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Particle filters for very high dimensional systems

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Particle filters are one of the new data-assimilation methods that allow us to infer the characteristics of the full posterior probability density function. Up to very recently the general knowledge has been that particle filters are not applicable in high-dimensional systems. Recent developments have shown this to be incorrect, and I will discuss a few of these.

They are all based on the freedom related to the fact that we can choose particle movements from a different density than that described by the model under study, as long as we adapt the relative weight of the particle. This allows us to pull the particles to future observations, reducing and even avoiding filter degeneracy. Using this we can explore more traditional data-assimilation and inverse modelling techniques that are based on linearisations to find very efficient particles that allow particle filtering in systems of arbitrary dimensions.

Different methods will be described and high-dimensional applications, including climate models, will illustrate the quality of the methods. I will also touch upon an ensemble data-assimilation framework that allows very easy and efficient coupling of models to ensemble data-assimilation methods without the need to change the model structure or model work flow.

This talk is part of the Isaac Newton Institute Seminar Series series.

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