RT Journal Article
ID 38e180c44b56a39c
A1 Altaf, Muhammad Umer
A1 Heemink, Arnold W.
A1 Verlaan, Martin
T1 Inverse Shallow-Water Flow Modeling Using Model Reduction
JF International Journal for Multiscale Computational Engineering
JO JMC
YR 2009
FD 2009-11-10
VO 7
IS 6
SP 577
OP 594
K1 inverse modeling
K1 proper orthogonal decomposition
K1 shallow-water tides
AB The idea presented in this paper is variational data assimilation based on model reduction using proper orthogonal decomposition. An ensemble of forward model simulations is used to determine the approximation of the covariance matrix of the model variability, and only the dominant eigenvectors of this matrix are used to define a model subspace. An approximate linear reduced model is obtained by projecting the original model onto this reduced subspace. Compared to the classical variational method, the adjoint of the tangent linear model is replaced by the adjoint of a linear reduced forward model. Thus, it does not require the implementation of the adjoint of the tangent linear model. The minimization process is carried out in reduced subspace and hence reduces the computational cost. Twin experiments using an operational storm surge prediction model in the Netherlands, the Dutch Continental Shelf Model are performed to estimate the water depth, with the findings that the approach with relatively little computational cost and without the burden of implementation of the adjoint model can be used in variational data assimilation.
PB Begell House
LK http://dl.begellhouse.com/journals/61fd1b191cf7e96f,042e955841389759,38e180c44b56a39c.html