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Article

Global Spatiotemporal Variability of Integrated Water Vapor Derived from GPS, GOME/SCIAMACHY and ERA-Interim: Annual Cycle, Frequency Distribution and Linear Trends

1
Royal Meteorological Institute of Belgium (RMIB), 1180 Uccle, Belgium
2
Royal Observatory of Belgium (ROB), 1180 Uccle, Belgium
3
Department of Hydrology and Climatology, Faculty of Chemistry and Geosciences, Institute of Geosciences, Vilnius University, 01513 Vilnius, Lithuania
4
Max Planck Institute for Chemistry (MPI-C), 55128 Mainz, Germany
5
Royal Belgium Institute for Space Aeronomy (BIRA), 1180 Uccle, Belgium
6
Met Office, Exeter EX1 3PB, UK
*
Author to whom correspondence should be addressed.
Academic Editor: Giuseppe Casula
Remote Sens. 2022, 14(4), 1050; https://doi.org/10.3390/rs14041050
Received: 23 December 2021 / Revised: 17 February 2022 / Accepted: 17 February 2022 / Published: 21 February 2022
(This article belongs to the Special Issue Climate Modelling and Monitoring Using GNSS)
Atmospheric water vapor plays a prominent role in climate change and atmospheric, meteorological, and hydrological processes. Because of its high spatiotemporal variability, precise quantification of water vapor is challenging. This study investigates Integrated Water Vapor (IWV) variability for the period 1995–2010 at 118 globally distributed Global Positioning System (GPS) sites, using additional UV/VIS satellite retrievals by GOME, SCIAMACHY, and GOME-2 (denoted as GOMESCIA below), plus ERA-Interim reanalysis output. Apart from spatial representativeness differences, particularly at coastal and island sites, all three IWV datasets correlate well with the lowest mean correlation coefficient of 0.878 (averaged over all the sites) between GPS and GOMESCIA. We confirm the dominance of standard lognormal distribution of the IWV time series, which can be explained by the combination of a lower mode (dry season characterized by a standard lognormal distribution with a low median value) and an upper mode (wet season characterized by a reverse lognormal distribution with high median value) in European, Western American, and subtropical sites. Despite the relatively short length of the time series, we found a good consistency in the sign of the continental IWV trends, not only between the different datasets, but also compared to temperature and precipitation trends. View Full-Text
Keywords: GNSS; integrated water vapor; climate change; spatiotemporal; lognormal distribution; ERA-Interim; GOMESCIA GNSS; integrated water vapor; climate change; spatiotemporal; lognormal distribution; ERA-Interim; GOMESCIA
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MDPI and ACS Style

Van Malderen, R.; Pottiaux, E.; Stankunavicius, G.; Beirle, S.; Wagner, T.; Brenot, H.; Bruyninx, C.; Jones, J. Global Spatiotemporal Variability of Integrated Water Vapor Derived from GPS, GOME/SCIAMACHY and ERA-Interim: Annual Cycle, Frequency Distribution and Linear Trends. Remote Sens. 2022, 14, 1050. https://doi.org/10.3390/rs14041050

AMA Style

Van Malderen R, Pottiaux E, Stankunavicius G, Beirle S, Wagner T, Brenot H, Bruyninx C, Jones J. Global Spatiotemporal Variability of Integrated Water Vapor Derived from GPS, GOME/SCIAMACHY and ERA-Interim: Annual Cycle, Frequency Distribution and Linear Trends. Remote Sensing. 2022; 14(4):1050. https://doi.org/10.3390/rs14041050

Chicago/Turabian Style

Van Malderen, Roeland, Eric Pottiaux, Gintautas Stankunavicius, Steffen Beirle, Thomas Wagner, Hugues Brenot, Carine Bruyninx, and Jonathan Jones. 2022. "Global Spatiotemporal Variability of Integrated Water Vapor Derived from GPS, GOME/SCIAMACHY and ERA-Interim: Annual Cycle, Frequency Distribution and Linear Trends" Remote Sensing 14, no. 4: 1050. https://doi.org/10.3390/rs14041050

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