- Bachelor Study of Communication Engineering (2001-2006)
- Master Study of Communication Engineering (MSCE) at TU Munich (2007-2009)
- Promotion, Remote sensing and atmospheric research at Institut für Photogrammetrie und Fernerkundung of the KIT (2010-2013)
- Postdoc researcher (2013–present)
- Determination of the atmospheric water vapor content using GNSS and meteorological data
- Persistent Scatterer Interferometrie-SAR (PSI)
- Estimation of atmospheric signals in InSAR interferograms
- Constructing maps of the absolute precipitable water vapor by combining PSI and GNSS data
- Geostatistical data fusion with data from numerical weather models
Fusion of GNSS and InSAR data to obtain the precipitable water vapor content in the atmosphere
This research aims at exploiting the time delay in microwave geodetic observations to provide accurate knowledge about temporal and spatial variations of atmospheric signals, particularly water vapor. Water vapor is a key constituent of the Earth's atmosphere although its average volumetric concentration does not exceed 4%. Due to the dynamic nature of the electrically neutral atmosphere (neutrosphere) and the complex energy exchange with the Earth's surface, the distribution of water vapor in time and space can be highly variable. Accurate information about its content and tendency is the main prerequisite for precise predictions of clouds and precipitation as for studies of climate and natural disasters.
Due to their high temporal resolution, observations from GNSS (Global Navigation Satellite System(s)) have been used to analyze the temporal variability of water vapor at individual sites. On the contrary, data from Interferometric Synthetic Aperture Radar (InSAR) are available at high spatial resolution over wide areas. Therefore, we will use InSAR data to estimate and evaluate the spatial variations of water vapor. Ultimately, we will develop an approach to combine GNSS and InSAR data to produce absolute, high-resolution maps of atmospheric water vapor. Remote sensing data together with numerical atmospheric models, for example, the weather research and forecasting (WRF), can provide accurate, continuous knowledge about atmospheric water vapor.
The cooperation involves: