Dual-Doppler Radar Characterization during the Krummendeich Field Experiment: A Campaign Overview and Performance Assessment

Jordan, Arianna Marie; Hung, Lin-Ya; Wolken-Möhlmann, Gerrit; Gottschall, Julia

doi:10.5194/wes-2026-114

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https://doi.org/10.5194/wes-2026-114

Preprints

28 Jul 2026

| 28 Jul 2026

Status: this preprint is currently under review for the journal WES.

Dual-Doppler Radar Characterization during the Krummendeich Field Experiment: A Campaign Overview and Performance Assessment

Arianna Marie Jordan, Lin-Ya Hung, Gerrit Wolken-Möhlmann, and Julia Gottschall

Abstract. The Krummendeich campaign in northern Germany was a field experiment designed to test dual-Doppler radar (DDR) technology for resolving flow fields. The campaign took place at the WiValdi research wind farm containing two operating turbines. Throughout the measurement period, a comprehensive observational network consisted of DDR, scanning and profiling lidars, a microwave radiometer, a meteorological mast, and laser disdrometers. Capitalizing on these datasets, the study herein offers an overview of the campaign while demonstrating the utility of the DDR method as a wind measurement tool for wind energy applications, specifically. This is done through validations against a co-located Doppler scanning lidar, assessments of data availability across meteorological conditions, and analyses of the method's capability to resolve flow features, i.e., turbine wake signatures. The site's temperate maritime climate, marked by frequent precipitation and wind direction-dependent stability regimes, provided a naturally diverse atmospheric environment for testing the method across conditions relevant to central European wind energy deployments. Validation showed great agreement with scanning lidar across most height levels, with discrepancies mainly occurring at near-surface levels. Hydrometeor presence strongly governed data availability, where precipitating periods sustained near-complete domain coverage. While radar returns were occasionally affected by artifacts even during otherwise well-covered scans, the method successfully captured both low-level jet and turbine wake within its flow field. Together, these results performance verification contribute to a continuously growing body of evidence supporting DDR as a viable and scalable tool for wind energy research and industry, with plans for deeper analysis and additional fieldwork to follow.

Received: 26 Jun 2026 – Discussion started: 28 Jul 2026

Competing interests: At least one of the (co-)authors is a member of the editorial board of Wind Energy Science.

Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims made in the text, published maps, institutional affiliations, or any other geographical representation in this paper. While Copernicus Publications makes every effort to include appropriate place names, the final responsibility lies with the authors. Views expressed in the text are those of the authors and do not necessarily reflect the views of the publisher.

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Arianna Marie Jordan, Lin-Ya Hung, Gerrit Wolken-Möhlmann, and Julia Gottschall

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'Comment on wes-2026-114', Anonymous Referee #1, 14 Aug 2026 reply
The study contributes to already existing Dual-Doppler Radar (DDR) experiments with a new measurement campaign. The main focus is testing whether the DDR setup holds up for a smaller domain than were used in the previous studies and explore its applicability in the wind turbine wake research. Generally, the findings are split into two groups, adding to one of those research directions. First, the authors confirm that the DDR results lie within the expectations and do not contradict recent similar studies. Second, the examples of wakes captured or failed to be captured by DDR are presented, giving a good overview of how the wake measurements may be hindered. The authors mostly refrain from the specific recommendations for the DDR setup, as the primary goal was to confirm whether the DDR measurement of wake features or atmospheric events like low-level jets were possible at all.

Since the study builds upon the known and well-researched DDR method, it is naturally expected that the reader is already familiar with its basics. If not, they may check the detailed description in one of the cited articles. Still, I noticed an occasional lack of technical details specific to the current study which I will list in minor suggestions.

The plan and goals are laid out nicely, focusing on the features that will be explored in the measured data. The findings set a foundation for further research in the compact DDR setup.
Specific comments:
I see a discrepancy in how the problem of insect backscatters is constantly highlighted as affecting the measurements prior to filtering and how little information is presented on the exact scale of this influence. Judging from the remarks, filtering the noise from biological backscatters may warrant a separate article, but there are a couple of points which I would like to see clarified:
Line 130: A reproducibility question: Which filters were used? If any research group encounters a similar issue, would it be easy for them to remove the insect noise with conventional methods, like median filter? If the filters are completely new, how were they validated?

What percentage of the data was affected by the insects and had to be filtered eventually? How filtering the noise caused by the insect backscatter compares to filtering by other criteria?

Lines 235-236, Fig. 8: If I understand correctly, the discrepancy between pre-filtered DDR profiles and reference data behaved similar to the profiles shown in the cited articles. Still, seeing the pre-filtered profiles for the Krummendeich dataset would give an idea on how the initial results were similar to the previous studies that, presumably, did not filter insect backscattering, and how much the filtered improved the agreement to the reference data.

In general, the only metric of the effect of insect noise is found at line 364, in Discussion, which is quite late, considering that this was an important part of the data processing and even required a special procedure.

Lines 131-134: I would like a clarification here to reduce ambiguity: are the resolutions of the coarse and fine grid also unknown? If the grid resolutions are known and only the processing details are hidden, then the values should be provided for reproducibility. It would be also interesting to see a schematic on how coarse and fine grid relate to the points scanned by the radars -- how many lidar points, on average, define a Cartesian grid point. This question is tackled later in Discussion (lines 380-384), although without exact values, so it is confusing why the grid size was not brought up earlier to build the later discussion upon it.

Figure 2. j) What is the cause of the DDR fine grid data being absent after September 2025, while the coarse grid data are still present? I could have missed that in the text.

Line 174: A minor question: which range of the bulk Richardson number is considered here as neutral conditions?

Figure 11. Having the wind direction series plotted would be helpful to understand Fig. 12: the fields for T2-T5 are quite noisy to be certain whether they show a wake at about 280⁰ wind direction, or is it a measurement fluctuation, while the wake is lost to the noise.

Line 349 and Fig.12. Clarification needed: What is exactly referred to in the part 'certain wind directions that avoid the bulk of the artifacts can be targeted (in the Fig. 12 example'? Is it the T1 timestamp that produced good quality scans, so this wind direction can be considered? Or wind directions for all azimuths that were not affected by the artifacts as seen in the T2-T5 columns? Again, as in the previous comment: knowing the wind direction for this period would helpful for the interpretation.

Technical corrections
Line 112: a typo in the experiment period. That should be 19 March 2025–9 January 2026, I suppose?

Figure 2
a) Channel A is almost overlaid by Channel B, so this panel conveys little on whether the channels mostly agree or have discrepancies and whether it was necessary to plot both channels. Since the figure focuses on the data availability, the scale makes it mildly ambiguous on what happens near 0 mm/hour. Is it: zero values are not plotted but there are numerous instances of near-zero precipitation, which are shown in the plot. Or: any line means recorded data even if the value is 0 mm/hour, no lines -- no data from both disdrometer channels. I would presume the first, based on how the precipitation is usually visualised. If it is the latter, then the remark would be appreciated.

c) Why does a non-centered colour bar goes down to -50⁰C, if the temperatures seem to stay within 0-20⁰? The colour bar range should be adjusted.

Figure in general: from the position of the minor ticks, I infer that the minor ticks mark the weeks and the grid lines separate the days – is that understanding correct? A remark on that in the caption would be nice.

Figure 2 is referred to only once as a general reference. Pointing out to specific panels when the respective instrument is mentioned on page 7-8 would be nice.

Figure 3: A personal preference, but I would try to adjust the radial range to be the same for both wind roses to make the comparison fair at a glance.

Figure 6: Mean Absolute Difference (in the subplot title and main text) and Mean Absolute Deviation (in the caption) are different metrics. Which one is actually plotted? Please, check that correcting the term does not affect the reasoning.

Figure 10. Although the plot is simple and remains readable, the image quality is very low compared to other figures. Some other images are also pixelated, but not to that extent. If it is planned to update all figures with vectorised versions or better resolution in the final version, then disregard this comment.

Figure 11: The colour bars for panels (b) and (c) are missing the quantity label, although it is present for panel (a) colour bar.

There were several instances of missing words or unclear phrasing. I might have not caught all of them, so I recommend another round of proofreading to ensure that no important information was accidentally omitted during the editing.
Line 63: 'Moreover, how DDR performance varies with the prevailing atmospheric regime is necessary, especially in environments that differ from Oklahoma’s semi-arid climate' -- the part in bold may be incomplete.

Line 259: Unclear line or editing artifact here: 'Seasonally, there were major differences in the actual fractional values: precipitating conditions in the colder months, foggy conditions yielded much higher levels of availability than warmer months, and vice-versa with precipitating conditions, reflecting the relevance of the types of hydrometeor droplets having an effect'. The first mention of 'precipitating conditions' looks out of place.

Lines 262-271: please, double-check the paragraph, as it lacks proofreading compared to the rest of the text. Particularly, the following sentence reads as incomplete: 'As such, Figure 10 portrays the relationship between DDR data availability, where the rain particle number (Fig. 10a), the rain intensity used in Fig. 9 (Fig. 10b), and the kinetic energy of the droplets'. Is it just listing the data series for comparison (then 'where' should be removed) or is a part of the sentence after 'where' missing?

Line 391: 'Further, the DDR method is that strong data availability requires substantial hydrometers for the radar beams to scatter off' -- either a word is missing after 'is' or 'is' should be replaced with a verb.

Line 442: 'At the heights in Fig. 7, the scanning lidar was seen to measure well, the were well aligned with r between 0.93 and 0.95...' is a noun missing between 'the' and 'were'?

Line 444: 'Note that the rain-exclusion filter applied in the profiling–scanning lidar comparisons of is intentionally not applied here' -- another instance of a missing text

There is an inconsistency in referring to the panels in figure captions. For example, Fig. 1, 3 refer to panels as left-right, while Fig. 2, 5, 7 use letter indices, Fig. 6 refers to panels without any indices even though they are marked as a-b. I leave that to the editor to decide whether this has to be corrected for the consistency or can be kept as it is.

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Citation: https://doi.org/10.5194/wes-2026-114-RC1

Arianna Marie Jordan, Lin-Ya Hung, Gerrit Wolken-Möhlmann, and Julia Gottschall

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Short summary

Wind energy needs accurate airflow tools around turbines. We tested dual-Doppler radar at a research wind farm in Germany alongside lidar (a point-based wind sensor). The method matched lidar well at most heights and mapped wind shadows behind turbines, including wake meandering. Data quality depended on rainfall, with rainy periods giving the best coverage and fog the worst. This information is useful for implementation of dual-Doppler radar as a wind measurement tool for the industry.


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