Name: Design and Implementation of a Parallel Multivariate Ensemble Kalman Filter for the Poseidon Ocean General Circulation Model (en Inglés)
SKU: 59974708
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Author: Nasa Technical Reports Server (Ntrs)

portada Design and Implementation of a Parallel Multivariate Ensemble Kalman Filter for the Poseidon Ocean General Circulation Model (en Inglés)

Design and Implementation of a Parallel Multivariate Ensemble Kalman Filter for the Poseidon Ocean General Circulation Model (en Inglés)

Formato

Libro Físico

Autor

Nasa Technical Reports Server (Ntrs)

Editorial

Bibliogov

Idioma

Inglés

N° páginas

Encuadernación

Tapa Blanda

Dimensiones

24.6x18.9x0.3 cm

Peso

0.11 kg.

ISBN13

9781289272913

Categorías

Política y gobierno

Design and Implementation of a Parallel Multivariate Ensemble Kalman Filter for the Poseidon Ocean General Circulation Model (en Inglés)

Nasa Technical Reports Server (Ntrs) (Autor) · Bibliogov · Tapa Blanda

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Reseña del libro "Design and Implementation of a Parallel Multivariate Ensemble Kalman Filter for the Poseidon Ocean General Circulation Model (en Inglés)"

A multivariate ensemble Kalman filter (MvEnKF) implemented on a massively parallel computer architecture has been implemented for the Poseidon ocean circulation model and tested with a Pacific Basin model configuration. There are about two 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 configuration are discussed. It is shown that the regionalization of the background covariances; has a negligible impact on the quality of the analyses. The parallel algorithm is very efficient for large numbers of observations but does not scale well beyond 100 PEs at the current model resolution. On a platform with distributed memory, memory rather than speed is the limiting factor.