Articles | Volume 19, issue 12
https://doi.org/10.5194/hess-19-4747-2015
https://doi.org/10.5194/hess-19-4747-2015
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, B. Fersch, S. Hinz, H. Kunstmann, M. Mayer, and F. J. Meyer

Related authors

Evaluating the Feasibility of Scaling the FIER Framework for Large-Scale Flood Inundation Prediction
Kel N. Markert, Hyongki Lee, Gustavious P. Williams, E. James Nelson, Daniel P. Ames, Robert E. Griffin, and Franz J. Meyer
EGUsphere, https://doi.org/10.5194/egusphere-2024-3491,https://doi.org/10.5194/egusphere-2024-3491, 2024
Short summary
Enhanced hydrological modelling with the WRF-Hydro lake/reservoir module at Convection-Permitting scale: a case study of the Tana River basin in East Africa
Ling Zhang, Lu Li, Zhongshi Zhang, Joël Arnault, Stefan Sobolowski, Anthony Musili Mwanthi, Pratik Kad, Mohammed Abdullahi Hassan, Tanja Portele, and Harald Kunstmann
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2024-278,https://doi.org/10.5194/hess-2024-278, 2024
Preprint under review for HESS
Short summary
Toward long-term monitoring of regional permafrost thaw with satellite interferometric synthetic aperture radar
Taha Sadeghi Chorsi, Franz J. Meyer, and Timothy H. Dixon
The Cryosphere, 18, 3723–3740, https://doi.org/10.5194/tc-18-3723-2024,https://doi.org/10.5194/tc-18-3723-2024, 2024
Short summary
Deep learning based sub-seasonal precipitation and streamflow forecasting over the source region of the Yangtze River
Ningpeng Dong, Haoran Hao, Mingxiang Yang, Jianhui Wei, Shiqin Xu, and Harald Kunstmann
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2024-212,https://doi.org/10.5194/hess-2024-212, 2024
Revised manuscript under review for HESS
Short summary
Novel View Synthesis with Neural Radiance Fields for Industrial Robot Applications
Markus Hillemann, Robert Langendörfer, Max Heiken, Max Mehltretter, Andreas Schenk, Martin Weinmann, Stefan Hinz, Christian Heipke, and Markus Ulrich
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVIII-2-2024, 137–144, https://doi.org/10.5194/isprs-archives-XLVIII-2-2024-137-2024,https://doi.org/10.5194/isprs-archives-XLVIII-2-2024-137-2024, 2024

Related subject area

Subject: Hydrometeorology | Techniques and Approaches: Modelling approaches
Mapping soil moisture across the UK: assimilating cosmic-ray neutron sensors, remotely sensed indices, rainfall radar and catchment water balance data in a Bayesian hierarchical model
Peter E. Levy and the COSMOS-UK team
Hydrol. Earth Syst. Sci., 28, 4819–4836, https://doi.org/10.5194/hess-28-4819-2024,https://doi.org/10.5194/hess-28-4819-2024, 2024
Short summary
Assessing rainfall radar errors with an inverse stochastic modelling framework
Amy C. Green, Chris Kilsby, and András Bárdossy
Hydrol. Earth Syst. Sci., 28, 4539–4558, https://doi.org/10.5194/hess-28-4539-2024,https://doi.org/10.5194/hess-28-4539-2024, 2024
Short summary
Multi-objective calibration and evaluation of the ORCHIDEE land surface model over France at high resolution
Peng Huang, Agnès Ducharne, Lucia Rinchiuso, Jan Polcher, Laure Baratgin, Vladislav Bastrikov, and Eric Sauquet
Hydrol. Earth Syst. Sci., 28, 4455–4476, https://doi.org/10.5194/hess-28-4455-2024,https://doi.org/10.5194/hess-28-4455-2024, 2024
Short summary
Spatiotemporal responses of runoff to climate change in the southern Tibetan Plateau
He Sun, Tandong Yao, Fengge Su, Wei Yang, and Deliang Chen
Hydrol. Earth Syst. Sci., 28, 4361–4381, https://doi.org/10.5194/hess-28-4361-2024,https://doi.org/10.5194/hess-28-4361-2024, 2024
Short summary
FROSTBYTE: a reproducible data-driven workflow for probabilistic seasonal streamflow forecasting in snow-fed river basins across North America
Louise Arnal, Martyn P. Clark, Alain Pietroniro, Vincent Vionnet, David R. Casson, Paul H. Whitfield, Vincent Fortin, Andrew W. Wood, Wouter J. M. Knoben, Brandi W. Newton, and Colleen Walford
Hydrol. Earth Syst. Sci., 28, 4127–4155, https://doi.org/10.5194/hess-28-4127-2024,https://doi.org/10.5194/hess-28-4127-2024, 2024
Short summary

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.
Download
Short summary
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.