Data assimilation, encompassing methods such as the ensemble Kalman filter (EnKF), variational approaches, and emerging techniques in machine learning and AI, is renowned for its remarkable capabilities in diverse domains such as atmospheric, oceanic, hydrologic, biomedical, biologic, and petroleum reservoir systems. By providing a platform for thought-provoking presentations and meaningful discussions, the workshop seeks to foster collaboration among technical experts, practitioners, researchers, and students. Together, we will showcase cutting-edge research findings, exchange practical insights, and collectively explore uncharted territories by identifying crucial challenges in data assimilation methods and applications. Join us to deepen your knowledge, expand your network, and contribute to the advancement of data assimilation science.
Venue
- Rosendal Fjordhotel, Norway.
- June 15 - 18, 2026.
- The local climatology
- Physical attendance only (no virtual participation or videoconferencing).
- There are excellent hiking opportunities in the area, and the program includes a guided hike, possibly including vistas of the Folgefonna glacier.
- There are quite a few other activities suggested by the hotel.
- The hotel has access to the fjord for daring swimmers.
Travel to the conference hotel by ferry from Bergen harbour is included with the conference fee, idem for the chartered bus on the return (exact time/place to be communicated by email). Inbound flights (or train) to Bergen will need to be on June 14 or earlier; outbound on June 18, 12:30pm or later. Each participant is responsible for making these arrangements after paying the registration fee.
Program
- The WS/WS will be a communal effort to advance DA research and applicability.
- Posters are on display throughout the workshop, and should be presented during both poster sessions. Size: max A0, preferably upright (portrait).
- Talks should have a run time of 22 min, to be followed by 8 min. Q&A.
Invited speakers
-
Lili Lei
Nanjing University, China
Integration of deep learning with ensemble-based data assimilation -
Matthias Morzfeld
Scripps Institution of Oceanography, UC San Diego, USA
Neural ensemble Kalman filter: Data assimilation for compressible flows with shocks -
Jana de Wiljes
TU Ilmenau, Germany
Sequential Learning Methods for High-Dimensional Data Assimilation -
Alexandre A. Emerick (pending)
Petrobras, Brazil
Topic TBD -
Geir Evensen
NORCE, Norway
Topic TBD
Abstracts and registration
- Deadline for submitting talks: March 11, CET 18:00.
- Posters and non-presenting attendees can sign up until May 1 (subject to availability)
by selecting
PosterorNonein the form below. The fee is the same as for talks. - The conference fee of NOK 16,000 (around 1,400 €, TBC) also covers the ferry, meals and a hotel room for 3 nights (ref. schedule). Participants selected for admission by the scientific committee will receive a link to pay the fee and thereby confirm their registration.
- To submit both a poster and a talk (encouraged! No additional charges) please submit the form twice.
- It is possible to bring a companion and/or children. Please specify this in the “additional information” field of the form. The price will be published later, and will need to be paid directly to the hotel.
Submission form
Scientific committee
This conference does not publish papers or proceedings.
- NORCE: Geir Evensen, Dean Oliver, Patrick N. Raanes
- NERSC: Yue (Michael) Ying
- Equinor: Remus Hanea
Presentations
Will be uploaded following workshop (if permission granted by presenter)
Photos
Will be uploaded following workshop.
Background
The EnKF is a data assimilation method that was co-invented and has been continuously developed by researchers at NORCE and NERSC. Over the last three decades, ensemble-based and variational data assimilation methods, including EnKF, have become highly effective and widely adopted approaches for integrating data into large-scale models. These advances have made profound impacts on the progress of various disciplines and promoted value creation for relevant industries. The field continues to evolve, with new algorithms and workflows—including those leveraging machine learning and AI—expanding the possibilities for data assimilation.
Sponsors:
REMEDY
Please contact enkf@data-assimilation.no if you want to sponsor the workshop.
Purpose
While the fundamental concepts of data assimilation methods are simple, practical implementations often demand problem-specific modifications for success. Our workshop aims to bridge the expertise of specialists from various fields to investigate the foundations and interconnections of ensemble, variational, and hybrid data assimilation methods, as well as new approaches in machine learning and AI, that have demonstrated effectiveness in diverse environments. In so doing, we strive to enhance robustness and efficiency in applications. Beyond practical applications, this gathering also facilitates the exchange of innovative research ideas, methods, algorithms, and workflows with the potential to further advance the performance and scope of data assimilation techniques.
History
In 2006, the inaugural EnKF workshop was held in Voss, Norway, marking the beginning of an annual tradition. Throughout the years, the EnKF workshop has consistently attracted participants from a wide range of scientific disciplines. The cross-pollination of ideas and the exchange of scientific advancements have fostered vibrant discussions and elevated the workshop’s scientific calibre to a high level. As a result, the annual international EnKF workshop has now established itself as a paramount event within the data assimilation community.
