Ocean Dynamics

, 59:697 | Cite as

Variational data assimilation for parameter estimation: application to a simple morphodynamic model

  • Polly J. Smith
  • Sarah L. Dance
  • Michael J. Baines
  • Nancy K. Nichols
  • Tania R. Scott
Article
First Online:
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Accepted:

Abstract

Data assimilation is a sophisticated mathematical technique for combining observational data with model predictions to produce state and parameter estimates that most accurately approximate the current and future states of the true system. The technique is commonly used in atmospheric and oceanic modelling, combining empirical observations with model predictions to produce more accurate and well-calibrated forecasts. Here, we consider a novel application within a coastal environment and describe how the method can also be used to deliver improved estimates of uncertain morphodynamic model parameters. This is achieved using a technique known as state augmentation. Earlier applications of state augmentation have typically employed the 4D-Var, Kalman filter or ensemble Kalman filter assimilation schemes. Our new method is based on a computationally inexpensive 3D-Var scheme, where the specification of the error covariance matrices is crucial for success. A simple 1D model of bed-form propagation is used to demonstrate the method. The scheme is capable of recovering near-perfect parameter values and, therefore, improves the capability of our model to predict future bathymetry. Such positive results suggest the potential for application to more complex morphodynamic models.

Keywords

Data assimilation Morphodynamic modelling Parameter estimation State augmentation 
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Notes

Acknowledgements

This work is funded under the UK Natural Environmental Research Council (NERC) Flood Risk From Extreme Events (FREE) programme, with additional funding provided by the Environment Agency as part of the Co-operative Awards in Science and Engineering (CASE) scheme. We would like to thank HR Wallingford for visits and useful discussions.

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Copyright information

© Springer-Verlag 2009

Authors and Affiliations

  • Polly J. Smith
    • 1
  • Sarah L. Dance
    • 1
  • Michael J. Baines
    • 1
  • Nancy K. Nichols
    • 1
  • Tania R. Scott
    • 2
  1. 1.Department of MathematicsUniversity of ReadingReadingUK
  2. 2.Environmental Systems Science CentreUniversity of ReadingReadingUK

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