General comments

This paper investigates the impact of sea breezes on the diurnal cycle of offshore wind energy capacity factors across eight potential offshore wind areas in southeastern and southwestern Australia. The authors use a novel sea breeze frontal object dataset derived from the BARRA-C2 reanalysis (1979 - 2024), combined with a moisture frontogenesis diagnostic. The key findings are that sea breeze days tend to have higher afternoon capacity factors (15–30% more available wind resources for six out of eight areas), that the timing of peak wind energy production on sea breeze days aligns favourably with peak electricity demand, and that anti-correlations in sea breeze occurrence across opposite-facing coastlines have implications for portfolio diversification

The paper addresses a timely and relevant topic for the Australian energy transition. With offshore wind farm development actively being pursued in these regions and no operational offshore farms yet, understanding the diurnal characteristics of the resource - and specifically the role of sea breezes - is valuable for energy system planning. The combination of a mesoscale sea breeze identification method with a wind energy resource framing is novel and well-motivated.

Overall, the paper is well-written, logically structured, and makes a useful contribution to the field. The methodology is generally sound, and the authors are transparent about limitations. I recommend minor revisions to address several points that would strengthen the paper.

Concerns and suggestions

• P3-4L80-100: One area that could be improved is the description of the filtering and post-processing steps for the frontal objects. The cascade of filters - spatial smoothing, morphology criteria, constraints on object-averaged variables, positive temporal trend in specific humidity - is described qualitatively, but the specific thresholds and implementation details for some of these steps are only available in the companion paper (Brown et al., 2025b). Providing a concise summary table of all filter parameters and thresholds in this paper (perhaps as a supplement) would make the method more self-contained and reproducible without requiring the reader to cross-reference multiple sources.
• Figure 4 (P11): The authors show that the seasonal cycle of sea breeze occurrences broadly follows the monthly mean daily maximum land-sea temperature difference (T_land - T_sea), but acknowledge that this metric cannot explain all regional variations (e.g., Newcastle peaking in October rather than December-January) and that there is no consensus on how to define it. This is a thoughtful discussion, but the practical utility of T_land-T_sea as a predictor could be strengthened by reporting a correlation coefficient between the two seasonal cycles across all regions.
• Figure 5 (P13): This is a key figure. The use of local standard time on the x-axis is practical, but makes direct comparison between SE Australian sites (AEST, UTC+10) and SW Australian sites (AWST, UTC+8) slightly awkward. The authors should ensure this time zone difference is clearly noted so readers do not inadvertently compare absolute times across the two panels.
• The discussion of how prevailing wind direction influences sea breeze type and, hence, offshore wind speeds is qualitatively interesting (P22L379-393). However, the connection between the wind direction analysis (Figures 6-8) and the capacity factor composites (Figure 5) could be made more quantitative. For example, could the authors condition the capacity factor analysis on prevailing wind direction categories to demonstrate this mechanism more directly?
• The detailed diurnal composites (Fig. 5) focus on the austral summer. While this is the season of peak sea breeze activity (Fig. 2), sea breezes do occur in shoulder seasons - particularly in lower-latitude sites like Newcastle, where occurrence peaks in October. Energy demand patterns also differ outside of summer. A brief discussion of what might be expected in other seasons - or a supplementary figure showing spring (SON) composites - would round out the analysis.
• Related to the point above. I would encourage the authors to more explicitly quantify the practical significance of their findings. For instance, what is the magnitude of the sea breeze contribution to total annual energy production at each site? The current framing focuses on the austral summer (DJF) and on relative differences between SBF and non-SBF days, but a reader interested in energy planning would benefit from understanding how often sea breezes occur and how much energy they account for in absolute terms across the full year.
  
  

Minor and technical comments

• P3L85: The moisture frontogenesis threshold of 16.1 g/kg/100 km/3h is quite specific. While the authors refer to Brown et al. (2025b) for justification, a brief note on the sensitivity of results to this threshold would be helpful for readers of this paper.
• P7L162-175: The power curve is interpolated using cubic splines with a cut-in at 3 m/s and a cut-out at 25 m/s. Is there any hysteresis modelled for the cut-out behaviour? This is a minor point, but it could affect capacity factor estimates during high-wind events.
• P7L162-175: The paper focuses on capacity factors from a single turbine and does not discuss wake effects, which would reduce actual energy production in an operational wind farm. A brief note acknowledging this limitation would be appropriate, particularly since sea breeze wind directions may differ from the prevailing directions for which farm layouts are optimised.
• The use of a single 10 MW reference turbine is acknowledged, but it would be helpful to briefly discuss sensitivity to turbine choice, particularly since some of the offshore wind areas with lower mean wind speeds may be better suited to turbines optimised for lower wind regimes (e.g., larger rotors relative to generator capacity).
• P7, Section 2.4: For the demand data analysis, the authors should briefly note whether the demand data represents operational demand (which is increasingly depressed during daytime hours by behind-the-meter rooftop solar PV in Australia) or underlying demand. This distinction matters for interpreting the alignment between sea breeze capacity factors and demand peaks (P22L370-375).

This well written manuscript deals with sea-breezes in connection with off-shore wind energy production.  The analysis is based on the BARRA-C” 4.4 km horizontal grid spacing reanalysis product.

A new method, based on “moisture frontogenesis”is used  to determine the sea-breeze front. The results are interesting both from a theoretical and applied point of view.

A few comments:

1. Line 82 “sufficiently”, please be more specific.
2. Line 83: Provide details on the Gaussian smoothing filter and how effective it is.
3. Line 87. Please discuss the sensitivity of the choice of the threshold value for the frontogenesis
4. Line 87: How does this value compare to similar threshold values in other studies.
5. Line 87: I suggest adding the theshold value (16.1) with standard scientific units.
6. Line 87-88. Please help the reader to better comprehend the text “before a series of filters are applied intended to remove non-sea-breeze fronts. These filters include conditions on the morphology of the objects, as well as constraints on object-averaged physical variables”
7. Line 164: between which height was the interpolation done.
8. Line 164: Were stability effects considered in the interpolation?
9. Line 175: which are the other reanalysis models – be specific.
10. Line 338: ”robust assessment”, I think you have mentioned in the text a number of examples where the method would fail, so I do not see how the word “robust” can be justified.
11. Line 375: I do not see the relevance of introducing Alpine meteorology in a paper on sea breezes in Australia