## 3 Results  
### Corrections for Two Single Points  
Figure 2a shows a comparison of EOF ellipses calculated from the HF radar and model currents. It can be seen that the degree of agreement between the HF radar and model ellipses can be divided into three regions: (1) Along the coast, the EOF ellipses are quite different in their sizes and orientations, which indicates that the complex coastline and islands might be the key factor causing these discrepancies. (2) In the region between 21\({}^{\circ}\) N and 21.5\({}^{\circ}\) N, with the influence of islands and coastlines decreases. The radar and model ellipses almost overlap with each other, indicating that the current fields derived from HF radars and model simulations are well consistent; (3) Further offshore, between 20.5\({}^{\circ}\) N and 21\({}^{\circ}\) N, the two sets of EOF ellipses are not well matched. The orientations of the radar ellipses in some areas are opposite to those of the model. To quantify the difference between the radar and model ellipses, Figure 2b shows their errors formulated by  
\[\sqrt{\left(\text{L}maj_{radar}-\text{L}maj_{model}\right)^{2}+\left(\text{L} min_{radar}-\text{L}min_{model}\right)^{2}}.\]  
According to the error distributions, the radar observing area can easily be marked by three regions from north to south. As shown, area B denotes the minimum errors and the best agreement between the radar and model ellipses. The errors in areas A and C are relatively large, and these will be the targeting areas for the error corrections. We selected two typical points (marked by A and B in Figure 2a) with relatively large errors for testing the correction process. Figure 3 shows the comparison of velocities at point A with the radar, model, and LSTM training results. We can see that, in the training period from 1-25 March, the v-component of the radar and model velocities at point A do not match well with a Pearson correlation coefficient (PCC) of 0.15 and a root-mean-square error (RMSE) of 0.47 m/s (Figure 3a). That is the reason why the two EOF ellipses do not match each other in point A. The training set of LSTM velocities (dashed red line) shows a very good agreement with the model velocities, with a PCC of 0.99 and RMSE of 0.01 m/s. Note that we used 128 neurons in the LSTM network, and therefore the model currents can be exactly reproduced by the LSTM training process. To validate the effectiveness of  
Figure 2: (**a**) EOF ellipses calculated from the HF radar (blue) currents and model (red) currents within the radar observing area. The data points are plotted at 0.25\({}^{\circ}\)\(\times\) 0.15\({}^{\circ}\) intervals. Point A (113.35\({}^{\circ}\)E, 21.95\({}^{\circ}\)N) and point B (113.85\({}^{\circ}\)E, 20.6\({}^{\circ}\)N) are marked with “**”. (**b**) Errors between the radar and model ellipse were calculated using \(\sqrt{\left(\text{L}maj_{radar}-\text{L}maj_{model}\right)^{2}+\left(\text{L} min_{radar}-\text{L}min_{model}\right)^{2}}\), where \(\text{L}maj\) and \(\text{L}min\) denote the major and minor axes of the ellipses. the LSTM training process, the trained regression weights between the input factors and model (outputs) were examined during the validation period 26-30 March. That is, given the radar velocities, the trained weights saved in the LSTM were used to calculate the validation set of the velocities and this was compared with the model. It can be seen that the LSTM velocities in the validation period (purple line) also shows good agreement with the model, with a PCC of 0.88 and RMSE of 0.06 m/s. This is acceptable, as all validation data were not used in the training process. The u-component shows the same features and the LSTM can also produce good training and validating sets of velocities, compared to the model currents (Figure 3b). Figure 4 is the same comparisons as in Figure 3, but for point B in area C. It is noteworthy that the u-component shows almost opposite phase difference between the radar and model currents during the training period (Figure 4b).