scholarly journals Particle filters for data assimilation based on reduced order data models †

2021 ◽  
Author(s):  
John Maclean ◽  
Erik S. Van Vleck
Keyword(s):  
Data Assimilation ◽  
Particle Filters ◽  
Data Models ◽  
Reduced Order

scholarly journals A Survey of Implicit Particle Filters for Data Assimilation

2013 ◽  
pp. 63-88 ◽  
Author(s):  
Alexandre J. Chorin ◽  
Matthias Morzfeld ◽  
Xuemin Tu
Keyword(s):  
Data Assimilation ◽  
Particle Filters

scholarly journals Parameter Estimation for a 2D Tidal Model with POD 4D VAR Data Assimilation

2016 ◽  
Vol 2016 ◽  
pp. 1-14 ◽  
Author(s):  
Shouguo Qian ◽  
Xianqing Lv ◽  
Yanhua Cao ◽  
Fenjing Shao
Keyword(s):  
Data Assimilation ◽  
Altimeter Data ◽  
Open Boundary ◽  
Phase Lag ◽  
Open Boundary Conditions ◽  
Control Variables ◽  
Tidal Model ◽  
Reduced Order ◽  
Spatially Varying ◽  
Proper Orthogonal

Combining the proper orthogonal decomposition (POD) reduced order method and 4D VAR (four-dimensional Variational) data assimilation method with a two-dimensional (2D) tidal model, a model is constructed to simulate theM2tide in the Bohai, Yellow, and East China Seas (BYECS). This model consists of two submodels: the POD reduced order forward model is used to simulate the tides, while its adjoint model is used to optimize the control variables. Numerical experiment is carried out to assimilate the harmonic constants, which are derived from TOPEX/Poseidon (T/P) altimeter data, into the 2D tidal model through optimizing the initial values and the temporally and spatially varying open boundary conditions (OBCs). The absolute mean difference between the model results and observations is 3.2 cm and 2.9∘for amplitude and phase-lag, respectively, better than the results of Lu and Zhang (2006), suggesting that the construction of the POD reduced order model and the inversion of control variables are successful.

scholarly journals A reduced-order strategy for 4D-Var data assimilation

2005 ◽  
Vol 57 (1-2) ◽  
pp. 70-82 ◽  
Author(s):  
C. Robert ◽  
S. Durbiano ◽  
E. Blayo ◽  
J. Verron ◽  
J. Blum ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Reduced Order

scholarly journals Continuous data assimilation reduced order models of fluid flow

2019 ◽  
Vol 357 ◽  
pp. 112596 ◽  
Author(s):  
Camille Zerfas ◽  
Leo G. Rebholz ◽  
Michael Schneier ◽  
Traian Iliescu
Keyword(s):  
Fluid Flow ◽  
Data Assimilation ◽  
Continuous Data ◽  
Reduced Order Models ◽  
Reduced Order

scholarly journals A Method of Successive Corrections of the Control Subspace in the Reduced-Order Variational Data Assimilation*

2009 ◽  
Vol 137 (9) ◽  
pp. 2966-2978 ◽  
Author(s):  
Max Yaremchuk ◽  
Dmitri Nechaev ◽  
Gleb Panteleev
Keyword(s):  
Data Assimilation ◽  
Numerical Models ◽  
Control Space ◽  
Strong Constraint ◽  
Reduced Order ◽  
Direct Model ◽  
Quasigeostrophic Model ◽  
Low Dimensional ◽  
The Cost ◽  
Better Than

Abstract A version of the reduced control space four-dimensional variational method (R4DVAR) of data assimilation into numerical models is proposed. In contrast to the conventional 4DVAR schemes, the method does not require development of the tangent linear and adjoint codes for implementation. The proposed R4DVAR technique is based on minimization of the cost function in a sequence of low-dimensional subspaces of the control space. Performance of the method is demonstrated in a series of twin-data assimilation experiments into a nonlinear quasigeostrophic model utilized as a strong constraint. When the adjoint code is stable, R4DVAR’s convergence rate is comparable to that of the standard 4DVAR algorithm. In the presence of strong instabilities in the direct model, R4DVAR works better than 4DVAR whose performance is deteriorated because of the breakdown of the tangent linear approximation. Comparison of the 4DVAR and R4DVAR also shows that R4DVAR becomes advantageous when observations are sparse and noisy.

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