the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 13 Mar 2025
Max Planck WinDarts: High-Resolution Atmospheric Boundary Layer Measurements with the Max Planck CloudKite platform and Ground Weather Station – A Data Overview
Abstract. This paper presents the data set collected during the Pallas Cloud Experiment (PaCE) campaign, conducted at Pallas, Finland, between September 15 and September 28, 2022. The data set includes measurements of turbulence in the atmospheric boundary layer in both cloudy and cloud-free conditions, collected using the Max Planck CloudKite (MPCK) platform, the WinDarts, and a ground weather station for near surface data. The airborne observations span altitudes from the surface up to 1510 m above ground level, with flight durations ranging from 1 hour to nearly 6 hours, while the ground weather station provides continuous measurements throughout the entire campaign. This data set provides high-resolution meteorological measurements to analyse boundary layer dynamics under different atmospheric conditions encountered during PaCE campaign. This paper describes the data collection process, the structure of the data set, and guidelines for users.
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Status: open (until 01 May 2025)
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RC1: 'Comment on essd-2025-111', Anonymous Referee #1, 13 Apr 2025
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Review of: Max Planck WinDarts: High-Resolution Atmospheric Boundary Layer Measurements with the Max Planck CloudKite platform and Ground Weather Station – A Data Overview
Authors: V. Chávez-Medina, H. Khodamoradi, O. Schlenczek, F. Nordsiek, C.E. Brunner, E. Bodenschatz, and G. Bagheri
General Comments: This paper attempts to provide an overview of the WinDarts measurement system deployed on the Max Planck CloudKite during the PaCE field campaign. In general, this is a worthwhile effort and interesting activity. Having said that there is a lack of detail in this paper that makes it challenging to really know what is going on. Additional work is required to provide the reader with a solid basis for using these data.
Major Comments:
- Lines 42-53: These two paragraphs are a bit odd.The paper isn’t about PaCE, per se. I understand that ESSD articles shouldn’t be analysis focused, but I think that the authors need to determine whether this paper is specifically about the data collected during PaCE, or whether it’s meant to be an overview of the WinDarts systems (in which case ESSD may not be appropriate. The subsequent text describes PaCE at a very, very high level, and primarily sends the reader to the Brus et al article (which is ok). But then why spend the real estate on PaCE at all? I would recommend rewriting these sections to truly focus on the details that are most important to the CloudKite deployment.
- Winds: It doesn’t see as though the authors have truly calculated the 3D wind vector.They don’t mention system pitch, roll, and yaw calculations at all (presumably these can be obtained through the BNO 055 measurements?), and they only show system relative airflow in the Figure 9. This is not very helpful to those wanting to understand the winds. It would also prove challenging to use for fluxes, as you don’t know whether the airflow is vertical or horizontal (you would need to correct for system pitch and roll to do this correctly). This is a major shortcoming of the dataset currently. Is there a reason that the winds and airflow angles aren’t converted to a normal wind coordinate system? Vectoflow is discussed, but no details are provided. Clearly system pitch, roll, and yaw (along with a calibrated airspeed) would be required here. How are those obtained?
- Data quality: On line 166, it is said that “defective data were identified graphically”.What does this mean? Were any quantitative measures or any formalized thresholds used to evaluate where data may be bad? If so, lay out those details in this article. If not, why not? This is another major shortcoming of the dataset and the paper at this time. Data QC should be done in a reproducible manner, not simply by having someone review the plots and make their own decisions on what are good or bad data.
- Time stamping:More information on this would be very helpful (and on logging in general). Was there an onboard microprocessor used for logging? If so, did you log the computer clock values with each logging event? If not, how do you know that the GPS time logged was accurate for the time that the sensor pulled data? And to what level?
- Figure 6: The large differences between sensors are a bit worrisome.These are likely outside of the accuracy specs of the sensors, correct? If so, how can you explain this and/or how can you justify selecting one sensor over another? More details on sensor selection for data products should be included in the manuscript. Also, if there other sensors are ever used for redundancy, that would also be good to explain.
Minor Comments:
Line 14: “fir example” should be “for example”
Line 16: “for developing a thorough”
Lines 23-28: What about smaller, unmanned aircraft? These are used frequently for this purpose, can fly lower, and operated at lower airspeeds. You mention UAVs were used during PaCE, so clearly you are aware of using these systems for atmospheric science.
Section 2 header: “… the PaCE Campaign”
Line 55: “conducting”
Line 56: “characterizing”
Line 56: “the vertical column”
Line 65: “included” – the campaign is over, correct?
Line 75: 34 m3 what?
Line 76: wind wind?
Line 81: There is something wrong with how the references are presented.
Line 98: Not sure what you mean by “combined”, here.
Line 273: Fluxes of what? Heat? Momentum? Moisture? CO2? Aerosols?
Citation: https://doi.org/10.5194/essd-2025-111-RC1
Data sets
Data from the Max Planck WinDarts and Ground Weather Station during the Pallas Cloud Experiment 2022 Venecia Chávez-Medina, Hossein Khodamoradi, Oliver Schlenczek, Freja Nordsiek, Claudia E. Brunner, Eberhard Bodenschatz, and Gholamhossein Bagheri https://doi.org/10.5281/zenodo.14858142
Data from the Max Planck WinDarts and Ground Weather Station during the Pallas Cloud Experiment 2022 Venecia Chávez-Medina, Hossein Khodamoradi, Oliver Schlenczek, Freja Nordsiek, Claudia E. Brunner, Eberhard Bodenschatz, and Gholamhossein Bagheri https://doi.org/10.5281/zenodo.14774327
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