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    Correlation between System and Observation Errors in Data Assimilation

    Source: Monthly Weather Review:;2018:;volume 146:;issue 009::page 2913
    Author:
    Berry, Tyrus
    ,
    Sauer, Timothy
    DOI: 10.1175/MWR-D-17-0331.1
    Publisher: 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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      Correlation between System and Observation Errors in Data Assimilation

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    http://yetl.yabesh.ir/yetl1/handle/yetl/4261262
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    contributor authorBerry, Tyrus
    contributor authorSauer, Timothy
    date accessioned2019-09-19T10:04:37Z
    date available2019-09-19T10:04:37Z
    date copyright6/6/2018 12:00:00 AM
    date issued2018
    identifier othermwr-d-17-0331.1.pdf
    identifier urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4261262
    description abstractAbstractAccurate 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.
    publisherAmerican Meteorological Society
    titleCorrelation between System and Observation Errors in Data Assimilation
    typeJournal Paper
    journal volume146
    journal issue9
    journal titleMonthly Weather Review
    identifier doi10.1175/MWR-D-17-0331.1
    journal fristpage2913
    journal lastpage2931
    treeMonthly Weather Review:;2018:;volume 146:;issue 009
    contenttypeFulltext
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    DSpace software copyright © 2002-2015  DuraSpace
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