Inferring flow energy, space scales, and timescales: Freely drifting vs. fixed-point observations

Abstract

A novel method for the inference of spatiotemporal decomposition of oceanic surface flow variability is presented and its performance assessed in a synthetic idealized configuration with horizontally divergentless flow. Inference methodology is designed for observations of surface velocity. The ability of networks of surface drifters and moorings to infer the spatiotemporal scales of surface ocean flow variability is quantified. The sensitivity of inference performance for both types of platforms to the number of observations, geometrical configurations, and flow regimes is presented. As drifters simultaneously sample spatial and temporal variability, they are shown to be able to capture both spatial and temporal flow properties even when deployed in isolation. Moorings are particularly adept for the characterization of the flow's temporal variability and may also capture spatial scales provided they are deployed as arrays. In particular, we show that our method correctly identifies whether drifters are preferentially sampling spatial vs.Temporal variability. Pending further developments, this method opens novel avenues for the analysis of existing datasets as well as the design of future experimental campaigns targeting the characterization of small-scale (e.g., <100 km) ocean variability.