Project start: 01.01.2018
Duration: 3 Jahre
Project lead: Harald Kunstmann
Involved scientists: Andreas Wagner (IMK-IFU), Benjamin Fersch (IMK-IFU)
Prof. Dr. Hansjörg Kutterer, KIT/GIK, Geodetic Institute, Karlsruhe
Prof. Dr. Stefan Hinz, KIT/IPF, Institute of Photogrammetry and Remote Sensing, Karlsruhe
Prof. Dr. Alain Geiger, ETH/IGP, Institute of Geodesy and Photogrammetry, Zürich
Water vapor is a highly effective greenhouse gas that is directly related to global climate change and its impact on natural disasters such as floods, droughts or the melting of glaciers. It is also an essential driver for the generation and spatio-temporal distribution of clouds and precipitation. Vertically integrated water vapor shows a high temporal and spatial variability. Although regional atmospheric models allow simulation of the distribution of hydrometeorological variables in space and time with high resolution, there is still a great need for improvement in terms of quality. At the same time, only limited records exist for the validation of high-resolution atmospheric water vapor.
Water vapor is an important signal in meteorology and climate research and is considered as a source of noise, especially in geodesy and remote sensing. The humidity of the Earth's atmosphere leads to delays and distortions of high temporal and spatial fluctuations in microwave signals, which can not be eliminated by multi-frequency measurements and must be quantified during data processing. Therefore, observations of global navigation satellite systems (GNSS) and interferometric synthetic aperture radar (InSAR) provide valuable contributions (GNSS: high temporal resolution; InSAR: high spatial resolution) for the reconstruction of integrated water vapor (IWV) on its way from satellite to observational site Earth's surface. In addition, the sophisticated tomographic analysis of this data enables the generation of 3D fields of 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, tomographic-based approaches for the evaluation of geodetic and remote sensing data will be further developed to improve the vertical and horizontal resolution of the investigated atmospheric state variables. The generated products are used for comparison and assimilation with atmospheric model-based information to finally obtain an optimal estimate of the atmospheric water vapor distribution.