Shallow-water REA using autonomous ocean sampling networks

Abstract

Environmental management and naval operations in coastal waters are severely limited by a lack of oceanographic predictive capability associated with the strong spatial gradients and temporal variability inherent to such environments. A key to improving the predictive capability is a measurement system which can provide data in close to real time for assimilation into oceanographic circulation models. In that regard, the Autonomous Ocean Sampling Network (AOSN) combines small, autonomous underwater vehicles (AUV) with an underwater communication and navigation network to provide an ocean measurement system with real-time data access and adaptive sampling capabilities. The acoustic communication capability of the AOSN allows for feedback to the sensor platforms of uncertainties of the field estimates, information which can then be used to redistribute the AOSN resources optimally. The development of a new observation and prediction capability combining the AOSN with efficient coastal circulation models is the objective of an ongoing multidisciplinary research effort, but the feasibility has already been demonstrated through a series of field deployments. The results of these experiments are reviewed, and the potential for this new rapid environmental assessment capability for littoral environments is discussed.