An important practical problem is the recovery of a turbulent velocity field from Lagrangian tracers that move with the fluid flow. Despite the inherent nonlinearity in measuring noisy Lagrangian tracers, it is shown that there are exact closed analytic formulas for the optimal filter. When the underlying velocity field is incompressible, the tracersâ€™ distribution converge to the uniform distribution geometrically fast; concrete asymptotic features, such as information barriers, are obtained...
Events List

Xin Tong: Filtering with Noisy Lagrangian Tracers

Laura Slivinski: A hybrid particleensemble Kalman filter for Lagrangian data assimilation
Lagrangian data assimilation involves using observations of the positions of passive drifters in a flow in order to obtain a probability distribution on the underlying Eulerian flow field. Several data assimilation schemes have been studied in the context of geophysical fluid flows (such as the particle filter and the ensemble Kalman filter), but these methods often have disadvantages. I will give an overview of Lagrangian data assimilation and present results from a new hybrid filter scheme...

Laura Slivinski: A hybrid particleensemble Kalman filter for Lagrangian data assimilation
Lagrangian data assimilation involves using observations of the positions of passive drifters in a flow in order to obtain a probability distribution on the underlying Eulerian flow field. Several data assimilation schemes have been studied in the context of geophysical fluid flows (such as the particle filter and the ensemble Kalman filter), but these methods often have disadvantages. I will give an overview of Lagrangian data assimilation and present results from a new hybrid filter scheme...

Karthik Gurumoorthy: The Mathematical Background of AUS
Karthik Gurumoorthy describes the mathematical background behind the Assimilation in Unstable Subspaces (AUS) approach in data assimilation which relies on efficiently computing the Liapunov vectors. He will cover what is meant by Liapunov vectors and the standard procedure behind computing them.

Naratip Santitissadeekorn: Joint parameterstate estimation using a twostage filter Part 2
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Naratip Santitissadeekorn: Joint parameterstate estimation using a twostage filter
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