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Open AccessArticle

Cross-Comparison and Methodological Improvement in GPS Tomography

1
Royal Belgian Institute for Space Aeronomy, 1180 Brussels, Belgium
2
Wroclaw University of Environmental and Life Sciences, 50-357 Wrocław, Poland
3
Department of Geoinformatics, VŠB-Technical University of Ostrava, 70800 Ostrava, Czech Republic
4
Ionospheric and Atmospheric Remote Sensing Group, Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA 91109, USA
5
Polytechnic Institute of Guarda, 6300-559 Guarda, Portugal
6
IT4Innovations, VŠB-Technical University of Ostrava, 70800 Ostrava, Czech Republic
7
Dipartimento di Geoscienze, Università degli Studi di Padova, 35131 Padua, Italy
8
Royal Melbourne Institute of Technology University, GPO Box 2476, 3001 Melbourne, Australia
9
Géosciences Montpellier, CNRS, Univ. Montpellier, UA, 34095 Montpellier, France
*
Author to whom correspondence should be addressed.
Remote Sens. 2020, 12(1), 30; https://doi.org/10.3390/rs12010030
Received: 13 November 2019 / Revised: 9 December 2019 / Accepted: 17 December 2019 / Published: 19 December 2019
(This article belongs to the Special Issue GPS/GNSS for Earth Science and Applications)
GPS tomography has been investigated since 2000 as an attractive tool for retrieving the 3D field of water vapour and wet refractivity. However, this observational technique still remains a challenging task that requires improvement of its methodology. This was the purpose of this study, and for this, GPS data from the Australian Continuously Operating Research Station (CORS) network during a severe weather event were used. Sensitivity tests and statistical cross-comparisons of tomography retrievals with independent observations from radiosonde and radio-occultation profiles showed improved results using the presented methodology. The initial conditions, which were associated with different time-convergence of tomography inversion, play a critical role in GPS tomography. The best strategy can reduce the normalised root mean square (RMS) of the tomography solution by more than 3 with respect to radiosonde estimates. Data stacking and pseudo-slant observations can also significantly improve tomography retrievals with respect to non-stacked solutions. A normalised RMS improvement up to 17% in the 0–8 km layer was found by using 30 min data stacking, and RMS values were divided by 5 for all the layers by using pseudo-observations. This result was due to a better geometrical distribution of mid- and low-tropospheric parts (a 30% coverage improvement). Our study of the impact of the uncertainty of GPS observations shows that there is an interest in evaluating tomography retrievals in comparison to independent external measurements and in estimating simultaneously the quality of weather forecasts. Finally, a comparison of multi-model tomography with numerical weather prediction shows the relevant use of tomography retrievals to improving the understanding of such severe weather conditions. View Full-Text
Keywords: GPS tomography; methodological improvement; a priori condition; data stacking; pseudo-slant observations; severe weather GPS tomography; methodological improvement; a priori condition; data stacking; pseudo-slant observations; severe weather
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MDPI and ACS Style

Brenot, H.; Rohm, W.; Kačmařík, M.; Möller, G.; Sá, A.; Tondaś, D.; Rapant, L.; Biondi, R.; Manning, T.; Champollion, C. Cross-Comparison and Methodological Improvement in GPS Tomography. Remote Sens. 2020, 12, 30. https://doi.org/10.3390/rs12010030

AMA Style

Brenot H, Rohm W, Kačmařík M, Möller G, Sá A, Tondaś D, Rapant L, Biondi R, Manning T, Champollion C. Cross-Comparison and Methodological Improvement in GPS Tomography. Remote Sensing. 2020; 12(1):30. https://doi.org/10.3390/rs12010030

Chicago/Turabian Style

Brenot, Hugues; Rohm, Witold; Kačmařík, Michal; Möller, Gregor; Sá, André; Tondaś, Damian; Rapant, Lukas; Biondi, Riccardo; Manning, Toby; Champollion, Cédric. 2020. "Cross-Comparison and Methodological Improvement in GPS Tomography" Remote Sens. 12, no. 1: 30. https://doi.org/10.3390/rs12010030

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