## 4 Conclusions  
Since no in situ observations are available in the radar observing domain, a regional high-resolution ocean model covering the entire PRE and its adjacent seas was first established and validated with in situ measurements. The HF radar data quality was then examined against the model simulations. Many previous studies on the flow field characteristics of the PRE and its adjacent waters were based on model simulations [3; 4; 5; 6]. In this study, the FVCOM modeling skill assessment suggested that the model can satisfactorily simulate the tidal currents in the PRE, and therefore it is used as a benchmark to assess the HF radar data quality. With a set of sensitivity experiments, we can diagnose the roles of tides, winds, river discharges, and topography on the radar data quality. Although the radar measurements are derived from the inversion algorithm of electromagnetic echoes, the radar data quality apparently is affected by the above-mentioned physical factors. Based on the sensitivity experiments, the coastal zone to the left-hand side of the PRE mouth is affected primarily by river discharges and tides. These two factors, together with complex coastlines and islands, prevent HF radar from delivering high-quality information for determining surface currents. Over the shelf, the effects of river discharges and tides weakened, and the winds become the major physical factors. The HF radars obtain very high-quality data in area B, but poor data in area C. This does not make sense, as the physical processes in the two areas are almost the same. The radial projection of model-radar comparisons indicates that the main errors in area C are very likely from the Shangchuan radar. We note that the model results are not perfect, but it provides useful information to identify the radar error sources, which can be done by pointwise in situ observations. In summary, the HF radar provided wide-range measurements of ocean surface currents and waves, while the radar data quality is subject to many potential errors [38],  
Figure 11: The standard deviation (cm/s, shaded) in the sensitivity experiments: **(a)** control experiment; **(b)** the difference between control and no_wind cases; **(c)** the difference between control and no_tide cases; **(d)** the difference between control and no_river cases. for example, the environmental noises, the inversion algorithms, the ocean physical factors, local topography, and so on. Given that the radar data were derived from airborne electromagnetic echoes, the local bathymetry, and island effects apparently could not be resolved. Therefore, a machine-learning-based inversion algorithm including traditional electromagnetic analysis and a variety of physical processes is needed to develop and improve radar data quality. So far, different types of machine-learning methods (clustering neural network, convolution neural network, etc.) have been included in the extraction of physical variables from the electromagnetic spectrum [39; 40; 41; 42; 43]. In this regard, this study is a first step toward the full deployment of the PRE radar network. It provides a physical analysis of tides, river discharges, winds, and topography, assesses the data quality of the existing radars, and identifies the error sources, all of which is useful information to proceed with the improvement of the radar network. Conceptualization, J.W. ; Data curation, F.Y. ; Formal analysis, L.Z. and T.L. ; Funding acquisition, J.W. ; Investigation, L.Z. and T.L. ; Methodology, L.W. ; Resources, F.Y. ; Supervision, B.L. ; Validation, L.W. ; Visualization, L.Z. and T.L. ; 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), the National Natural Science Foundation of China (41976007, 91958101) and the project supported by Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai) No. SML2020 SP009.