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A semi-evolutive partially local filter for data assimilation.

I Hoteit1, D T Pham, J Blum

  • 1Laboratoire de Modélisation et calcul, Tour IRMA BP 53, Grenoble, France. Ibrahim.Hoteit@imag.fr

Marine Pollution Bulletin
|January 5, 2002
PubMed
Summary

A new semi-evolutive partially local filter reduces the computational cost of data assimilation in oceanic models. This method improves representativity and results compared to the singular evolutive extended Kalman filter.

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Area of Science:

  • Oceanography
  • Numerical modeling
  • Data assimilation

Background:

  • The singular evolutive extended Kalman (SEEK) filter is effective for data assimilation in numerical oceanic models.
  • However, the SEEK filter's high computational cost limits its operational use.

Purpose of the Study:

  • To develop a more cost-effective data assimilation filter for oceanic models.
  • To improve representativity and results in operational assimilation.

Main Methods:

  • Introduction of a 'local correction basis' combined with a few evolving global basis vectors.
  • Development of a semi-evolutive partially local filter.
  • Validation through twin experiments using the OPA model in the tropical Pacific.

Main Results:

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  • The new filter is significantly less costly to implement than the SEEK filter.
  • The semi-evolutive partially local filter yields improved results compared to the SEEK filter.
  • Successful application in a realistic OPA model setting.

Conclusions:

  • The semi-evolutive partially local filter offers a computationally efficient and effective alternative for data assimilation in oceanic models.
  • This approach balances cost reduction with enhanced representativity and performance.
  • The method shows promise for operational oceanographic data assimilation.