the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Three months of combined high resolution rainfall and wind data collected on a wind farm
- Hydrologie Météorologie et Complexité (HM&Co), Ecole des Ponts Paris-Tech, France
- Hydrologie Météorologie et Complexité (HM&Co), Ecole des Ponts Paris-Tech, France
Abstract. The Hydrology, Meteorology, and Complexity laboratory of École des Ponts ParisTech (hmco.enpc.fr) has made a data set of atmospheric measurements available. It comes from a campaign carried out on a meteorological mast located on a wind farm in the framework of the RW-Turb project (supported by the French National Research Agency – ANR-19- CE05-0022). Six devices are used : two 3D sonic anemometers (manufactured by Thies), two mini meteorological stations (manufactured by Thies), and two disdrometers (Parsivel2, manufactured by OTT). They are installed at two heights (approx. 45 m and 80 m), which enables to monitor potential effects of altitude will be used. The temporal resolution is of 100 Hz for the 3D sonic anemometers, 1 Hz for the meteorological stations and 30 s for the disdrometers. A multifractal analysis is implemented to assess the effective resolution of the devices and it suggested that anemometers and stations are able to measure expected variability only down to 1 s and 16 s respectively. Link to the data set: https://doi.org/10.5281/zenodo.5801900 (Gires et al., 2021).
Auguste Gires et al.
Status: final response (author comments only)
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RC1: 'Comment on essd-2021-463', Anonymous Referee #1, 29 Apr 2022
This paper describes a dataset of very high resolution wind and rainfall data collected at a wind farm location in central France. A thorough description of the measurement site and equipment is given, followed by details of the data collected and some analysis on the potential limitations of the instruments when measuring at high frequencies.
From what I can see the data is complete and well documented on the zenodo repository. The inclusion of the python scripts and quick looks are a very useful addition for those wishing to quickly explore the data. I would comment that having to download the whole 9.7GB of data at once is an issue that may inhibit the use of the data to those who do not need everything. You comment in the manuscript that the large raw data files would only be needed by an expert user. Future datasets could be potentially be grouped by beginner and expert data users to improve accessibility of the data, and potentially improve the uptake of it’s use.
At present although I believe the dataset to be complete and well documented the manuscript quality is currently not high enough for publication. This could be improved by implementing the following comments.
Main comments:
- It would be helpful to mention the location of the wind turbine in the title and abstract of the paper as knowledge of the climatic region the turbine is located in would be very useful for those wanting to use the dataset. The time period the data is collected for would also be useful in the abstract.
- Throughout the text there are numerous spelling and grammar errors which made it quite difficult to interpret the key messages. I would suggest a thorough proof read of the document to pick up on these. A number are highlighted in the minor comments below.
- Ending the manuscript with a summary section of the data and details of some potential use cases would be helpful. Are there other applications as well as for the wind industry where these observations could be useful? How is this dataset better than others mentioned in the discussion that are already available?
- From what you say in Line 22, can you demonstrate in this paper how the rain rate impacts the conversion to wind power in this paper? This would further highlight the usefulness of these measurements. Adding a section demonstrating this would significantly increase the value of the paper.
- Within section 2.5 can you put the measurement period into more context. In terms of wind energy generation is there a large seasonal cycle at this location? And do you know what point in the cycle this is, and whether it is a particularly high/low wind year based on the large scale circulation conditions? Can you also comment on why there are large differences between the disdrometers and the stations?
- I would remove the database structure from Section 3. Similarly for the bullet points in line 180-196 and 230-260. Alternatively this could all be moved to a supplement or changed to tables of variables available.
Minor comments.
- Consider editing the spelling and/or grammar in lines 11, 13, 21, 31-33, 45, 75, 95, 115, 119, 124, 128, 207, 288, 299-301, 330.
- Lines 15-19 Where are the previous studies that have looked at rain rates around wind turbines based? The climatic region that the wind turbines are in will be important for this relationship and would be worth commenting on (for example whether all in tropical or extra-tropical regions).
- Line 29: Can you comment on the complexity of the atmospheric boundary layer and how that will impact the wind turbines?
- Line 42: Can you give the dates of the measurement period?
- Line 52: Figure 3 is mentioned before Figure 2.
- Figure 2: are there photo credits required here for the publication of the images?
- Figure 3: Can you include what the different colours mean in the caption. You could also possibly include the prevailing wind direction for some context and comment in the text on if it is influenced by local orography.
- Line 73, define U_L
- Line 88 ‘Built-in’ rather than ‘Build-in’.
- Line 94: What does OTT stand for?
- Line 103: Check the display of drop size distribution information. The mixture of italics and normal font is confusing.
- Section 3: You oscillate between the use of database and data base, please check for consistency.
- Section 3.6 can you give an indication on how long the python code takes to run?
- Line 343: Three month, rather than two month field campaign.
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RC2: 'Comment on essd-2021-463', Anonymous Referee #2, 01 May 2022
Review comments:
In this paper, the authors present a three-month long atmospheric measurements dataset from a meteorological mast installed at a wind farm in France. The dataset comes out of a campaign using two 3D sonic anemometers, two meteorological stations, and two disdrometers.
While reviewing this data paper, I came across some issues which need to be addressed before they can be published.
Major comments:
1. The authors include the python scripts along with the dataset which is really helpful for the potential users for their analysis. That said, I feel that a single dataset file is presented which is too large to download (9.6 GB) and view if one wants to check just a file of interest. For better accessibility, it would be better to perhaps break the dataset into multiple subsets, for example by each device, or with a better strategy for sharing and accessing the data with ease.
2. A brief description of how the updates/curation of the dataset will be handled is important to include in the manuscript.
3. What is the value of such field campaigns and data measurements on wind and rainfall? The answer to this question is missing in the manuscript.
4. The premise of this dataset is built around exploring the impact of rainfall on wind energy as you mention right at the beginning of your manuscript. The manuscript, however, does not touch anywhere on this.
5. Most of the figures can be generally improved in terms of their size, presentation, and texts included. More detailed suggestions are made wherever relevant in the minor comments.
6. Will there be any difference in the multifractal analysis results if one uses the entire 3-month dataset as opposed to 1 month dataset only? My concern is primarily from the seasonality point of view.
Minor comments:
7. The abstract should include the location where the data was taken. Also, including a sentence on the target users of this dataset would benefit the readers.
8. L5-6: The sentence doesn't read well. Rephrase it!
9. Check parenthesis in the Figure 2 caption.
10. L61: Add a sentence or two further describing the terrain settings around the mast.
11. L66: “of” missing in the sentence after “are located in one ……”?
12. Add North arrows in Figures 3 and 4. In figure 4, instead of the elevation product in the legend, use "Elevation [m]".
13. L75: Begin the sentence with a lowercase “which”.
14. L108: There are several pieces of literature on this topic estimating rain rate from disdrometer data that are worth noting here. Include some key ones.
15. L112: Are you referring “Anemometer #2”?
16. Figure 6: I suggest rearranging this figure with the R vs time plot in the first row while the rest in the second row. This might provide a better distinction of rain rate between each station/disdrometer.
17. L176: Remove “the” after “This is done through ….”.
18. L179: Correct the sentence as “It provides a summary….”.
19. Figure 8: It needs many details to be easily comprehensible to the readers. Each of the subplots should be labeled and properly described in the caption or referred to in the description in the text. Also, you have enough space to make it bigger for readability.
20. L226: Check the sentence for correctness.
21. L271: Should it be “more than…”?
22. Figure 9: This figure should follow the text describing it rather than the other way around. Also, in the caption, there is no subplot (d) in the figure. Make sure you add it.
23. L313: The paragraph should include a brief description of what Trace Moment (TM) analysis is and how it can support the Universal Multifractal analysis.
24. L319: Maybe you could use a column here to remove confusion on whether it is a minus sign.
25. L339: You mean “…It is….?
Auguste Gires et al.
Data sets
Data for : "Three months of combined high resolution rainfall and wind data collected on a wind farm" Auguste Gires, Jerry Jose, Ioulia Tchiguirinskaia, and Daniel Schertzer https://doi.org/10.5281/zenodo.5801900
Auguste Gires et al.
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