Correlation between System and Observation Errors in Data AssimilationSource: Monthly Weather Review:;2018:;volume 146:;issue 009::page 2913DOI: 10.1175/MWR-D-17-0331.1Publisher: American Meteorological Society
Abstract: AbstractAccurate knowledge of two types of noise, system and observational, is an important aspect of Bayesian filtering methodology. Traditionally, this knowledge is reflected in individual covariance matrices for the two noise contributions, while correlations between the system and observational noises are ignored. We contend that in practical problems, it is unlikely that system and observational errors are uncorrelated, in particular for geophysically motivated examples where errors are dominated by model and observation truncations. Moreover, it is shown that accounting for the cross correlations in the filtering algorithm, for example in a correlated ensemble Kalman filter, can result in significant improvements in filter accuracy for data from typical dynamical systems. In particular, we discuss the extreme case where the two types of errors are maximally correlated relative to the individual covariances.
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contributor author | Berry, Tyrus | |
contributor author | Sauer, Timothy | |
date accessioned | 2019-09-19T10:04:37Z | |
date available | 2019-09-19T10:04:37Z | |
date copyright | 6/6/2018 12:00:00 AM | |
date issued | 2018 | |
identifier other | mwr-d-17-0331.1.pdf | |
identifier uri | http://yetl.yabesh.ir/yetl1/handle/yetl/4261262 | |
description abstract | AbstractAccurate knowledge of two types of noise, system and observational, is an important aspect of Bayesian filtering methodology. Traditionally, this knowledge is reflected in individual covariance matrices for the two noise contributions, while correlations between the system and observational noises are ignored. We contend that in practical problems, it is unlikely that system and observational errors are uncorrelated, in particular for geophysically motivated examples where errors are dominated by model and observation truncations. Moreover, it is shown that accounting for the cross correlations in the filtering algorithm, for example in a correlated ensemble Kalman filter, can result in significant improvements in filter accuracy for data from typical dynamical systems. In particular, we discuss the extreme case where the two types of errors are maximally correlated relative to the individual covariances. | |
publisher | American Meteorological Society | |
title | Correlation between System and Observation Errors in Data Assimilation | |
type | Journal Paper | |
journal volume | 146 | |
journal issue | 9 | |
journal title | Monthly Weather Review | |
identifier doi | 10.1175/MWR-D-17-0331.1 | |
journal fristpage | 2913 | |
journal lastpage | 2931 | |
tree | Monthly Weather Review:;2018:;volume 146:;issue 009 | |
contenttype | Fulltext |