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

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

doi:https://doi.org/10.5194/amt-18-4025-2025

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:

Observing atmosphere and climate with occultation techniques...

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

Data sets

GNSS deep SNR retrievals of marine atmosphere boundary layer (MABL) specific humidity Jie Gong and Wu Dong https://doi.org/10.5281/zenodo.13946112

Radiosonde measurements from the EUREC4A field campaign (v3.0.0) C. C. Stephan et al. https://doi.org/10.25326/137

NCAR/EOL ISS Radiosonde Data NCAR https://doi.org/10.5065/D69P30HG

Standard RO Inversions in the Neutral Atmosphere 2013-2020 (Processing Steps and Explanation of Data) S. Sokolovskiy https://doi.org/10.5065/8r12-hs65

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.