Covariance localization in the ensemble transform Kalman filter based on an augmented ensemble
Author:
Affiliation:
1. China University of Petroleum, China; Ministry of Natural Resources, China
2. China University of Petroleum, China
3. Ministry of Natural Resources, China
Publisher
FapUNIFESP (SciELO)
Subject
Water Science and Technology,Aquatic Science,Oceanography
Link
http://www.scielo.br/pdf/ocr/v68/2675-2824-ocr-68-e20309.pdf
Reference26 articles.
1. A Monte Carlo implementation of the nonlinear filtering problem to produce ensemble assimilations and forecasts;ANDERSON J. L.;Monthly Weather Review,1999
2. Adaptive sampling with the ensemble transform kalman filter. Part I: Theoretical aspects;BISHOP C. H.;Monthly Weather Review,2001
3. Ensemble covariances adaptively localized with ECO-RAP. Part 1: Tests on simple error models;BISHOP C. H.;Tellus A: Dynamic Meteorology and Oceanography,2009
4. Ensemble covariances adaptively localized with ECO-RAP. Part 2: A strategy for the atmosphere;BISHOP C. H.;Tellus A: Dynamic Meteorology and Oceanography,2009
5. Gain form of the ensemble transform Kalman filter and its relevance to satellite data assimilation with model space ensemble covariance localization;BISHOP C. H.;Monthly Weather Review,2017
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