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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. Alshawaf et al.
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Cited articles  
Alshawaf, F., Fersch, B., Hinz, S., Kunstmann, H., Mayer, M., Thiele, A., Westerhaus, M., and Meyer, F.: Analysis of atmospheric signals in spaceborne InSAR – toward water vapor mapping based on multiple sources, in: Geoscience and Remote Sensing Symposium (IGARSS), 2012 IEEE International, 1960–1963, 2012.
Alshawaf, F., Fuhrmann, T., Knopfler, A., Luo, X., Mayer, M., Hinz, S., and Heck, B.: A}ccurate Estimation of Atmospheric Water Vapor Using GNSS {Observations and Surface Meteorological Data, IEEE T. Geosci. Remote Sens., 53, 3764–3771, https://doi.org/10.1109/TGRS.2014.2382713, 2015a.
Alshawaf, F., Hinz, S., Mayer, M., and Meyer, F. J.: C}onstructing accurate maps of atmospheric water vapor by combining interferometric synthetic aperture radar and GNSS observations, J. {Geophys. Res.-Atmos., 120, 1391–1403, 2015b.
Awan, N. K., Truhetz, H., and Gobiet, A.: Parametrization-Induced Error Characteristics of MM5 and WRF Operated in Climate Mode over the Alpine Region: An Ensemble-Based Analysis., J. Climate, 24, 3107–3123, 2011.
Bender, M., Dick, G., Wickert, J., Schmidt, T., Song, S., Gendt, G., Ge, M., and Rothacher, M.: Validation of GPS slant delays using water vapor radiometers and weather models, Meteorol. Z., 17, 807–812, 2008.
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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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