the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 03 Apr 2025
In situ cloud surface measurements dataset from four cloud spectrometers during the Pallas Cloud Experiment (PaCE) 2022
Abstract. This data paper presents an overview of the cloud spectrometers deployed during the Pallas Cloud Experiment (PaCE) in autumn 2022, a coordinated measurement campaign in the Finnish subarctic that took place between September 12th and December 15th, 2022. Four cloud spectrometers, the Cloud Aerosol Spectrometer (CAS), the Forward Scattering Spectrometer Probe (FSSP-100), the Cloud Droplet Analyzer (CDA), and the ICEMET were operated as ground-based setups, providing high-resolution, in-cloud measurements of droplet size distributions and key microphysical properties, such as number concentration (Nc), liquid water content (LWC), median volume diameter (MVD), and effective diameter (ED). The dataset is complemented by meteorological observations of temperature, humidity, wind speed, and visibility at a 1 min resolution. The measurements collected during PaCE 2022 offer valuable insights into aerosol-cloud interactions and cloud evolution in subarctic cloud systems. This dataset is suitable for researchers in cloud microphysics, atmospheric science, and climate modeling, as well as for instrument calibration and validation in future campaigns. The data can also be integrated with complementary concurrent in-situ aerosol, remote sensing, UAV, and balloon-borne observations during PaCE 2022 to provide a more comprehensive understanding of cloud microphysics and atmospheric processes in the subarctic environment. The dataset is publicly available here: https://doi.org/10.5281/zenodo.15045295, Doulgeris et al., 2025.
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RC1: 'Comment on essd-2025-163', Anonymous Referee #1, 02 May 2025
The presented document by Doulgeris et al. (2025) introduces a comprehensive and well-structured dataset of in-situ cloud microphysics measurements collected during the Pallas Cloud Experiment (PaCE) 2022.
The authors provide a clear and detailed overview of the instruments used, including the Cloud Aerosol Spectrometer (CAS), the Forward Scattering Spectrometer Probe (FSSP-100), the Cloud Droplet Analyzer (CDA), and the holographic ICEMET sensor. Particularly noteworthy is the transparent description of instrument characteristics and their respective limitations, such as measurement losses due to icing and alignment issues.
The methodology of data collection and processing, along with accompanying meteorological measurements, is comprehensively described and easy to follow. A central aspect of the document is the detailed quality control, clearly identifying potential sources of error and providing suitable solutions and recommendations for data use, particularly concerning the CAS data due to its fixed orientation.
Issues:
The metadata of the individual instruments could be further expanded (e.g., serial number, calibration values, calibration dates, first installation date, etc.).
To make the dataset more transparent and easier to interpret for future analyses, I suggest introducing a QA flag. This would support the well-documented quality controls and help reduce potential misinterpretations, particularly with regard to CAS and wind direction. One example: 2.November 11:56 – 15:04 Is this gap caused by icing?The meteorological data from the individual devices differ — for example, the ICE-MET temperature and wind direction are not the same as those in the CDA dataset. Does the CDA dataset include parameters from its internal weather station? This should be clearly stated in the manuscript, as well as in the metadata and the dataset itself.
The ICE-MET dataset contains noticeable LWC outliers that could affect the data when grouped temporally. It is caused by values in the upper bins. Eg. 22. October 3:05 UTC Bin 187
Is there an explanation for that —is it already precipitation?Nevertheless, the dataset presented constitutes an extremely valuable resource for researchers in the fields of cloud physics, climate research, and meteorology. The careful documentation and provision of data, including uncertainties and boundary conditions, enhance reliability and facilitate their use in future studies.
Citation: https://doi.org/10.5194/essd-2025-163-RC1 -
RC2: 'Comment on essd-2025-163', Anonymous Referee #2, 18 May 2025
The manuscript "In situ cloud surface measurements dataset from four cloud spectrometers during the Pallas Cloud Experiment (PaCE) 2022" provides information about cloud microphysical data collected during several months with four different in situ cloud probes.
Description of the instrumentation, calibration procedures, operations and related difficulties and challenges are clearly presented. Especially part linked to limitations for individual instruments is really good and useful for potential users.
Details comments and questions
- Title: I would like to propose change to "In situ surface cloud measurements….." Current title "cloud surface measurements" is confusing as no cloud surface was measured.
- CAPS fixed orientation. I would like to propose adding one more "flag" parameter to data file which will include if measurements were from good and bad wind sector as defined by authors.
- CAS clearly suffered significant losses due to inlet orientation. What was the reason to install the inlet vertically? Then losses are extremely difficult to quantify.
- Although this is not research paper I wonder what is the local orographic effect at the site. This is valuable and important information for potential users if they would like to extrapolate the measurements to larger scale or compare with other sites.
- I am curios how was done correction for flow changes due to icing (page 9 lines 208-211). It is not only about changes in airflow in wind tunnel. Icing will also change the flow pattern and increase losses dur to impaction and turbulence.
- Page 9 (lines 233-241). Listing of all size bins is not necessary in the manuscript. This info should be metadata in data repository.
- 2 does not add to the manuscript much of valuable information and can be reduced to one text paragraph
- Page 14, Line 319: MVD and ED can be accurately derived only for periods when instruments measure properly and did not suffer losses and which cannot be accurately corrected. I am curios what instrument did provide most of the good quality data for the MVD and ED? FSSP-100?
Overall this is well written dataset description with very good instrumental section and after some revisions suitable for publication in ESSD.
Citation: https://doi.org/10.5194/essd-2025-163-RC2
Data sets
Dataset of in situ cloud surface measurements from cloud spectrometers during Pallas Cloud Experiment 2022 (PaCE2022). Konstantinos Doulgeris et al. https://doi.org/10.5281/zenodo.15045295
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