Ensemble Kalman Filtering

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Ensemble Kalman filtering uses a set of simulated states to estimate uncertainty and update model forecasts when new observations arrive, making it useful in weather, geophysical, and fluid-flow data assimilation. Common approaches include the standard EnKF and ensemble square-root filters, often combined with localization and inflation to reduce sampling error in high-dimensional systems. Recent methods also learn updates in latent spaces or with generative models, and are tested on chaotic dynamics, sparse observations, and flows with sharp shocks. A central challenge is balancing computational cost and reliable uncertainty estimates when ensembles are small, dynamics are strongly nonlinear, or observations are incomplete.

Latest papers

13 papers1 paper in the last four weeks, against 1 the four weeks before. 0.0% of all new papers.

Accounting for model error in ensemble variational data assimilation through learned generative dynamics
Oct 12, 2026Marc Bocquet, Tobias S. Finn, Sibo ChengData Assimilation Ensemble Kalman Filtering
CEREA, ENPC, EDF R&D, Institut Polytechnique de Paris, Île-de-France, France
Improving Ensemble Filters with Flow Matching
Sep 23, 2026Haoyuan Chen, Alexandre ThiéryData Assimilation Ensemble Kalman Filtering
Department of Statistics and Data Science National University of Singapore 6 Science Drive 2, Singapore 117546
Residual-Based Adaptive Kalman Filtering for Legged Robot State Estimation
Aug 3, 2026Mihaela Popescu, Dennis Mronga, Shivesh Kumar +1Legged Robotics Kalman Filtering
Learning a System-Level Surrogate for Hydraulic Excavators: A Simulation-to-Real LSTM Approach
Jul 17, 2026Shuai Wang, Shen Wang, Qiang Wang +4Neural Surrogate Modeling Robotic Construction
National Engineering Research Center of Mobile Network Technologies, Beijing University of Posts and Telecommunications, Beijing 100876, China · Yaowu (Shenzhen) Technology Co., Ltd., Shenzhen 518052, China
Event-RGB Adaptive Tracking for Nighttime Highway Perception
Jul 13, 2026Haidong Wang, Hengxing Cai, Wanlei Li +2Autonomous Driving Datasets Intelligent Transportation Systems
School of Intelligent Systems Engineering, Sun Yat-sen University, Shenzhen, China · School of Intelligence Science and Engineering, Harbin Institute of Technology, Shenzhen, China
Feature-preserving Latent-EnKF for Data Assimilation of Flows with Shocks
Jun 10, 2026Hemanth Chandravamsi, Hangchuan Hu, Ponkrshnan Thiagarajan +1Kalman Filtering Data Assimilation
Department of Mechanical Engineering, Johns Hopkins University, Baltimore, MD 21218, USA
Resilient Navigation for Autonomous Farm Robots by Leveraging Jerk-Augmented Models with IMU-Only Disturbance Rejection
Jun 9, 2026Batu Candan, Mohammed Atallah, Simone Servadio +1Kalman Filtering Embodied Navigation
Batu Candan, PhD Candidate, Iowa State University, Ames, IA 50011, USA · Mohammed Atallah, PhD Candidate, Iowa State University, Ames, IA 50011, USA · Dr. Simone Servadio, Assistant Professor, Iowa State University, Ames, IA 50011, USA +1
Hybrid Adaptive Kalman Filtering for Data-Efficient Joint Tracking and Classification
Jun 1, 2026Jiho Lee, Nisar R. Ahmed, Rebecca RussellKalman Filtering Self-Supervised Learning
Charles Stark Draper Laboratory, Inc., Cambridge, MA · Ann and H. J. Smead Department of Aerospace Engineering Sciences at the University of Colorado, Boulder, Boulder, CO · Draper
Learned Memory Attenuation in Sage-Husa Kalman Filters for Robust UAV State Estimation
May 18, 2026Kenan Majewski, Marcin ŻugajKalman Filtering UAV Navigation
Institute of Aeronautics and Applied Mechanics, Warsaw University of Technology, Nowowiejska 24, Warsaw, 00-665, Poland
Pose Tracking with a Foundation Pose Model and an Ensemble Directional Kalman Filter
May 4, 2026Tianlu Lu, Asif Sijan, Thomas Noh +2Kalman Filtering Object Pose Estimation
Information and Computer Sciences Department at the University of Hawaii at Manoa · John A. Burns School of Medicine at the University of Hawaii at Manoa
Latent Autoencoder Ensemble Kalman Filter for Nonlinear Data assimilation
Mar 6, 2026Xin T. Tong, Yanyan Wang, Liang YanData Assimilation Ensemble Kalman Filtering
Department of Mathematics, National University of Singapore, 119076, Singapore · School of Mathematics, Southeast University, Nanjing 210096, China
Neural ensemble Kalman filter: Data assimilation for compressible flows with shocks
Feb 26, 2026Xu-Hui Zhou, Lorenzo Beronilla, Michael K. Sleeman +4Data Assimilation Ensemble Kalman Filtering
Scripps Institution of Oceanography, University of California San Diego, La Jolla, 92093, CA, USA · Computing + Mathematical Sciences, California Institute of Technology, Pasadena, 91125, CA, USA · Department of Mechanical Engineering, Johns Hopkins University, Baltimore, 21218, MD, USA
Flow Matching for Efficient and Scalable Data Assimilation
Aug 18, 2025Taos Transue, Bohan Chen, So Takao +1Flow Matching Kalman Filtering
Department of Mathematics and Scientific Computing and Imaging Institute, University of Utah, Salt Lake City, UT, 84112 · The Computing and Mathematical Sciences Department, California Institute of Technology, 1200 E California Blvd, Pasadena, CA 91125

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Accounting for model error in ensemble variational data assimilation through learned generative dynamics

Marc Bocquet, Tobias S. Finn, Sibo Cheng

CEREA, ENPC, EDF R&D, Institut Polytechnique de Paris, Île-de-France, France

Data Assimilation Ensemble Kalman Filtering

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Many approaches have recently been proposed to combine data assimilation with machine learning for improved forecasts with geophysical models. Here, we focus on combining advanced sequential data assimilation schemes with a generative forecasting model which accounts for complex model errors. This generative model provides a stochastic approximation of the conditional distribution of the successor state given the current state. The proposed variational formulation is a novel one-step weak-constraint 4D-Var scheme in which a learned conditional stochastic forecast replaces the prescribed additive Gaussian model error representation. We derive and implement this formulation as an ensemble variational algorithm, the generative iterative ensemble Kalman filter (IEnKF-G), which generalises the iterative ensemble Kalman filter with additive Gaussian model error (IEnKF-Q). We extend the method to a smoother with an assimilation window spanning multiple time steps and propose a simplified variant. The IEnKF-G is evaluated on a stochastic variant of the Lorenz 1963 model to assess its robustness. It is then tested on a multi-scale variant of the one-dimensional Lorenz 1996 model, in which the resolved variables are affected by model error originating from the hidden small-scale variables. The algorithm is then compared to an ensemble Kalman filter that adaptively mitigates model and sampling errors, and to the IEnKF-Q. The IEnKF-G is generally more accurate in the tested configurations, with substantial gains under partial observations and significant model error. This demonstrates the potential of generative data-driven models when paired with classical data assimilation methods, particularly when model error is substantial.