Articles | Volume 14, issue 10
https://doi.org/10.5194/amt-14-6851-2021
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, Jacopo Grazioli, and Alexis Berne

Related authors

Exploiting radar polarimetry for nowcasting thunderstorm hazards using deep learning
Nathalie Rombeek, Jussi Leinonen, and Ulrich Hamann
Nat. Hazards Earth Syst. Sci., 24, 133–144, https://doi.org/10.5194/nhess-24-133-2024,https://doi.org/10.5194/nhess-24-133-2024, 2024
Short summary
Nowcasting thunderstorm hazards using machine learning: the impact of data sources on performance
Jussi Leinonen, Ulrich Hamann, Urs Germann, and John R. Mecikalski
Nat. Hazards Earth Syst. Sci., 22, 577–597, https://doi.org/10.5194/nhess-22-577-2022,https://doi.org/10.5194/nhess-22-577-2022, 2022
Short summary
Unsupervised classification of snowflake images using a generative adversarial network and K-medoids classification
Jussi Leinonen and Alexis Berne
Atmos. Meas. Tech., 13, 2949–2964, https://doi.org/10.5194/amt-13-2949-2020,https://doi.org/10.5194/amt-13-2949-2020, 2020
Short summary
Marine liquid cloud geometric thickness retrieved from OCO-2's oxygen A-band spectrometer
Mark Richardson, Jussi Leinonen, Heather Q. Cronk, James McDuffie, Matthew D. Lebsock, and Graeme L. Stephens
Atmos. Meas. Tech., 12, 1717–1737, https://doi.org/10.5194/amt-12-1717-2019,https://doi.org/10.5194/amt-12-1717-2019, 2019
Short summary
Retrieval of snowflake microphysical properties from multifrequency radar observations
Jussi Leinonen, Matthew D. Lebsock, Simone Tanelli, Ousmane O. Sy, Brenda Dolan, Randy J. Chase, Joseph A. Finlon, Annakaisa von Lerber, and Dmitri Moisseev
Atmos. Meas. Tech., 11, 5471–5488, https://doi.org/10.5194/amt-11-5471-2018,https://doi.org/10.5194/amt-11-5471-2018, 2018
Short summary

Related subject area

Subject: Others (Wind, Precipitation, Temperature, etc.) | Technique: In Situ Measurement | Topic: Data Processing and Information Retrieval
Double-moment normalization of hail size number distributions over Switzerland
Alfonso Ferrone, Jérôme Kopp, Martin Lainer, Marco Gabella, Urs Germann, and Alexis Berne
Atmos. Meas. Tech., 17, 7143–7168, https://doi.org/10.5194/amt-17-7143-2024,https://doi.org/10.5194/amt-17-7143-2024, 2024
Short summary
The role of time averaging of eddy covariance fluxes on water use efficiency dynamics of maize
Arun Rao Karimindla, Shweta Kumari, Saipriya S R, Syam Chintala, and BVN P. Kambhammettu​​​​​​​
Atmos. Meas. Tech., 17, 5477–5490, https://doi.org/10.5194/amt-17-5477-2024,https://doi.org/10.5194/amt-17-5477-2024, 2024
Short summary
Number- and size-controlled rainfall regimes in the Netherlands: physical reality or statistical mirage?
Marc Schleiss
Atmos. Meas. Tech., 17, 4789–4802, https://doi.org/10.5194/amt-17-4789-2024,https://doi.org/10.5194/amt-17-4789-2024, 2024
Short summary
The Far-INfrarEd Spectrometer for Surface Emissivity (FINESSE) – Part 2: First measurements of the emissivity of water in the far-infrared
Laura Warwick, Jonathan E. Murray, and Helen Brindley
Atmos. Meas. Tech., 17, 4777–4787, https://doi.org/10.5194/amt-17-4777-2024,https://doi.org/10.5194/amt-17-4777-2024, 2024
Short summary
Bias Correction and Application of Labeled Smartphone Pressure Data for Evaluating the Best Track of Landfalling Tropical Cyclones
Ge Qiao, Yuyao Cao, Qinghong Zhang, and Juanzhen Sun
EGUsphere, https://doi.org/10.5194/egusphere-2024-1505,https://doi.org/10.5194/egusphere-2024-1505, 2024
Short summary

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
Download
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.