We are pleased the reviewer has found our manuscript and data to be useful for researchers interested in the region. 

Technical comments:

1. Beside studies on long-term variation, research on rapid varying current is very important (see, e.g., DOI: 10.1109/JOE.2016.2591718).

Answer:  Thank you for your suggestion. The rapid fluctuation of the ocean currents is indeed important. Therefore, we added text and citations in the revised manuscript in line 47.

1. You may also consider comparison of the currents results obtained by Seasonde and WERA systems.

Answer:  This is a good point and has been investigated in other regions, however, the two operating radar systems in the east Australia current are situated far away from each other (more than 300 km, beyond the observational range of both radars). Which prohibits this analysis in our region.

1. Add explanation about how W^u is chosen.

Answer:  In this work, we performed a similar practice as in Yaremchuk et al. (2017), section 4.1, for identifying the weight parameters of the 2dVar approach. The was roughly estimated based on the formula: W^u = 0.05 * sigma^2 * l^4, in which sigma^2 is the sum of the diagonal values of the noise covariance matrix from the radial data and l is the spatial resolution of the radial data (which are 6 km in NEWC and 1.5 km in COF). The equation represents the cut-off scale, which is approximately twice the radial resolution. After fixing the value, we adjusted the value of W^u and used the drifter data for NEWC and mooring data for COF radar until the optimal values were found. New text was added to the section 3.2 for clarity.

Other comments:

1. Line 245, delete “the” before “K”.

Answer: Thank you for your suggestion. The phrase was removed in the revised manuscript.

This article utilizes 2D-Var and HF radar data to measure surface currents both upstream and downstream of the East Australian Current (EAC). It then analyzes the variability of the EAC across multiple spatial and temporal scales, highlighting its critical role in influencing continental shelf dynamics, regional circulation, coastal weather, and global climate patterns.

However, the description of the second term in the 2D-Var cost function J is unclear. Specifically, what is meant by “facilitating the extraction of the large-scale circulation pattern while limiting the generation of spurious small-scale variations in the reconstructed velocity field”? Why is this approach effective in achieving these outcomes?

Answer: The phrase was modified for clarity as “…to facilitate the smoothness of the circulation pattern while limiting the generation of spurious small-scale variations in the reconstructed velocity field” as well as section 3.2 for describing the 2dVar approach.

The second term in the 2dVar cost function is introduced to constrain the kinematic of the flow field which was introduced by Kaplan et al. (2007). In the 2dVar algorithm, the Laplacian operator acts as a high-pass filter which can result in a more ‘violent’ circulation regime or noisy reconstructed field. As demonstrated by Yaremchuk and Sentchev (2009), enforcing the smoothness of the divergence and vorticity patterns is beneficial to facilitate the smoothness of the circulation pattern.

Additionally, why was 2D-Var chosen over 3D-Var or 4D-Var?

Answer:  The 2dVar approach is used here because it was developed as an inexpensive algorithm for real-time interpolation of surface currents by a typical HFR system. Besides its ability to gap-fill data, the algorithm is simpler compared to more sophisticated methods like the open-boundary modal analysis (OMA) (Kaplan et al., 2007). The interpolation field can be simplified by adjusting three weight parameters. Additionally, the method has shown good performance in several studies, such as those in Bodega Bay (Yaremchuk and Sentchev, 2009), the Iroise Sea (Sentchev et al., 2013), and the Gulf of Tonkin (Tran et al., 2020), etc. Therefore, we believe this method is suitable for the purpose of this study to create a long-term radar dataset in eastern Australia. Meanwhile, new methods are being researched to enhance data accuracy, representing a promising area for future investigation.

Could the reconstruction be improved by including surface wind stress and other atmospheric variables? These could provide valuable constraints and enhance the robustness of the results.

Answer:  The reviewer makes a good point, however, we do not have spatially resolved data at the same resolution as the radar. This could be an area for further investigation in the future.

Finally, consider exploring broader climate connections. For example, how does EAC variability relate to larger climate phenomena, such as the El Niño-Southern Oscillation (ENSO) or the Indian Ocean Dipole (IOD)? Additionally, assessing the impact of EAC variability on regional ecosystems, fisheries, and biodiversity—particularly in the context of climate change—could provide important insights and expand the study’s relevance.

Answer:  The reviewer makes a good point, and this dataset will be useful for further investigation – however it is outside the scope of this data description paper to answer these science questions here.

We greatly appreciate the constructive comments from the Reviewer. Below we provide our detailed point-by-point responses and any description of actions taken regarding the comments by the Reviewer.