Calibration and Data Assimilation algorithms combine a numerical model with observations in a quantitative way. This is done to drive models towards real world observations. To achieve an optimal combination, either variational minimization algorithms or ensemble-based estimation methods are applied. The ensemble-based methods have been shown to exhibit a particularly good scalability due to the natural parallelism inherent in the integration of an ensemble of model states. However, the scalability of these estimation methods, their computational efficiency, and possible challenges that exist for applying them in data intense research studies have formed our research with the focus on integrating satellite geodetic and Earth Observation data into models to improve the representation of the regional and global water cycle, as well as upper atmosphere and lower atmosphere.
GRACE(-FO) and multi-sensor C/DA Systems
Model Parameter Calibration
Ensemble-based Data Assimilation (DA)
