Regional ocean data assimilation

This article reviews the past 15 years of developments in regional ocean data assimilation. A variety of scientific, management, and safety-related objectives motivate marine scientists to characterize many ocean environments, including coastal regions. As in weather prediction, the accurate representation of physical, chemical, and/or biological properties in the ocean is challenging. Models and observations alone provide imperfect representations of the ocean state, but together they can offer improved estimates. Variational and sequential methods are among the most widely used in regional ocean systems, and there have been exciting recent advances in ensemble and four-dimensional variational approaches. These techniques are increasingly being tested and adapted for biogeochemical applications.

A semi-evolutive partially local filter for data assimilation

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.