Nonlinear parameter estimation: Comparison of an ensemble Kalman smoother with a Markov chain monte carlo algorithm

Abstract

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..