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.
Venue: May 2 - 5, Thon Hotel Sandven, Norheimsund, Norway
The cultural event/excursion will likely consist of a hike to the Steindal waterfall (with an optional extension with some beautiful vistas), and a tasting session from a local apple cider farm. Another option will be a guided tour of the Hardanger Fartøyvernsenter (Maritime center), with the option of renting a rowboat thereafter.
Caltech, MIT, USA
Toward consistent nonlinear filtering and smoothing via measure transport
Attempts at adaptively localising the Troll field
Consistent ensemble formulation and solution of the history-matching problem
Stein Variational Gradient Descent for Reservoir History Matching Problems
The EnKF is a data assimilation method was co-invented and has been continuously developed by researchers at NORCE and NERSC. In the past two decades, the EnKF and related “ensemble” methods have been established as a school of viable and popular methods for data assimilation in very large models, and have made immense impacts on the advancements of various disciplines and promoted value creations for relevant industries.
Although the basic concept ensemble methods is straightforward, successful practical implementations often require modifications that are problem-specific. By bringing together experts from diverse areas, we aim to explore the bases and connections among EnKF-related methods that are proven to work in different environments, so that the resulting applications are more robust and efficient. Other than the aspect of practical applications, this workshop also aims to exchange and communicate novel research ideas, methods, algorithms and/or workflows that have the potential of further improving the performance of EnKF and its related methods.
The first EnKF workshop took place in Voss (Norway) in 2006. Since then, the EnKF workshop has been held annually (except for a disruption in 2020 due to COVID-19). The EnKF workshop has always accommodated participants coming from diverse scientific disciplines (e.g., meteorology, oceanography, hydrology, to name a few). The communication and exchange of scientific progresses and advancements have led to even more fruitful discussions and raised the scientific quality of the workshop to a very high level. As such, the annual international EnKF workshop has now become one of the most influential events within the data assimilation community.
email@example.com if you want to sponsor the workshop.
- NORCE: Geir Evensen, Dean Oliver, Patrick N. Raanes
- NERSC: Laurent Bertino
- Equinor: Remus Hanea
|Patrick N. Raanes||talk||'Ensemblized' linear least squares (LLS)|
|Maximilian Ramgraber||talk||Adaptive localization in nonlinear ensemble transport filtering|
|Maximilian Ramgraber||poster||Adaptive localization in nonlinear ensemble transport filtering|
|Maximilian Ramgraber||script||Supplementary material: how I generate my histograms.|
|Moha Gharamti||talk||A Randomized Dormant Ensemble Kalman Filter: Dealing with Extreme Sampling Errors|
|Moha Gharamti||poster||A Quantile Conserving Ensemble Filtering Framework: Next Generation Nonlinear and Non-Gaussian Data Assimilation Capabilities for DART|
|Mathieu Le Provost||talk||An ensemble filter for heavy tailed t-distributions|
|Mathieu Le Provost||poster||Regularization of the ensemble Kalman filter for non-local observations: application to elliptic observations|
|Hamed Ali Diab Montero||poster||A Particle Flow Filter for Estimating Future Earthquake Occurrences|
|Samantha S.R. Kim||poster||Optimal proposal for the assimilation of geodetic data with a particle filter: an application to compacting reservoirs|
|Antoine Bernigaud||poster||Lp-norm regularization in variational data assimilation|
|Xiaoling Jin||poster||Ensemble Kalman-based data-driven identification of stochastic model from state data|
|Heng Xiao||poster||Inference of relative permeability curves in reservoir rocks with ensemble Kalman method|
|Manon Verberne||poster||Ensemble smoother with multiple data assimilation to disentangle shallow and deep subsidence|
|Rolf J. Lorentzen||poster||Ensemble-based history matching of the Edvard Grieg field using 4D seismic data|
|Kjersti Solberg Eikrem||poster||Ensemble-based optimization applied to an offshore wind farm layout problem|
|Geir Evensen||talk||Consistent ensemble formulation and solution of the history-matching problem|
|Mathias Methlie Nilsen||talk||An Exponential Class of Ensemble based Optimization Algorithms|
|Lars Nerger||talk||A hybrid nonlinear-Kalman ensemble transform filter for data assimilation in systems with different degrees of nonlinearity|
|Lars Nerger||poster||Coupled assimilation of satellite temperature and chlorophyll observations for improved ecosystem predictions in the Baltic Sea|
|Femke Vossepoel||talk||Quantifying information loss in a Particle Method for Subsidence Estimation|
|Luxi Yu||talk||Soft sensing of intracellular states in bioprocessing with Ensemble Kalman Filters|
|Luxi Yu||poster||Soft sensing of intracellular states in bioprocessing with Ensemble Kalman Filters|
|Yue Ying||talk||Assimilating observations of deformation to improve short-term ensemble forecasts of sea ice features|
|Sophie Mauran||talk||A kernel extension of the Ensemble Transform Kalman Filter|
|Jiping Xie||talk||Clutch effect in an Arctic coupled ocean and sea ice data assimilation system|
|Marina Durán Moro||talk||Data assimilation of SIC satellite observations in the Barents Sea region|
|Ricardo Baptista||talk||Toward consistent nonlinear filtering and smoothing via measure transport|
|Feda Curic||talk||Attempts at adaptively localising the Troll field|
|Andreas Størksen Stordal||talk||Marginalized Iterative Ensemble Smoothers for Data assimilation|
|Xiao-Hui Wu||talk||Stein Variational Gradient Descent for Reservoir History Matching Problems|
|Xiaodong Luo||talk||Hyper parameter optimization for improving the performance of localization in an iterative ensemble smoother|