Tags: data assimilation

Presentations (1-20 of 22)

  1. 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.

  2. 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.

  3. Hybrid Data Assimilation Techniques and Applications

    25 May 2016 | Contributor(s):: Erik Van Vleck

    Data assimilation provides a framework for incorporating data or observations into models for both state space and parameter estimation. In this talk we discuss the development of some hybrid data assimilation techniques based upon shadowing refinement. These techniques employ dimension reduction...

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

    14 Apr 2016 | | Contributor(s):: Juan Durazo

    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,...

  5. An application of Lagrangian data assimilation to Katama Bay, MA

    02 Feb 2016 | | Contributor(s):: Laura Slivinski

    Data assimilation is the process of combining predictions from numerical models with observations of the system. Fully Lagrangian data assimilation seeks to directly assimilate trajectories from drifters into some circulation model. This talk will provide a brief overview of Lagrangian data...

  6. Hybrid EnKF and Particle Filter: Lagrangian DA and Parameter Estimation

    06 Nov 2015 | | Contributor(s):: Naratip Santitssadeekorn, Christopher KRT Jones

    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...

  7. Midwest Mathematics and Climate Conference - Day 2 Morning Session

    12 May 2015 |

    Juan Restrepo, Oregon State UniversityData AssimilationAccounting for uncertainties has led us to alter our expectations of what is predictable and how such predictions compare to nature. A significant effort, in recent years, has been placed on creating new uncertainty quantification techniques,...

  8. Midwest Mathematics and Climate Conference - Day 1 Afternoon Session

    12 May 2015 |

    Graham Feingold, National Oceanic & Atmospheric AdministratonDynamical System Analogues to Cloud SystemsShallow convection exhibits fascinating cellular structures at scales of a few to several hundred kilometers. Configurations of relatively cloud free open cell states, or much cloudier...

  9. Realistic modeling and analysis of synchronization dynamics in power-grid networks

    08 May 2015 | | Contributor(s):: Takashi Nishikawa

    An imperative condition for the functioning of a power-grid network is that its power generators remain synchronized. Disturbances can prompt desynchronization, which is a process that has been involved in large power outages. In this talk I will first give a comparative review of three leading...

  10. Complex Energy Systems

    07 May 2015 | | Contributor(s):: Michael Chertkov

    This is the opening guest lecture for the Electric Grid Focus Group.  Dr. Michael Chertkov of Los Alamos National Lab describes the statistical and mathematical problems arising in complex energy systems, including the electric grid and gas networks.  Systems with high penetration of...

  11. 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...

  12. 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,...

  13. 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...

  14. 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...

  15. 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...

  16. 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...

  17. 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.

  18. 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.

  19. 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....

  20. 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.