Adjoint-based sensitivity analysis for a numerical storm surge model

Warder, Simon C.; Horsburgh, Kevin J. ORCID: https://orcid.org/0000-0003-4803-9919; Piggott, Matthew D.. 2021 Adjoint-based sensitivity analysis for a numerical storm surge model. Ocean Modelling, 160, 101766. https://doi.org/10.1016/j.ocemod.2021.101766

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Official URL: http://dx.doi.org/10.1016/j.ocemod.2021.101766

Abstract/Summary

Numerical storm surge models are essential to forecasting coastal flood hazard and informing the design of coastal defences. However, such models rely on a variety of inputs, many of which carry uncertainty. An awareness and understanding of the sensitivity of model outputs with respect to those uncertain inputs is therefore essential when interpreting model results. Here, we use an unstructured-mesh numerical coastal ocean model, Thetis, and its adjoint, to perform a sensitivity analysis for a hindcast of the 5th/6th December2013 North Sea surge event, with respect to the bottom friction coefficient, bathymetry and wind stress forcing. The results reveal spatial and temporal patterns of sensitivity, providing physical insight into the mechanisms of surge generation and propagation. For example, the sensitivity of the skew surge to the bathymetry reveals the protective effect of a sand bank off the UK east coast. The results can also be used to propagate uncertainties through the numerical model; based on estimates of model input uncertainties, we estimate that modelled skew surges carry uncertainties of around 5cmand 15cmdue to bathymetry and bottom friction, respectively. While these uncertainties are small compared with the typical spread in an ensemble storm surge forecast due to uncertain meteorological inputs, the adjoint-derived model sensitivities can nevertheless be used to inform future model calibration and data acquisition efforts in order to reduce uncertainty. Our results demonstrate the power of adjoint methods to gain insight into a storm surge model, providing information complementary to traditional ensemble uncertainty quantification methods.

Item Type:	Publication - Article
Digital Object Identifier (DOI):	https://doi.org/10.1016/j.ocemod.2021.101766
Programmes:	NOC Programmes > Marine Physics and Ocean Climate
ISSN:	14635003
Date made live:	28 Jun 2021 15:49 +0 (UTC)
URI:	https://nora.nerc.ac.uk/id/eprint/530311

Item Type:

Publication - Article

Digital Object Identifier (DOI):

https://doi.org/10.1016/j.ocemod.2021.101766

Programmes:

NOC Programmes > Marine Physics and Ocean Climate

ISSN:

14635003

Date made live:

28 Jun 2021 15:49 +0 (UTC)

URI:

https://nora.nerc.ac.uk/id/eprint/530311