Optimal Sampling Strategies for Oceanic Applications

We have developed a method for optimal array design and applied it to a suite of applications, including the design of a surface mooring array in the tropical Indian Ocean (Sakov and Oke 2007). The method builds on the work of Bishop et al. (2001), using data assimilation theory to determine the observation locations that best constrain a data assimilating ocean model. The method seeks to identify the set of observations that minimize the analysis error variance of a predefined variable or quantity. We assume that the purpose of making a series of observations is to initialize a data assimilating ocean model. At present, we assume that the data assimilation scheme used to assimilate the observations is ensemble optimal interpolation (EnOI; e.g., Oke et al. 2006). Under this project, we propose to modify our approach, to accommodate other data assimilation methods (e.g., Ensemble Kalman Filter, EnKF).