Optimization of stochastic parameterizations for model error treatment using nested ensemble Kalman filters

Guillermo Scheffler
University of Buenos Aires, Argentina
Seminar Date: 
16. August 2018 - 12:30 - 13:00
Lecture room, Ground Floor, NERSC

Stochastic parameterizations have been successfully used to represent the uncertainty associated with the parameterization of unresolved scale processes for ensemble forecasting and data assimilation systems. In order to accurately describe the uncertainty associated with numerical predictions, these parameterizations have to be optimized. We will introduce a novel technique based on hierarchical ensemble Kalman filters for the optimization of stochastic parameterizations for data assimilation applications. The technique is proposed to be applied offline as part of an a priori optimization of the data assimilation system and could in principle be extended to the estimation of other hyperparameters of a data assimilation system.