Articles | Volume 18, issue 1
https://doi.org/10.5194/amt-18-57-2025
https://doi.org/10.5194/amt-18-57-2025
Research article
 | 
07 Jan 2025
Research article |  | 07 Jan 2025

Global Navigation Satellite System (GNSS) radio occultation climatologies mapped by machine learning and Bayesian interpolation

Endrit Shehaj, Stephen Leroy, Kerri Cahoy, Alain Geiger, Laura Crocetti, Gregor Moeller, Benedikt Soja, and Markus Rothacher

Related authors

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
EGUsphere, https://doi.org/10.5194/egusphere-2025-1516,https://doi.org/10.5194/egusphere-2025-1516, 2025
This preprint is open for discussion and under review for Atmospheric Measurement Techniques (AMT).
Short summary
Tropospheric water vapor: a comprehensive high-resolution data collection for the transnational Upper Rhine Graben region
Benjamin Fersch, Andreas Wagner, Bettina Kamm, Endrit Shehaj, Andreas Schenk, Peng Yuan, Alain Geiger, Gregor Moeller, Bernhard Heck, Stefan Hinz, Hansjörg Kutterer, and Harald Kunstmann
Earth Syst. Sci. Data, 14, 5287–5307, https://doi.org/10.5194/essd-14-5287-2022,https://doi.org/10.5194/essd-14-5287-2022, 2022
Short summary

Related subject area

Subject: Others (Wind, Precipitation, Temperature, etc.) | Technique: Remote Sensing | Topic: Data Processing and Information Retrieval
Gravity waves above the northern Atlantic and Europe during streamer events using Aeolus
Sabine Wüst, Lisa Küchelbacher, Franziska Trinkl, and Michael Bittner
Atmos. Meas. Tech., 18, 1591–1607, https://doi.org/10.5194/amt-18-1591-2025,https://doi.org/10.5194/amt-18-1591-2025, 2025
Short summary
Observations of tall-building wakes using a scanning Doppler lidar
Natalie E. Theeuwes, Janet F. Barlow, Antti Mannisenaho, Denise Hertwig, Ewan O'Connor, and Alan Robins
Atmos. Meas. Tech., 18, 1355–1371, https://doi.org/10.5194/amt-18-1355-2025,https://doi.org/10.5194/amt-18-1355-2025, 2025
Short summary
Mid-Atlantic nocturnal low-level jet characteristics: a machine learning analysis of radar wind profiles
Maurice Roots, John T. Sullivan, and Belay Demoz
Atmos. Meas. Tech., 18, 1269–1282, https://doi.org/10.5194/amt-18-1269-2025,https://doi.org/10.5194/amt-18-1269-2025, 2025
Short summary
Mitigating radome-induced bias in X-band weather radar polarimetric moments using an adaptive discrete Fourier transform algorithm
Padmanabhan Thiruvengadam, Guillaume Lesage, Ambinintsoa Volatiana Ramanamahefa, and Joël Van Baelen
Atmos. Meas. Tech., 18, 1185–1191, https://doi.org/10.5194/amt-18-1185-2025,https://doi.org/10.5194/amt-18-1185-2025, 2025
Short summary
GNSS-RO residual ionospheric error (RIE): a new method and assessment
Dong L. Wu, Valery A. Yudin, Kyu-Myong Kim, Mohar Chattopadhyay, Lawrence Coy, Ruth S. Lieberman, C. C. Jude H. Salinas, Jae N. Lee, Jie Gong, and Guiping Liu
Atmos. Meas. Tech., 18, 843–863, https://doi.org/10.5194/amt-18-843-2025,https://doi.org/10.5194/amt-18-843-2025, 2025
Short summary

Cited articles

Aichinger-Rosenberger, M., Brockmann, E., Crocetti, L., Soja, B., and Moeller, G.: Machine learning-based prediction of Alpine foehn events using GNSS troposphere products: first results for Altdorf, Switzerland, Atmos. Meas. Tech., 15, 5821–5839, https://doi.org/10.5194/amt-15-5821-2022, 2022. 
Crocetti, L., Schartner, M., and Soja, B.: Discontinuity Detection in GNSS Station Coordinate Time Series Using Machine Learning, Remote Sens., 13, 3906, https://doi.org/10.3390/rs13193906, 2021. 
Crocetti, L., Schartner, M., Zus, F., Zhang, W., Moeller, G., Navarro, V., See, L., Schindler, K., and Soja, B.: Global, spatially explicit modelling of zenith wet delay with XGBoost, J. Geod., 98, 23, https://doi.org/10.1007/s00190-024-01829-2, 2024. 
ECMWF: IFS documentation, https://www.ecmwf.int/en/publications/ifs-documentation (last access: 31 July 2023), 2023. 
Foelsche, U., Borsche, M., Steiner, A., Gobiet, A., Pirscher, B., Kirchengast, G., Wickert, J., and Schmidt, T.: Observing upper troposphere–lower stratosphere climate with radio occultation data from the CHAMP satellite, Clim. Dynam., 31, 49–65, https://doi.org/10.1007/s00382-007-0337-7, 2008. 
Download
Short summary
This work investigates whether machine learning (ML) can offer an alternative to existing methods to map radio occultation (RO) products, allowing the extraction of information not visible in direct observations. ML can further improve the results of Bayesian interpolation, a state-of-the-art method to map RO observations. The results display improvements in horizontal and temporal domains, at heights ranging from the planetary boundary layer up to the lower stratosphere, and for all seasons.
Share