## 3 Results
### Corrections for the Whole Domain  
Since the LSTM enabled successful error correction at sites A and B, we applied this algorithm to the whole radar observing area. Figure 5 shows the comparison of the EOF ellipses between the model velocities and the LSTM results. Compared to Figure 2a, all ellipses in the three areas have been corrected to some extent. Table 1 shows the area-averaged PCCs and RMSEs for the three areas. Overall, the LSTM-corrected velocities show significant improvement in the three areas, compared with the original radar velocities.  
Figure 3: Line charts of point A current velocity for (**a**) the v component and (**b**) the u component. Solid black line: 1–30 March model data. Solid blue line: 1–25 March HF radar data. Dashed red line: 1–25 March LSTM training results. Solid purple line: 26–30 March LSTM validation results.  
The LSTM-corrected velocities obtain the smallest RMSE and highest PCC in area C, and the highest RMSE and smallest PCC in area A. This implies that, with physical information with respect to winds and tides, the LSTM works effectively in area C, where the current variability is mostly modulated by tides and winds. In contrast, the current variability in area A is much more complicated. In addition to the winds and tides, the Pearl River runoff and the local topography and coastlines are also important influencers of the currents in area A.  
Figure 4: Line charts of point B current velocity for (**a**) the v component and (**b**) the u component. Solid black line: 1–30 March model data. Solid blue line: 1–25 March HF radar data. Dashed red line: 1–25 March LSTM training results. Solid purple line: 26–30 March LSTM validation results.  
Figure 5: EOF ellipses calculated from LSTM validation results (blue) and model (red) within the radar observation area.  
In the above experiments, since we treated the model currents as the true values, we had enough targeting outputs in each data point for the LSTM training processes, which meant that we could build one LSTM network for one individual data point. This also implies that in future field work, we need in situ measurements at all data points, which is infeasible in practical situations. To provide a feasible observing strategy for the field work, we performed the following experiment: building the LSTM training network at certain points to see if this well-trained network could be applied to other points in areas A to C.  
The experiment was first carried out in area C. We tested LSTM corrections at each point and used the one-point LSTM trained network for all other points in the whole of domain C. As a result, we found that the LSTM network built at point C obtained a PCC of 0.92 and an RMSE of 0.08 m/s (Figure 6). If the LSTM network at point C was applied to the whole of area C, it obtained the smallest domain-averaged RMSE. In view of the good results in area C, this experiment was further carried out in area B. The best site is point D, which produced PCCs between 0.74 and 0.93 for the other points in area B except one point. The worst correction point is located in the upper-left one in area B, near the island in the northwest corner. This point is most likely affected by the island. This experimental strategy does not work for area A (not shown), which implies that local factors such as topography, surrounding islands and coastlines, and their interactions with winds and tides, play more important roles in determining the current variability at each point of area A, so that a single-point LSTM network cannot be used for correcting other points.