## 3 Results
### Model and Data Comparisons
Figure 6: Same as Figure 5, only using (**a**,**b**) close to a month’s worth of data at 5 mooring locations (Exp 1) and (**c**,**d**) extra hypothetical observation points (Exp 1 plus) to improve the radar data for the whole field. Original observation points are marked with R1–R5. The extra hypothetical points are marked with S1–S3. \begin{table}
\begin{tabular}{c c c c} \hline \multirow{2}{*}{**Experiment**} & \multicolumn{2}{c}{**Training Points**} & \multirow{2}{*}{**Validation Duration**} & \multirow{2}{*}{**Results**} \\  & **(Data Duration for Each Point)** & & \\ \hline Exp 1 & 5 (28 days) & 28 days & Figure 6a,b \\ Exp 1 plus & 7 (28 days) & 28 days & Figure 6c,d \\ Exp 2 & 5 (10 days) & 10–28 days & Figure 7 \\ Exp 3 & 5 (6 days continuous) & 5–28 days & Figure 8 \\ Exp 4 & 20 (6 h continuous) & 5–28 days & Figure 9 \\ Exp 5 & 20 + 12 + 24 (6 h continuous) & 5–28 days & Figure 10 \\ \end{tabular}
\end{table} 
Table 2: LSTM model setup for the sensitivity experiments. Figure 8: Schematic representation of the experiment with shipborne ADCP survey trajectories (Exp 2). R1-R5 represent the location of ADCPs. Figure 7: Time series of full-field mean RMSE and correlation coefficient before (solid gray line) and after (solid black line) correction by neural network algorithms. The design of the experiment was the same as in Figure 6 but with a 10-day correction. Figure 9: Spatial distribution of (**a,b**) 5 days and (**c,d**) 28 days, and time series of Pearson correlation coefficient (PCC) and root mean square error (RMSE) calculated from radar data and model results for simulated shipborne ADCP survey (Exp 4). The plus sign indicates the position where the data was collected. Sensitivity experiments with extra points (Exp 1 plus, Figure 6c,d) indicate that additional data points can effectively enhance the quality of radar data in their vicinity without significantly affecting the rest of the high-precision area (Exp 1 plus). This provides the basis for the design and testing of a neural network model for a theoretical shipborne ADCP survey. However, the impact range when data points at different locations are added is not uniform. This is mainly attributed to the influence of the terrain on ocean currents: in relatively wide and open seas, the continuity of ocean currents allows for significant improvement with only a small number of data points (S1, S2), whereas in the multi-island area near Hong Kong Island, the complexity of ocean currents reduces the range of influence of additional data points (S3). The above experiments were based on a few fixed points with 1 month of data to improve the quality of overall field radar data for the entire month. We are also curious about how the improvement effect of entire month radar data if the duration of training data becomes shorter. To explore the evolution of the overall field error following the application of the neural network algorithm's correction over time, a sensitivity experiment spanning 10 days was devised. Figure 7 shows the evolution of the RMSE and PCC over time following the algorithm correction. The RMSE between the original radar data and the model results remained consistent, ranging between 0.33 and 0.35 over nearly a month. Figure 10: Spatial distribution of Pearson correlation coefficient (PCC) and root mean square error (RMSE) for (**a,b**) original radar data and (**c,d**) data improved by shipborne ADCP experiments (Exp 5) calculated from radar data and model results. The plus sign indicates the position where the data was collected. Similarly, the RMSE of the improved data demonstrates stability, with values between 0.18 and 0.2 that exhibit a minimal increase over time. However, the variation in the correlation coefficient is different. The original radar data remain low, at around 0.38, while the PCC of the data corrected by the neural network reaches 0.55 and gradually decreases to the level before correction.