TY - JOUR

T1 - Optimization of the spatial distribution of oceanographic sensors in a highly variable estuarine environment

AU - Rogowski, P.

AU - Stolkin, R.

AU - Bruno, M.

N1 - Copyright 2012 Elsevier B.V., All rights reserved.

PY - 2012/1/1

Y1 - 2012/1/1

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?partnerID=yv4JPVwI&eid=2-s2.0-84859519387&md5=8125277bab2fa54e1688128f5732071c

M3 - Article

AN - SCOPUS:84859519387

SN - 1061-026X

VL - 9

SP - 211

EP - 224

JO - Journal of Marine Environmental Engineering

JF - Journal of Marine Environmental Engineering

IS - 3

ER -