Journal article

Errors in Ensemble Kalman Smoother Estimates of Cloud Microphysical Parameters

Derek J Posselt, Daniel Hodyss, Craig H Bishop

MONTHLY WEATHER REVIEW | AMER METEOROLOGICAL SOC | Published : 2014

Abstract

If forecast or observation error distributions are non-Gaussian, the true posterior mean and covariance depends on the distribution of observation errors and the observed values. The posterior distribution of analysis errors obtained from ensemble Kalman filters and smoothers is independent of observed values. Hence, the error in ensemble Kalman smoother (EnKS) state estimates is closely linked to the sensitivity of the true posterior to observed values. Here a Markov chain Monte Carlo (MCMC) algorithm is used to document the dependence of the errors in EnKS-based estimates of cloud microphysical parameters on observed values. It is shown that EnKS analysis distributions are grossly inaccura..

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University of Melbourne Researchers

Grants

Awarded by U.S. Office of Naval Research


Awarded by Chief of Naval Research


Funding Acknowledgements

DJP's contribution to this paper was funded by the U.S. Office of Naval Research Grant N00173-10-1-G035 while CHB was supported by the U.S. Office of Naval Research Grant 4596-0-1-5. Support for DH was provided by the Chief of Naval Research PE-0601153N.