Atmospheric Measurement Techniques
Atmospheric Measurement Techniques
AMT
Articles | Volume 18, issue 16
Articles | Volume 18, issue 16
https://doi.org/10.5194/amt-18-4025-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
Special issue:
https://doi.org/10.5194/amt-18-4025-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 18, issue 16
Research article
 | 
27 Aug 2025
Research article |  | 27 Aug 2025

A machine-learning-based marine atmosphere boundary layer (MABL) moisture profile retrieval product from GNSS-RO deep refraction signals

Jie Gong, Dong L. Wu, Michelle Badalov, Manisha Ganeshan, and Minghua Zheng

Viewed

Total article views: 3,129 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
2,404 282 443 3,129 98 111
  • HTML: 2,404
  • PDF: 282
  • XML: 443
  • Total: 3,129
  • BibTeX: 98
  • EndNote: 111
Views and downloads (calculated since 20 Jun 2024)
Cumulative views and downloads (calculated since 20 Jun 2024)

Viewed (geographical distribution)

Total article views: 3,129 (including HTML, PDF, and XML) Thereof 3,001 with geography defined and 128 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 06 Jan 2026
Download
Short summary
Marine boundary layer (MABL) water vapor is among the key factors to couple the ocean and atmosphere, but it is also among the hardest to retrieve from a satellite remote sensing perspective. Here we propose a novel way to retrieve MABL specific humidity profiles using the GNSS (Global Navigation Satellite System) Level-1 signal-to-noise ratio. Using a machine learning approach, we successfully obtained a retrieval product that outperforms the ERA-5 reanalysis and operational Level-2 retrievals globally, except in the deep tropics.
Read more
Share
Special issue
Observing atmosphere and climate with occultation techniques...