the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 19 Jan 2026
50-year Wind Speed Maps for Tropical Cyclone-affected Regions using Best Track data
Abstract. Accurate estimation of extreme wind speeds from tropical cyclones is a significant challenge within tropical cyclone prone regions. This study presents a method to estimate the 50-year return wind speed at heights relevant to wind turbines. The International Best Track Archive for Climate Stewardship data is combined with the Holland parametric model and the Gumbel distribution to assess extreme winds within three tropical cyclone-affected regions within the Northern Hemisphere. These regions are Taiwan, Japan, and the east coast of the United States of America. To assess the uncertainty within the results from differing input parameters, Monte Carlo simulations are used. The method performs well in Taiwan and Japan which can be attributed to the large sample size of data points located within a limited spatial area. The east coast of the United States performs less well, which conversely, is due to the smaller sample size and wider spatial region of which they cover. This study shows that combining International Best Track Archive for Climate Stewardship data with parametric and statistical models provides a practical approach to estimate extreme wind speeds while highlighting the need for an understanding of regional characteristics to ensure reliability of the results.
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Status: final response (author comments only)
- RC1: 'Comment on wes-2025-269', Anonymous Referee #1, 22 Jan 2026
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RC2: 'Comment on wes-2025-269', Anonymous Referee #2, 23 Mar 2026
This paper presents a practical computational method for estimating 50-year return wind speeds at turbine hub heights across three tropical cyclone-prone regions: Taiwan, Japan, and the East Coast of the United States. The approach integrates IBTracks, the Holland model and Gumbel extreme value distribution, propagating uncertainties through Monte Carlo simulations. This is a standard industry practice, but usually performed at a particular offshore wind site, so I see the innovation on expanding the study to cover entire sea basins. The method performs robustly in Taiwan and Japan but results over US east coast exhibit spatial fragmentation and unexpected latitudinal patterns coming from the low data coverage. The uncertainty analysis effectively identifies input parameters and the sensitivity of each component.
Overall Assessment: The paper is well written and provides new insights for design of offshore wind turbines in regions where 50-year winds approach the limit for IEC Class S turbines. The contribution of this methodology is incremental but substantive, with limitations that are properly discussed. I have minor revisions throughout the paper that need to be addressed before publication. See below:
Line 6 – “method performs well in Taiwan and Japan”. What do you mean by this? Be specific in terms of statistics and concrete numbers
Line 8 – “…performs less well”. Same comment as above.
Line 106 – I suggest you already explain here the reason for the choice of time frame for each region. It is strange to see such short period for ECUS, for example.
Figure 2- Improve the plots by adding lat/long coordinates and scale.
Table 4 – this table is not needed if you properly make the plots in Figure 2.
Figure 3 – Same comment. Improve the quality and geo referencing of this plot, showing lat/long coordinates in the axis and a scale for reference.
Line 174-175 – The methodology to calibrate the geostrophic drag law by tuning z0 is indeed non-realistic. How did you arrive to a single z0 value for the entire region? I suggest to plot the wind speed comparisons between the downscaled winds with the IBTracks, so one could see what are the spatial differences even with the calibrated z0.
Figure 4 – The 95th quantile of Gumbell does not seem to be the best way to illustrate the spatial uncertainty in these results. I would expect the 50-year winds to be much worse where we have lower data samples. Can you clarify why you chose this parameter? Also, since you have chosen an annual maxima to select your peaks, I think it is worth mentioning how many points are used for the Gumbel fit for each region.
Line 275 – For the uncertainty analysis, I understand that you assume a Gaussian distribution of all parameters for your Monte Carlo simulations. However, according to IEC 610400-1, Annex J, these parameters might actually have a Weibull distribution. Can you justify why you chose to stick with a Gaussian? What is the impact of this choice on your results?
Discussion – One uncertainty component not taken into account nor discussed is the fact that IBTracks dataset has a coarse spatial resolution, usually providing track data every six hours or so. How do you tackle the coarse spatial resolution of this underlying dataset? I know your method is grid-based, so it compiles all data within each grid cell. However, there is no spatial interpolation or uncertainty quantification related to the spatial resolution. Please add some lines explaining if your results account for this effect and how.
Line 471 – I suggest you explicitly mention some other uncertainty components that are not taken into account. For example, the impact of climate change on the track database when looking at 50-year winds.
Line 475 – Another methodology to generate synthetic datasets is clearly mentioned and suggested by IEC 61400-1 in Annex J. I suggest this is also mentioned as a potential solution to increase sample sizes and compute extremes from synthetic tropical cyclones from Monte Carlo simulations. This is particularly of interest since higher return periods are also needed for design.
Conclusions – I suggest you expand your conclusions to summarize the potential for future studies and more detailed analysis with variations of Holand, synthetic tracks, other extreme value analysis methods, etc.. This section should not be just a bullet list of your findings.
Citation: https://doi.org/10.5194/wes-2025-269-RC2
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The method is clear and results are useful. A few points need attention:
Annual maxima sampling: Using the single yearly max wind speed for the Gumbel distribution is standard but may miss years with several strong typhoons. The second-strongest wind in an active year might be greater than the annual max in a quiet year. This could affect the U50 estimate. Please discuss this limitation.
Comparison request: Please compare your 100m U50 results with those from Imberger et al. (2024). This will help show consistency with other recent methods.
Minor corrections:
Table 4: Longitude units for Taiwan/Japan should be "E", not "W". Please check the whole text.
Figure 2: Add latitude/longitude coordinate labels to the maps for clarity.