• Corpus ID: 256105802

Efficient optimization of a regional water elevation model with an automatically generated adjoint

  title={Efficient optimization of a regional water elevation model with an automatically generated adjoint},
  author={Tuomas Karna and Joseph G. Wallwork and Stephan C. Kramer},
Calibration of unknown model parameters is a necessary task in most ocean model applications. We present an adjoint-based optimization of an unstructured mesh shallow water model for the North Sea and the Baltic Sea. Spatially varying bottom friction parameter is tuned to minimize the misfit with respect to tide gauge sea surface height (SSH) observations. A key benefit of adjoint-based optimization is that computational cost does not depend on the number of unknown variables. Adjoint models are… 



Adjoint-based optimization of a regional water elevation model

By optimizing the bottom friction, the model’s capability to predict sea surface height improves significantly and the optimization process is robust and computationally efficient.

Calibration, inversion and sensitivity analysis for hydro-morphodynamic models

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A novel discontinuous Galerkin (DG) finite element discretization for the hydrostatic equations that is fully conservative and second-order accurate in space and time and capable of simulating baroclinic flows in the eddying regime.

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Convergence analysis indicates that meshes resulting from the goal-oriented adaptation strategies permit accurate QoI estimation using fewer computational resources than uniform refinement.

Bottom Topography Mapping via Nonlinear Data Assimilation

AbstractA variational data assimilation method is described for bottom topography mapping in rivers and estuaries using remotely sensed observations of water surface currents. The velocity field and