Efficient optimization of a regional water elevation model with an automatically generated adjoint
Tuomas K\"arn\"a, Joseph G. Wallwork, Stephan C. Kramer

TL;DR
This paper presents an efficient adjoint-based optimization method for calibrating a shallow water model of the Baltic Sea, utilizing automatic adjoint generation to improve model accuracy with minimal computational cost.
Contribution
The work introduces an automated framework for generating discrete adjoint models within a domain-specific language, enabling efficient and robust optimization of spatially-varying parameters in complex ocean models.
Findings
Significant improvement in model performance after optimization.
Effective regularization prevents over-fitting of parameters.
Automated adjoint generation reduces implementation effort.
Abstract
Calibration of unknown model parameters is a common task in many ocean model applications. We present an adjoint-based optimization of an unstructured mesh shallow water model for 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, however, typically very laborious to implement. In this work, we leverage a domain specific language framework in which the discrete adjoint model can be obtained automatically. The adjoint model is both exactly compatible with the discrete forward model and computationally efficient. A gradient-based quasi-Newton method is used to minimize the misfit. Optimizing spatially-variable parameters is typically an…
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Taxonomy
TopicsOceanographic and Atmospheric Processes · Geophysics and Gravity Measurements · Climate variability and models
