Estimation of Spatially-Correlated Ocean Currents from Ensemble Forecasts and Online Measurements

TL;DR

This paper introduces a computationally efficient method combining kernel techniques and Bayesian estimation to accurately estimate two-dimensional ocean currents from ensemble forecasts and real-time measurements, aiding marine robotics.

Contribution

The paper presents a novel approach that integrates ensemble data and online measurements for ocean current estimation using kernel methods and recursive Bayesian filtering.

Findings

Method achieves accurate flow field estimates with real-world data.

Computational efficiency surpasses existing techniques.

Demonstrated applicability in marine robotics path planning.

Abstract

We present a method to estimate two-dimensional, time-invariant oceanic flow fields based on data from both ensemble forecasts and online measurements. Our method produces a realistic estimate in a computationally efficient manner suitable for use in marine robotics for path planning and related applications. We use kernel methods and singular value decomposition to find a compact model of the ensemble data that is represented as a linear combination of basis flow fields and that preserves the spatial correlations present in the data. Online measurements of ocean current, taken for example by marine robots, can then be incorporated using recursive Bayesian estimation. We provide computational analysis, performance comparisons with related methods, and demonstration with real-world ensemble data to show the computational efficiency and validity of our method. Possible applications in addition to path planning include active perception for model improvement through deliberate choice of measurement locations.