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Implicit Particle Methods and Their Connection with Variational Data Assimilation
2013
Monthly Weather Review
The implicit particle filter is a sequential Monte Carlo method for data assimilation that guides the particles to the high-probability regions via a sequence of steps that includes minimizations. A new and more general derivation of this approach is presented and the method is extended to particle smoothing as well as to data assimilation for perfect models. Minimizations required by implicit particle methods are shown to be similar to those that one encounters in variational data
doi:10.1175/mwr-d-12-00145.1
fatcat:qeafyh6ea5cbph764sfqg4qoem