the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 18 Feb 2025
Data collected by a drone backpack for air quality and atmospheric state measurements during Pallas Cloud Experiment 2022 (PaCE2022)
Abstract. A lightweight custom built drone backpack for air quality and atmospheric state variables measurements on top of consumer-grade drone was used during Pallas Cloud Experiment (PaCE) campaign’s intensive operation period (IOP) between September 12th and October 10th, 2022. The drone backpack measurements include 63 vertical profile flights from two close by locations at Pallasjarvi lake and 12 flights against the reference at Sammaltunturi station. The observations include aerosol number concentrations and size distributions, and meteorological parameters up to 500 m above the ground level. The dataset has been uploaded to the common Zenodo PaCE 2022 community archive (https://zenodo.org/communities/pace2022/, last access: 5 February, 2025). The datasets in two formats NetCDF and CSV are available here: https://doi.org/10.5281/zenodo.14780929, Brus et al. (2025a) and https://doi.org/10.5281/zenodo.14778422, Brus et al. (2025b), respectively.
- Preprint
(1332 KB) - Metadata XML
- BibTeX
- EndNote
Status: open (until 27 Mar 2025)
-
RC1: 'Comment on essd-2025-61', Anonymous Referee #1, 17 Mar 2025
reply
This study presents an extensive dataset collected using a drone-based backpack system for air quality and atmospheric state measurements during the Pallas Cloud Experiment 2022 (PaCE2022). The authors detail the instrumentation, calibration/validation, and data collection methods used during the campaign, emphasizing the advantages of drone-based measurements for atmospheric studies, especially in subarctic areas. The dataset includes information on aerosol concentrations, and meteorological parameters, providing insights into the atmospheric conditions of the studied area.
Strong points:
The dataset has been rigorously validated through comparisons with reference measurements, which enhances the credibility and usability of the collected data.
The dataset offers valuable information for the atmospheric science community, particularly regarding the use of UAV-based measurement techniques in complex and/or under-studied atmospheric conditions.
Suggested improvements:
While the introduction provides a solid background on the importance of UAV-based measurements, it lacks a clear structure that outlines the research objectives and the organization of the paper. Providing a more structured introduction would enhance readability and help guide the reader through the study.
Although the dataset is well-documented, the discussion on its potential applications and future uses is relatively limited. Expanding the conclusion to explicitly address how this dataset could be utilized by the scientific community and integrated into broader atmospheric research (e.g., CCN, INP) would strengthen the impact of the study.
Minor comments line by line:
L10: "against the reference" - Please explain the meaning.
L11: "meteorological parameters" - Which ones?
L12-14 - The provided links include the coma at the end, thus are not working when direct click on them.
L24 - "our previous research" - Please specify and cite.
L42 - Also check https://doi.org/10.5194/amt-2024-162. Can be also interesting to consider as this study used the same OPC in a different drone system to study volcanic aerosols.
L42 - "FMI" - Please specify it properly the first time for people that does not know the Finish Meteorological Institute.
L59 - "minimize the propeller airflow" - Based on what ? You could add some references that indeed show propeller airflow is minimal in this drone area (e.g., https://doi.org/10.2514/6.2018-1266, https://doi.org/10.2514/1.C032828, https://doi.org/10.3390/drones6110329).
L110 - Might be useful to specify here the size range of the measured particles.
L128 - I suppose that PM are calculated from the raw particle counts of the OPC, but based on which particle density?
Figure 5 - Not really clear why you have such errors/uncertainties on the OPC measurements.
Citation: https://doi.org/10.5194/essd-2025-61-RC1 -
RC2: 'Comment on essd-2025-61', Anonymous Referee #2, 20 Mar 2025
reply
This data description paper is a very important addition to the already published data from the Pallas Cloud Experiment 2022. It contains information about the measurement system, data acquisition, measurement strategy and some intercomparison with other instruments. It is good to see researchers presenting their methods and instruments alongside published datasets!
Most information is sufficient to understand and further use the data, including its peculiarities, but especially for the csv-Files with its high accessibility, details are needed for people not being part of the experiment to make most out of the data.
Although I don't feel qualified to make comments on the language, the text might benefit a lot from using a language tool or a native speaker, in addition to some basic decisions (data as singular or plural? Direct or indirect speech?) to be followed throughout the text.
In the following, you'll find some specific comments with line numbers:
L17: "..all [atmospheric?] models"; add a reference to the statement?
L47: please omit the word "please"
L55: Why is it suitable (include a reference for the statement)? Or is it intended to be used for .. ?
L62: rather mention the relative airflow in the drone coordinate system than wind speed? E.g. a racetrack with the wind would decrease the relative airflow (and therefore affect the sensor ventilation)
L63-70: from my perspective, there are too many details not relevant for working with the data (for example it is not important to name the interfaces used for the sensors, e.g. I2C/SPI/Serial/..), please consider shortening this paragraph.
L72-75: Is the source code available on git? Maybe describe some more your wind estimation algorithm to allow the user to estimate its strength and weakness?
Table 1: This is a good starting point to show the manufacturers estimates on resolution/accuracy/uncertainty and response time; maybe fill up the values not provided in datasheets with your estimations? E.g. Res/Acc for OPC, Vertical positioning for GNSS. I somehow missed a concluding table with your guess on resolution/accuracy/uncertainty and response time of your whole measurement system, which likely will achieve slightly less accurate measurements on a drone than in the lab. In addition, isn't the response time of RH temperature dependent? If so, please make a comment (at least reference temperature for the response time).
L84..86: Can you provide some links to the mentioned networks (ACTRIS/ICOS/..) and mention implications/benefits for the station and its measurements?
L87ff: A map (although referred to a map in another publication later in the text) and especially a picture of the sites and conditions during the experiment could help a lot to understand data and the general environment (snow/grass/flora), including the low level clouds during fall.
L100: consider omitting the text about programming the mission
L110: add a reference to the picture/section where one can see the RH bias?
L122: please explain the altitude further - is it mean sea level in addition to a specific geoid (e.g. EGM96)? Consider using GNSS instead of GPS.
L132: Good to read how the data was synchronized. You might add a short note of the error you expect in the time synchronization.
L146: consider adding a reference about sampling losses
L168: please add a reference/table for the dataset levels (b1 here) so the reader is able to understand it.
L195: is all information about the processing steps (some nonlinear corrections / wind estimation algorithm and parameters / .. ) present in the publication to allow dataset users to understand the (pre-)processed data?Regarding dataset users:
For the nc-Files, metadata description within the file is clear (although no instrument is mentioned in the variable attributes for e.g. particle concentration), but for the CSV file, more information within this data description paper would be helpful (e.g. bin numbering and bin size/edges in a table, not just within the text[L127]). A concluding table with estimated overall uncertainty, response time and e.g. repeatability for each variable in your datasets might strongly increase the ability of dataset users to work and publish with the provided data.This publication is a very important addition to the provided datasets once all (or at least most of) the meta-information is well presented!
Citation: https://doi.org/10.5194/essd-2025-61-RC2
Data sets
Data collected by a drone backpack for air quality and atmospheric state measurements during Pallas Cloud Experiment 2022 (PaCE2022) David Brus, Viet Le, Joel Kuula, and Konstantinos Doulgeris https://doi.org/10.5281/zenodo.14780929
Data collected by a drone backpack for air quality and atmospheric state measurements during Pallas Cloud Experiment 2022 (PaCE2022) David Brus, Viet Le, Joel Kuula, and Konstantinos Doulgeris https://doi.org/10.5281/zenodo.14778422
Viewed
| HTML | XML | Total | BibTeX | EndNote | |
|---|---|---|---|---|---|
| 124 | 17 | 6 | 147 | 7 | 8 |
- HTML: 124
- PDF: 17
- XML: 6
- Total: 147
- BibTeX: 7
- EndNote: 8
Viewed (geographical distribution)
| Country | # | Views | % |
|---|
| Total: | 0 |
| HTML: | 0 |
| PDF: | 0 |
| XML: | 0 |
- 1