Initial testing of a massively parallel ensemble Kalman filter with the Poseidon isopycnal ocean general circulation model

Abstract

A multivariate ensemble Kalman filter (MvEnKF) implemented on a massively parallel computer architecture has been developed for the Poseidon ocean circulation model and tested with a Pacific basin model configuration. There are about 2 million prognostic state-vector variables. Parallelism for the data assimilation step is achieved by regionalization of the background-error covariances that are calculated from the phase-space distribution of the ensemble. Each processing element (PE) collects elements of a matrix measurement functional from nearby PEs. To avoid the introduction of spurious long-range covariances associated with finite ensemble sizes, the background-error covariances are given compact support by means of a Hadamard (element by element) product with a three-dimensional canonical correlation function. The methodology and the MvEnKF implementation are discussed. To verify the proper functioning of the algorithms, results from an initial experiment with in situ temperature data are presented. Furthermore, it is shown that the regionalization of the background covariances has a negligible impact on the quality of the analyses. Even though the parallel algorithm is very efficient for large numbers of observations, individual PE memory. rather than speed, dictates how large an ensemble can be used in practice on a platform with distributed memory.