## 4 Conclusions  
In this study, we proposed a LSTM algorithm to reduce velocity errors from HF surface wave radar current measurements. Sea surface winds and tides are included in the LSTM algorithm to provide ocean physical information by which to constrain and improve its training efficiency. The results showed that the LSTM algorithm is able to significantly improve radar data quality. The model-simulated velocity fields were treated as the targeting outputs (true values) for the LSTM training process, as no current in situ observations are available at this stage. A practical observation strategy for future field work was tested based on a set of sensitivity experiments.  
The results of this study show that the improvement of the radar current data in the whole domain through the LSTM correction is obvious (Tables 1 and 2). The degree of improvement varies in the three sub-domains, in which the current variability is dominated by different physical factors. Note that in this study we treated the model currents as the true values, which provided us with enough training data for testing various sensitivity experiments. In reality, the deployment of in situ measurements at certain points of this area is necessary for validating the radar data. Therefore, a hypothesis was proposed to test; that is, whether a well-trained LSTM network at one single point could be applied to the whole domain. Testing such a hypothesis has provided a low-cost and feasible observing strategy for future field work. As we can see, this hypothesis applies in areas B and C, where the current variability is determined by large-scale winds and tides. In contrast, the hypothesis does not apply in area A, as the local bathymetry and coastlines are the major interference factors along the coastal area. This implies that an LSTM network including more local factors or sufficient observations might provide a better correction solution for coastal area.  
Conceptualization, J.W.; methodology, J.W. and Y.Y.; validation, C.W.; formal analysis, Y.Y.; investigation, T.L.; resources, F.Y.; data curation, F.Y., L.Z. and T.L.; writing--original draft preparation, Y.Y.; writing--review and editing, J.W.; visualization, Y.Y. and L.Z.; supervision, C.W.; funding acquisition, 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), the National Natural Science Foundation of China (91958101, 41976007).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Experiments** & **Inputs** & **PCCs** & **RMSEs (m/s)** \\ \hline Exp. \#1 & Radar + Winds & 0.7482 & 0.1222 \\ Exp. 2 & Radar + Tides & 0.7696 & 0.1312 \\ Exp. 3 & Radar + Winds + Tides & 0.8794 & 0.0902 \\ Reference & Original radar data & 0.3734 & 0.2174 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Domain-averaged PCCs and RMSEs for the sensitivity experiments.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**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.  
**Acknowledgments:** We thank the two anonymous reviewers for their valuable suggestions that substantially improved the paper.  
**Conflicts of Interest:** The authors declare no conflict of interest.