techniques
Atmospheric Measurement Techniques
AMT
Articles | Volume 18, issue 1
Articles | Volume 18, issue 1
https://doi.org/10.5194/amt-18-57-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
Special issue:
https://doi.org/10.5194/amt-18-57-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 18, issue 1
Research article
 | 
07 Jan 2025
Research article |  | 07 Jan 2025

Global Navigation Satellite System (GNSS) radio occultation climatologies mapped by machine learning and Bayesian interpolation

Endrit Shehaj, Stephen Leroy, Kerri Cahoy, Alain Geiger, Laura Crocetti, Gregor Moeller, Benedikt Soja, and Markus Rothacher

Viewed

Total article views: 2,248 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
1,630 538 80 2,248 93 117
  • HTML: 1,630
  • PDF: 538
  • XML: 80
  • Total: 2,248
  • BibTeX: 93
  • EndNote: 117
Views and downloads (calculated since 23 Oct 2023)
Cumulative views and downloads (calculated since 23 Oct 2023)

Viewed (geographical distribution)

Total article views: 2,248 (including HTML, PDF, and XML) Thereof 2,212 with geography defined and 36 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 27 Nov 2025
Download
Short summary
This work investigates whether machine learning (ML) can offer an alternative to existing methods to map radio occultation (RO) products, allowing the extraction of information not visible in direct observations. ML can further improve the results of Bayesian interpolation, a state-of-the-art method to map RO observations. The results display improvements in horizontal and temporal domains, at heights ranging from the planetary boundary layer up to the lower stratosphere, and for all seasons.
Read more
Share
Special issue
Observing atmosphere and climate with occultation techniques...