Model‐Space Localization in Serial Ensemble Filters
نویسندگان
چکیده
منابع مشابه
Localization techniques for ensemble transform Kalman filters∗
Ensemble Kalman filter techniques are widely used to assimilate observations into dynamical models. The dimension of phase is typically much larger than the number of ensemble members which leads to inaccurate results in the computed covariance matrices. These inaccuracies lead, among others, to spurious long range correlations which can be eliminated by Schur-product-based localization techniq...
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Localization is an essential element of ensemble-based Kalman filters in largescale systems. Two localization methods are commonly used: Covariance localization and domain localization. The former applies a localizing weight to the forecast covariance matrix while the latter splits the assimilation into local regions in which independent assimilation updates are performed. The domain localizati...
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A new type of ensemble filter is proposed, which combines an ensemble Kalman filter (EnKF) with the ideas of morphing and registration from image processing. This results in filters suitable for nonlinear problems whose solutions exhibit moving coherent features, such as thin interfaces in wildfire modeling. The ensemble members are represented as the composition of one common state with a spat...
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Data assimilation in meteorology seeks to provide a current analysis of the state of the atmosphere to use as initial conditions in a weather forecast. This is achieved by using an estimate of a previous state of the system and merging that with observations of the true state of the system. Ensemble Kalman filtering is one method of data assimilation. Ensemble Kalman filters operate by using an...
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ژورنال
عنوان ژورنال: Journal of Advances in Modeling Earth Systems
سال: 2019
ISSN: 1942-2466,1942-2466
DOI: 10.1029/2018ms001514