|Review of WES-2020-134_R1|
Seasonal effects in the long-term correction of short-term wind measurements using reanalysis data
1. Abstract: The abstract elaborates quite a lot on the activities that has been employed during the study. However, the abstract should report about the conclusions and findings of the paper, i.e. what have we learnt?. Now that is covered in only a single sentence.
2. Readability: Section 3.1 as an example: From the beginning this section is difficult to follow. I think some additional explanation is needed that explains the research strategy in a more concrete way. E.g. the first sentence “Short-term periods with a duration of 90 consecutive days are selected starting at the first day of year and running through the data with an increment 135 of three days” does not mentioned that you do this operation on the reanalysis products, so for the reader it is a bit shaky here: where are we suddenly heading to? Moreover, so far often wordings like “analyse” and “statistics are generated” are used, but for the reader it is difficult to follow why this is exactly done. Perhaps a flowchart that explains the design of the research would be helpful. Also the text can be more precise in terms of using the word “data” which is used for and the observations and for the re-analysis products, which makes the reader easily lost what is exactly done. For example, in ln 136 “ensuring that 122 90-day measurement periods can be investigated”, the measurement periods that are mentioned are subsets of the reanalyses, not from the observations in Table 1. So this makes the paper confusing. I suggest to use the words “observations” and “re-analysis product”, or if you sample from a reanalysis to mimic a measurement campaign, please call this subset “pseudo observations” (or something like that). In addition, in ln 139 you talk about regression parameters but at this point the reader has no concrete idea about which regression parameters you talk about concretely! It remains all a bit vague in a cloud. In general I have confidence in the work the authors present, but less confusing wordings can help to make the paper more attractive for the readership, which will help to have a bigger impact in the field.
3. Discussion: the paper should sharpen the discussion section in which the paper shows how it has extended the science. The paper now refers continuously to three papers, but I wonder whether that can be strengthened? The reference list contains a lot of grey literature, the paper can be brought to a higher scientific level.
Ln 32: MCP: the MCP method should be explained briefly or with a schematic (see below as well), since from section 3.1 the manuscript is difficult to follow, which can be circumvented by including a flowchart or other schematic about how the MCP works (inputs, output, procedures, regressions; so an extension of Figure 1), and a flowchart illustrating the experiment. From Fig 1 it is still not clear what is the difference between a u and capitalized U.
Ln 41: scientific publications -> studies
Ln 41: In Carta et al. (2013) an extensive review is given on -> Carta et al. (2013) presents an extensive review on
Ln 42: It is concluded that -> They concluded that....
Ln 78: An overview of the measurement campaigns is given in Tab. 1. -> Table 1 presents an overview of the measurement campaigns used in this study.
Ln 102: WRF (2020))-> the usual reference to the WRF model is either Powers et al 2017 (BAMS) or Skamarock et al 2019.
Ln 132: I recommend to replace the labelling of the subscript “ref” with the more direct subscript “reanalysis”. I understand in general other reference wind speeds can be used as well, but for the readability of the paper I think it is wise to make the terminology as direct as possible.
Ln 138: three-month -> to avoid confusion, just call it “90-day periods”, so the wording remains consistent with the previous paragraph.
Ln 138: In a first step, the data in these three-month data portions are analyzed with respect to, e.g., mean and variance of wind speed. For the reader it is unclear why these statistics must be generated at this point. Again, adding a flowchart or a scheme in the beginning of section 3.1 can help to make the workflow more easy to understand for the reader.
Ln 153: few negative wind speed values can occur. -> You mean that the MCP method may generate a few negative wind speed values. Be more precise in the wording, it will help the reader!
Figure 1: the location and role of the grey bars “benchmark Umeas” are unclear. The grey bars next to the dark blue bar now look purely like they act to refine the layout. Also, there a no black arrows pointing to/from it. Please revise.
Ln 166: reference wind speed. For readability purposes, it would be good that uref refers to the reanalysis products in this case right?
Ln 173: please add that the subscript LR stands for linear regression
Ln 193: what is the difference between Eq 4 and Eq. 6 since they effectively do the same job? Is it not more confusing to mention them twice?
Ln 215: one-year time series: please be more precise. It is a daily, hourly or sub-hourly time series?
Ln 254: Inserting in Eq. (10). What are you inserting in Eq. 10? A is already present in that Equation.
Ln 265: suddenly overbars appear above the U’s in the equation, but without further explanation. What was the averaging timescale? Unclear (again).
Ln 335: the authors should introduce better why d_mean is needed. It is well explained what it does and how you can calculate it, but why do we need it? Which research question does it answer?
Figure 10: the caption should mention the height of the wind speed that is discussed
Ln 561: much -> much more?