Developing a Stabilized Ensemble Kalman Filter for integrating daily GRACE/GRACE-FO data into process models (S-ENKF)

• Applicant: Professor Dr.-Ing. Jürgen Kusche
• Subject Area: Geodesy, Photogrammetry, Remote Sensing, Geoinformatics, Cartography
• Term: from 2017 to 2023
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 329114959

Recent studies have suggested integrating global mass redistribution data obtained from the Gravity Recovery and Climate Experiment (GRACE) mission with hydrological models, using the Ensemble Kalman Filter (EnKF) approach. This would offer great potential for flood (i.e. discharge) and drought forecasting applications, in particular when applied to GRACE solutions at sub-monthly resolution. However, results so far have not met theoretical expectations and the reason for this is likely to be found in the inherent ill-conditioning of the GRACE lateral and vertical disaggregation problem, combined with the numerical peculiarities of limited-size ensemble approaches for optimal estimation. Here, we suggest developing a stabilized EnKF; through implementing/tailoring regularization and optimal weighting techniques, synthesizing methods from meteorology and from geodesy. The stabilized method will be applied to integrate daily GRACE data with a hydrological model at 0.5-degree spatial resolution. The approach will be applied to real data over the Ganges-Brahmaputra-Meghna region, and evaluated though simulations as well as using independent data.

• DFG Programme: Research Grants