Optimal temperature sampling with SPOTS to improve acoustic predictions

Abstract

The Modular Ocean Data Assimilation System (MODAS) uses optimal interpolation to assimilate data (e.g., XBTs), and to create temperature nowcasts and associated uncertainties. When XBTs are dropped in a uniform grid (during surveys) or in random patterns and spaced according to resources available their assimilation can lead to nowcast errors in complex, littoral regions, especially when only a few measurements are available. To mitigate, Sensor Placement for Optimal Temperature Sampling (SPOTS) [Rike and DelBalzo, Proc. IEEE Oceans (2003)] was developed to rapidly optimize placement of a few XBTs and to maximize MODAS accuracy. This work involves high-density, in situ data assimilation into MODAS to create a ground-truth temperature field from which a ground-truth transmission loss field was computed. Optimal XBT location sets were chosen by SPOTS, based on original MODAS uncertainties, and additional sets were chosen, based on subjective choices by an oceanographer. For each XBT set, a MODAS temperature nowcast and associated transmission losses were computed. This work discusses the relationship between temperature uncertainty, temperature error, and acoustic error for the objective SPOTS approach and the subjective oceanographer approach. The SPOTS approach allowed significantly more accurate acoustic calculations, especially when few XBTS were used. [Work sponsored by NAVAIR.]