## 4 Conclusions  
On the one hand, ocean current data derived from high-frequency radar are obtained via electromagnetic inversion, which has high-resolution and long-distance detection capabilities. However, it also has the characteristics of being insensitive to physical oceanography and susceptible to electromagnetic interference. On the other hand, bottom-mounted acoustic Doppler current profilers (ADCPs) can allow for the real-time monitoring of ocean currents at a single point. However, bottom-mounted ADCPs require installation on the seafloor, which may increase the difficulty of equipment deployment in certain sea areas. In addition, maintenance is relatively difficult due to the high cost. In this study, we proposed a method by which to monitor a relatively large area at a lower cost by using the ocean current data obtained from a towed ADCP and training them with a long short-term  
Figure 8: Elliptical representation of corrected radar data from bottom-mounted ADCP training, corrected radar data from towed ADCP training, raw radar data, and bottom-mounted ADCP observations using EOF analysis. memory (LSTM) algorithm to reduce the error of the high-frequency radar monitoring of ocean currents. By adjusting the input parameters and introducing wind and tide information, we demonstrated that the addition of this missing oceanographic information can supplement radar flow speed and improve the training efficiency of the LSTM algorithm, thereby improving the effectiveness of the radar monitoring of ocean currents. By training the LSTM algorithm using ocean current data of the same length obtained from both bottom-mounted and towed ADCPs, we found that as the time for correcting the radar increased, the correction effect became worse. However, the reduction in towed ADCP training was more gradual than that of the bottom-mounted ADCP. Through experiments on the time sensitivity of the bottom-mounted and towed ADCPs as the target values, we found that both became more effective in correcting radar currents as more data were available for LSTM learning. However, compared to the bottom-mounted ADCP, the towed ADCP could achieve better radar current correction effects with shorter periods of data. Furthermore, by constructing an EOF ellipse from the corrected 21-day radar currents obtained from bottom-mounted and towed ADCPs, as well as the original ocean currents from bottom-mounted ADCPs and untrained radar currents, we found that the towed ADCP data for 3 days could obtain the magnitude of and variation in the spatial ocean currents for 21 days; in contrast, a bottom-mounted ADCP required more time. However, for a specific area, we could not provide a specific range of the size of the area that the LSTM model trained with towed ADCP data can be used for, as we lacked more bottom-mounted ADCP data for different areas at the same time. Nevertheless, from Figure 8, we can see that as the correction area moved further away from the training area, the effect became worse. In summary, although training with a bottom-mounted ADCP to explore ocean currents can achieve good results in a specific area, it requires a long detection time and has a high cost, as well as a risk of equipment loss. In contrast, training with a towed ADCP to explore ocean currents can improve the correction area of the radar and obtain the magnitude and direction of the ocean currents in this area. Therefore, as long as we can reasonably calculate the time and space scales for towing to correct the radar ocean currents according to our needs, we can obtain safe, low-cost, and high-yield corrected radar ocean current data. Conceptualization, J.W. ; data curation, F.Y. and L.Z. ; formal analysis, Z.X. and C.W. ; funding acquisition, J.W. ; investigation, Z.X. and F.Y. ; methodology, J.W. and Z.X. ; resources, F.Y. and C.W. ; supervision, J.W. and R.H.; validation, F.Y. and C.W. ; visualization, Z.X. ; writing--original draft, Z.X. ; writing--review and editing, J.W. All authors have read and agreed to the published version of the manuscript.