Global spatiotemporal variability of integrated water vapor derived from GPS, GOME/SCIAMACHY and ERA-Interim: annual cycle, frequency distribution and linear trends
Van Malderen, R.; Pottiaux, E.; Stankunavicius, G.; Beirle, S.; Wagner, T.; Brenot, H.; Bruyninx, C.; Jones, J. (2022). Global spatiotemporal variability of integrated water vapor derived from GPS, GOME/SCIAMACHY and ERA-Interim: annual cycle, frequency distribution and linear trends. Remote Sens. 14(4): 1050. https://dx.doi.org/10.3390/rs14041050
In: Remote Sensing. MDPI: Basel. ISSN 2072-4292; e-ISSN 2072-4292, more
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| Author keywords |
GNSS; integrated water vapor; climate change; spatiotemporal; lognormal distribution; ERA-Interim; GOMESCIA |
| Authors | | Top |
- Van Malderen, R., more
- Pottiaux, E., more
- Stankunavicius, G.
- Beirle, S.
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- Wagner, T.
- Brenot, H., more
- Bruyninx, C., more
- Jones, J.
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| Abstract |
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. |
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