Exploitation of GNSS tropospheric gradients for severe weather monitoring and prediction

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Global Navigation Satellite Systems (GNSS) have revolutionized positioning, navigation, and timing, becoming a common part of our everyday life. Aside from these well-known civilian and commercial applications, GNSS is currently in process to be established as a powerful and versatile observation tool for geosciences, which can accurately sense atmospheric temperature, water vapor, ionospheric electron content, Earth surface properties, co-seismic deformation and other geophysical parameters. A geophysical key application is atmospheric water vapor monitoring using GNSS ground station data. Water vapor, the most abundant greenhouse gas, accounts for about 70% of atmospheric warming and plays a key role in the atmospheric energy exchange. The precise knowledge of its highly variable spatial and temporal distribution is precondition for precise modeling of the atmospheric state as a base for numerical weather forecasts with focus to the strong precipitation and severe weather events, one of the current key challenges in weather research. GNSS water vapor data, derived from regional ground networks hereby close gaps in the established meteorological observing systems, e.g., at Germany with currently about 270 stations. No other observing system provides data with such high temporal and spatial resolution. The data from European GNSS networks are therefore already widely operationally used to improve regional weather forecasts in several countries. However, the impact of the currently provided data products to the forecast systems is still limited due to the limited atmospheric information content, which is provided by the currently used Zenith Total Delay (ZTD) data. The proposed project will pioneer the development and usage of next generation data products; tropospheric gradients. The new data products, developed and provided within the project, are expected to improve the impact of the currently provided GNSS data to weather forecast systems.The main innovations, which will be addressed by the project are:(1) Developments to provide high quality ZTDs and tropospheric gradientsin near-real-time for the German SAPOS network(see attached letter of support SAPOS);(2) Developments to make use of ZTDs and tropospheric gradients innumerical weather prediction, i.e., implement operators in thevariational/ensemble data assimilation system of theWeather Research and Forecasting (WRF) model;(3) Impact studies with the state of the art numerical weather model.