Designing a Large Scale Autonomous Observing Network: A Set Theory Approach

Abstract

A well designed observing network is vital to improve our understanding of the oceans and to obtain better predictions of the future. As autonomous marine technology develops, the potential for deploying large autonomous observing systems becomes feasible. Though there are many design considerations to take into account (according to the target data use cases), a fundamental requirement is to take observations that capture the variability at the appropriate length scales. In doing so, a balance must be struck between the limited observation resources available and how well they are able to represent different areas of the ocean. In this paper we present and evaluate a new method to aid decision makers in designing near-optimal observing networks. The method uses ideas from set theory to recommend an irregular network of observations which provides a guaranteed level of representation (correlation) across a domain. We show that our method places more observations in areas with smaller characteristic length scales and vice versa, as desired. We compare the method to two other grid types: regular and randomly allocated observation locations. Our new method is able to provide comparable average representation of data across the domain, whilst efficiently targeting resource to regions with shorter length scale and thereby elevating the minimum skill baseline, compared to the other two grid types. The method is also able to provide a network that represents up to 15% more of the domain area. Assessing error metrics such as Root Mean Square Error and correlation shows that our method is able to reconstruct data more consistently across all length scales, especially at smaller scales where we see RMSE 2-3 times lower and correlations of over 0.2 higher. We provide an additional discussion on the variability inherent in such methods as well as practical advice for the user. We show that considerations must be made based on time filtering, seasonality, depth and horizontal resolution.