## 3 Results
### Model and Data Comparisons
It is noteworthy that there is an abnormal increase in PCC after the 20 th day, which may be attributed to the improved echo quality of the raw radar data affected by the environment during this period. To summarize, it has been demonstrated that utilizing short-term fixed observations from multiple points is a feasible and effective approach for enhancing the accuracy of full-field radar detection data. Figure 8 shows the schematic of the experiment with shipborne ADCP survey trajectories, which is called Exp 2. In Exp 1 (Figure 6a,b), the data from five fixed-point observations during the same period were sequentially connected to form a learning sequence for the neural network. In Exp 2, which simulates ideal cruise observations, a cruise observation route following the five points was formed by extracting one-fifth of the data from each ADCP fixed observation and connecting them in chronological order. As the number of fixed points increases and the time spent at each point decreases, continuous observations of fixed points gradually approach the real shipborne ADCP survey. A simple 5-day towpath was designed in the high-precision area of the radar, including 20 points with 6-h data. Figure 9a,b (Exp 4) shows the effect of using this section of shipborne data to improve the raw data for the first 5 days of the full-field radar. Figure 9c,d shows improved data for close to one month, which is reasonably weaker in the high-value region. The time series of RMSE, as in the experiments above, shows steady enhancement over time. Conversely, the time series of PCC, compared to Figure 7, presents a noteworthy feature: the change curve of data improved by the neural network model is approximately parallel to the curve before machine learning. This proves, to some extent, that although the duration of shipborne observation data is not as long as that of fixed-point observation data, the overall improvement effect on radar data quality can remain relatively stable over time due to its longer coverage route. This experiment demonstrates the effectiveness of using shipborne data to improve radar data quality at the numerical simulation level. In addition, a series of sensitivity tests were conducted to test the effect of single-point residence time and the feasibility of multiple towing routes, etc. The results, which are not presented here, indicate that the area covered by the towing route matters for the improvement effect. Based on the circulation characteristics of the Pearl River Estuary [16; 36; 42; 43; 44; 45; 46], the area covered by the radar network data can be roughly divided into three areas (Figure 10a). In Region 1, the movement of currents is mainly affected by river discharge and tides propagating from the open sea. Region 2, located at the end of the river discharge estuary, is affected by runoff to a certain extent and is mainly controlled by nearshore circulation and topography. However, due to the Coriolis force, region 2 is almost unaffected by river discharge, and tides and multi-island landforms are the main factors affecting ocean current movement. Based on the results of sensitivity tests, if the towing route spans any two zones and is utilized to correct the full-field data, the improvement will be significantly worse than if the route is distributed in a single zone. Therefore, the radar data were divided based on the above three regions and were improved using the corresponding towing data to conduct the machine learning (Exp 5; Figure 10c,d). In this way, the PCC between radar data and mode data can be enhanced to 0.59 while RMSE is reduced to 0.19 cm/s. Moreover, such an improvement effect does not require that three routes be carried out simultaneously or continuously, which provides us with strong flexibility from simulation research to actual operation. This approach is adopted to not only improve the effectiveness of machine learning but also to reduce the cost of offshore observation and thus ultimately develop a feasible and practical solution for shipborne ADCP surveying.