Optimizing fixed observational assets in a coastal observatory

Abstract Proliferation of coastal observatories necessitates an objective approach to managing of observational assets. In this article, we used our experience in the coastal observatory for the Columbia River estuary and plume to identify and address common problems in managing of fixed observational assets, such as salinity, temperature, and water level sensors attached to pilings and moorings. Specifically, we addressed the following problems: assessing the quality of an existing array, adding stations to an existing array, removing stations from an existing array, validating an array design, and targeting of an array toward data assimilation or monitoring. Our analysis was based on a combination of methods from oceanographic and statistical literature, mainly on the statistical machinery of the best linear unbiased estimator. The key information required for our analysis was the covariance structure for a field of interest, which was computed from the output of assimilated and non-assimilated models of the Columbia River estuary and plume. The network optimization experiments in the Columbia River estuary and plume proved to be successful, largely withstanding the scrutiny of sensitivity and validation studies, and hence providing valuable insight into optimization and operation of the existing observational network. Our success in the Columbia River estuary and plume suggest that algorithms for optimal placement of sensors are reaching maturity and are likely to play a significant role in the design of emerging ocean observatories, such as the United State's ocean observation initiative (OOI) and integrated ocean observing system (IOOS) observatories, and smaller regional observatories.