Jun 20 2016
11th International Ensemble Kalman Filter Workshop
The ensemble Kalman filter (EnKF) and its many variants have been proven effective for data assimilation in large models, including those in atmospheric, oceanic, hydrologic, and petroleum...
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.
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.
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...
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,...
AMS Special Session on Challenges in Data Assimilation and the Mathematics of Planet Earth and Its Climate
15 Mar 2016 |
Posted by Karna Gowda
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...
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...
Sarah Louise Dance
OptIC project: An intercomparison of optimization techniques for parameter estimation in terrestrial biogeochemical models
20 Sep 2015 | | Contributor(s)::
A comparison of different optimization techniques for parameter estimation for a very simple ecosystem model (2 ODEs).
The REFLEX project: Comparing different algorithms and implementations for the inversion of a terrestrial ecosystem model against eddy covariance data
20 Sep 2015 | | Contributor(s)::
A comparison of parameter estimation techniques for a simplified ecosystem model. A follow-on to the OpTIC study with a more complex model (DALEC).
Sep 07 2015
The overall theme of the 15th Annual Meeting of the European Meteorological Society (EMS) and the 12th European Conference on Applications of Meteorology (ECAM) is High impact weather and...
Aneesh C Subramanian
Kody JH Law
Kody Law is a staff research scientist in the Computational Applied Math group at Oak Ridge National Laboratory. He received his PhD in Mathematics from the University of Massachusetts in...
Trevor Joseph Gionet