Constructing water vapor maps by fusing InSAR, GNSS and WRF data

The work aims at constructing maps of the total water vapor in the atmosphere by fusing InSAR, GNSS, and WRF data

Compressive sensing reconstruction of 3D wet refractivity based on GNSS and InSAR observations

In this work, the reconstruction quality of an approach for neutrospheric water vapor tomography based on Slant Wet Delays (SWDs) obtained from Global Navigation Satellite Systems (GNSS) and Interferometric Synthetic Aperture Radar (InSAR) is investigated. The novelties of this approach are (1) the use of both absolute GNSS and absolute InSAR SWDs for tomography and (2) the solution of the tomographic system by means of compressive sensing (CS). The tomographic reconstruction is performed based on (i) a synthetic SWD dataset generated using wet refractivity information from the Weather Research and Forecasting (WRF) model and (ii) a real dataset using GNSS and InSAR SWDs. Thus, the validation of the achieved results focuses (i) on a comparison of the refractivity estimates with the input WRF refractivities and (ii) on radiosonde profiles. In case of the synthetic dataset, the results show that the CS approach yields a more accurate and more precise solution than least squares (LSQ). In addition, the benefit of adding synthetic InSAR SWDs into the tomographic system is analyzed. When applying CS, adding synthetic InSAR SWDs into the tomographic system improves the solution both in magnitude and in scattering. When solving the tomographic system by means of LSQ, no clear behavior is observed. In case of the real dataset, the estimated refractivities of both methodologies show a consistent behavior although the LSQ and CS solution strategies differ

Towards a rigorous fusion of GNSS and InSAR observations for the purpose of water vapor retrieval

In the framework of the rigorous fusion of GNSS and InSAR observations, the presented work carries out at a straightforward comparison of the wet delay, caused by water vapor, derived from GNSS and InSAR. The contributions of the two sensors to the wet delay are compared in the line of sight towards the SAR satellite. Comparisons of GNSS observations with the satellite-directed InSAR data show that only a partial component of the wet delay remains after the interferogram formation

Compressive sensing for neutrospheric water vapor tomography using GNSS and InSAR observations

This paper presents the innovative Compressive Sensing (CS) concept for tomographic reconstruction of 3D neutrospheric water vapor fields using data from Global Navigation Satellite Systems (GNSS) and Interferometric Synthetic Aperture Radar (InSAR). The Precipitable Water Vapor (PWV) input data are derived from simulations of the Weather Research and Forecasting modeling system. We apply a Compressive Sensing based approach for tomographic inversion. Using the Cosine transform, a sparse representation of the water vapor field is obtained. 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 combination of GNSS and InSAR data shows a significant improvement in 3D water vapor reconstruction; and the CS estimation produces better results than a traditional Tikhonov regulari-zation with l2 norm penalty term

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

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

Towards a zero-difference approach for homogenizing GNSS tropospheric products

A data homogenization method based on singular spectrum analysis (SSA) was developed and tested on real and simulated datasets. The method identifies abrupt changes in the atmospheric time series derived from Global Navigation Satellite System (GNSS) observations. For simulation and verification purposes, we used the ERA-Interim reanalysis data. Our method of change detection is independently applied to the precipitable water vapor (PWV) time series from GNSS, ERA-Interim and their difference. Then the detected offsets in the difference time series can be related to inconsistencies in the datasets or to abrupt changes due to climatic effects. The issue of missing data is also discussed and addressed using SSA. We appraised the performance of our method using a Monte Carlo simulation, which suggests a promising success rate of 81.1% for detecting mean shifts with values between 0.5 and 3 mm in PWV time series. A GNSS-derived PWV dataset, consisting of 214 stations in Germany, was investigated for possible inhomogeneities and systematic changes. We homogenized the dataset by identifying and correcting 96 inhomogeneous time series containing 134 detected and verified mean shifts from which 45 changes, accounting for approximately 34% of the offsets, were undocumented. The linear trends from the GNSS and ERA-Interim PWV datasets were estimated and compared, indicating a significant improvement after homogenization. The correlation between the trends was increased by 39% after correcting the mean shifts in the GNSS data. The method can be used to detect possible changes induced by climatic or meteorological effects

Chances and challenges of creating a research information platform for the Berlin University Alliance

Extended abstract presented at the CRIS2022 conference in Dubrovnik.-- Event programme available at https://cris2022.srce.hr/#section-program17 slides.-- Presentation delivered within the session "The evolving CRIS landscape"This paper describes some of the challenges we face while developing a research information platform for the Berlin University Alliance (BUA). Since its founding in 2018, the BUA has put its focus on promoting the networking of researchers and their research activities and connecting many research groups within the clusters of excellence as well as beyond. The platform aims at presenting structured, transparent, categorized, and linked information about researchers and their research activities to improve the discoverability of expertise, connect researchers to their work across disciplines and boundaries, and facilitate new research collaborations. The platform is established using the open-source, web-based VIVO software, which uses semantic web techniques to connect research outputs, organizations, people, things, and research activities