Tags: JDAS

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  1. A Tutorial on Kalman Filters

    01 Jun 2016 | Contributor(s):: Colin James Grudzien

    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.

  2. Particle filters for geophysical applications

    30 Apr 2015 | | Contributor(s):: Javier Amezcua

    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...

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

    30 Apr 2015 | | Contributor(s):: Elaine Spiller

    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...

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

    30 Apr 2015 | | Contributor(s):: Peter Challenor

    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...

  5. Statistical Data Assimilation For Parameter Estimation In Costal Ocean Hydrodynamics Modeling

    30 Apr 2015 | | Contributor(s):: Talea Mayo

    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...

  6. Correlated Observation Errors in Data Assimilation

    30 Apr 2015 | | Contributor(s):: Sarah Dance

    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...

  7. Ionospheric weather forecasting using the LETKF

    23 Mar 2015 | | Contributor(s):: Juan Durazo

    We track the three-dimensional global distribution of electron density in the ionosphere using theLocal Ensemble Kalman Filter (LETKF) and the Thermosphere-Ionosphere-Electrodynamics GlobalCirculation Model (TIEGCM) by assimilating globally distributed electron density profiles. TheTIEGCM is a...

  8. An Introduction to Lagrangian Data Assimilation - Part II

    23 Mar 2015 | | Contributor(s):: Laura Slivinski

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

  9. An Introduction to Lagrangian Data Assimilation - Part I

    23 Mar 2015 | | Contributor(s):: Laura Slivinski

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

  10. Andy Reagan: Masters Thesis

    23 Mar 2015 | | Contributor(s):: Andy Reagan

    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....

  11. An Introduction to Variational Data Assimilation

    23 Mar 2015 | | Contributor(s):: Naratip Santitissadeekorn

    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.

  12. Computational Techniques for Lyapunov Exponents and Vectors

    23 Mar 2015 | | Contributor(s):: Erik Van Vleck

    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...

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

    25 Feb 2015 | | Contributor(s):: Patrick Raanes

    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...