MASCDB, a database of images, descriptors and microphysical properties of individual snowflakes in free fall

<strong>Dataset overview</strong> This dataset provides data and images of snowflakes in free fall collected with a Multi-Angle Snowflake Camera (MASC) The dataset includes, for each recorded snowflakes: A triplet of gray-scale images corresponding to the three cameras of the MASC A large quantity of geometrical, textural descriptors and the pre-compiled output of published retrieval algorithms as well as basic environmental information at the location and time of each measurement. The pre-computed descriptors and retrievals are available either individually for each camera view or, some of them, available as descriptors of the triplet as a whole. A non exhaustive list of precomputed quantities includes for example: Textural and geometrical descriptors as in <em>Praz et al 2017</em> Hydrometeor classification, riming degree estimation, melting identification, as in <em>Praz et al 2017</em> Blowing snow identification, as in <em>Schaer et al 2020 </em> Mass, volume, gyration estimation<em>, as in Leinonen et al 2021</em> <strong>Data format and structure</strong> The dataset is divided into four <em>.parquet</em> file (for scalar descriptors) and a <em>Zarr</em> database (for the images). A detailed description of the data content and of the data records is available here. <strong>Supporting code</strong> A python-based API is available to manipulate, display and organize the data of our dataset. It can be found on GitHub. See also the code documentation on ReadTheDocs. <strong>Download notes</strong> All files available here for download should be stored in the same folder, if the python-based API is used <em>MASCdb.zarr.zip</em> must be unzipped after download <strong>Field campaigns</strong> A list of campaigns included in the dataset, with a minimal description is given in the following table <strong>Campaign_name</strong> <strong>Information</strong> <strong>Shielded / Not shielded</strong> <em>DFIR = Double Fence Intercomparison Reference</em> <em>APRES3-2016 &amp; APRES3-2017</em> Installed in Antarctica in the context of the APRES3 project. See for example Genthon et al, 2018 or Grazioli et al 2017 Not shielded <em>Davos-2015</em> Installed in the Swiss Alps within the context of SPICE (Solid Precipitation InterComparison Experiment) Shielded (DFIR) <em>Davos-2019</em> Installed in the Swiss Alps within the context of RACLETS (<em>Role of Aerosols and CLouds Enhanced by Topography on Snow</em>) Not shielded <em>ICEGENESIS-2021</em> Installed in the Swiss Jura in a MeteoSwiss ground measurement site, within the context of ICE-GENESIS. See for example Billault-Roux et al, 2023 Not shielded <em>ICEPOP-2018</em> Installed in Korea, in the context of ICEPOP. See for example Gehring et al 2021. Shielded (DFIR) <em>Jura-2019 &amp; Jura-2023</em> Installed in the Swiss Jura within a MeteoSwiss measurement site Not shielded <em>Norway-2016</em> Installed in Norway during the High-Latitude Measurement of Snowfall (HiLaMS). See for example Cooper et al, 2022. Not shielded <em>PLATO-2019</em> Installed in the "Davis" Antarctic base during the PLATO field campaign Not shielded <em>POPE-2020</em> Installed in the "Princess Elizabeth Antarctica" base during the POPE campaign. See for example Ferrone et al, 2023. Not shielded <em>Remoray-2022</em> Installed in the French Jura. Not shielded <em>Valais-2016</em> Installed in the Swiss Alps in a ski resort. Not shielded ISLAS-2022 Installed in Norway during the ISLAS campaign Not shielded Norway-2023 Installed in Norway during the MC2-ICEPACKS campaign Not shielded <strong>Version</strong> 1.1 - Two new campaigns ("ISLAS-2022", "Norway-2023") added. 1.0 - Two new campaigns ("Jura-2023", "Norway-2016") added. Added references and list of campaigns. 0.3 - a new campaign is added to the dataset ("Remoray-2022") 0.2 - rename of variables. Variable precision (digits) standardized 0.1 - first upload