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MCRN

Statistical Data Assimilation For Parameter Estimation In Costal Ocean Hydrodynamics Modeling

Coastal ocean models are used for a variety of applications, including modeling tides and hurricane storm surge. These models numerically solve the shallow water equations, which are derived by depth integrating the Navier-Stokes equations. The inherent uncertainties in coastal ocean models are a re…
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MCRN

Hybrid EnKF and Particle Filter: Lagrangian DA and Parameter Estimation

Dealing with high dimensional systems is one of the central problems of data assimilation. A strategy is proposed here for systems that enjoys a skew-product structure. Christopher Jones, University of North Carolina at Chapel Hill, presents joint work with Naratip Santitssadeekorn to the Stochastic…
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MCRN

Extending the square root method to account for model noise in the ensemble Kalman filter

Presented by Patrick Raanes on 2-18-2015 Abstract:A novel approach to account for model noise in the forecast step of the Ensemble Kalman filter (EnKF) is proposed. The core method is based on the approach of the analysis step of ensemble square root filters (ETKF), and consists in right-multiplying…
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MCRN

Andy Reagan: Masters Thesis

Andy Reagan at UVM gives a talk about his masters work on uncertainty quantification and data assimilation to improve the numerical prediction of fluids in the thermosyphon. This talk will involve several data assimilation methods, including variational techniques, but will focus on LETKF. This talk…
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MCRN

Toward a hybrid particle-ensemble Kalman filter for assimilating data from Lagrangian instruments into high dimensional models

Presented by Elaine Spiller on 3-26-15Abstract:We discuss a recently proposed hybrid particle-ensemble Kalman filter for assimilating Lagrangian data, and apply it to a high-dimensional quasi-geostrophic ocean model. Effectively the hybrid filter applies a particle filter to the highly nonlinear, lo…
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MCRN

A Tutorial on Kalman Filters

This is an introduction to the Kalman filter, explaining some underlying assumptions, use and extensions of the method.This talk was given on 10-7-2015. This is an introduction to the Kalman filter, explaining some underlying assumptions, use and extensions of the method.This talk was given on 10-7-…
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MCRN

Hybrid EnKF and Particle Filter: Langrangian DA and Parameter Estimation

This is a talk delivered at the Imperial College meeting on Stochastic Modeling in GFD, Data Assimilation and Non-equilibrium Phenomena on 11-4-2015. This talk presents joint research by Chris Jones at UNC-CH and Naratip Santitissandeekorn on hybrid data assimilation methods. This is a talk delivere…
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MCRN

An Introduction to Lagrangian Data Assimilation - Part I

Laura Slivinski at Woods Hole Oceanographic Institute gives an introduction to Lagrangian Data Assimlation on 10-30-2014. Laura Slivinski at Woods Hole Oceanographic Institute gives an introduction to Lagrangian Data Assimlation on 10-30-2014. …
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MCRN

An Introduction to Lagrangian Data Assimilation - Part II

Lauara Slivinski at Woods Hole Oceanographic Institution gives an introduction to Lagrangian Data Assimilation on 11-13-2014. Lauara Slivinski at Woods Hole Oceanographic Institution gives an introduction to Lagrangian Data Assimilation on 11-13-2014. …
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MCRN

Data assimilation for extreme ionospheric events: observing system experiments of the September 26, 2011 geomagnetic storm

As increasingly sophisticated ground- and space-based technological systems that depend on the near-Earth space environment are being built, vulnerabilities to variations in the ionosphere are also increasing. These vulnerabilities are especially prominent during extreme space-weather events, where …
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