Assimilation of advanced GNSS atmospheric remote sensing observations into the MPAS (Model Prediction Across Scales) system

• Applicant: Dr. Rohith Thundathil
• Subject Area: Geodesy, Photogrammetry, Remote Sensing, Geoinformatics, Cartography ; Atmospheric Science
• Term: since 2025
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 562592497

Global Navigation Satellite Systems (GNSS) are vital for our daily lives, primarily in positioning, navigation, and timing. It plays a crucial role in accurately measuring atmospheric and surface properties in geosciences. For example, using GNSS regional ground networks to monitor atmospheric water vapor has improved existing meteorological observation systems. GNSS stands out from other observation systems for its many benefits, including low operating costs, all-weather availability, and excellent spatio-temporal resolution. Zenith Total Delays (ZTDs) are the only source of moisture data used operationally; however, they provide limited atmospheric information. Tropospheric Gradients (TGs) offer valuable insights into moisture variations in the lower troposphere, but their operational implementation is still pending. These gradients have demonstrated significant improvements in regional models and can be easily integrated. Furthermore, the Slant Total Delays (STDs) provides another important source of information that gives detailed data on moisture distribution and should also be utilized operationally. GNSS Reflectometry (GNSS-R) is an emerging field in satellite remote sensing that utilizes GNSS signals as bistatic radar sources to analyze the characteristics of reflecting surfaces. For instance, the Cyclone Global Navigation Satellite System (CYGNSS) is a constellation of satellites designed to study severe weather events, such as tropical cyclones. The mission aims to provide valuable data on wind speeds over the ocean's surface, especially within the intense cores of cyclones, where traditional measurements are challenging to obtain. Additionally, artificial intelligence is used to enhance the accuracy of these wind speed measurements. The project aims to enhance global weather forecasts using advanced GNSS data. The primary objectives include producing high-quality GNSS-based observations, such as ZTDs, TGs, STDs, and GNSS-R wind speeds. These observations will be integrated into the Model Prediction Across Scales (MPAS) system. Furthermore, the project will investigate how these GNSS data contribute to improved weather forecasts using state-of-the-art weather modeling systems through impact studies. Ultimately, this initiative seeks to make GNSS data more accessible and impactful for global weather prediction.

• DFG Programme: Research Grants
• International Connection: United Kingdom
• Co-Investigator: Professor Dr. Jens Wickert
• Cooperation Partner: Dr. Jonathan Jones