## 4 Summary  
Traditionally, high-frequency radar-derived ocean current data are obtained based solely on electromagnetic inversion without the constraints of physical oceanography. On the other hand, acquiring single-point in situ current observation data is expensive and their utilization is inadequate, typically limited to the comparison and small-scale correction of radar data. In this study, we proposed a relatively low-cost method that can spatially and comprehensively improve the quality of radar data by using a shipborne ADCP towing approach to obtain actual ocean current data and incorporate that data into the corresponding radar data through machine learning. Based on simulations using model data (Exps. 1-5), we found that the advantages of incorporating fixed-point observation data into the machine learning model are the persistent influence over time on regions with similar surrounding circulation conditions and spatial stability. Meanwhile, the characteristics exhibited when data obtained through shipborne ADCP towing are included in the learning model are different. This method improves the quality of the overall radar data for a longer period but lacks spatial stability: when the towing route spans two areas with different circulation conditions, the improvement effect significantly decreases. Therefore, after a basic evaluation and prediction of the circulation conditions in the Pearl River Estuary, we divided the radar area into three regions and conducted towing experiments separately, resulting in a prototype of our actual operation.  
Due to the limited temporal overlap between radar data and ADCP observational data, the quantity of data available for testing the survey route in this study was relatively small. However, it still reflects, to some extent, the significant improvement in data quality that this method can achieve within a season. In the future, as radar data accumulates gradually, we will consider simulating seasonal shipborne ADCP data to assess the improvement in radar data quality. Considering the cost and practical operational constraints of fieldwork, obtaining long-duration shipborne ADCP data is not feasible. Therefore, in our simulations, the duration of ADCP data is constrained to a few days, which can serve as a reference for field operations. Because of the difficulty and cost of acquiring actual current data for the full radar field, we could not demonstrate the feasibility of this shipborne ADCP method to improve the radar data with the available observational data. However, this method has been validated with the aid of model data, which provides guidance for the fieldwork in the next stage.  
Conceptualization, J.W.; Data curation, F.Y.; Formal analysis, L.Z. and Y.Y.; Funding acquisition, J.W.; Investigation, L.Z. and Z.X.; Methodology, J.W. and L.Z.; Resources, F.Y.; Supervision, J.W.; Validation, F.Y.; Visualization, L.Z.; Writing--original draft, L.Z.; Writing--review& editing, J.W. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Key Research and Development Program of Guangdong Province (2020B1111020003) and the National Basic Research and Development Project of China (2022 YEF0802000). The project was also supported by Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai) No. SML2020 SP009 and the National Natural Science Foundation of China (41976007).  
Not applicable.  
Informed Consent Statement:Informed consent was obtained from all subjects involved in the study.  
Data Availability Statement:The data presented in this study are available on request from the corresponding author. The data are not publicly available due to the fact that parts of radar data on oceanic state information are classified.  
We thank the three anonymous reviewers for their valuable suggestions that substantially improved the paper.  
The authors declare no conflict of interest.