Integration of a 3D variational data assimilation scheme with a coastal area morphodynamic model of Morecambe Bay

• Published in: Coastal engineering (Amsterdam) Vol. 69; pp. 82 - 96
• Main Authors: Thornhill, G.D., Mason, D.C., Dance, S.L., Lawless, A.S., Nichols, N.K., Forbes, H.R.
• Format: Journal Article
• Language: English
• Published: Kidlington Elsevier B.V 01-11-2012; Elsevier
• Subjects: 3D-Var; Applied geophysics; Assimilation; Calibration; Coastal morphology; Data assimilation; Earth sciences; Earth, ocean, space; Engineering and environment geology. Geothermics; Engineering geology; Exact sciences and technology; Internal geophysics; Marine and continental quaternary; Mathematical analysis; Mathematical models; Morecambe Bay; Morphodynamic modelling; Sediment transport; Skills; Surficial geology; Three dimensional; Great Britain; Europe; Atlantic Ocean; England; United Kingdom; North Atlantic; North Sea; Northeast Atlantic; Morecambe Bay; Western Europe; ANE, British Isles, England, Morecambe Bay; 3D-Var; Coastal morphology; Morecambe Bay; Morphodynamic modelling; Data assimilation; morphology; accuracy; remote sensing; covariance; hydrodynamics; coastal environment; interpolation; sediment transport; imagery; three-dimensional models; bathymetry; calibration; errors; landform evolution; Synthetic aperture radar
• ISSN: 0378-3839; 1872-7379
• DOI: 10.1016/j.coastaleng.2012.05.010

This paper describes the implementation of a 3D variational (3D-Var) data assimilation scheme for a morphodynamic model applied to Morecambe Bay, UK. A simple decoupled hydrodynamic and sediment transport model is combined with a data assimilation scheme to investigate the ability of such methods to improve the accuracy of the predicted bathymetry. The inverse forecast error covariance matrix is modelled using a Laplacian approximation which is calibrated for the length scale parameter required. Calibration is also performed for the Soulsby–van Rijn sediment transport equations. The data used for assimilation purposes comprises waterlines derived from SAR imagery covering the entire period of the model run, and swath bathymetry data collected by a ship-borne survey for one date towards the end of the model run. A LiDAR survey of the entire bay carried out in November 2005 is used for validation purposes. The comparison of the predictive ability of the model alone with the model-forecast–assimilation system demonstrates that using data assimilation significantly improves the forecast skill. An investigation of the assimilation of the swath bathymetry as well as the waterlines demonstrates that the overall improvement is initially large, but decreases over time as the bathymetry evolves away from that observed by the survey. The result of combining the calibration runs into a pseudo-ensemble provides a higher skill score than for a single optimized model run. A brief comparison of the Optimal Interpolation assimilation method with the 3D-Var method shows that the two schemes give similar results.
► Data assimilation improves prediction of bathymetry changes in morphodynamic model. ► Limited observations provide a significant improvement in predictive capability. ► Remote sensing observations are combined with in situ observations. ► Combining model runs in an ensemble improves model predictions further.