Variational assimilation of surface wave data for bathymetry reconstruction. Part I: algorithm and test cases

Accurate mapping of ocean bathymetry is needed for effective modelling of ocean dynamics, such as tsunami prediction. Available bathymetry data does not always provide the resolution to model such nonlinear waves accurately, and collection of accurate data is logistically challenging. As an alternat...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	R. A. Khan, N. K.-R. Kevlahan
Formato:	article
Lenguaje:	EN
Publicado:	Taylor & Francis Group 2021
Materias:	bathymetry estimation density-based sensitivity analysis (dbsa) global sensitivity analysis (gsa) shallow water equations tsunami modelling Oceanography GC1-1581 Meteorology. Climatology QC851-999
Acceso en línea:	https://doaj.org/article/2397821e664f41c6a303e106e503b495
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:2397821e664f41c6a303e106e503b495
record_format	dspace
spelling	oai:doaj.org-article:2397821e664f41c6a303e106e503b4952021-12-01T14:40:59ZVariational assimilation of surface wave data for bathymetry reconstruction. Part I: algorithm and test cases1600-087010.1080/16000870.2021.1976907https://doaj.org/article/2397821e664f41c6a303e106e503b4952021-01-01T00:00:00Zhttp://dx.doi.org/10.1080/16000870.2021.1976907https://doaj.org/toc/1600-0870Accurate mapping of ocean bathymetry is needed for effective modelling of ocean dynamics, such as tsunami prediction. Available bathymetry data does not always provide the resolution to model such nonlinear waves accurately, and collection of accurate data is logistically challenging. As an alternative, in this study we develop and evaluate a variational data assimilation scheme for the one-dimensional nonlinear shallow water equations that estimates bathymetry using a finite set of observations of surface wave height. We demonstrate that convergence to exact bathymetry is improved by including more observation locations and by implementing a low-pass filter in the data assimilation algorithm to remove small-scale noise. A necessary condition for convergence of the bathymetry reconstruction is that the amplitude of the initial conditions is less than 1% of the bathymetry height. We use density-based global sensitivity analysis (GSA) to assess the sensitivity of the surface wave and reconstruction error to model parameters. By demonstrating low sensitivity of the surface wave to the reconstruction error, we show that reconstructing the bathymetry with a relative error of about 10% is sufficiently accurate for surface wave modelling in most cases. These results can be used to guide the development of similar assimilation schemes in higher dimensions and more realistic geometries.R. A. KhanN. K.-R. KevlahanTaylor & Francis Grouparticlebathymetry estimationdensity-based sensitivity analysis (dbsa)global sensitivity analysis (gsa)shallow water equationstsunami modellingOceanographyGC1-1581Meteorology. ClimatologyQC851-999ENTellus: Series A, Dynamic Meteorology and Oceanography, Vol 73, Iss 1, Pp 1-25 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	bathymetry estimation density-based sensitivity analysis (dbsa) global sensitivity analysis (gsa) shallow water equations tsunami modelling Oceanography GC1-1581 Meteorology. Climatology QC851-999
spellingShingle	bathymetry estimation density-based sensitivity analysis (dbsa) global sensitivity analysis (gsa) shallow water equations tsunami modelling Oceanography GC1-1581 Meteorology. Climatology QC851-999 R. A. Khan N. K.-R. Kevlahan Variational assimilation of surface wave data for bathymetry reconstruction. Part I: algorithm and test cases
description	Accurate mapping of ocean bathymetry is needed for effective modelling of ocean dynamics, such as tsunami prediction. Available bathymetry data does not always provide the resolution to model such nonlinear waves accurately, and collection of accurate data is logistically challenging. As an alternative, in this study we develop and evaluate a variational data assimilation scheme for the one-dimensional nonlinear shallow water equations that estimates bathymetry using a finite set of observations of surface wave height. We demonstrate that convergence to exact bathymetry is improved by including more observation locations and by implementing a low-pass filter in the data assimilation algorithm to remove small-scale noise. A necessary condition for convergence of the bathymetry reconstruction is that the amplitude of the initial conditions is less than 1% of the bathymetry height. We use density-based global sensitivity analysis (GSA) to assess the sensitivity of the surface wave and reconstruction error to model parameters. By demonstrating low sensitivity of the surface wave to the reconstruction error, we show that reconstructing the bathymetry with a relative error of about 10% is sufficiently accurate for surface wave modelling in most cases. These results can be used to guide the development of similar assimilation schemes in higher dimensions and more realistic geometries.
format	article
author	R. A. Khan N. K.-R. Kevlahan
author_facet	R. A. Khan N. K.-R. Kevlahan
author_sort	R. A. Khan
title	Variational assimilation of surface wave data for bathymetry reconstruction. Part I: algorithm and test cases
title_short	Variational assimilation of surface wave data for bathymetry reconstruction. Part I: algorithm and test cases
title_full	Variational assimilation of surface wave data for bathymetry reconstruction. Part I: algorithm and test cases
title_fullStr	Variational assimilation of surface wave data for bathymetry reconstruction. Part I: algorithm and test cases
title_full_unstemmed	Variational assimilation of surface wave data for bathymetry reconstruction. Part I: algorithm and test cases
title_sort	variational assimilation of surface wave data for bathymetry reconstruction. part i: algorithm and test cases
publisher	Taylor & Francis Group
publishDate	2021
url	https://doaj.org/article/2397821e664f41c6a303e106e503b495
work_keys_str_mv	AT rakhan variationalassimilationofsurfacewavedataforbathymetryreconstructionpartialgorithmandtestcases AT nkrkevlahan variationalassimilationofsurfacewavedataforbathymetryreconstructionpartialgorithmandtestcases
_version_	1718404981889957888

Variational assimilation of surface wave data for bathymetry reconstruction. Part I: algorithm and test cases

Ejemplares similares