Data Assimilation The Ensemble Kalman Filter
Data Assimilation comprehensively covers data assimilation and inverse methods, including both traditional state estimation and parameter estimation. This text and reference focuses on various popular data assimilation methods, such as weak and strong con
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Geir Evensen
Data Assimilation The Ensemble Kalman Filter Second Edition
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Prof. Geir Evensen Statoil Research Centre PO box 7200 5020 Bergen Norway and Mohn-Sverdrup Center for Global Ocean Studies and Operational Oceanography at Nansen Environmental and Remote Sensing Center Thormølensgt 47 5600 Bergen Norway [email protected]
ISBN 978-3-642-03710-8 e-ISBN 978-3-642-03711-5 DOI 10.1007/978-3-642-03711-5 Springer Dordrecht Heidelberg London New York Library of Congress Control Number: 2009933770 c Springer-Verlag Berlin Heidelberg 2009 This work is subject to copyright. All rights are reserved, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilm or in any other way, and storage in data banks. Duplication of this publication or parts thereof is permitted only under the provisions of the German Copyright Law of September 9, 1965, in its current version, and permission for use must always be obtained from Springer. Violations are liable to prosecution under the German Copyright Law. The use of general descriptive names, registered names, trademarks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. Cover design: deblik, Berlin Printed on acid-free paper Springer is part of Springer Science+Business Media (www.springer.com)
To Tina, Endre, and Linn
Preface to the second edition
The second edition of this book provides a more consistent presentation of the square root algorithm in Chap 13. The presentation in the first edition is less mature and there has been a significant development and enhanced understanding of the square root algorithm following the publication of the first edition. A new chapter “Spurious correlations, localization, and inflation” is included and discusses and quantifies the impact of spurious correlations in ensemble filters caused by the use of a limited ensemble size. The chapter suggests and discusses inflation and localization methods for reducing the impact of spurious correlations and among others presents a new adaptive inflation algorithm. The improved sampling algorithm in Chap. 11 is improved and takes into account the fact that sampling using too few singular vectors can lead to physically unrealistic and too smooth realizations. The experiments in Chapters 13 and 14 are all repeated with the updated square root algorithms. In Chap. 14 a new section on the validity of the analysis equation, when using an ensemble representation of the measurement error covariance matrix, is included. Finally the material in the Appendix is reorganized and the list of references is updated with many of the more recent publications on the EnKF. I am greateful for the interaction and many discussions with Pavel Sakov and Laurent Bertino during the preparation of the second edition of this book.
Bergen, June
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