## 3 Results  
### Model and Data Comparisons  
To examine the original radar data quality and model performance, the radar, model, and ADCP EOF ellipses were compared at five mooring locations (Figure 4: R1-R5). The model-derived EOF ellipses are consistent with the ellipses derived from ADCPs, indicating that the model simulations captured the velocity variance recorded by the ADCPs. The radar-derived EOF ellipse at the R2 position is in very good agreement with the model and ADCPs; however, at the other four stations, the radar ellipses do not accurately capture the recorded velocity variance in terms of their sizes and orientations. The degraded quality of the radar data is likely due to island obstruction and complex topography and coastlines. Since the simulated velocities in the model were validated through comparison with ADCPs, it is reasonable to evaluate the quality of radar data for the whole domain by comparing it with model simulations (Figure 5). The spatial distribution of Pearson correlation coefficient (PCC) and root mean square error (RMSE) show that the radar network has higher PCC values in the model simulations of the estuarine area along the left-side coast of the PRE (from Qi'ao Island to Hengqin Island). However, the PCC values decrease significantly toward the open ocean, with a minimum value near Lantau Island. The RMSE distribution shows a similar pattern, indicating that the radars perform well within the PRE, but the data quality is degraded at the mouth of the PRE, where several islands are embedded. Around these areas with islands, both statistical indicators show that errors are likely to increase due to the complexity of current movement and the occlusion and reflection of signals from the islands. The spatially averaged PCC between the original radar network data and model results is 0.3689, with an average RMSE velocity of 0.3488 m/s. As is known, radar data are obtained using conventional electromagnetic inversion techniques. In traditional inversion algorithms, sea surface velocity is directly extracted from electromagnetic echoes, with constraints from oceanographic processes. Therefore, the aim of our work was to enhance the quality of unprocessed radar data by imposing restrictions on the physical procedures using neural network learning algorithms. The movement of currents in estuaries is primarily impacted by physical elements, including wind, tides, and river discharge. These physical processes, which are concealed in individual point observations, can be relatively accurately and clearly represented in the model of the entire estuary area. Since adequate spatially realistic data on ocean currents are lacking, it is reasonable to utilize model simulation to conduct experiments and validate the effectiveness and feasibility of our learning algorithms. The ocean model simulated data were used in the training processes for testing the LSTM model to design the theoretical survey trajectory, which will provide very useful guidance for our future in-situ fieldwork. As shown in Figure 6a,b, model data from five points in the same locations as the actual observations were used to build the LSTM neural network, combined with corresponding physical information such as tides and winds, to improve the quality of the full-field radar data (Exp 1). The results indicate a noteworthy enhancement of PCC subsequent to machine learning correction. Specifically, the domain-averaged PCC increased from 0.3689 to 0.5575, while the domain-averaged RMSE decreased from 0.3488 to 0.2014 m/s. Furthermore, the closer the location to the selected data points, the more noticeable the improvement of the machine learning model. The settings of all the sensitivity experiments for the machine learning models are given in Table 2.  
Figure 5: Spatial distribution of (**a**) Pearson correlation coefficient (PCC) and (**b**) root mean square errors (RMSE) calculated from radar data and model results. The Qi’ao, Lantau, and Hengqin Islands are marked.