A parameterization scheme for the floating wind farm in a coupled atmosphere-wave model (COAWST v3.7)

Deng, Shaokun; Yang, Shengmu; Chen, Shengli; Yang, Xuefeng; Cui, Shanshan

doi:https://doi.org/10.22541/essoar.169774544.48666734/v1

Preprints

https://doi.org/10.22541/essoar.169774544.48666734/v1

Preprints

15 Dec 2023

| 15 Dec 2023

A parameterization scheme for the floating wind farm in a coupled atmosphere-wave model (COAWST v3.7)

Shaokun Deng, Shengmu Yang, Shengli Chen, Xuefeng Yang, and Shanshan Cui

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 m²s⁻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.

Received: 24 Nov 2023 – Discussion started: 15 Dec 2023

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Journal article(s) based on this preprint

21 Jun 2024

A parameterization scheme for the floating wind farm in a coupled atmosphere–wave model (COAWST v3.7)

Shaokun Deng, Shengmu Yang, Shengli Chen, Daoyi Chen, Xuefeng Yang, and Shanshan Cui

Geosci. Model Dev., 17, 4891–4909, https://doi.org/10.5194/gmd-17-4891-2024,https://doi.org/10.5194/gmd-17-4891-2024, 2024

Short summary

Shaokun Deng, Shengmu Yang, Shengli Chen, Xuefeng Yang, and Shanshan Cui

Interactive discussion

Status: closed

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
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.
  
  Citation: https://doi.org/10.5194/egusphere-2023-2805-AC2
RC2:
'Comment on egusphere-2023-2805', Anonymous Referee #2, 24 Jan 2024

Please see the attached document.

Citation: https://doi.org/10.5194/egusphere-2023-2805-RC2
- 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.
  
  Citation: https://doi.org/10.5194/egusphere-2023-2805-AC3

Interactive discussion

Status: closed

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
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.
  
  Citation: https://doi.org/10.5194/egusphere-2023-2805-AC2
RC2:
'Comment on egusphere-2023-2805', Anonymous Referee #2, 24 Jan 2024

Please see the attached document.

Citation: https://doi.org/10.5194/egusphere-2023-2805-RC2
- 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.
  
  Citation: https://doi.org/10.5194/egusphere-2023-2805-AC3

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision | EF: Editorial file upload

AR by Shaokun Deng on behalf of the Authors (13 Feb 2024) Author's response Author's tracked changes Manuscript

ED: Referee Nomination & Report Request started (19 Feb 2024) by Nicola Bodini

RR by Anonymous Referee #1 (12 Mar 2024)

Suggestions for revision or reasons for rejection

I appreciate the authors addressing virtually all of my comments satisfactorily, and greatly increasing the clarity and readability of the manuscript. And it remains an interesting application and one of potential great importance.

Unfortunately I am still troubled by my Comment 8 on old Line 184, which I noted at the time was my biggest concern. (And Reviewer 2's Comment 2 suggests they have a similar concern.) Specifically, the inclusion of an elevated momentum source due to increased friction velocity from the turbines. I think there is some general confusion on these matters, so some leeway can be permitted, but I would make the following points.

*) I am still not sure how new equation (22) is derived, specifically the derivation of the momentum flux term (delta_tau S Vijk (zk+1 - zk) in the mean kinetic energy budget, where delta_tau is the change in rho * ustar * ustar induced by turbine-impacted waves. Momentum flux times mean velocity is not a tendency of kinetic energy. (Negative) vertical gradient of momentum flux times mean velocity can be a tendency of mean kinetic energy. If the momentum flux really had this (constant with height) value up to 100 m and then became zero, you would have infinite accelerations at 100 m, and no accelerations above and below.

*) In reality, vertical turbulent momentum mixing in WRF is done with a turbulence closure parameterization, usually a function of the TKE equation and some diagnostics. Momentum flux convergence / divergence is one of these terms. The only role of surface roughness / momentum stress in this framework is as a lower boundary condition such that these divergences can be computed.

*) I would also say that there is not generally a 'constant momentum flux layer' in the atmosphere -- it is more correct to say that the surface layer is defined as a layer thin enough such that the momentum flux may be treated as approximately constant. Since it generally decreases on the scale of the boundary layer height, this is generally 10% of that value. But this means that wave impacts on surface roughness can cause drag to be transmitted throughout the depth of the PBL given enough turbulent transport.

*) Less essentially, it should be noted that 100 m can be well above the surface layer in marine conditions, in which case a constant stress formulation would be inappropriate.

*) In summary, without working through all details, I would advocate for wave-roughness modifications on momentum flux to be applied at the surface only, and then either transmitted throughout the PBL by the PBL parameterization, or perhaps somehow specified to decrease with height to the boundary layer top as long as double counting is avoided.

Minor comments:

Line 276: You say that ‘the model is run for an additional 150 hours for further validation (0000 UTC 01 January to 0000 UTC 07 January)’ but 00 UTC 01 Jan to 00 UTC 07 Jan is six days = 144 hours, and the six hour period for all later analyses falls within this interval. So I am still a little confused about the time intervals involved.

Response to comment 12, line 351: I could quibble about the degree that the Taylor Yelland parameterization is iterative. While this is technically true, the iterative aspect of the scheme is in a viscous term that is actually derived from laminar theory, and in practice provides a roughness length in conditions for which wave roughness is zero. In regions of interest for wave roughness, this term should hopefully have negligible effect, in which case iteration is not needed to determine the roughness height.
However, it is certainly true that the similarity schemes and turbulence physics of WRF overall are complex, and there are a number of reasons why reductions of SWH might not increase near-surface wind speeds. What I might suggest is to just remove the reference to ‘(Taylor and Yelland 2001)’ here but leave it in Table 1.

Referee Report: PDF

Hide

RR by Anonymous Referee #2 (18 Mar 2024)

ED: Reconsider after major revisions (18 Mar 2024) by Nicola Bodini

AR by Shaokun Deng on behalf of the Authors (08 Apr 2024) Author's response Author's tracked changes Manuscript

ED: Referee Nomination & Report Request started (10 Apr 2024) by Nicola Bodini

RR by Anonymous Referee #2 (29 Apr 2024)

RR by Anonymous Referee #1 (07 May 2024)

Suggestions for revision or reasons for rejection

I appreciate the authors revising their scheme to take into account my concerns and redoing the experiments. The new results seem reasonable, and I think the manuscript is almost there. However, I just have a few hopefully minor concerns:

1) Section 5.1: A little more explanation of these equations / references might be good. What is C_P in equation 26? What is '|wt'? Isn't (23) just a rearrangement of (22)? Can you explicitly state what the change relative to Fitch is? (I think just V|wt which comes from machine learning surface layer with SWAN inputs?)

2) Figure 13 caption, lines 305-315, Figure 16 caption: when you mention differences and their sign, please indicate the order of subtraction of the two terms.

3) Figure 12, 14, 15: please indicate in the cross sections which end is 'upstream'.

4) Conclusion: When you discuss the relative differences between Fitch and FWFP, I would first mention the downstream differences, because in general those are substantially larger than the upstream differences.

5) Conclusion, throughout: if you can provide it, it would be nice to have a concise statement of how the expected impact of floating wind turbines on waves (I think, a reduction of SWH and roughness), which is the prime innovation of your scheme, leads to your modeled sensitivities on power production (more power than in default scheme, along with less wind speeds downstream).

6) Last sentence of conclusion: 'In order to better evaluate the power output of floating wind farms and their impacts on the environment, it is necessary to improve the offshore wind farms parameterization.' Can you give just a few reasons why you conclude this?

7) Though the grammar and clarity are improved, a further technical edit could be used.

Hide

ED: Publish subject to minor revisions (review by editor) (07 May 2024) by Nicola Bodini

AR by Shaokun Deng on behalf of the Authors (09 May 2024) Author's response Author's tracked changes Manuscript

ED: Publish as is (14 May 2024) by Nicola Bodini

AR by Shaokun Deng on behalf of the Authors (15 May 2024) Manuscript

Journal article(s) based on this preprint

21 Jun 2024

A parameterization scheme for the floating wind farm in a coupled atmosphere–wave model (COAWST v3.7)

Shaokun Deng, Shengmu Yang, Shengli Chen, Daoyi Chen, Xuefeng Yang, and Shanshan Cui

Geosci. Model Dev., 17, 4891–4909, https://doi.org/10.5194/gmd-17-4891-2024,https://doi.org/10.5194/gmd-17-4891-2024, 2024

Short summary

Shaokun Deng, Shengmu Yang, Shengli Chen, Xuefeng Yang, and Shanshan Cui

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The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.

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Short summary

Global offshore wind power development is moving from offshore to deeper waters, where floating offshore wind turbines have advantage over bottom fixed. However, current wind farm parameterization schemes in mesoscale models are not applicable to floating turbines. In this study, we propose a floating wind farm parameterization scheme that accounts for the attenuation of the significant wave height by floating turbines. The results indicate that it has a significant effect on the power output.


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