the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 15 Dec 2023
A parameterization scheme for the floating wind farm in a coupled atmosphere-wave model (COAWST v3.7)
Abstract. Coupling Weather Research and Forecasting (WRF) model with wind farm parameterization can be effective in examining the performance of large-scale wind farms. However, the current scheme is not suitable for floating wind turbines. In this study, a new scheme is developed for floating wind farm parameterization (FWFP) in the WRF model. The impacts of the side columns of a semi-submersible floating wind turbine on waves are firstly parameterized in the spectral wave model (SWAN) where the key idea is to consider both inertial and drag forces on side columns. A machine learning model is trained using results of idealized high-resolution SWAN simulations and then implemented in the WRF to form the FWFP. The difference between our new scheme and the original scheme in a realistic case is investigated using a coupled atmosphere-wave model. Results indicate that the original scheme underestimates the power output of the entire floating wind farm in the winter scenario. On average, the power output of a single turbine is underestimated by a maximum of 694 kW (12 %). The turbulent kinetic energy decreases within the wind farm, with the greatest drop of 0.4 m2 s−2 at the top of the turbine. This demonstrates that the FWFP is necessary for both predicting the power generated by floating wind farms and evaluating the impact of floating wind farms on the surrounding environment.
Status: final response (author comments only)
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CEC1: 'Comment on egusphere-2023-2805', Astrid Kerkweg, 19 Dec 2023
Dear authors,
why are you presenting a *docx file as preprint? For a .docx-file it can not be assured that we are assessing the information provided correctly. For example, if someone does not use Microsoft Word, the compatibility and integrity of contents are not assured if the document is opened e.g. with LibreOffice.
You as authors should be the first interested in providing the information in a non-editable format.
Would it be possible for you to provide your pre-print as PDF file? Otherwise I do not see how we should provide proper enforcing, reviewing and discussion of your paper.
Best regards,
Astrid Kerkweg (Executive Editor of GMD)
Citation: https://doi.org/10.5194/egusphere-2023-2805-CEC1 -
AC1: 'Reply on CEC1', Shaokun Deng, 20 Dec 2023
Thanks for the reminder, we've uploaded a PDF version of the preprint.
Citation: https://doi.org/10.5194/egusphere-2023-2805-AC1
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AC1: 'Reply on CEC1', Shaokun Deng, 20 Dec 2023
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RC1: 'Comment on egusphere-2023-2805', Anonymous Referee #1, 19 Jan 2024
General comments:
The submitted manuscript describes a new parameterization of the impact of wind farms on the wind speed and TKE within the boundary layer, whose novelty is that it is adapted to the characteristics of floating offshore turbines. As wind farms move to deeper waters, necessitating this design, such a parameterization will certainly be of great scientific value.
There is a lot of innovative analysis here, such as the consideration of impacts on wave heights, inertial effects from the piles, and the adaptation of a SWAN vegetation model to account for these effects. The machine learning approach is also interesting. However, overall I find the manuscript leaves out too many details of the theoretical justification, is unclear in presentation and organization (especially of the experimental design), and seems to provide insufficient evidence to justify its conclusions. So I can only recommend acceptance after major revisions have been performed.
Specific comments:
Line 27: You mention explicit and implicit methods here, and then state that explicit methods are superior, but you never describe what an implicit method is – you should put in either a brief description or remove the first sentence of the paragraph.
Line 43: ‘This suggests that the current wind farm parameterization is not suitable for floating wind farms because it does not account for the change in roughness length caused by large floating platforms.’ Do you have additional evidence that existing parameterizations are deficient? Why would this deficiency only affect floating wind farms, and not also offshore but fixed wind farms?
Lines 80 and following: Maybe include more brief descriptions of where all these equations and values are coming from, and why?
Line 102: I don’t follow the Rayleigh equations. Why would H^3 equal its integral over p(H) dH?
Line 160: Maybe this works for the South China Sea, but a range of water depth from only 53 m to 98 m leaves out other potential deep water applications, such as off the U.S. West Coast, where the depth could be hundreds of meters (though at some point additional depth won’t matter, I suppose).
Line 162: Your machine learning output variable is SWH, but roughness can also be a function of wavelength and wave age – would it be possible to include these parameters as well?
Line 164: For non-specialists, could you include at least a little more description of what these ML methods mean? What is ‘Matern 5/2 kernel’? Any explanation why its fit is so much closer than for the other methods?
Line 184: My biggest issue might be with the justification of (17). Why does a change in surface momentum flux get translated to a change in mean wind kinetic energy for an elevated layer? Aren’t changes in wind speed related to vertical gradients of momentum fluxes? And changes in kinetic energy related to the product of momentum flux times vertical gradients of mean winds? Why would the impact of surface roughness be omitted above 100 m if it can exert a drag on the whole boundary layer?
Line 230: You validate SWH with satellite data – are there any in situ measurements of waves available?
Line 231: You say the total model simulation time is 18 hours, but here you say the model is run for an additional 2 days for further validation, and then you show figures of model output over apparently four days. You also mention ‘winter scenario’ (line 243) but there is no mention of other scenarios. Can you clarify the simulation periods used in the evaluation, and state the relevant times in the figure captions?
Line 250: In the caption ‘Power output differences’ – between what? I assume this is default Fitch – new FWFP scheme. But then it is incorrect to call these ‘underestimates’ of the power output because you don’t know what the truth is, they are sensitivities.
Line 351 ff: The first mention of Taylor and Yelland belongs in the experimental design, not at the end of the conclusions. I also would not agree that it is a ‘complex iterative computational method’. It is a simple expression of roughness length as a function of SWH and wavelength (not frictional velocity), unless I am missing something?
Technical corrections:
Note that this whole manuscript could greatly benefit by a technical edit for English usage. I have only indicated the most noteworthy instances below.
Line 35: ‘The installed capacity of offshore wind energy…’ This should be the beginning of a new paragraph. The previous sentence does not clearly relate to the rest of the paragraph.
Line 36: change ‘offshore’ to ‘near shore’.
Line 71: ‘Morrison’ should be ‘Morison’.
Line 225: In Table 1, ‘Duhia’ should be ‘Dudhia’, ‘CORE’ should be ‘COARE’, ‘Talyor’ should be ‘Taylor’.
Citation: https://doi.org/10.5194/egusphere-2023-2805-RC1 -
AC2: 'Reply on RC1', Shaokun Deng, 13 Feb 2024
We sincerely thank the reviewer for the suggestions and comments that help us improve the quality of our manuscripts. A point-by-point response to the reviewers’ comments is included in the Reply. Changes in the revised manuscript are tracked and highlighted.
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AC2: 'Reply on RC1', Shaokun Deng, 13 Feb 2024
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RC2: 'Comment on egusphere-2023-2805', Anonymous Referee #2, 24 Jan 2024
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AC3: 'Reply on RC2', Shaokun Deng, 13 Feb 2024
We sincerely thank the reviewer for the suggestions and comments that help us improve the quality of our manuscripts. A point-by-point response to the reviewers’ comments is included in the Reply. Changes in the revised manuscript are tracked and highlighted.
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AC3: 'Reply on RC2', Shaokun Deng, 13 Feb 2024
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