A data compression and sampling methodology for ocean observing systems

MBARI, a nonprofit, privately funded research institute devoted to the development of technology to support research in ocean sciences has been developing systems for long term environmental monitoring in Monterey Bay since 1987. The institute has initiated a project for expanding its ocean observing system capabilities through an expansion of existing moored data acquisition systems as well as the additional use of a benthic network, ROV and AUV based data acquisition systems. The goal of this project is to provide semi-continuous observations of important physical, biological, and chemical variables extended in space and time to support event detection, such as the onset of an El Nino, as well as support for focused intermediate-term process studies. In addition to the problems associated with managing a large variety and quantity of data associated to systems of this nature, there is the additional problem of how to optimize the data sampling topology. That is, what spacing and frequency of the system measurement resources will best meet the specific scientific goals of researchers using the system. Given the enormous cost of developing and deploying high technology instrumentation and systems a modest effort to understand, and to develop a sampling methodology for such systems is clearly warranted. In this paper, an approach to the multi-dimensional sampling problem based on data compression is developed. The method is empirically based and does not depend on, or require, assumptions about the underlying data field or processes. The approach can also be used by a system to analyze its own sampling efficiency, and adjust sampling rates and spacing (assuming the system has this capability) for improved efficiency and accuracy. The methodology is illustrated with practical applications to one-dimensional bio-chemical data from the WOCE program, as well as prototypical multi-dimensional problems for the MBARI Ocean Observing System (MOOS).