Preprints
https://doi.org/10.5194/amt-2021-176
https://doi.org/10.5194/amt-2021-176

  12 Jul 2021

12 Jul 2021

Review status: this preprint is currently under review for the journal AMT.

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

Jussi Leinonen1,2,, Jacopo Grazioli1,, and Berne Alexis1 Jussi Leinonen et al.
  • 1Environmental Remote Sensing Laboratory, École polytechnique fédérale de Lausanne, Lausanne, Switzerland
  • 2Federal Office of Meteorology and Climatology MeteoSwiss, Locarno-Monti, Switzerland
  • These authors contributed equally to this work.

Abstract. This paper presents a method named 3D-GAN, based on a generative adversarial network (GAN), to retrieve the total mass, 3D structure and the internal mass distribution of snowflakes. The method uses as input a triplet of binary silhouettes of particles, corresponding to the triplet of stereoscopic images of snowflakes in free fall captured by a Multi-Angle Snowflake Camera (MASC). 3D-GAN is trained on simulated snowflakes of known characteristics whose silhouettes are statistically similar to real MASC observations and it is evaluated by means of snowflake replicas printed in 3D at 1 : 1 scale. The estimation of mass obtained by 3D-GAN has a normalized RMSE (NRMSE) of 40 %, a mean normalized bias (MNB) of 8 % and largely outperforms standard relationships based on maximum size and compactness. The volume of the convex hull of the particles is retrieved with MNRSE of 35 % and MNB of +19 %. In order to illustrate the potential of 3D-GAN to study snowfall microphysics and highlight its complementarity with existing retrieval algorithms, some application examples and ideas are provided, using as showcases the large available datasets of MASC images collected worldwide during various field campaigns. The combination of mass estimates (from 3D-GAN) and hydrometeor classification or riming degree estimation (from independent methods) allows for example to obtain mass-to-size power law parameters stratified on hydrometeor type or riming degree. The parameters obtained in this way are consistent with previous findings, with exponents overall around 2 and increasing with the degree of riming.

Jussi Leinonen et al.

Status: open (until 02 Sep 2021)

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Jussi Leinonen et al.

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, A. Berne https://10.5281/zenodo.4790962

Model code and software

Masc3dgan J. Leinonen, J. Grazioli https://github.com/jleinonen/masc3dgan

Jussi Leinonen et al.

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Short summary
Measuring the shape, size and mass of a large number of snowflakes is a challenging task; 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.