Time-resolved reconstruction of turbulent flows using linear stochastic estimation and sequential data assimilation

2020 ◽  
Vol 32 (7) ◽  
pp. 075106
Author(s):  
Chuangxin He ◽  
Yingzheng Liu
Keyword(s):  
Data Assimilation ◽  
Turbulent Flows ◽  
Stochastic Estimation ◽  
Sequential Data ◽  
Time Resolved ◽  
Linear Stochastic Estimation ◽  
Sequential Data Assimilation

TREATMENT OF THE ERROR DUE TO UNRESOLVED SCALES IN SEQUENTIAL DATA ASSIMILATION

2011 ◽  
Vol 21 (12) ◽  
pp. 3619-3626 ◽  
Author(s):  
ALBERTO CARRASSI ◽  
STÉPHANE VANNITSEM
Keyword(s):  
Dynamical System ◽  
Kalman Filter ◽  
Data Assimilation ◽  
Large Scale ◽  
Model Error ◽  
Environmental Modeling ◽  
Sequential Data ◽  
Chaotic Dynamical System ◽  
Error Covariance ◽  
Sequential Data Assimilation

In this paper, a method to account for model error due to unresolved scales in sequential data assimilation, is proposed. An equation for the model error covariance required in the extended Kalman filter update is derived along with an approximation suitable for application with large scale dynamics typical in environmental modeling. This approach is tested in the context of a low order chaotic dynamical system. The results show that the filter skill is significantly improved by implementing the proposed scheme for the treatment of the unresolved scales.

Error covariance modeling in sequential data assimilation

2001 ◽  
Vol 15 (1) ◽  
pp. 65-86 ◽  
Author(s):  
J. Sénégas ◽  
H. Wackernagel ◽  
W. Rosenthal ◽  
T. Wolf
Keyword(s):  
Data Assimilation ◽  
Sequential Data ◽  
Error Covariance ◽  
Covariance Modeling ◽  
Sequential Data Assimilation
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