Related Experiment Video
Updated: Mar 21, 2026
10:53
Simultaneous Measurement of Turbulence and Particle Kinematics Using Flow Imaging Techniques
Published on: March 12, 2019
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Multiscale data assimilation in turbulent models
Francesco Fossella1,2,3, Luca Biferale2,3, Alberto Carrassi4
1Telecom Paris, LTCI, 19 Place Marguerite Perey, 91120 Palaiseau, France.
Physical Review. E
|March 20, 2026
Summary
Data assimilation using the ensemble Kalman filter (EnKF) in turbulence models improves predictions. Measuring mesoscales synchronizes larger scales, offering a computationally efficient alternative to other data assimilation methods.
Area of Science:
- Fluid Dynamics
- Computational Physics
- Data Assimilation
Background:
- Turbulence modeling involves complex multiscale dynamics.
- Data assimilation (DA) is crucial for improving forecast accuracy.
- Shell models offer a simplified framework for studying turbulence.
Purpose of the Study:
- To investigate the impact of mesoscale measurements on turbulence prediction using data assimilation.
- To evaluate the ensemble Kalman filter (EnKF) within a multiscale shell model.
- To compare EnKF with nudging and ensemble four-dimensional variational methods.
Main Methods:
- Utilized a shell model of turbulence.
- Implemented the ensemble Kalman filter (EnKF) for data assimilation.
- Systematically varied observation frequency and measured scales.
- Benchmarked EnKF against nudging and 4D-Var.
Main Results:
- Mesoscale measurements, exceeding observed scale frequencies, synchronize larger scales when at least two adjacent scales are measured.
- EnKF demonstrated superior performance compared to nudging.
- EnKF showed comparable results to 4D-Var with lower computational cost.
- A scale-aware inflation technique is necessary for stable assimilation.
Conclusions:
- EnKF is an effective and computationally efficient DA method for turbulent flows.
- Targeted mesoscale observations significantly enhance predictions across scales.
- Scale-aware inflation is critical for robust data assimilation in turbulence models.
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