A Machine Learning-Based Correction Method for High-Frequency Surface Wave Radar Current Measurements

Abstract

An algorithm based on a long short-term memory (LSTM) network is proposed to reduce errors from high-frequency surface wave radar current measurements. In traditional inversion algorithms, the radar velocities are derived from electromagnetic echo signals, with no constraints imposed by physical oceanographic processes. In this study, sea surface winds and tides are included in the LSTM algorithm to improve radar data. These physical factors provide the LSTM network with more oceanic information by which to constrain and improve its training efficiency. The results show that the domain-averaged root-mean-square errors of the radar-derived velocities are reduced from 0.22 to 0.09 m/s for the whole radar observation area. The overall correlation coefficient increases from 0.37 to 0.88. To provide a practical strategy for future field work, we conduct a set of sensitivity experiments, showing that the LSTM network based on one single point can be applied to other data points within a sub-domain.

Keywords

• HF radar
• machine learning
• LSTM
• EOF ellipse