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 reservoir systems. By bringing together technical experts, practitioners, researchers and students for presentations and informal interchange of information, we aim to share research results and suggest important challenges that have yet to be addressed.
Program
Download the workshop program here.
Venue
- Location: Solstrand Hotel & Bad, Os
- Dates: June 12-14
Registration Fee
10,830 NOK (with accommodation) - Includes conference, three lunches, two dinners, and two nights at hotel. 4,170 NOK (without accommodation) - Includes conference, three lunches, and two dinners.
Invited Speakers
|
Andreas Størksen Stordal
IRIS, Norway |
Iterative ensemble smoothers in the annealed importance sampling framework |
|
Dan Crisan
Imperial College London, UK |
Data Assimilation for Stochastic Transport Models |
|
John Harlim
The Pennsylvania State University, USA |
Improving EnKF with machine learning algorithms |
|
Peter Jan van Leeuwen
University of Reading, UK |
Nonlinear ensemble data assimilation in high-dimensional spaces |
|
Chris Snyder
UCAR, USA |
Iterative ensemble smoothers in the annealed importance sampling framework |
Presentations
| Peter Jan van Leeuwen | talk | Nonlinear ensemble data assimilation in high-dimensional spaces |
| Jana de Wiljes | talk | EnKF-based particle filters |
| Dan Crisan | talk | Data Assimilation for Stochastic Transport Models |
| Femke Vossepoel | talk | On estimating geomechanical parameters from surface deformation with a particle method |
| Marc Bocquet | talk | An iterative ensemble Kalman filter in presence of additive model error |
| Anthony Fillion | talk | Quasi static ensemble variational data assimilation |
| Sammy Metref | talk | Estimating model evidence using ensemble-based data assimilation with localization |
| Colin Grudzien | talk | Dynamically constrained uncertainty for the Kalman filter covariance in the presence of model error |
| John Harlim | talk | Improving EnKF with machine learning algorithms |
| Renping Lin | talk | Applications of ocean data assimilation into a coupled climate model to East Asian summer monsoon simulations |
| Paula Maldonado | talk | Parameter sensitivity of the LETKF-WRF system for assimilation of radar observations in a case of deep convection in Argentina |
| Olivier Pannekoucke | talk | Parametric Kalman filter : toward an alternative to the EnKF? |
| John Maclean | talk | Coherent structure approaches for Lagrangian data assimilation |
| Kristian Fossum | talk | Multi-level ensemble based data assimilation |
| Frank Wilschut | talk | OLYMPUS Field Development Optimization Challenge |
| Al Reynolds | talk | Modified ES-MDA algorithms for data assimilation and uncertainty quantification |
| Pål Næverlid Sævik | talk | Fracture parameters as inversion variables for the EnKF and RML |
| Dongxiao Zhang | talk | Inverse Modeling with the aid of Surrogate Models |
| Hans Wackernagel | talk | Geostatistical multivariate spatial and temporal modelling |
| Trond Mannseth | talk | Assimilating spatially dense data for subsurface applications --- balancing information and degrees of freedom |
Additional presentations may be available by contacting the presenters.
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