Articles | Volume 18, issue 22
https://doi.org/10.5194/amt-18-6659-2025
© 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-18-6659-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
18 Nov 2025
Research article |
| 18 Nov 2025
| 18 Nov 2025
A feasibility study to reconstruct atmospheric rivers using space- and ground-based GNSS observations
STAR lab, Department of Aeronautics and Astronautics, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
Stephen Leroy
Atmospheric and Environmental Research (AER), JANUS Research Group, Lexington, MA 02421, USA
Kerri Cahoy
STAR lab, Department of Aeronautics and Astronautics, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
Juliana Chew
STAR lab, Department of Aeronautics and Astronautics, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
Benedikt Soja
Institute of Geodesy and Photogrammetry, ETH Zurich, Zurich, 8093 Zurich, Switzerland
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Short summary
We investigate the capability of machine learning models, trained with space and ground Global Navigation Satellite Systems (GNSS) observations, to produce fields of refractivity and water vapor that describe the spatiotemporal morphology of atmospheric rivers (ARs) and quantify moisture associated with them to a degree sufficient for atmospheric studies. The reconstructed fields can be used to monitor ARs. It studies what low-Earth orbit (LEO) radio occultation (RO) constellation is appropriate to quantify the structure, location and timing of ARs.
We investigate the capability of machine learning models, trained with space and ground Global...
