the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Performance of wind assessment datasets in United States coastal areas
Abstract. The atmospheric dynamics that occur near the intersection of land and water offer exciting and challenging opportunities for wind energy deployment in coastal locations. New models and tools are continually being developed in support of wind resource assessment, and three recent products are explored in this work for their performance in representing characteristics of the wind resource at coastal locations: the Global Wind Atlas 3 (GWA3), the 2023 National Offshore Wind data set (NOW-23), and the wind climate simulations that are a component of the Wind Integration National Dataset (WIND) Toolkit Long-term Ensemble Dataset (WTK-LED Climate). These relatively new products are freely available and user-friendly so that anyone from a utility-scale developer to a resident or business owner can evaluate the potential for wind energy generation at their location of interest.
The validations in this work provide guidance on the accuracy of wind resource assessments for coastal customers interested in installing small or midsize wind turbines (≤ 1 MW in capacity) to support energy needs at the residential, business, or community scale, such as the island and remotely located participants of the U.S. Department of Energy’s Energy Transitions Initiative Partnership Project. At 23 coastal locations across the United States, dataset performance varies according to different evaluation metrics. All three recent datasets tend to overestimate the observed coastal wind resource. GWA3 produces the smallest annual average wind speed relative errors, whereas WTK-LED Climate is in best agreement in terms of representing diurnal wind speed cycles. NOW-23 is the highest performing of the datasets for representing seasonal and inter-annual trends in the coastal wind resource. While GWA3 and WTK-LED Climate are relatively insensitive to the dataset output heights selected for wind resource assessment at small and midsize wind turbine hub heights (20 m – 60 m), significant variation in the NOW-23 representation of wind shear across the wind profile in the lowest 100 m of the atmosphere leads to notable differences in wind speed estimates according to the dataset output heights selected for evaluation. GWA3 exhibits challenges in representation of observed wind speed diurnal cycles at small and midsize turbine hub heights, likely due to the dataset’s consistent treatment of hourly wind speed trends regardless of altitude.
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RC1: 'Comment on wes-2024-115', Anonymous Referee #1, 29 Oct 2024
Dear team, please find attached my review. BR. Rémi.
- AC2: 'Reply on RC1', Lindsay Sheridan, 02 Dec 2024
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RC2: 'Comment on wes-2024-115', Anonymous Referee #2, 05 Nov 2024
Comments to the manuscript “Performance of wind assessment datasets in United States coastal areas”
General comments
This manuscript compares the existing wind speed datasets in the coastal regions of the United States by using measurement data at 23 points. The discussion is clear and the reviewer could not find any fundamental mistakes in their analysis. However, at the same time, the reviewer could not find any scientific or engineering value which is worth for this manuscript to be published as a research paper in Wind Energy Science journal. In other words, what are the findings in this work and what is the contribution of this work for wind energy community? The authors do not answer this most important question. As seen in the table 1, which is the summary of the datasets used in this work, the characteristics of the data are very different by datasets. For example, the method of downscaling is different, the temporal resolution is different and the spatial resolution is different, making it very difficult to investigate the cause of the error for each site. It seems that the discussion in this manuscript is very specific to the datasets and measurement sites, and it is hard to generalize the conclusion. Thus, the reviewer does not agree this manuscript to be published even with major revisions, unless substantial change are made with the introduction of the new viewpoints which brings more general conclusions.
Technical comments
Table 1.: What is the meaning of “Annual, seasonal, diurnal” in the temporal resolution rowof GWA3 and WTK-LED Climate? Does it mean annual average value per each year, seasonal average per season and diurnal average per a day are provided? But if so, the “temporal” resolutions is “once a day”, isn’t it?? (I mean annual or seasonal average value can be calculated from diurnal average data….) And the averaging time is different from the resolution and has to be specified separately. Anyway, more clarification needed
Equation 4.: The numerator of the right side of the equation looks like a ceiling function. But it does not make sense. Is it a simple bracket or absolute value?? From the following discussion, the relative error is always positive and the reviewer assumes this is an absolute value. But in that case, equation 4 has to be modified to the absolute value.
Figure 3: The difference of the different method to calculate the shear exponent is a little unclear. It’s better to explicitly show by equation.
Line 296-302: NOW-23 is based on different PBL scheme by different locations, right? However it does not justify to discuss the results as the difference of the PBL scheme as figure 8. They are based on different sites. This discussion is misleading and unacceptable.
Line 347-: What is the meaning to discuss the relative diurnal cycle?? The meaning of discussing the diurnal cycle for wind power application is not clear. The authors needs to clarify the justification of this discussion.
Citation: https://doi.org/10.5194/wes-2024-115-RC2 - AC1: 'Reply on RC2', Lindsay Sheridan, 02 Dec 2024
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