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

Data sets

GNSS Radio Occultation Climatologies mapped by Machine Learning and Bayesian Interpolation (dataset and code example) Endrit Shehaj https://doi.org/10.3929/ethz-b-000670139

COSMIC-2 Data, Index of /gnss-ro/cosmic2/nrt/level2/ UCAR https://data.cosmic.ucar.edu/gnss-ro/cosmic2/nrt/level2/

Model code and software

GNSS Radio Occultation Climatologies mapped by Machine Learning and Bayesian Interpolation (dataset and code example) Endrit Shehaj https://doi.org/10.3929/ethz-b-000670139

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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.
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Special issue
Observing atmosphere and climate with occultation techniques...