the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 03 Apr 2025
Dataset of airborne measurements of aerosol, cloud droplets and meteorology by tethered balloon during PaCE 2022
Abstract. Aerosol, cloud droplet, and meteorological measurements were carried out by the Finnish Meteorological Institute's payload onboard the tethered balloon systems during the Pallas Cloud Experiment 2022 in Finland. This dataset includes 21 flights between September 16th and October 10th. The observations include vertical profiles and time series of aerosol number concentration and size distribution; cloud droplet number concentration and size distribution; and meteorological parameters. This dataset has been uploaded to the common Zenodo PaCE 2022 community archive (https://zenodo.org/communities/pace2022/, last access: Jan 20, 2025). The dataset (Le et al., 2025) is available at: https://doi.org/10.5281/zenodo.14932882.
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RC1: 'Comment on essd-2025-148', Anonymous Referee #1, 14 Apr 2025
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Feedback on ESSD manuscript 2025-148
General comment
Le et al. present a dataset of balloon-borne meteorology, aerosol, and cloud microphysics observations collected at the Pallas Station, Finland, during the PaCE 2022 campaign. A custom set of mobile instruments was operated with two tethered balloon systems on 21 days, with the end of the campaign set by losing the second balloon system. A handheld condensation particle counter (CPC), a portable optical particle spectrometer (POPS), and a mini cloud droplet analyzer (mCDA) were deployed to study aerosol-cloud interactions with in situ measurements inside clouds. These measurements are extremely rare; therefore, such a dataset would in general be very valuable for the scientific community. However, because the methods, the dataset, and the manuscript show significant flaws, the work must be rejected for publication.
For the first 10 days of measurements, a diesel-powered winch system was used to maintain the balloon's altitude. Such a significant aerosol source near the measurements is unacceptable in the clean sub-Arctic environment, or at least needs a careful quantification, which was not provided. The measurements of the handheld CPC are most likely incorrect because the device was used outside its operating temperature range (Bezantakos et al. 2022). This instrument is very sensitive to the operating temperature, but doesn’t provide any housekeeping data such as condenser, saturator, or inlet temperature. Therefore, an appropriate data correction/flagging seems impossible. Aerosol sampling losses were entirely disregarded, which is a particular issue for the POPS measurements with a horizontal inlet. Without a wind vane, the payload must have constantly rotated during the balloon flights. The result is a change in sampling angles (iso- or anisoaxial) and flow conditions (sub- to super isokinetic sampling) that needs to be recorded with an inertial measurement unit and a wind speed sensor for correction. However, as the authors failed to do so, the particle number concentration of the POPS is very noisy with a standard deviation up to 100 % on a randomly picked time range of 5 min. The meteorological measurements are very rudimentary, with a limited representativeness of the ambient conditions. The temperature sensor without any shielding was likely affected by solar radiation, and the humidity sensor by wetting. Wind measurements essential for sampling corrections of the mCDA, the POPS, and the CPC are missing. The cloud microphysics data of the mCDA are questionable. As aerosol and cloud particles were sampled simultaneously, how were the two differentiated from each other?
A random data check revealed significant discrepancies that should have been filtered out or flagged to allow re-use of the data. For instance, on 17 September 2022, mean cloud droplet number concentrations inside a cloud (assumed from the rh measurements) of 2 cm-3 seem too low compared to reported values at this station. Also, the POPS flow measurements show unrealistically high values. Measurement uncertainties and other significant methodological errors were not assessed and corrected. Fundamental information, such as cloud occurrence, is missing and difficult to derive from the provided data. I would not recommend using the dataset as it is.
The manuscript is generally well written, but it doesn’t provide the necessary information for external users to foster re-use of the data. Essential info on the cloud microphysics measurements and data quality assurance is missing. What are the plus-minus values in section 4? A detailed uncertainty estimation is missing.
Detailed feedback on the manuscript:
Figure 4: The sketch of the payload system is very simplified and doesn’t provide any useful info. Please provide a photo with detailed descriptions instead.
Line 97 -100: BME 280 fully exposed to the environment? No shielding from the sun to avoid temperature increase? How did you handle condensation on the sensor and wetting inside clouds? Quantify the measurement uncertainties!
Line 107: This sentence is difficult to understand. Was the DP50 at 10 nm, and how was the uncertainty determined? What was the plateau efficiency? Rephrase and add the counting efficiency curve with error bars to the manuscript (supplementary is sufficient). Please also consider Bezantakos et al. 2022 (https://doi.org/10.1016/j.jaerosci.2021.105877) for the ambient temperature dependency of the TSI 3007’s detection limit.
Line 110: The maximum detectable diameter of the POPS is 3 µm. Please see Pilz et al. 2022 (https://doi.org/10.5194/amt-15-6889-2022) and Pohorsky et al. 2023 (https://doi.org/10.5194/amt-17-731-2024) for limited usage of the POPS size range and increased sizing uncertainties from 0.5 to 1 µm. Consider the limitations of the lowest and highest instrument bins and provide uncertainty estimations for your bin configuration.
Line 113: The intensity of scattered light does not depend only on particle size, but also on the refractive index and shape. The POPS provides equivalent particle diameters to the calibration material, e.g., PSL-equivalent diameters. Therefore, the calibration material should be mentioned here.
Line 120: Please add a plot with the results of the sizing calibration to the supplementary.
Table 1: Add a column with measurement uncertainties. Were the pm masses calibrated or validated? Which aerosol properties were chosen for the PM masses?
Line 130: You report wet particle diameters since no aerosol drying system has been installed.
Figure 5: Is not very useful for enhancing data usage. Instead, please provide a detailed table with start and end times of the balloon flight, since the data record also contains time on the ground. Also, add info for every flight, e.g., sampling strategy (profile or hovering), maximum altitude, and cloud occurrence.
Line 135: How do you differentiate between water droplets and aerosol particles?
Line 140: The POPS pump failure was not mentioned before.
Line 143: Provide details for the flight displayed in Fig. 6. Scientific aim? Clouds?
Line 151: What does the plus-minus range represent? The single Standard deviation? If so, there is probably a mistake, as negative values of LWC, CPC concentration, POPS concentration, and Nd result from the average minus the deviation. Please provide a detailed uncertainty estimation for all your measurements.
Figure 8d): Are these aerosol particle or cloud droplet number size distributions, or is it mixed? How do you differentiate between the two?
Citation: https://doi.org/10.5194/essd-2025-148-RC1
Data sets
Dataset of airborne measurements of aerosol, cloud droplets and meteorology by tethered balloon during PaCE 2022 Viet Le et al. https://doi.org/10.5281/zenodo.14932882
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