A semi-evolutive partially local filter for data assimilation

Ibrahim Hoteit, Dinh Tuan Pham, Jacques Blum

Research output: Contribution to journalArticlepeer-review

23 Scopus citations


The singular evolutive extended Kalman (SEEK) filter has been proposed recently by Pham et al. (1997) for data assimilation into numerical oceanic models. This filter has been applied in different realistic ocean frameworks and has provided satisfactory results (Pham et al., 1997; Verron et al., 1998). However, the SEEK filter remains expensive in real operational assimilation. To reduce cost and obtain a better representativity, we introduce the idea `local correction basis'. Such basis however cannot be made to evolve according to the model without destroying its locality property. Therefore we shall keep this basis fixed and we augment it by a few global basis vectors which evolve. The resulting semi-evolutive partially local filter is much less costly to implement than the SEEK filter and yet can yield better results. In the first application, validation twin experiments are conducted in a realistic setting of the OPA model over the tropical Pacific Ocean.

Original languageEnglish (US)
Pages (from-to)164-174
Number of pages11
JournalMarine pollution bulletin
Issue number7-12
StatePublished - 2001
Externally publishedYes


  • Data assimilation
  • EOF analysis
  • Reduced Kalman filtering
  • SEEK filter

ASJC Scopus subject areas

  • Oceanography
  • Aquatic Science
  • Pollution


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