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

Abstract

Stochastic parameterizations are increasingly being used to represent the uncertainty associated with model errors in ensemble forecasting and data assimilation. One of the challenges associated with the use of these parameterizations is the optimization of the properties of the stochastic forcings within their formulation. In this work a hierarchical data assimilation approach based on two nested ensemble Kalman filters is proposed for inferring parameters associated with a stochastic parameterization. The proposed technique is based on the Rao-Blackwellization of the parameter estimation problem. The technique consists in using an ensemble of ensemble Kalman filters, each of them using a different set of stochastic parameter values. We show the ability of the technique to infer parameters related to the covariance structure of stochastic representations of model error in the Lorenz-96 dynamical system. The evaluation is conducted with stochastic twin experiments and imperfect model experiments with unresolved physics in the forecast model. The proposed technique performs successfully under different model error covariance structures. 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.

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• Stochastic Parameterization: Towards a new view of Weather and Climate Models
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Cited by in corpus (3)

• A Review of Innovation-Based Methods to Jointly Estimate Model and Observation Error Covariance Matrices in Ensemble Data Assimilation
• Evaluation of Machine Learning Techniques for Forecast Uncertainty Quantification
• Model error covariance estimation in particle and ensemble Kalman filters using an online expectation-maximization algorithm