High-Resolution Atmospheric Water Vapor Fields by Spaceborne Geodetic Sensing, Tomographic Fusion, and Atmospheric Modeling

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Although the atmosphere contains only up to 4% water vapor by volume, water vapor is one of the central atmospheric gases. Water vapor is a highly effective greenhouse gas that is directly intertwined with global climate change and its implications for natural disasters such as floods, droughts, deluge or glacier melting. As a vital component of the hydrological cycle, water vapor represents a main driver for the generation and spatio-temporal distribution of clouds and precipitation. The quantification of water vapor remains a challenge: while regional atmospheric models (Limited Area Model, LAM) in general allow to simulate the distribution of hydro-meteorological variables in space and time in high resolution, their performance in reproducing in detail their high spatio-temporal variability remains still limited. At the same time only limited high resolution atmospheric water vapor validation records exist. Acting as an important signal in meteorology and climate research, water vapor principally is regarded as a source of noise in Geodesy and Remote Sensing applications. The humidity of the Earth's atmosphere induces delays and distortions of high temporal and spatial fluctuations in microwave signals, which cannot be eliminated by multi-frequency measurements and have to be quantified during the data processing. Thus observations of Global Navigation Satellite Systems (GNSS: high temporal resolution) and Interferometric Synthetic Aperture Radar (InSAR: high spatial resolution) provide valuable contributions for reconstructing the integrated water vapor along the path from the satellites to the observation site on the Earth's surface. In addition, the sophisticated tomography-based evaluation of these data even allows generating 3D fields of the water vapor distribution in space and time. By using GNSS and InSAR based techniques in combination with high resolution regional atmospheric weather models and geostatistical data merging techniques, the proposed project aims at developing and evaluating new approaches to derive improved spatio-temporal estimates of the atmospheric water vapor distribution. In particular, tomography-based approaches in the evaluation of geodetic and remote sensing data will be further developed to improve the vertical and horizontal resolution of the atmospheric state variable under research. The generated products are used for comparison and assimilation with atmospheric model-based information to finally get an optimal estimation of the atmospheric water vapor distribution.