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.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/amt-18-4025-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
27 Aug 2025
Research article |
| 27 Aug 2025
| 27 Aug 2025
A machine-learning-based marine atmosphere boundary layer (MABL) moisture profile retrieval product from GNSS-RO deep refraction signals
Climate and Radiation Lab, NASA Goddard Space Flight Center, Greenbelt, MD, USA
Dong L. Wu
Climate and Radiation Lab, NASA Goddard Space Flight Center, Greenbelt, MD, USA
Michelle Badalov
Dept. of Computer Science, Univ. of Maryland, College Park, MD, USA
Manisha Ganeshan
GESTAR-II/Morgan State University, Baltimore, MD, USA
Climate and Radiation Lab, NASA Goddard Space Flight Center, Greenbelt, MD, USA
Minghua Zheng
Scripps Institution of Oceanography, University of California at San Diego, La Jolla, CA, USA
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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.
Marine boundary layer (MABL) water vapor is among the key factors to couple the ocean and...
