Robust System Identification of Strongly Non-linear Dynamics Using a Polynomial Chaos-Based Sequential Data Assimilation Technique

2007 ◽  
Author(s):  
George Saad ◽  
Roger Ghanem ◽  
Sami Masri
Keyword(s):  
System Identification ◽  
Data Assimilation ◽  
Polynomial Chaos ◽  
Sequential Data ◽  
Linear Dynamics ◽  
Non Linear ◽  
Robust System ◽  
Data Assimilation Technique ◽  
Sequential Data Assimilation

scholarly journals Application of a Sequential Data Assimilation Technique to Improve Modeling of Surface Currents Using Radar Data at a Coastal Domain

10.29007/w7nn ◽  
2018 ◽  
Author(s):  
Lei Ren ◽  
Michael Hartnett
Keyword(s):  
Numerical Model ◽  
Data Assimilation ◽  
Radar Data ◽  
Surface Flow ◽  
Sequential Data ◽  
Surface Currents ◽  
Hydrodynamic Parameters ◽  
Definition Of ◽  
Data Assimilation Technique ◽  
Sequential Data Assimilation

Numerical model is generally to simulate hydrodynamic parameters such as surface currents. However, it has limits such as difficulty in definition of initial and boundary conditions. As remote sensing such as satellite and radars advances and is applied in practice. Data assimilation technique has becoming a promising means to improve modeling performance through taking advantages of available observations. In this paper, surface currents hourly monitored by a radar system were assimilated into a 3D numerical model to improve modeling performance using a sequential data assimilation algorithm. Results indicated that application proposed data assimilation approach not only improved hindcasting of surface flow fields, but also improved its forecasting.

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.

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