Nonlinear Data Assimilation

Nonfiction, Science & Nature, Mathematics, Counting & Numeration, Mathematical Analysis
Cover of the book Nonlinear Data Assimilation by Peter Jan Van Leeuwen, Yuan Cheng, Sebastian Reich, Springer International Publishing
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Author: Peter Jan Van Leeuwen, Yuan Cheng, Sebastian Reich ISBN: 9783319183473
Publisher: Springer International Publishing Publication: July 22, 2015
Imprint: Springer Language: English
Author: Peter Jan Van Leeuwen, Yuan Cheng, Sebastian Reich
ISBN: 9783319183473
Publisher: Springer International Publishing
Publication: July 22, 2015
Imprint: Springer
Language: English

This book contains two review articles on nonlinear data assimilation that deal with closely related topics but were written and can be read independently. Both contributions focus on so-called particle filters.

The first contribution by Jan van Leeuwen focuses on the potential of proposal densities. It discusses the issues with present-day particle filters and explorers new ideas for proposal densities to solve them, converging to particle filters that work well in systems of any dimension, closing the contribution with a high-dimensional example. The second contribution by Cheng and Reich discusses a unified framework for ensemble-transform particle filters. This allows one to bridge successful ensemble Kalman filters with fully nonlinear particle filters, and allows a proper introduction of localization in particle filters, which has been lacking up to now.

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This book contains two review articles on nonlinear data assimilation that deal with closely related topics but were written and can be read independently. Both contributions focus on so-called particle filters.

The first contribution by Jan van Leeuwen focuses on the potential of proposal densities. It discusses the issues with present-day particle filters and explorers new ideas for proposal densities to solve them, converging to particle filters that work well in systems of any dimension, closing the contribution with a high-dimensional example. The second contribution by Cheng and Reich discusses a unified framework for ensemble-transform particle filters. This allows one to bridge successful ensemble Kalman filters with fully nonlinear particle filters, and allows a proper introduction of localization in particle filters, which has been lacking up to now.

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