Articles | Volume 14, issue 10
Atmos. Meas. Tech., 14, 6851–6866, 2021
https://doi.org/10.5194/amt-14-6851-2021
Atmos. Meas. Tech., 14, 6851–6866, 2021
https://doi.org/10.5194/amt-14-6851-2021
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 et al.

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Cited articles

Alom, M. Z., Taha, T. M., Yakopcic, C., Westberg, S., Sidike, P., Nasrin, M. S., Hasan, M., Van Essen, B. C., Awwal, A. A. S., and Asari, V. K.: A State-of-the-Art Survey on Deep Learning Theory and Architectures, Electronics, 8, 292, https://doi.org/10.3390/electronics8030292, 2019. a
Arjovsky, M., Chintala, S., and Bottou, L.: Wasserstein GAN, arXiv [preprint], arXiv:1701.07875, 2017. a
Bagheri, A. and Jin, J.: Photopolymerization in 3D Printing, ACS Appl. Polym. Mat., 1, 593–611, https://doi.org/10.1021/acsapm.8b00165, 2019. a
Baker, B. and Lawson, R. P.: Improvement in determination of ice water content from two-dimensional particle imagery. Part I: Image-to-mass relationships, J. Appl. Meteorol. Clim., 45, 1282–1290, https://doi.org/10.1175/JAM2398.1, 2006. a, b
Chicco, D.: Siamese Neural Networks: An Overview, in: Artificial Neural Networks, edited by: Cartwright, H., Springer, New York, New York, USA, https://doi.org/10.1007/978-1-0716-0826-5_3, pp. 73–94, 2021. a
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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.