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: Park Hotel Vossevangen, Voss
- Dates: JUNE 3-5, 2019
Registration Fee
7,000 NOK (without accommodation) - Includes conference, three lunches, and two dinners.
Invited Speakers
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Pieter Houtekamer
Environment and Climate Change, Canada |
Estimation of model parameters using an evolutionary algorithm |
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Stephen G. Penny
University of Maryland, USA |
Transitioning to Strongly Coupled Data Assimilation |
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Alexandre Emerick
Petrobras, Brazil |
Recent Ensemble Smoother Applications: Data-Space Inversion and Deep Learning for Facies Models |
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Takemasa Miyoshi
RIKEN, Japan |
Big data assimilation: A new science for weather prediction and beyond |
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Tijana Janjic Pfander
Ludwig-Maximilians-Universität München, Germany |
Data assimilation on convective scale based on first physical principles |
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Patrick Nima Raanes
NORCE, Norway |
EnKF – FAQ |
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Jincong He
Chevron ETC, USA |
Ensemble Methods: Challenges Faced In and Lessons Learned From Practical Applications |
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Remus Hanea
Equinor ASA & UiS, Norway |
Decision making under uncertainties – a holistic ensemble approach |
Photos
