14th international EnKF workshop (2019)

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

Pieter Houtekamer Environment and Climate Change, Canada	Estimation of model parameters using an evolutionary algorithm
Stephen G. Penny University of Maryland, USA	Transitioning to Strongly Coupled Data Assimilation
Alexandre Emerick Petrobras, Brazil	Recent Ensemble Smoother Applications: Data-Space Inversion and Deep Learning for Facies Models
Takemasa Miyoshi RIKEN, Japan	Big data assimilation: A new science for weather prediction and beyond
Tijana Janjic Pfander Ludwig-Maximilians-Universität München, Germany	Data assimilation on convective scale based on first physical principles
Patrick Nima Raanes NORCE, Norway	EnKF – FAQ
Jincong He Chevron ETC, USA	Ensemble Methods: Challenges Faced In and Lessons Learned From Practical Applications
Remus Hanea Equinor ASA & UiS, Norway	Decision making under uncertainties – a holistic ensemble approach

Presentations

Luca Cantarello	talk	Investigating satellite data assimilation in an idealised framework using an ensemble Kalman filter
Alberto Carrassi	talk	Model error estimation and treatment in data assimilation
Francois Counillon	talk	Building and testing of the first super earth system model
Pieter Houtekamer	talk	Estimation of model parameters using an evolutionary algorithm
Patrick Laloyaux	talk	Bias-aware assimilation methods for numerical weather prediction
Santiago Lopez-Restrepo	talk	LOTOS-EUROS EnKF for PM10 and PM2.5 forecast in Colombia
Jesper Sandvig Mariegaard	talk	Implementation of ensemble Kalman filter data assimilation in a high resolution spectral wave model with application in the southern North Sea
Stephen G. Penny	talk	Transitioning to strongly coupled data assimilation
Tijana Janjic Pfander	talk	Data assimilation on convective scale based on first physical principles
Seoleun Shin	talk	Experiments of RTPS methods for the 4D-LETKF system implemented to a global NWP model on the cubed-sphere
Roland. M. B. Young	talk	Assimilation of ExoMars Trace Gas Orbiter thermal infrared observations into the LMD Mars GCM using the LETKF
Ricardo Baptista	talk	High-dimensional Bayesian filtering with nonlinear local couplings
Maxime Conjard	talk	Multimodality in the ensemble Kalman filter using a selection-Gaussian initial distribution
Fred Daum	talk	Particle flow for nonlinear filters with Gromov’s method
Alban Farchi	talk	On the efficiency of covariance localisation of the ensemble Kalman filter using augmented ensembles
Takemasa Miyoshi	talk	Big data assimilation: A new science for weather prediction and beyond
Patrick Nima Raanes	talk	EnKF – FAQ
Yiguo Wang	talk	Empirical anisotropic multivariate localization in the ensemble Kalman filter for earth system models
Sigurd I. Aanonsen	talk	Using Bayesian model probability with ensemble methods to quantify uncertainty in reservoir modelling with multiple prior scenarios
Julien Brajard	talk	Combining data assimilation and machine learning to emulate a numerical model from noisy and sparse observations
Yuqing Chang	talk	Olympus optimization under geological uncertainty
Alexandre Emerick	talk	Recent ensemble smoother applications: Data-space inversion and deep learning for facies models
Geir Evensen	talk	Implementation of IES in ERT and validation on an FMU example
Jincong He	talk	Ensemble methods: Challenges faced in and lessons learned from practical applications
Xiaodong Luo	talk	Ensemble-based kernel learning for a class of data assimilation problems with imperfect forward simulators
Trond Mannseth	talk	Assimilation of multiple linearly dependent data vectors

Additional presentations may be available by contacting the presenters.

Photos

Photos from the workshop.

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