MASCDB, a database of images, descriptors and microphysical properties of individual snowflakes in free fall
Contributors
Data collector (11):
Researcher:
Supervisor:
- 1. EPFL-ENAC-IIE-LTE
- 2. MeteoSwiss
- 3. MeteoSwiss / EPFL-LTE
- 4. Institut des Géosciences de l'Environnement, Université Grenoble Alpes
- 5. University of Utah
- 6. University of Oslo
- 7. Univesity of Oslo
Description
Dataset overview
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 Praz et al 2017
- Hydrometeor classification, riming degree estimation, melting identification, as in Praz et al 2017
- Blowing snow identification, as in Schaer et al 2020
- Mass, volume, gyration estimation, as in Leinonen et al 2021
Data format and structure
The dataset is divided into four .parquet file (for scalar descriptors) and a Zarr database (for the images). A detailed description of the data content and of the data records is available here.
Supporting code
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.
Download notes
- All files available here for download should be stored in the same folder, if the python-based API is used
- MASCdb.zarr.zip must be unzipped after download
Field campaigns
A list of campaigns included in the dataset, with a minimal description is given in the following table
| Campaign_name | Information |
Shielded / Not shielded DFIR = Double Fence Intercomparison Reference |
|
APRES3-2016 & APRES3-2017 |
Installed in Antarctica in the context of the APRES3 project. See for example Genthon et al, 2018 or Grazioli et al 2017 | Not shielded |
| Davos-2015 | Installed in the Swiss Alps within the context of SPICE (Solid Precipitation InterComparison Experiment) | Shielded (DFIR) |
| Davos-2019 | Installed in the Swiss Alps within the context of RACLETS (Role of Aerosols and CLouds Enhanced by Topography on Snow) | Not shielded |
| ICEGENESIS-2021 | 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 |
| ICEPOP-2018 | Installed in Korea, in the context of ICEPOP. See for example Gehring et al 2021. | Shielded (DFIR) |
| Jura-2019 & Jura-2023 | Installed in the Swiss Jura within a MeteoSwiss measurement site | Not shielded |
| Norway-2016 | Installed in Norway during the High-Latitude Measurement of Snowfall (HiLaMS). See for example Cooper et al, 2022. | Not shielded |
| PLATO-2019 | Installed in the "Davis" Antarctic base during the PLATO field campaign | Not shielded |
| POPE-2020 | Installed in the "Princess Elizabeth Antarctica" base during the POPE campaign. See for example Ferrone et al, 2023. | Not shielded |
| Remoray-2022 | Installed in the French Jura. | Not shielded |
| Valais-2016 | 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 |
Version
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
Files
MASCdb.zarr.zip
Files
(6.1 GB)
Additional details
References
- Billault-Roux, A., and Coauthors, 2023: ICE GENESIS: Synergetic Aircraft and Ground-Based Remote Sensing and In Situ Measurements of Snowfall Microphysical Properties. Bull. Amer. Meteor. Soc., 104, E367–E388, https://doi.org/10.1175/BAMS-D-21-0184.1.
- Cooper S.J., T.S. L'Ecuyer, M.A. Wolff, T. Kuhn, C. Pettersen, C. Schirle, J. Shates, N.B. Wood, S. Eliasson, F. Hellmuth, B.J.K. Engdahl, T. Ilmo, and K. Nygård, 2022: Exploring Snowfall Variability through the High-Latitude Measurement of Snowfall (HiLaMS) Field Campaign, Bull American Met Society, pp. E1762–E1780, https://doi.org/10.1175/BAMS-D-21-0007.1
- Ferrone, A. and Berne, A.: Radar and ground-level measurements of clouds and precipitation collected during the POPE 2020 campaign at Princess Elisabeth Antarctica, Earth Syst. Sci. Data, 15, 1115–1132, https://doi.org/10.5194/essd-15-1115-2023, 2023.
- Garrett, T. J., Fallgatter, C., Shkurko, K., and Howlett, D.: Fall speed measurement and high-resolution multi-angle photography of hydrometeors in free fall, Atmos. Meas. Tech., 5, 2625–2633, https://doi.org/10.5194/amt-5-2625-2012, 2012.
- Gehring, J., Ferrone, A., Billault-Roux, A.-C., Besic, N., Ahn, K. D., Lee, G., and Berne, A.: Radar and ground-level measurements of precipitation collected by the École Polytechnique Fédérale de Lausanne during the International Collaborative Experiments for PyeongChang 2018 Olympic and Paralympic winter games, Earth Syst. Sci. Data, 13, 417–433, https://doi.org/10.5194/essd-13-417-2021, 2021.
- Genthon, C., Berne, A., Grazioli, J., Durán Alarcón, C., Praz, C., and Boudevillain, B.: Precipitation at Dumont d'Urville, Adélie Land, East Antarctica: the APRES3 field campaigns dataset, Earth Syst. Sci. Data, 10, 1605–1612, https://doi.org/10.5194/essd-10-1605-2018, 2018.
- Grazioli, J., Genthon, C., Boudevillain, B., Duran-Alarcon, C., Del Guasta, M., Madeleine, J.-B., and Berne, A.: Measurements of precipitation in Dumont d'Urville, Adélie Land, East Antarctica, The Cryosphere, 11, 1797–1811, https://doi.org/10.5194/tc-11-1797-2017, 2017.
- Leinonen, J., Grazioli, J., and Berne, A.: Reconstruction of the mass and geometry of snowfall particles from multi-angle snowflake camera (MASC) images, Atmos. Meas. Tech., 14, 6851–6866, https://doi.org/10.5194/amt-14-6851-2021, 2021.
- Praz, C., Roulet, Y.-A., and Berne, A.: Solid hydrometeor classification and riming degree estimation from pictures collected with a Multi-Angle Snowflake Camera, Atmos. Meas. Tech., 10, 1335–1357, https://doi.org/10.5194/amt-10-1335-2017, 2017.
- Schaer, M., Praz, C., and Berne, A.: Identification of blowing snow particles in images from a Multi-Angle Snowflake Camera, The Cryosphere, 14, 367–384, https://doi.org/10.5194/tc-14-367-2020, 2020.