Atmospheric Measurement Techniques
Atmospheric Measurement Techniques
AMT
Articles | Volume 18, issue 22
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.
https://doi.org/10.5194/amt-18-6659-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 18, issue 22
Research article
 | 
18 Nov 2025
Research article |  | 18 Nov 2025

A feasibility study to reconstruct atmospheric rivers using space- and ground-based GNSS observations

Endrit Shehaj, Stephen Leroy, Kerri Cahoy, Juliana Chew, and Benedikt Soja

Viewed

Total article views: 3,877 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
2,750 984 143 3,877 239 125 192
  • HTML: 2,750
  • PDF: 984
  • XML: 143
  • Total: 3,877
  • Supplement: 239
  • BibTeX: 125
  • EndNote: 192
Views and downloads (calculated since 11 Apr 2025)
Cumulative views and downloads (calculated since 11 Apr 2025)

Viewed (geographical distribution)

Total article views: 3,877 (including HTML, PDF, and XML) Thereof 3,877 with geography defined and 0 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Saved (final revised paper)

Latest update: 22 Jun 2026
Download
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.
Read more
Share