Articles | Volume 12, issue 1
https://doi.org/10.5194/amt-12-23-2019
https://doi.org/10.5194/amt-12-23-2019
Research article
 | 
03 Jan 2019
Research article |  | 03 Jan 2019

Atmospheric bending effects in GNSS tomography

Gregor Möller and Daniel Landskron

Related authors

Determination of high-precision tropospheric delays using crowdsourced smartphone GNSS data
Yuanxin Pan, Grzegorz Kłopotek, Laura Crocetti, Rudi Weinacker, Tobias Sturn, Linda See, Galina Dick, Gregor Möller, Markus Rothacher, Ian McCallum, Vicente Navarro, and Benedikt Soja
Atmos. Meas. Tech., 17, 4303–4316, https://doi.org/10.5194/amt-17-4303-2024,https://doi.org/10.5194/amt-17-4303-2024, 2024
Short summary
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
Atmos. Meas. Tech. Discuss., https://doi.org/10.5194/amt-2023-205,https://doi.org/10.5194/amt-2023-205, 2023
Revised manuscript accepted for 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
Machine learning-based prediction of Alpine foehn events using GNSS troposphere products: first results for Altdorf, Switzerland
Matthias Aichinger-Rosenberger, Elmar Brockmann, Laura Crocetti, Benedikt Soja, and Gregor Moeller
Atmos. Meas. Tech., 15, 5821–5839, https://doi.org/10.5194/amt-15-5821-2022,https://doi.org/10.5194/amt-15-5821-2022, 2022
Short summary
Assimilation of GNSS tomography products into the Weather Research and Forecasting model using radio occultation data assimilation operator
Natalia Hanna, Estera Trzcina, Gregor Möller, Witold Rohm, and Robert Weber
Atmos. Meas. Tech., 12, 4829–4848, https://doi.org/10.5194/amt-12-4829-2019,https://doi.org/10.5194/amt-12-4829-2019, 2019
Short summary

Related subject area

Subject: Others (Wind, Precipitation, Temperature, etc.) | Technique: Remote Sensing | Topic: Data Processing and Information Retrieval
Global-scale gravity wave analysis methodology for the ESA Earth Explorer 11 candidate CAIRT
Sebastian Rhode, Peter Preusse, Jörn Ungermann, Inna Polichtchouk, Kaoru Sato, Shingo Watanabe, Manfred Ern, Karlheinz Nogai, Björn-Martin Sinnhuber, and Martin Riese
Atmos. Meas. Tech., 17, 5785–5819, https://doi.org/10.5194/amt-17-5785-2024,https://doi.org/10.5194/amt-17-5785-2024, 2024
Short summary
Retrieval of pseudo-BRDF-adjusted surface reflectance at 440 nm from the Geostationary Environmental Monitoring Spectrometer (GEMS)
Suyoung Sim, Sungwon Choi, Daeseong Jung, Jongho Woo, Nayeon Kim, Sungwoo Park, Honghee Kim, Ukkyo Jeong, Hyunkee​​​​​​​ Hong, and Kyung-Soo Han
Atmos. Meas. Tech., 17, 5601–5618, https://doi.org/10.5194/amt-17-5601-2024,https://doi.org/10.5194/amt-17-5601-2024, 2024
Short summary
Drop size distribution retrieval using dual-polarization radar at C-band and S-band
Daniel Durbin, Yadong Wang, and Pao-Liang Chang
Atmos. Meas. Tech., 17, 5397–5411, https://doi.org/10.5194/amt-17-5397-2024,https://doi.org/10.5194/amt-17-5397-2024, 2024
Short summary
Thermal tides in the middle atmosphere at mid-latitudes measured with a ground-based microwave radiometer
Witali Krochin, Axel Murk, and Gunter Stober
Atmos. Meas. Tech., 17, 5015–5028, https://doi.org/10.5194/amt-17-5015-2024,https://doi.org/10.5194/amt-17-5015-2024, 2024
Short summary
Global sensitivity analysis of simulated remote sensing polarimetric observations over snow
Matteo Ottaviani, Gabriel Harris Myers, and Nan Chen
Atmos. Meas. Tech., 17, 4737–4756, https://doi.org/10.5194/amt-17-4737-2024,https://doi.org/10.5194/amt-17-4737-2024, 2024
Short summary

Cited articles

Aghajany, S. H. and Amerian, Y.: Three dimensional ray tracing technique for tropospheric water vapor tomography using GPS measurements, J. Atmos. Sol.-Terr. Phy., 164, 81–88, https://doi.org/10.1016/j.jastp.2017.08.003, 2017. a
Anderson, D. N., Mendillo, M., and Herniter, B.: A semi-empirical low-latitude ionospheric model, Radio Sci., 22, 292–306, https://doi.org/10.1029/RS022i002p00292, 1987. a
Bender, M. and Raabe, A.: Preconditions to ground based GPS water vapour tomography, Ann. Geophys., 25, 1727–1734, https://doi.org/10.5194/angeo-25-1727-2007, 2007. a
Bender, M., Stosius, R., Zus, F., Dick, G., Wickert, J., and Raabe, A.: GNSS water vapour tomography - Expected improvements by combining GPS, GLONASS and Galileo observations, Adv. Space Res., 47, 886–897, https://doi.org/10.1016/j.asr.2010.09.011, 2011. a
Bevis, M., Businger, S., Herring, T. A., Rocken, C., Anthes, R. A., and Ware, R. H.: GPS meteorology: Remote sensing of atmospheric water vapor using the Global Positioning System, J. Geophys. Res., 97, 15787–15801, https://doi.org/10.1029/92JD01517, 1992. a
Download

The requested paper has a corresponding corrigendum published. Please read the corrigendum first before downloading the article.

Short summary
The paper describes a ray-tracing approach for the proper reconstruction of GNSS signal paths through the lower atmosphere, identifies possible error sources during ray tracing and provides a strategy for reducing their effect on the GNSS tomography solution, thereby contributing to a more reliable reconstruction of the 3-D water vapor distribution in the lower atmosphere from GNSS measurements.