Nonlinear Data Assimilation electronic resource by Peter Jan Van Leeuwen, Yuan Cheng, Sebastian Reich.
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TextSeries: Frontiers in Applied Dynamical Systems: Reviews and TutorialsPublication details: Cham : Springer International Publishing : Imprint: Springer, 2015Edition: 1st ed. 2015Description: XII, 118 p. 19 illus., 15 illus. in color. online resourceContent type: text Media type: computer Carrier type: online resourceISBN: 9783319183473Subject(s): mathematics | Dynamics | Ergodic theory | Mathematical physics | Computer mathematics | Mathematics | Dynamical Systems and Ergodic Theory | Computational Mathematics and Numerical Analysis | Mathematical Applications in the Physical SciencesDDC classification: 515.39 | 515.48 LOC classification: QA313Online resources: Click here to access online
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Springer eBooksSummary: 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.
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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