Hybrid EnKF and Particle Filter: Langrangian DA and Parameter Estimation
02 Jun 2016 | Contributor(s):: Christopher KRT Jones, naratip santitissadeekorn
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.
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...
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 nonlinear,...
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...
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 a...
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...
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...
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....
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...
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...