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Hydrology and Earth System Sciences An interactive open-access journal of the European Geosciences Union
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Volume 19, issue 12
Hydrol. Earth Syst. Sci., 19, 4747–4764, 2015
https://doi.org/10.5194/hess-19-4747-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.
Hydrol. Earth Syst. Sci., 19, 4747–4764, 2015
https://doi.org/10.5194/hess-19-4747-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.

Research article 03 Dec 2015

Research article | 03 Dec 2015

Water vapor mapping by fusing InSAR and GNSS remote sensing data and atmospheric simulations

F. Alshawaf1,3, B. Fersch2, S. Hinz1, H. Kunstmann2, M. Mayer3, and F. J. Meyer4 F. Alshawaf et al.
  • 1Institute of Photogrammetry and Remote Sensing, Karlsruhe Institute of Technology (KIT), 76131 Karlsruhe, Germany
  • 2Institute of Meteorology and Climate Research, Campus Alpin, KIT, 82467 Garmisch-Partenkirchen, Germany
  • 3Geodetic Institute, KIT, 76131 Karlsruhe, Germany
  • 4Geophysical Institute, University of Alaska Fairbanks, Fairbanks, AK 99775, USA

Abstract. Data fusion aims at integrating multiple data sources that can be redundant or complementary to produce complete, accurate information of the parameter of interest. In this work, data fusion of precipitable water vapor (PWV) estimated from remote sensing observations and data from the Weather Research and Forecasting (WRF) modeling system are applied to provide complete grids of PWV with high quality. Our goal is to correctly infer PWV at spatially continuous, highly resolved grids from heterogeneous data sets. This is done by a geostatistical data fusion approach based on the method of fixed-rank kriging. The first data set contains absolute maps of atmospheric PWV produced by combining observations from the Global Navigation Satellite Systems (GNSS) and Interferometric Synthetic Aperture Radar (InSAR). These PWV maps have a high spatial density and a millimeter accuracy; however, the data are missing in regions of low coherence (e.g., forests and vegetated areas). The PWV maps simulated by the WRF model represent the second data set. The model maps are available for wide areas, but they have a coarse spatial resolution and a still limited accuracy. The PWV maps inferred by the data fusion at any spatial resolution show better qualities than those inferred from single data sets. In addition, by using the fixed-rank kriging method, the computational burden is significantly lower than that for ordinary kriging.

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This work aims at deriving high spatially resolved maps of atmospheric water vapor by the fusion data from Interferometric Synthetic Aperture Radar (InSAR), Global Navigation Satellite Systems (GNSS), and the Weather Research and Forecasting (WRF) model. The data fusion approach exploits the redundant and complementary spatial properties of all data sets to provide more accurate and high-resolution maps of water vapor. The comparison with maps from MERIS shows rms values of less than 1 mm.
This work aims at deriving high spatially resolved maps of atmospheric water vapor by the fusion...
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