Nonlinear Data Assimilation [electronic resource] / by Peter Jan Van Leeuwen, Yuan Cheng, Sebastian Reich
- Leeuwen, Peter Jan van
- Cham : Springer International Publishing : Imprint: Springer, 2015.
- 1st ed. 2015.
- Physical Description:
- XII, 116 pages 19 illustrations, 15 illustrations in color : online resource
- Additional Creators:
- Cheng, Yuan, Reich, Sebastian, and SpringerLink (Online service)
- Frontiers in Applied Dynamical Systems: Reviews and Tutorials, 2364-4532 ; 2
- 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.
- Digital File Characteristics:
- text file PDF
- AVAILABLE ONLINE TO AUTHORIZED PSU USERS.
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