High-Frequency Radar Ocean Current Mapping at Rapid Scale with Autoregressive Modeling

TL;DR

This paper introduces an Autoregressive combined with Maximum Entropy Method to improve high-frequency radar surface current mapping, enabling high-quality, rapid, and high-coverage estimations with very short integration times.

Contribution

The novel AR-MEM approach significantly enhances surface current estimation accuracy and success rate over classical methods, especially with minimal data integration time.

Findings

AR-MEM outperforms classical spectral methods in synthetic data tests.

High-quality surface current maps are achievable with about 1-minute data.

Rapid surface current variations are detected and align with turbulent spectral decay.

Abstract

We use an Autoregressive (AR) approach combined with a Maximum Entropy Method (MEM) to estimate radial surface currents from coastal High-Frequency Radar (HFR) complex voltage time series. The performances of this combined AR-MEM model are investigated with synthetic HFR data and compared with the classical Doppler spectrum approach. It is shown that AR-MEM drastically improves the quality and the rate of success of the surface current estimation for short integration time. To confirm these numerical results, the same analysis is conducted with an experimental data set acquired with a 16.3 MHz HFR in Toulon. It is found that the AR-MEM technique is able to provide high-quality and high-coverage maps of surface currents even with very short integration time (about 1 minute) where the classical spectral approach can only fulfill the quality tests on a sparse coverage. Further useful application of the technique is found in the tracking of surface current at high-temporal resolution. Rapid variations of the surface current at the time scale of the minute are unveiled and shown consistent with a $f^{-5/3}$ decay of turbulent spectra.