[9] Mathematical aspect 3

[9-2] February 28, 15:20-15:40

Bayesian estimation of the observation-error covariance matrix

G. Ueno (The Institute of Statistical Mathematics)


We develop a Bayesian technique for estimating the parameters in the observation-noise covariance matrix Rt for ensemble data assimilation. We design a posterior distribution by using the ensemble-approximated likelihood and a Wishart prior distribution and present an iterative algorithm for parameter estimation. The temporal smoothness of Rt can be controlled by an adequate choice of two parameters of the prior distribution, the covariance matrix S and the number of degrees of freedom nu. The nu parameter can be estimated by maximizing the marginal likelihood. The present formalism can handle cases in which the number of data points or data positions varies with time, the former of which is exemplified in the experiments.

  Presentation file: 09_2_G.Ueno.pdf