Reconstruction of the mass and geometry of snowfall particles  from multi-angle snowflake camera (MASC) images

Leinonen, Jussi; Grazioli, Jacopo; Berne, Alexis

doi:https://doi.org/10.5194/amt-14-6851-2021

Articles | Volume 14, issue 10

https://doi.org/10.5194/amt-14-6851-2021

© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/amt-14-6851-2021

© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 14, issue 10

Research article

|

25 Oct 2021

Research article |

| 25 Oct 2021

Reconstruction of the mass and geometry of snowfall particles from multi-angle snowflake camera (MASC) images

Jussi Leinonen, Jacopo Grazioli, and Alexis Berne

Data sets

Support data and codes for the evaluation experiment section of the paper: "Mass and geometry reconstruction of snowfall particles from multi angle snowflake camera (MASC) images" J. Grazioli, J. Leinonen, and A. Berne https://doi.org/10.5281/zenodo.4790962

Model code and software

3D GAN code and example J. Leinonen and J. Grazioli https://github.com/jleinonen/masc3dgan

jleinonen/aggregation J. Leinonen https://github.com/jleinonen/aggregation

Short summary

Measuring the shape, size and mass of a large number of snowflakes is a challenging task; it is hard to achieve in an automatic and instrumented manner. We present a method to retrieve these properties of individual snowflakes using as input a triplet of images/pictures automatically collected by a multi-angle snowflake camera (MASC) instrument. Our method, based on machine learning, is trained on artificially generated snowflakes and evaluated on 3D-printed snowflake replicas.