Nonlinear Data Assimilation

Nonlinear Data Assimilation

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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.Consider the forecast PDF Zn;f and the analysis PDF Zn;a at iteration index n. For simplicity of notion we drop the iteration index and simply write Zf and Za, respectively. Definition 2. A coupling of Zf and Za consists of a pair ZfWa D .Zf;Za/ ofanbsp;...

Title:Nonlinear Data Assimilation
Author:Peter Jan Van Leeuwen, Yuan Cheng, Sebastian Reich
Publisher:Springer - 2015-07-22


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