The ensemble Kalman filter (EnKF) and its versatile variants are renowned for their remarkable data assimilation 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 EnKF 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. Join us to deepen your knowledge, expand your network, and contribute to the advancement of data assimilation.


Indoor pool. Also connected to an outdoor (heated) pool Bird's eye view of hotel Fjord swimming access
Images from venue



  • Participants must arrange their own travel to/from Bergen.
  • A chartered bus will take us from downtown Bergen to Solstrand hotel.
  • The unsession will consist of an open discussion on benchmarking. The sharing and adaption of open benchmark models and datasets is one of the fuels powering the rapid progress of ML and AI. The DA community has a handful of well-known benchmark models, but implementations vary and comparisons are not always straightforward. On the data side, there is much scope for improvement. In this guided discussion, we will ask, “What do we have?”, “What do we need?”, and “How will we get there?”. We will not solve the issue, but we will emerge with a better understanding of what needs to be done, and who is up for doing it!
  • There are some moderate hiking opportunities in the area.
  • The hotel includes pools with spa facilities, with access to the fjord for more daring swimmers.

Invited speakers

  • Alban Farchi

    Alban Farchi

    Reseacher, CEREA, École des Ponts ParisTech, France
    Online model error correction with neural networks – from theory to the ECMWF forecasting system
  • Tijana Janjic

    Tijana Janjic

    Professor, KU Eichstätt Ingolstadt, Germany
    Learning model parameters from observations by combining data assimilation and machine learning
  • Chris Snyder

    Chris Snyder

    Researcher, NSF NCAR, USA
    Sampling error in the ensemble Kalman filter for small ensembles and high-dimensional states
  • Julien Brajard

    Julien Brajard

    Researcher, NERSC, Norway
    The interplay between data assimilation and artificial intelligence
  • Berent Å. S. Lunde

    Berent Å. S. Lunde

    Researcher, Equinor, Norway
    Linear triangular transport at scale


Dean S. Oliver talk Importance Weighting in Hybrid Iterative Ensemble Smoothers for Data Assimilation
Berent Å. S. Lunde talk Linear triangular transport at scale
Xiaohui Wang talk Improving coastal flooding forecasts with data assimilation using crowdsourced observations
Matthias Morzfeld talk Noise informed covariance estimation
Chris Snyder talk Sampling error in the ensemble Kalman filter for small ensembles and high-dimensional states
Marco Bajo talk Modelling the sea level in the Mediterranean Sea with an Ensemble Kalman Filter
Simone Spada talk Ensemble Kalman Filter Strategies for Efficient Data Assimilation in Geosciences
Xiaodong Luo talk Cross-validation in an iterative ensemble smoother: Stopping earlier for better
Yan Chen talk A Global Ocean Assimilation System using the localized weighted ensemble Kalman filter
Tijana Janjic talk Learning model parameters from observations by combining data assimilation and machine learning
Eliott Lumet talk Reduced-cost EnKF for parameter estimation of microscale atmospheric pollutant dispersion models
Luxi Yu talk Online State and Dynamic Parameter Estimation in Biotherapeutic Production through Ensemble Kalman Filtering
Ian Grooms talk An ensemble adjustment Kalman filter with model-space localization
Alban Farchi talk Online model error correction with neural networks -- from theory to the ECMWF forecasting system
Yiguo Wang talk Post-processing climate reanalysis with the ensemble Kalman smoother
Mélanie Rochoux talk Challenges in building a system for assimilating airborne thermal infrared data to predict wildland fire behavior
Jeffrey van der Voort talk A Multi-Fidelity Ensemble Kalman Filter with a machine learned surrogate model
Yue (Michael) Ying talk Introducing NEDAS: the Next-generation Ensemble Data Assimilation System
Naratip Santitissadeekorn talk Ensemble-based method for Hawkes-process network construction from time-series of count data
Julien Brajard talk The interplay between data assimilation and artificial intelligence
Patrick N. Raanes talk Ensemble control algorithms
Martin Verlaan poster Hybrid physics-AI-model applied to estuarine hydrodynamics
Femke Vossepoel poster Localisation in iterative ensemble smoothers for coupled nonlinear multiscale models
Jenny Soonthornrangsan poster ESMDA for improving land subsidence prediction from a data-driven and physics-based modeling approach: An application to Bangkok, Thailand
Luisa d'Amore poster Space-Time Decomposition of Kalman Filter
Gaël Descombes poster Satellite air quality data assimilation using the Chimere-DART EAKF for the CAMs EvOlution project
Maryam Ramezani Ziarani poster Towards the assimilation of dual-polarization radar data 
Heng Xiao poster Ensemble Neural Filtering: Overcoming Small-Ensemble Limitations in Data Assimilation
Luxi Yu poster Real-time Optimization and Control of Biotherapeutic Product Quality through EnKF and Machine Learning with EnOpt
Matthias Morzfeld poster A theory for why even simple covariance localization is so useful in ensemble Kalman filtering
Laurent Bertino poster Reconstruction of Arctic sea ice thickness (1991-2010) based on hybrid machine learning and data assimilation.
Rolf J. Lorentzen poster Data assimilation featuring deep learning of petrophysical model errors
Kristian Fossum poster Improved subsurface uncertainty quantification with multi-fidelity scenario evaluation
Mathias Methlie Nilsen poster Robust Wind-powered Well Control Optimization
Sergey Alyaev poster AI-based multi-modal interpretation and extrapolation of geophysical logs


Group photo from outside hotel Promenade Group photo from outside hotel Group photo from outside hotel
Pictures from workshop


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, the EnKF and related ensemble methods, has emerged as a highly effective and widely adopted approach for data assimilation in large-scale models, and have made profound impacts on the advancements of various disciplines and promoted value creations for relevant industries.


While the fundamental concept of ensemble methods is 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 EnKF-related methods that have demonstrated effectiveness in diverse environments. In so doing, we strive to enhance the 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 enhance the performance of EnKF and related approaches.


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.

Listing of previous workshops



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