Ocean Science Journal

, 46:179

Improving the description of the suspended particulate matter concentrations in the southern North Sea through assimilating remotely sensed data

  • Ghada Y. El Serafy
  • Marieke A. Eleveld
  • Meinte Blaas
  • Thijs van Kessel
  • Sandra Gaytan Aguilar
  • Hendrik J. Van der Woerd
Article

DOI: 10.1007/s12601-011-0015-x

Cite this article as:
El Serafy, G.Y., Eleveld, M.A., Blaas, M. et al. Ocean Sci. J. (2011) 46: 179. doi:10.1007/s12601-011-0015-x
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Abstract

The integration of remote sensing data of suspended particulate matter (SPM) into numerical models is useful to improve the understanding of the temporal and spatial behaviour of SPM in dynamic shelf seas. In this paper a generic method based on the Ensemble Kalman Filtering (EnKF) for assimilating remote sensing SPM data into a transport model is presented. The EnKF technique is used to assimilate SPM data of the North Sea retrieved from the MERIS sensor, into the computational water quality and sediment transport model, Delft3D-WAQ. The satellite data were processed with the HYDROPT algorithm that provides SPM concentrations and error information per pixel, which enables their use in data assimilation. The uncertainty of the transport model, expressed in the system noise covariance matrix, was quantified by means of a Monte Carlo approach. From a case study covering the first half of 2003, it is demonstrated that the MERIS observations and transport model application are sufficiently robust for a successful generic assimilation. The assimilation results provide a consistent description of the spatial-temporal variability of SPM in the southern North Sea and show a clear decrease of the model bias with respect to independent in-situ observations. This study also identifies some shortcomings in the assimilated results, such as over prediction of surface SPM concentrations in regions experiencing periods of rapid stratification/de-stratification. Overall this feasibility study leads to a range of suggestions for improving and enhancing the model, the observations and the assimilation scheme.

Key words

data assimilation sediment transport remote sensing Kalman filter North Sea 

Copyright information

© Korea Ocean Research & Development Institute (KORDI) and the Korean Society of Oceanography (KSO) and Springer Netherlands 2011

Authors and Affiliations

  • Ghada Y. El Serafy
    • 1
    • 2
  • Marieke A. Eleveld
    • 3
  • Meinte Blaas
    • 1
  • Thijs van Kessel
    • 1
  • Sandra Gaytan Aguilar
    • 1
  • Hendrik J. Van der Woerd
    • 3
  1. 1.Marine and Coastal Systems, DeltaresDelftNetherlands
  2. 2.Delft Institute of Applied MathematicsDelft University of TechnologyDelftNetherlands
  3. 3.Institute for Environmental Studies (VU-IVM)Vrije Universiteit AmsterdamAmsterdamNetherlands

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