The primary goal of the MIT component is to provide expertise in ocean modeling, data assimilation and uncertainty quantification for coupled oceanographic-acoustic predictions. The specific goals are to:
Characterize and Forecast Oceanographic Variability and Uncertainty -- The MIT team provides forecast and hindcast simulations of the uncertainty in the environment. In such high-fidelity multi-resolution simulations, the probability density function of the full ocean state is predicted by an ensemble of simulations that integrate model estimates with observations. The scheme for such data-assimilative field and uncertainty prediction is the Error Subspace Statistical Estimation (Lermusiaux, 2007). Dynamically-balanced stochastic forcing are included (Lermusiaux, 2006), so as to represent effects of sub-grid-scales not resolved by the deterministic model equations. The results are stochastic partial differential equations that allow to capture both deterministic effects (advection, Coriolis, etc.) and statistical effects (smaller-scale turbulence, internal wave variability, etc.) on the environment. With this modeling and data assimilation, accurate estimates of the probability density functions (pdf) of oceanographic variability are available. They become inputs to our end-to-end oceanographic-seabed-acoustic-sonar probabilistic TDAs and thus allow us to estimate and forecast realistic acoustic vulnerability. These oceanographic pdf estimates are provided by the MIT team for the East China Sea, Taiwan and Kuroshio region. Other regions which involve operationally relevant ocean-acoustic studies, e.g. the Middle Atlantic Bight Shelfbreak front region, as well as the Kauai Strait and Hawaiian Islands region (see also here) can also be utilized.
Combine MSEAS with OASIS acoustics to generate realistic scenario in East Asian Seas -- The above components are combined to form an interdisciplinary system for assessing sonar system performance and vulnerabilities. Such an end-to-end system (Lermusiaux et al, 2002; Lermusiaux and Robinson, 2004), couples and integrates data and models from meteorology, physical oceanography, geoacoustics, ocean acoustics, bottom, noise, target and sonar. This approach was exercised and validated in the northeastern Taiwan region within the context of both the 2008 Quantifying, Predicting, and Exploiting Uncertainty (QPE 2008) pilot experiment (Lermusiaux et al, 2010) and the QPE IOP09 real-time exercise.