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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ftzenodo:oai:zenodo.org:8046497 2024-09-15T17:45:05+00:00 MASCDB, a database of images, descriptors and microphysical properties of individual snowflakes in free fall Grazioli, Jacopo Ghiggi, Gionata Berne, Alexis Berne, Alexis Billault-Roux, Anne-Claire Gehring, Josué Roulet, Yves-Alain Praz, Christophe Leinonen, Jussi Ferrone, Alfonso Durán-Alarcón, Claudio Cooper, Steven J 2023-06-16 https://doi.org/10.5281/zenodo.8046497 eng eng Zenodo https://doi.org/10.5194/amt-10-1335-2017 https://doi.org/10.5194/tc-14-367-2020 https://doi.org/10.5194/amt-2021-176 https://github.com/ltelab/pymascdb https://doi.org/10.5194/amt-5-2625-2012 https://doi.org/10.1175/BAMS-D-21-0007.1 https://zenodo.org/communities/epfl https://zenodo.org/communities/climate-cryosphere https://doi.org/10.5281/zenodo.5578920 https://doi.org/10.5281/zenodo.8046497 oai:zenodo.org:8046497 info:eu-repo/semantics/openAccess Creative Commons Attribution 4.0 International https://creativecommons.org/licenses/by/4.0/legalcode Snowfall ice crystals snow images snowflakes multi angle snowflake camera (MASC) image classification meteorology info:eu-repo/semantics/other 2023 ftzenodo https://doi.org/10.5281/zenodo.804649710.5194/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.5578920 2024-07-25T21:35:10Z 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 Instrument 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 Instrument installed in the Swiss Alps within the context of SPICE (Solid ... Other/Unknown Material Antarc* Antarctica Zenodo |
institution |
Open Polar |
collection |
Zenodo |
op_collection_id |
ftzenodo |
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 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 Instrument 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 Instrument installed in the Swiss Alps within the context of SPICE (Solid ... |
author2 |
Berne, Alexis Billault-Roux, Anne-Claire Gehring, Josué Roulet, Yves-Alain Praz, Christophe Leinonen, Jussi Ferrone, Alfonso Durán-Alarcón, Claudio Cooper, Steven J |
format |
Other/Unknown Material |
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://doi.org/10.5281/zenodo.8046497 |
genre |
Antarc* Antarctica |
genre_facet |
Antarc* Antarctica |
op_relation |
https://doi.org/10.5194/amt-10-1335-2017 https://doi.org/10.5194/tc-14-367-2020 https://doi.org/10.5194/amt-2021-176 https://github.com/ltelab/pymascdb https://doi.org/10.5194/amt-5-2625-2012 https://doi.org/10.1175/BAMS-D-21-0007.1 https://zenodo.org/communities/epfl https://zenodo.org/communities/climate-cryosphere https://doi.org/10.5281/zenodo.5578920 https://doi.org/10.5281/zenodo.8046497 oai:zenodo.org:8046497 |
op_rights |
info:eu-repo/semantics/openAccess Creative Commons Attribution 4.0 International https://creativecommons.org/licenses/by/4.0/legalcode |
op_doi |
https://doi.org/10.5281/zenodo.804649710.5194/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.5578920 |
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1810492805278072832 |