Nonlinear Data Assimilation - Sebastian Reich,Yuan Cheng,Peter Jan Van Leeuwen
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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 ... Pilns apraksts
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Aprašymas
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.
Vairāk informācijas
| Autors | Sebastian Reich, Yuan Cheng, Peter Jan Van Leeuwen |
|---|---|
| Izdevējs | Springer Nature Switzerland |
| Series | Frontiers in Applied Dynamical Systems: Reviews and Tutorials |
| Izlaides gads | 2015 |
| Vāka tips | Mīkstais vāks |
| EAN | 9783319183466 |