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| Titel |
Water vapor mapping by fusing InSAR and GNSS remote sensing data and atmospheric simulations |
| VerfasserIn |
F. Alshawaf, B. Fersch, S. Hinz, H. Kunstmann, M. Mayer, F. J. Meyer |
| Medientyp |
Artikel
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| Sprache |
Englisch
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| ISSN |
1027-5606
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| Digitales Dokument |
URL |
| Erschienen |
In: Hydrology and Earth System Sciences ; 19, no. 12 ; Nr. 19, no. 12 (2015-12-03), S.4747-4764 |
| Datensatznummer |
250120862
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| Publikation (Nr.) |
 copernicus.org/hess-19-4747-2015.pdf |
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| Zusammenfassung |
| 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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