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Sparsity-driven tomographic reconstruction of atmospheric water vapor using GNSS and InSAR observations

Heublein, Marion and Alshawaf, Fadwa and Zhu, Xiao Xiang and Hinz, Stefan (2016) Sparsity-driven tomographic reconstruction of atmospheric water vapor using GNSS and InSAR observations. In: Geophysical Research Abstracts, 18, p. 7326. EGU 2016, 17–22 April 2016, Wien, Österreich.

Full text not available from this repository.

Official URL: http://meetingorganizer.copernicus.org/EGU2016/EGU2016-7326-1.pdf

Abstract

An accurate knowledge of the 3D distribution of water vapor in the atmosphere is a key element for weather forecasting and climate research. On the other hand, as water vapor causes a delay in the microwave signal propagation within the atmosphere, a precise determination of water vapor is required for accurate positioning and deformation monitoring using Global Navigation Satellite Systems (GNSS) and Interferometric Synthetic Aperture Radar (InSAR). However, due to its high variability in time and space, the atmospheric water vapor distribution is difficult to model. Since GNSS meteorology was introduced about twenty years ago, it has increasingly been used as a geodetic technique to generate maps of 2D Precipitable Water Vapor (PWV). Moreover, several approaches for 3D tomographic water vapor reconstruction from GNSS-based estimates using the simple least squares adjustment were presented. In this poster, we present an innovative and sophisticated Compressive Sensing (CS) concept for sparsity-driven tomographic reconstruction of 3D atmospheric wet refractivity fields using data from GNSS and InSAR. The 2D zenith wet delay (ZWD) estimates are obtained by a combination of point-wise estimates of the wet delay using GNSS observations and partial InSAR wet delay maps. These ZWD estimates are aggregated to derive realistic wet delay input data of 100 points as if corresponding to 100 GNSS sites within an area of 100 km � 100 km in the test region of the Upper Rhine Graben. The made-up ZWD values can be mapped into different elevation and azimuth angles. Using the Cosine transform, a sparse representation of the wet refractivity field is obtained. In contrast to existing tomographic approaches, we exploit sparsity as a prior for the regularization of the underdetermined inverse system. The new aspects of this work include both the combination of GNSS and InSAR data for water vapor tomography and the sophisticated CS estimation. The accuracy of the estimated 3D water vapor field is determined by comparing slant integrated wet delays computed from the estimated wet refractivities with real GNSS wet delay estimates. This comparison is performed along different elevation and azimuth angles.

Item URL in elib:https://elib.dlr.de/103113/
Document Type:Conference or Workshop Item (Speech)
Additional Information:Geophysical Research Abstracts Vol. 18, EGU2016-7326-1, 2016 EGU General Assembly 2016 © Author(s) 2016. CC Attribution 3.0 License.
Title:Sparsity-driven tomographic reconstruction of atmospheric water vapor using GNSS and InSAR observations
Authors:
AuthorsInstitution or Email of AuthorsAuthors ORCID iD
Heublein, MarionDLR-IMF/KIT-IPFUNSPECIFIED
Alshawaf, FadwaKIT-IPFUNSPECIFIED
Zhu, Xiao XiangDLR-IMF/TUM-LMFUNSPECIFIED
Hinz, StefanKIT-IPFUNSPECIFIED
Date:2016
Journal or Publication Title:Geophysical Research Abstracts
Refereed publication:No
Open Access:No
Gold Open Access:No
In SCOPUS:No
In ISI Web of Science:No
Volume:18
Page Range:p. 7326
Status:Published
Keywords:Tomography, atmospheric water vapor, GNSS, INSAR
Event Title:EGU 2016
Event Location:Wien, Österreich
Event Type:international Conference
Event Dates:17–22 April 2016
Organizer:EGU
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Space
HGF - Program Themes:Earth Observation
DLR - Research area:Raumfahrt
DLR - Program:R EO - Erdbeobachtung
DLR - Research theme (Project):R - Vorhaben hochauflösende Fernerkundungsverfahren
Location: Oberpfaffenhofen
Institutes and Institutions:Remote Sensing Technology Institute > SAR Signal Processing
Deposited By: Ge, Nan
Deposited On:17 Feb 2016 14:05
Last Modified:09 Dec 2016 11:36

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