MASCDB, a database of images, descriptors and microphysical properties of individual snowflakes in free fall ...
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, te...
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Online Access: | https://dx.doi.org/10.5281/zenodo.5578920 https://zenodo.org/record/5578920 |
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ftdatacite:10.5281/zenodo.5578920 2023-07-23T04:14:57+02:00 MASCDB, a database of images, descriptors and microphysical properties of individual snowflakes in free fall ... Grazioli, Jacopo Ghiggi, Gionata Berne, Alexis 2023 https://dx.doi.org/10.5281/zenodo.5578920 https://zenodo.org/record/5578920 en eng Zenodo https://github.com/ltelab/pymascdb https://dx.doi.org/10.5194/amt-10-1335-2017 https://dx.doi.org/10.5194/tc-14-367-2020 https://dx.doi.org/10.5194/amt-2021-176 https://github.com/ltelab/pymascdb https://dx.doi.org/10.5194/amt-5-2625-2012 https://dx.doi.org/10.1175/bams-d-21-0007.1 https://dx.doi.org/10.5281/zenodo.5578921 https://dx.doi.org/10.5281/zenodo.5709832 https://dx.doi.org/10.5281/zenodo.6905707 https://dx.doi.org/10.5281/zenodo.8046497 Open Access Creative Commons Attribution 4.0 International https://creativecommons.org/licenses/by/4.0/legalcode cc-by-4.0 info:eu-repo/semantics/openAccess Snowfall ice crystals snow images snowflakes multi angle snowflake camera MASC image classification meteorology dataset Dataset 2023 ftdatacite https://doi.org/10.5281/zenodo.557892010.5194/amt-10-1335-201710.5194/tc-14-367-202010.5194/amt-2021-17610.5194/amt-5-2625-201210.1175/bams-d-21-0007.110.5281/zenodo.557892110.5281/zenodo.570983210.5281/zenodo.690570710.5281/zenodo.8046497 2023-07-03T19:07:01Z 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 ... : {"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\u2013E388, 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. Nyg\u00e5rd, 2022: Exploring Snowfall Variability through the High-Latitude Measurement of Snowfall (HiLaMS) Field Campaign, Bull American Met Society, pp. E1762\u2013E1780, 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\u20131132, 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 ... Dataset Antarc* Antarctica DataCite Metadata Store (German National Library of Science and Technology) Leinonen ENVELOPE(24.993,24.993,66.148,66.148) |
institution |
Open Polar |
collection |
DataCite Metadata Store (German National Library of Science and Technology) |
op_collection_id |
ftdatacite |
language |
English |
topic |
Snowfall ice crystals snow images snowflakes multi angle snowflake camera MASC image classification meteorology |
spellingShingle |
Snowfall ice crystals snow images snowflakes multi angle snowflake camera MASC image classification meteorology Grazioli, Jacopo Ghiggi, Gionata Berne, Alexis MASCDB, a database of images, descriptors and microphysical properties of individual snowflakes in free fall ... |
topic_facet |
Snowfall ice crystals snow images snowflakes multi angle snowflake camera MASC image classification meteorology |
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 ... : {"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\u2013E388, 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. Nyg\u00e5rd, 2022: Exploring Snowfall Variability through the High-Latitude Measurement of Snowfall (HiLaMS) Field Campaign, Bull American Met Society, pp. E1762\u2013E1780, 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\u20131132, 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 ... |
format |
Dataset |
author |
Grazioli, Jacopo Ghiggi, Gionata Berne, Alexis |
author_facet |
Grazioli, Jacopo Ghiggi, Gionata Berne, Alexis |
author_sort |
Grazioli, Jacopo |
title |
MASCDB, a database of images, descriptors and microphysical properties of individual snowflakes in free fall ... |
title_short |
MASCDB, a database of images, descriptors and microphysical properties of individual snowflakes in free fall ... |
title_full |
MASCDB, a database of images, descriptors and microphysical properties of individual snowflakes in free fall ... |
title_fullStr |
MASCDB, a database of images, descriptors and microphysical properties of individual snowflakes in free fall ... |
title_full_unstemmed |
MASCDB, a database of images, descriptors and microphysical properties of individual snowflakes in free fall ... |
title_sort |
mascdb, a database of images, descriptors and microphysical properties of individual snowflakes in free fall ... |
publisher |
Zenodo |
publishDate |
2023 |
url |
https://dx.doi.org/10.5281/zenodo.5578920 https://zenodo.org/record/5578920 |
long_lat |
ENVELOPE(24.993,24.993,66.148,66.148) |
geographic |
Leinonen |
geographic_facet |
Leinonen |
genre |
Antarc* Antarctica |
genre_facet |
Antarc* Antarctica |
op_relation |
https://github.com/ltelab/pymascdb https://dx.doi.org/10.5194/amt-10-1335-2017 https://dx.doi.org/10.5194/tc-14-367-2020 https://dx.doi.org/10.5194/amt-2021-176 https://github.com/ltelab/pymascdb https://dx.doi.org/10.5194/amt-5-2625-2012 https://dx.doi.org/10.1175/bams-d-21-0007.1 https://dx.doi.org/10.5281/zenodo.5578921 https://dx.doi.org/10.5281/zenodo.5709832 https://dx.doi.org/10.5281/zenodo.6905707 https://dx.doi.org/10.5281/zenodo.8046497 |
op_rights |
Open Access Creative Commons Attribution 4.0 International https://creativecommons.org/licenses/by/4.0/legalcode cc-by-4.0 info:eu-repo/semantics/openAccess |
op_doi |
https://doi.org/10.5281/zenodo.557892010.5194/amt-10-1335-201710.5194/tc-14-367-202010.5194/amt-2021-17610.5194/amt-5-2625-201210.1175/bams-d-21-0007.110.5281/zenodo.557892110.5281/zenodo.570983210.5281/zenodo.690570710.5281/zenodo.8046497 |
_version_ |
1772188601376833536 |