Journal article

Nonlinear Parameter Estimation: Comparison of an Ensemble Kalman Smoother with a Markov Chain Monte Carlo Algorithm

Derek J Posselt, Craig H Bishop



This paper explores the temporal evolution of cloud microphysical parameter uncertainty using an ideal- ized 1D model of deep convection. Model parameter uncertainty is quantified using a Markov chain Monte Carlo (MCMC) algorithm. A new form of the ensemble transform Kalman smoother (ETKS) appropriate for the case where the number of ensemble members exceeds the number of observations is then used to obtain estimates of model uncertainty associated with variability in model physics parameters. Robustness of the parameter estimates and ensemble parameter distributions derived from ETKS is assessed via comparison with MCMC. Nonlinearity in the relationship between parameters and model output g..

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University of Melbourne Researchers


Awarded by Office of Naval Research

Awarded by NASA

Awarded by U.S. Office of Naval Research

Awarded by Div Atmospheric & Geospace Sciences

Funding Acknowledgements

DJP's contribution to this paper was funded by Office of Naval Research Grant N00173-10-1-G035, as well as by NASA Modeling, Analysis, and Prediction Grants NNX09AJ43G and NNX09AJ46G, while CHB was supported by the U.S. Office of Naval Research Grant 4596-0-1-5.