We propose a quality control method based on machine learning neural networks to enhance the quality of high-frequency (HF) radar data. Unlike traditional quality control methods that rely on radar signals as indicators and involve extensive data manipulation in specialized software, our approach employs a Bi-LSTM neural network model. This method aims to improve data quality and streamline the quality control process. Through a series of analyses, we demonstrate the feasibility of using machine learning techniques to enhance radar data quality.

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.

This paper proposes an algorithm based on the long short-term memory (LSTM) network to improve the quality of high-frequency surface wave radar current measurements. In order to address the limitations of traditional high-frequency radar inversion algorithms, which solely rely on electromagnetic inversion and disregard physical oceanography, this study incorporates a bottom-mounted acoustic Doppler current profiler (ADCP) and towed ADCP into LSTM training. Additionally, wind and tidal oceanography data were included as inputs. This study compared high-frequency radar current data before and after calibration. The results indicated that both towed and bottom-mounted ADCP enhanced the quality of HF radar monitoring data. By comparing two methods of calibrating radar, we found that less towed ADCP data input is required for the same high-frequency radar data calibration effect. Furthermore, towed ADCP has a significant advantage in calibrating high-frequency radar data due to its low cost and wide calibration range. However, as the location of the calibrated high-frequency radar data moves further away from the towing position, the calibration error also increases. This article conducted sensitivity studies on the times and different positions of using towed ADCP to calibrate high-frequency radar data, providing reference for the optimal towing path and towing time for future corrections of high-frequency radar data.

The performance of a high-frequency (HF) radar network situated within the Pearl River Estuary from 17 July to 13 August 2022 is described via a comparison with seven acoustic Doppler current profilers (ADCPs). The radar network consists of six OSMAR-S100 compact HF radars, with a transmitting frequency of 13–16 MHz and a direction-finding technique. Both the radial currents and vector velocities showed good agreement with the ADCP results (coefficient of determination r²: 0.42–0.78; RMS difference of radials: 11–21.6 $\mathrm{c}\mathrm{m} {\mathrm{s}}^{-1}$; bearing offset $\Delta \theta$: $-4.8°$–$16.1°$; complex correlation coefficient $\gamma$: 0.62–0.96; and phase angle $\alpha$: −24.3$°–$17.8$°$). For these radars, the $\Delta \theta$ values are not constant but vary with azimuthal angles. The relative positions between the HF radar and ADCPs, as well as factors such as the presence of island terrain obstructing the signal, significantly influence the errors. The results of spectral analysis also demonstrate a high level of consistency and the capability of HF radar to capture diurnal and semidiurnal tidal frequencies. The tidal characteristics and the Empirical Orthogonal Function (EOF) results measured by the HF radars also resemble the ADCPs and align with the characteristics of the estuarine current field.

The High-Frequency (HF) radar network has become an effective method for detecting coastal currents. In this study, we confirmed the effectiveness of the HF radar measurements by comparing with the Acoustic Doppler Current Profiler (ADCP) and explore the possibility of assimilating radar data into a regional coastal ocean model. A regional high-resolution model with resolution of 10 m was first built in the Pearl River Estuary (PRE). However, analysis of the Hovmöller diagrams from the model simulations in this study indicated a significant deficiency in representing Near-Inertial Oscillations (NIOs) in the PRE, particularly in the east–west direction, despite including wind fields in the input data, during the week from 3 to 8 August 2022. To overcome the model deficiency, we conducted a set of assimilation experiments and performed sensitivity analyses. The results of sensitivity experiments indicate that the model exhibits a sufficient capacity to replicate NIOs after assimilation, lasting approximately 5–6 days. To further analyze the reasons for the decay in the magnitude of the NIOs, data from the three ADCP stations were compared with model results by applying the momentum equation. The assimilated vertical diffusion term outperforms the unassimilated model in representing NIOs. These findings highlight the importance of the vertical diffusion term for simulating NIOs and the data assimilation in improving the model’s representation of physical processes.