Optimization of the spatial distribution of oceanographic sensors in a highly variable estuarine environment

Ocean observations are difficult and expensive to obtain. Optimal placement of oceanographic sensors can reduce the number of sensors used while improving observational accuracy. This paper presents a new technique for optimal placement of a set of oceanographic sensors in a highly variable environment. The study initially demonstrates how an objective analysis method, which incorporates an inverse distance weighting function, can be used to estimate salinity maps from a small number of sensors. Next, the effectiveness of a particular choice of sensor locations in terms of the expected errors is addressed. Subsequently it is shown how numerical, nonlinear optimization techniques can iteratively modify a set of sensor positions until the optimal choice of sensor placements is achieved by minimizing the expected error. The technique is first evaluated with a series of ground truth simulations using historical data. The paper concludes by presenting the results of a field trial, in which a small number of optimally placed sensor locations are used to develop accurate salinity maps for a complex region of the lower Hudson River with root mean square errors of approximately 1 psu or less for several comparison points.