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, 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 Precipitation InterComparison Experiment) Shielded (DFIR) Davos-2019 Instrument installed in the Swiss Alps within the context of RACLETS (Role of Aerosols and CLouds Enhanced by Topography on Snow) Not shielded ICEGENESIS-2021 Instrument 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 Instrument installed in Korea, in the context of ICEPOP. See for example Gehring et al 2021. Shielded (DFIR) Jura-2019 & Jura-2023 Instrument installed in the Swiss Jura within a MeteoSwiss measurement site Not shielded Norway-2016 Instrument installed in Norway during the High-Latitude Measurement of Snowfall (HiLaMS). See for example Cooper et al, 2022. Not shielded PLATO-2019 Instrument installed in the "Davis" Antarctic base during the PLATO field campaign Not shielded POPE-2020 Instrument installed in the "Princess Elizabeth Antarctica" base during the POPE campaign. See for example Ferrone et al, 2023. Not shielded Remoray-2022 Instrument installed in the French Jura. Not shielded Valais-2016 Instrument installed in the Swiss Alps in a ski resort. Not shielded Version 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

数据集概览
本数据集提供了使用多角度雪花相机（Multi-Angle Snowflake Camera, MASC）采集的自由下落雪花的数据与图像。本数据集为每一个记录到的雪花提供以下内容：一组对应MASC三台相机的灰度图像三元组；大量几何、纹理特征描述符，已预编译的已发表反演算法输出结果，以及每次测量时对应地点与时间的基础环境信息。预计算的特征描述符与反演结果既可以按每个相机视角单独获取，其中部分也可作为整个图像三元组的整体特征描述符获取。非详尽的预计算量示例包括：如Praz等人2017年研究中的纹理与几何特征描述符；如Praz等人2017年研究中的水凝物分类、结霜程度估计、融化识别；如Schaer等人2020年研究中的吹雪识别；如Leinonen等人2021年研究中的质量、体积、旋转估算。
数据格式与结构
本数据集分为四个用于存储标量特征描述符的.parquet文件，以及一个用于存储图像的Zarr数据库。有关数据内容与数据记录的详细说明可在此处查阅。
配套代码
本数据集配套有基于Python的应用程序接口（API），用于处理、展示与整理数据集内容，该API托管于GitHub平台。相关代码文档可在ReadTheDocs平台查阅。
下载说明
若使用基于Python的API，所有可下载文件均需存储在同一文件夹中，且MASCdb.zarr.zip需在下载后进行解压。
野外实验
本数据集包含的各野外实验及其简要说明如下表所示：
| 实验名称 | 说明 | 是否遮蔽 |
| ---- | ---- | ---- |
| DFIR | 双围栏比对参考站（Double Fence Intercomparison Reference） | - |
| APRES3-2016 & APRES3-2017 | 部署于南极，隶属于APRES3项目，相关研究可参考Genthon等人2018年与Grazioli等人2017年的文献 | 未遮蔽 |
| Davos-2015 | 部署于瑞士阿尔卑斯山区，隶属于SPICE（固态降水比对实验，Solid Precipitation InterComparison Experiment）项目 | 遮蔽（DFIR） |
| Davos-2019 | 部署于瑞士阿尔卑斯山区，隶属于RACLETS（地形增强气溶胶与云对降雪的作用，Role of Aerosols and CLouds Enhanced by Topography on Snow）项目 | 未遮蔽 |
| ICEGENESIS-2021 | 部署于瑞士汝拉山区的瑞士气象局（MeteoSwiss）地面观测站点，隶属于ICE-GENESIS项目，相关研究可参考Billault-Roux等人2023年的文献 | 未遮蔽 |
| ICEPOP-2018 | 部署于韩国，隶属于ICEPOP项目，相关研究可参考Gehring等人2021年的文献 | 遮蔽（DFIR） |
| Jura-2019 & Jura-2023 | 部署于瑞士汝拉山区的瑞士气象局观测站点 | 未遮蔽 |
| Norway-2016 | 部署于挪威，隶属于高纬度降雪测量（High-Latitude Measurement of Snowfall, HiLaMS）项目，相关研究可参考Cooper等人2022年的文献 | 未遮蔽 |
| PLATO-2019 | 在PLATO野外实验期间部署于南极戴维斯站 | 未遮蔽 |
| POPE-2020 | 在POPE实验期间部署于南极伊丽莎白公主站，相关研究可参考Ferrone等人2023年的文献 | 未遮蔽 |
| Remoray-2022 | 部署于法国汝拉山区 | 未遮蔽 |
| Valais-2016 | 部署于瑞士阿尔卑斯山区的一处滑雪胜地 | 未遮蔽 |
版本历史
1.0版本：新增两项野外实验（"Jura-2023"、"Norway-2016"），补充了参考文献与实验列表
0.3版本：新增一项野外实验（"Remoray-2022"）
0.2版本：修改变量命名，统一变量精度（位数）
0.1版本：首次上传