the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Database of the Italian disdrometer network
Elisa Adirosi
Federico Porcù
Mario Montopoli
Luca Baldini
Alessandro Bracci
Vincenzo Capozzi
Clizia Annella
Giorgio Budillon
Edoardo Bucchignani
Alessandra Lucia Zollo
Orietta Cazzuli
Giulio Camisani
Renzo Bechini
Roberto Cremonini
Andrea Antonini
Alberto Ortolani
Samantha Melani
Paolo Valisa
Simone Scapin
Abstract. In 2021, a group of seven italian institutions decided to bring together their know-how, experience, and instruments for measuring the drop size distribution (DSD) of atmospheric precipitation giving birth to the Italian Group of Disdrometry (in Italian named: Gruppo Italiano Disdrometria, GID, https://www.gid-net.it/). The GID has made freely available a database of 1-minute records of DSD collected by the disdrometer network along the Italian peninsula. At the time of writing, the disdrometer network is composed of eight laser disdrometers belonging to six different Italian institutions (including research centers, universities and environmental regional agencies). This work aims to document the technical aspects of the italian DSD database consisting of 1-minute sampling data from 2012 to 2021 in a uniform standard format defined within GID. Although not all the disdrometers have the same length of the data record, the DSD data collection effort is the first of its kind in Italy, and from here onwards, it opens new opportunities in the surface characterization of microphysical properties of precipitation in the perspective of climate records. The GID database can be downloaded here https://doi.org/10.5281/zenodo.6875801 (Adirosi et al., 2022).
Elisa Adirosi et al.
Status: final response (author comments only)
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RC1: 'Comment on essd-2022-317', Anonymous Referee #1, 22 Nov 2022
This paper presents the technical aspects of the new freely available drop size distribution (DSD) database in Italy contributed by the disdrometer network from the corporation of seven Italian institutions, namely the Italian Group of Disdrometry (GID). This paper documented the technical details of the two types of laser disdrometers in the GID, six Thies Clima Laser Precipitation Monitor and two OTT Parsivel 2. The raw data was filtered by the fall velocity criterion to the 1-minute size-velocity matrix before computing of the DSD, and further filtered by the rain/no-rain criterion. The data was stored and shared in yearly XLSX files. The work documented in this paper does contribute to the frontier in the field of precipitation measurements, and promotes the expansion of the disdrometer network.
Comments:
- The data was shared through webpage, https://doi.org/10.5281/zenodo.6875801. The most up-to-date data was in year 2021, which means data in this year 2022 is yet avaible. It is suggested to update the data on more frequently.
- It is also not very clear how the data sharing workflow is organized. It looks like we have some DSD data from eight disdrometers shared online, but not sure whether the data will be update in the future and if there is any delay for the latest data to be published online.
- In future work, I suggest using modern ICT methods to enable the automation and reduce the time delay in the data collection, data transferring, data processing and data sharing.
Citation: https://doi.org/10.5194/essd-2022-317-RC1 - AC1: 'Reply on RC1', Elisa Adirosi, 10 Feb 2023
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RC2: 'Comment on essd-2022-317', Anonymous Referee #2, 03 Dec 2022
The manuscript describes a new database of precipitation particle size measurements across Italy. These measurements are important since they provide insights about the type of precipitation, its microphysical origin, hydrologic impact, and enable calibration of remote sensing measurements and communication links. Although the manuscript provides some scientific analysis of this dataset, it fails to provide much detail or discussion of the results and as such it is not very useful for gaining new scientific insights about precipitation. Instead, the manuscript is more akin to an algorithm theoretical basis document in some regards, or serves as simply a means to document the new database.
Here are some major concerns with the manuscript:
- The manuscript lacks many key references related to existing disdrometer networks, instrument, and DSD studies.
- The background needs to include a few more references to existing disdrometer networks. The GPM Ground Validation (GV) Program (Petersen et al. 2020) has operated the Disdrometer and Radar Observations of Precipitation (DROP) Facility, which consists of a network of video and laser disdrometer that have been deployed to GPM-related GV activites since 2010. The DROP Facility continues to operate around the NASA Wallops Flight Facility. This vast dataset of particle size distribution measurements is archived at NASA's GHRC DAAC (https://ghrc.nsstc.nasa.gov/home/field-campaigns/gpmgv).
Petersen, W.A., Kirstetter, PE., Wang, J., Wolff, D.B., Tokay, A. (2020). The GPM Ground Validation Program. In: Levizzani, V., Kidd, C., Kirschbaum, D., Kummerow, C., Nakamura, K., Turk, F. (eds) Satellite Precipitation Measurement. Advances in Global Change Research, vol 69. Springer, Cham. https://doi.org/10.1007/978-3-030-35798-6_2 - Need to cite Loffler-Mang and Joss (2000) since that is the first study on the Parsivel disdrometer.
Löffler-Mang, M., and Joss, J. (2000). An Optical Disdrometer for Measuring Size and Velocity of Hydrometeors. Journal of Atmospheric and Oceanic Technology 17, 2, 130-139, available from: < https://doi.org/10.1175/1520-0426(2000)017<0130:AODFMS>2.0.CO;2> - In reference to computing radar reflectivity factor from Parsivel measurments, iite Loffler-Mang and Blahak (2001).
Löffler-Mang, M., and Blahak, U. (2001). Estimation of the Equivalent Radar Reflectivity Factor from Measured Snow Size Spectra. Journal of Applied Meteorology 40, 4, 843-849, available from: < https://doi.org/10.1175/1520-0450(2001)040<0843:EOTERR>2.0.CO;2> - Add source of reported Parsivel measurement accuarcy numbers provided in Section 2.2
- Give examples of studies that use filtering when analyzing disdrometer measurements.
- Section 3: Provide examples of studies that use DSD measurements to compute these additional integral parameters like LWC, Ze, etc.
- The background needs to include a few more references to existing disdrometer networks. The GPM Ground Validation (GV) Program (Petersen et al. 2020) has operated the Disdrometer and Radar Observations of Precipitation (DROP) Facility, which consists of a network of video and laser disdrometer that have been deployed to GPM-related GV activites since 2010. The DROP Facility continues to operate around the NASA Wallops Flight Facility. This vast dataset of particle size distribution measurements is archived at NASA's GHRC DAAC (https://ghrc.nsstc.nasa.gov/home/field-campaigns/gpmgv).
- The writing is very good, but the English grammar could be improved. It would be beneficial to have the next revision reviewed by a primarly English speaking proof-reading service before resubmitting.
- This study uses the Gunn-Kinzer terminal velocity reference, which were obtained for mean sea-level. Foote and Du Toit (1969) have demonstrated that density affects the terminal fallspeed of raindrops. Hence, there is a need to correct the fall-speed measurements for altitude, in particular the TC-MV site. It may also need to be done for the other sites (e.g., Thurai and Bringi corrected the terminal velocity computed from Atlas 1973 for a disdrometer located at an altitude of only 480-m ASL).
Foote, G. B., and Du Toit, P. S. (1969). Terminal Velocity of Raindrops Aloft. Journal of Applied Meteorology and Climatology 8, 2, 249-253, available from: < https://doi.org/10.1175/1520-0450(1969)008<0249:TVORA>2.0.CO;2>
Thurai, M., and Bringi, V. N. (2005). Drop Axis Ratios from a 2D Video Disdrometer. Journal of Atmospheric and Oceanic Technology 22, 7, 966-978, available from: < https://doi.org/10.1175/JTECH1767.1> - Need to include reason(s) why the TC and P2 disdrometer plots in Figures 4-6 show lower concentrations at the smallest drop diameters. The recent raindrop size measurements by Thurai et al. (2019) that use a disdrometer capable of better resolving the small diameter part of the spectrum is a good example.
Thurai M, Bringi V, Gatlin PN, Petersen WA, Wingo MT. Measurements and Modeling of the Full Rain Drop Size Distribution. Atmosphere. 2019; 10(1):39. https://doi.org/10.3390/atmos10010039 - Include the number of DSD spectra for each site (e.g., in Table 1) since those are needed to assess statistical significance of climatological results in Figure 5.
- What are possible reasons for the seasonal variability exhibited in the DSD results shown in Figures 5 and 6?
Minor comments:
- Several mentions of the "old version TC" are in the manuscript. Please clearly state which site(s) has or had this version.
- The Parsivel software computes the spherical volume-equivalent diameter (Deq) based on the measured particle diameter. The manuscript words this in a confusing manner that mentions the particle axis ratio (lines 153-155).
- Line 299: Spelling error..."expect" should be "except"
- Suggest including in Figure 4 the rainfall rate for each 1-min DSD (e.g., another entry in the legend)
Citation: https://doi.org/10.5194/essd-2022-317-RC2 - AC2: 'Reply on RC2', Elisa Adirosi, 10 Feb 2023
- The manuscript lacks many key references related to existing disdrometer networks, instrument, and DSD studies.
Elisa Adirosi et al.
Data sets
Database of the Italian disdrometer network Elisa Adirosi; Federico Porcù; Mario Montopoli; Luca Baldini; Alessandro Bracci; Vincenzo Capozzi; Clizia Annella; Giorgio Budillon; Edoardo Bucchignani; Alessandra Lucia Zollo; Orietta Cazzuli; Giulio Camisani; Renzo Bechini; Roberto Cremonini; Andrea Antonini; Alberto Ortolani; Samantha Melani; Paolo Valisa; Simone Scapin https://doi.org/10.5281/zenodo.6875801
Elisa Adirosi et al.
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