Published October 17, 2024 | Version v1
Dataset Open

GNSS deep SNR retrievals of marine atmosphere boundary layer (MABL) specific humidity

Authors/Creators

  • 1. ROR icon Goddard Space Flight Center

Description

This folder contains 5 prediction files ended with *_v2.h5. These files can be loaded using the provided code "prediction_data_loader.py". All variables are in their respective physical units.

The *.tgz file contains the training and validation codes as well as sample training and validation datasets from METOP-B satellite. Please refer to the paper for details. All variables had been normalized in the training and validation datasets so no real physical meaning attached.

Reference:

Gong, J., Wu, D. L., Badalov, M., Ganeshan, M., and Zheng, M.: A machine-learning-based marine atmosphere boundary layer (MABL) moisture profile retrieval product from GNSS-RO deep refraction signals, Atmos. Meas. Tech., 18, 4025–4043, https://doi.org/10.5194/amt-18-4025-2025, 2025.

 

POC: Jie.Gong@nasa.gov

10/17/2024

 

Update on 08/27/2025: The final paper has been published on AMT. Please see updated reference information above.

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Files

Files (661.2 MB)

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md5:9fad11b704e9c732815a4be81c6e9553
91.7 MB Download
md5:04abde564db0049e219788203f6684f8
109.3 MB Download
md5:035130f5c3284ed7f874ceaa70ed6277
38.8 MB Download
md5:e94478b49f41891d2dc7f0374af3a071
526 Bytes Download
md5:ff4d87ce573ea9994abb5d3e69dd9716
11.6 MB Download
md5:0c1a701e4e12c5112b7a590823aec8b4
4.5 MB Download
md5:95e48ad08f56547ce4204b263fe87a90
405.3 MB Download
md5:e194360e6f7357b11df85ae579efa5b9
1.0 kB Download

Additional details

References

  • Gong, J., Wu, D. L., Badalov, M., Ganeshan, M., and Zheng, M.: A Machine-learning Based Marine Planetary Boundary Layer (MPBL) Moisture Profile Retrieval Product from GNSS-RO Deep Refraction Signals, EGUsphere [preprint], https://doi.org/10.5194/egusphere-2024-973, 2024