This study compared the efficiencies of two widely used automatic eddy detection algorithms—that is, the winding-angle (WA) method and the vector geometry (VG) method—and investigated the submesoscale eddy properties using surface current observations derived from high-frequency radars (HFRs) in the Taiwan Strait. The results showed that the WA method using the streamline and the VG method based on the streamfunction field have almost the same capacity for identifying eddies, but the former is more competent than the latter in capturing the eddy size. The two algorithms simultaneously identified 1080 submesoscale eddies, with the centers and boundaries determined only by the WA method, and they were further used to investigate the eddy properties. In general, no significant difference was observed between the cyclonic and anticyclonic eddies in terms of radius, life span, and kinematics, as well as the evolution during their life cycles. The typical radius of the eddy in this region was 3–18 km. And a strong correlation was observed between the life span and the radius. The spatial distribution of the eddies indicated that topography played a significant role in the generation of the eddies. And the trajectories of the eddies suggested that all the eddies in this area mostly tended to move southeastward. Statistically, three different stages of the eddy’s life span could be identified by the significant growth and decay of the radius and the mean kinetic energy. This study shows the great capability of HFRs in oceanography research and applications, especially for observing the submesocale dynamics.

Wind speed is an important sea surface dynamic parameter which influences a wide variety of oceanic applications. Wave height and wind direction can be extracted from high frequency radar echo spectra with a relatively high accuracy, while the estimation of wind speed is still a challenge. This paper describes an artificial neural network based method to estimate the wind speed in HF radar which can be trained to store the specific but unknown wind-wave relationship by the historical buoy data sets. The method is validated by one-month-long data of SeaSonde radar, the correlation coefficient between the radar estimates and the buoy records is 0.68, and the root mean square error is 1.7 m/s. This method also performs well in a rather wide range of time and space (2 years around and 360 km away). This result shows that the ANN is an efficient tool to help make the wind speed an operational product of the HF radar.

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High-frequency (HF) radars are routinely used for remotely sensing ocean surface currents. However, the performance of the most widely used direction-finding HF radar is degraded due to the effect of the inevitable deviations of actual antenna pattern on the direction of arrival (DOA) estimation. In this paper, we quantify the DOA estimation error resulting from the deviation of the actual antenna pattern from the ideal one. Theoretical analysis and field experiment results suggest that the ratio of the deviations for the two loops dominates the DOA estimation error. Thus, eliminating the effect of the antenna pattern deviations on DOA estimation error is transformed into eliminating the effect of this ratio. From this, a calibration method based on the time-averaged local spatial coverage rate (TLSCR) is proposed to reduce the effect of the antenna pattern deviations on current extraction, which uses the ideal antenna pattern to estimate the DOA of the echoes. To validate this proposed calibration method, we assess the radar-derived radial velocities by comparing with in situ observations. The comparison results indicate that the proposed TLSCR calibration method can effectively reduce the DOA estimation error and improve the performance of the direction-finding HF radar in current observation.

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