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.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/amt-18-57-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
07 Jan 2025
Research article |
| 07 Jan 2025
| 07 Jan 2025
Global Navigation Satellite System (GNSS) radio occultation climatologies mapped by machine learning and Bayesian interpolation
Endrit Shehaj
CORRESPONDING AUTHOR
STAR Laboratory, Department of Aeronautics and Astronautics, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
Institute of Geodesy and Photogrammetry, ETH Zürich, Zurich, 8093, Switzerland
Stephen Leroy
Atmospheric and Environmental Research, Lexington, MA 02421, USA
Kerri Cahoy
STAR Laboratory, Department of Aeronautics and Astronautics, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
Alain Geiger
Institute of Geodesy and Photogrammetry, ETH Zürich, Zurich, 8093, Switzerland
Laura Crocetti
Institute of Geodesy and Photogrammetry, ETH Zürich, Zurich, 8093, Switzerland
Gregor Moeller
Institute of Geodesy and Photogrammetry, ETH Zürich, Zurich, 8093, Switzerland
Department of Geodesy and Geoinformation, TU Wien, Vienna, Austria
Benedikt Soja
Institute of Geodesy and Photogrammetry, ETH Zürich, Zurich, 8093, Switzerland
Markus Rothacher
Institute of Geodesy and Photogrammetry, ETH Zürich, Zurich, 8093, Switzerland
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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.
This work investigates whether machine learning (ML) can offer an alternative to existing...
