An error reduction algorithm to improve lidar turbulence estimates for wind energy

Newman, Jennifer F.; Clifton, Andrew

doi:https://doi.org/10.5194/wes-2-77-2017

Articles | Volume 2, issue 1

https://doi.org/10.5194/wes-2-77-2017

© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.

https://doi.org/10.5194/wes-2-77-2017

© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.

Articles | Volume 2, issue 1

Research article

|

10 Feb 2017

Research article |

| 10 Feb 2017

An error reduction algorithm to improve lidar turbulence estimates for wind energy

Jennifer F. Newman and Andrew Clifton

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Final revised paper (published on 10 Feb 2017)
Preprint (discussion started on 08 Jul 2016)

Interactive discussion

Status: closed

AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment

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- Supplement

SC1: 'Correction on the background section', Alfredo Peña, 11 Jul 2016
- AC1: 'Reply to interactive comment on “Improving Lidar-Derived Turbulence Estimates for Wind Energy”', Jennifer Newman, 15 Jul 2016
  - SC2: 'Just for clarification', Alfredo Peña, 19 Jul 2016
RC1: 'Review', Anonymous Referee #1, 22 Jul 2016
RC2: 'Paper Review', Anonymous Referee #2, 29 Jul 2016

Peer-review completion

AR: Author's response | RR: Referee report | ED: Editor decision

ED: Reconsider after major revisions (21 Aug 2016) by Jakob Mann

AR by Jennifer Newman on behalf of the Authors (28 Oct 2016) Author's response Manuscript

ED: Referee Nomination & Report Request started (07 Nov 2016) by Jakob Mann

RR by Anonymous Referee #1 (21 Nov 2016)

Suggestions for revision or reasons for rejection

In the second version of this manuscript, the first 5 pages are introductory material.
The shear parameter does not appear to be an adequate measure of stability and it could be that this is why the scatter and errors remain large. The shear parameter includes roughness and terrain effects and is likely direction sensitive. If the authors really insist that they do not have to compare with stability determined from sonic data (which they have available) then at least they should call this a shear parameter and not stability, or show that it is equivalent.
It is not clear which of the steps in sections 3.1-3.5 have been applied and whether they are needed. What are the physics-based corrections in L-TERRA? Maybe the errors in the large shear case are because the exponent used is wrong? It is unfortunate that the r2 values are so little impacted. The MAE improvement of 0.26 is very difficult to evaluate. Does it mean the difference between the met tower TI (height?) and the WC TI was previously larger than 1.50 so for example it could be that WCTI was 11.5% while the met tower TI is 10% and with TERRA it is now predicted to be 11.26%? Is that a correct interpretation? At the wind farm site the improvement in MAE appears to be 0.28. Are these really significant as stated in the abstract when there is very little or no change in the slope or r2 values?
Section 3.6 is called comparison to previous methods but there is no quantitative comparison. Nor is there a qualitative evaluation of which of the preprocessing steps are necessary and have utility. It is unfortunate because a quantitative comparison here would add value, even if the overall results show rather small improvements from using L-TERRA.
With a more quantitative approach to the processing there could be something here. But based on these results and with the lack of any physical detail of the model, or processing detail that indicates the value of the steps it is unfortunately not a very compelling analysis.

Referee Report: PDF

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RR by Rozenn Wagner (28 Nov 2016)

ED: Reconsider after major revisions (04 Dec 2016) by Jakob Mann

AR by Jennifer Newman on behalf of the Authors (22 Dec 2016) Author's response Manuscript

ED: Publish as is (16 Jan 2017) by Jakob Mann

ED: Publish as is (16 Jan 2017) by Jakob Mann (Chief editor)

AR by Jennifer Newman on behalf of the Authors (18 Jan 2017) Manuscript

Short summary

Remote-sensing devices such as lidars are often used for wind energy studies. Lidars measure mean wind speeds accurately but measure different values of turbulence than an instrument on a tower. In this paper, a model is described that improves lidar turbulence estimates. The model can be applied to commercially available lidars in real time or post-processing. Results indicate that the model performs well under most atmospheric conditions but retains some errors under daytime conditions.