the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Locating the Optimal Wind Resource within two Californian Offshore Wind Energy Areas
Abstract. The spatiotemporal variability of wind resource at two Californian offshore wind-energy areas (Humboldt and Morro Bay) is characterized using 23 years (2000–2022) of 2-km-resolution, hourly NOW-23 reanalysis data. Idealized power is estimated for the International Energy Agency 15 MW reference wind turbine. Wind speed and energy output are higher at Humboldt than Morro Bay and for summer months than winter months at both sites. Idealized daily energy output per turbine with one turbine within each model grid cell, peaks at 330 MWh in June for Humboldt and 300 MWh in May for Morro Bay. Energy output per turbine decreases from the oceanward to the coastward perimeter by ~20 %, dropping ~22 MWh across Humboldt and ~46 MWh across Morro Bay. Rotor-layer wind shear and veer exhibit strong seasonal variability, with summertime shear twice of wintertime shear at both sites. Daily wind resource variability is quantified through Fractional Variability (FV), defined as the ratio of the interquartile range of wind speeds/energy to the overall median value for that day of year. Locations and times with higher FV coincide with low wind-speeds (i.e., low output) for both sites. A linear optimization identifies the optimal wind resource locations (that maximizes energy output but minimizes output FV, wind shear, wind veer, and distance to shore) at the oceanward and coastward flanks of Humboldt and Morro Bay, respectively. The gradients in optimization scores are aligned parallel to the coast and are independent of the choice of power curves for rated powers of 8–16 MW.
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RC1: 'Comment on wes-2025-55', Anonymous Referee #1, 05 May 2025
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Review of WES-2025-55
The manuscript "Locating the Optimal Wind Resource within two Californian Offshore Wind Energy Areas" by Mitra et al. presents in general quite interesting and relevant research on offshore wind ressources in California. The further investigation of potential future offshore lease areas is from a scientific and industrial perspective very relevant.
However, I found several key deficiencies in the manuscript that lead to the conclusion that I can't recommend publication of the manuscript in wind energy science. Those points are - from my perspective - so severe that a revision of those points would lead to a completely different manuscript and results.
Specifically, I have identified the following shortcomings:
- The authors ignore wake effects in their design of the study. Thus, in principle, they are investigating the purely academic question of placing and moving around a single wind turbine in the two wind energy development areas. This results in a conclusion that the most optimal areas are for instance located at the southern edges of the wind farm, in areas with a very pronounced northerly wind component (see wind roses in supplementary material). In a real wind farm (and those are prospective real sites) those areas would be the ones with the strongest wake effects. Wake effects can lead to 10-30% yield loss, in extremely dense wind farm clusters even more. As the variability of the annual averaged daily energy output (Fig. 1) across the sites is in the same order of magnitude, the results would completely shift. There are layout optimization tools like floris, FOXES, pywake/TOPFARM that could be used for including wake effects in the optimization. Or at least using a geometric wind farm layout and an engineering wake model would help to increase the relevance of the results.
- The authors are making some random choices on data processing, which are not explained. For example: Why is there a resampling of the 2 km grid to a 0.02 degree reference grid? Why are there 25 random turbine power curves investigated by varying cut-in, cut-out and rated power where also other reference turbines could be used such as DTU 10 MW turbine, the IEA 22MW turbine, the IEA 5MW turbine or even publicly available power curves for some existing turbines (a web search can help) could be used?
- Why is the variability discussion not based on an annual energy production but on daily output variability? The first one would be an established measure from the wind energy community.
- There is no discussion at all on available reference measurements in the area, or how they went into the NOW-23 dataset, if they are sufficiently included there.
- The literature review refers at several points to a paper Mitra et al., 2024, which is not part of the list of references. There, an unpublished study (Mitra et al., 2025) is mentioned, which I couldn't find even in a web search. This also this part would need some very thorough revision.
- The manuscript has quite a long document of supplementary material, which is at some points very relevant for the interpretation of the results, such as the used power curve, or the wind roses. The choice of what went into supplementary material seems quite random to me. I would recommend putting them rather into an appendix or at some points even in the main part of the document.
Citation: https://doi.org/10.5194/wes-2025-55-RC1
Data sets
NOW-23 National Renewable Energy Laboratory (NREL), USA https://data.openei.org/submissions/4500
15 MW Reference Wind Turbine (RWT) Power Curve National Renewable Energy Laboratory (NREL), USA https://nrel.github.io/turbine-models/IEA_15MW_240_RWT.html
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