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Advanced Data Assimilation for GeosciencesLecture Notes of the Les Houches School of Physics: Special Issue, June 2012$
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Éric Blayo, Marc Bocquet, Emmanuel Cosme, and Leticia F. Cugliandolo

Print publication date: 2014

Print ISBN-13: 9780198723844

Published to Oxford Scholarship Online: March 2015

DOI: 10.1093/acprof:oso/9780198723844.001.0001

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Short-range error statistics in an ensemble Kalman filter

Short-range error statistics in an ensemble Kalman filter

Chapter:
(p.267) 11 Short-range error statistics in an ensemble Kalman filter
Source:
Advanced Data Assimilation for Geosciences
Author(s):

P. Houtekamer

Publisher:
Oxford University Press
DOI:10.1093/acprof:oso/9780198723844.003.0011

This chapter deals with short-range error statistics in the context of the ensemble Kalman filter (EnKF). To arrive at an optimal data-assimilation system, a good description of the uncertainty in the background field is needed. Historically, different approaches, with a solid comparison against either a ground truth or observations, have been used to obtain limited descriptions. The first category includes observation system simulation experiments (OSSEs), while the second includes methods based on statistical analysis of innovations. The EnKF is a relatively new method that simulates the effect of known sources of error to arrive at a Monte Carlo estimate of flow-dependent background error statistics. It is necessary to validate the ensemble statistics–in part by comparison with results from established methods–to identify areas of improvement for the EnKF. This chapter first summarizes existing methods and then studies the properties of a research version of the Canadian global EnKF.

Keywords:   ensemble Kalman filter, EnKF, uncertainty, observation system simulation experiments, OSSEs, Monte Carlo

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