Dealing with Nonlinearity in Lagrangian Data Assimilation
Christopher Jones
University of Warwick and University of North Carolina
(Abstract received 08/16/2009 for session D)
ABSTRACT
I will compare Kalman filter methods with statistical particle filtering methods in Lagrangian data assimilation. Even EnKF has problems handling certain nonlinear issues that arise from trajectories that sample from regions of high nonlinearity. Particle filter methods offer a way forward but also challenges in scaling up to large-dimensional problems.
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