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MCRN

An Introduction to Variational Data Assimilation

Naratip Santitissadeekorn gives an introductory survey of variational techniques in data assimilation including 3D-VAR and 4D-VAR. This talk was given on 10-2-14. Naratip Santitissadeekorn gives an introductory survey of variational techniques in data assimilation including 3D-VAR and 4D-VAR. This t…
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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

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

Correlated Observation Errors in Data Assimilation

Remote sensing observations often have correlated errors, but the correlations are typically ignored in data assimilation for numerical weather prediction. The assumption of zero correlations is often used with data thinning methods, resulting in a loss of information, and reduction in analysis accu…
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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

Particle filters for geophysical applications

Avoiding degeneracy is a crucial challenge for particle filters. Results have shown that the number of particles scales exponentially with respect to the number of independent observations. In this talk I will review attempts to counteract this phenomenon by exploiting proposal densities. I will put…
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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

Computational Techniques for Lyapunov Exponents and Vectors

In this talk we present computational techniques for Lyapunov exponents and vectors based upon continuous matrix factorizations (QR and SVD). We outline the techniques, their well-posedness, error analysis/perturbation theory, and describe codes we have developed. We then discuss application of thes…
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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

Emulators in climate science. Uncertainty, sensitivity, calibration and more

An emulator or a surrogate is a statistical approximation of a complex numerical model. Emulators are fast to run and include a measure of their own uncertainty. This makes them suitable for a number of applications in climate science. Emulators were originally devised for uncertainty quantification…
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