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.

Related authors

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
Performance assessment of a triple-frequency spaceborne cloud–precipitation radar concept using a global cloud-resolving model
J. Leinonen, M. D. Lebsock, S. Tanelli, K. Suzuki, H. Yashiro, and Y. Miyamoto
Atmos. Meas. Tech., 8, 3493–3517, https://doi.org/10.5194/amt-8-3493-2015,https://doi.org/10.5194/amt-8-3493-2015, 2015
Short summary

Related subject area

Subject: Others (Wind, Precipitation, Temperature, etc.) | Technique: In Situ Measurement | Topic: Data Processing and Information Retrieval
Estimates of the spatially complete, observational-data-driven planetary boundary layer height over the contiguous United States
Zolal Ayazpour, Shiqi Tao, Dan Li, Amy Jo Scarino, Ralph E. Kuehn, and Kang Sun
Atmos. Meas. Tech., 16, 563–580, https://doi.org/10.5194/amt-16-563-2023,https://doi.org/10.5194/amt-16-563-2023, 2023
Short summary
Detection of turbulence occurrences from temperature, pressure, and position measurements under superpressure balloons
Richard Wilson, Clara Pitois, Aurélien Podglajen, Albert Hertzog, Milena Corcos, and Riwal Plougonven
Atmos. Meas. Tech., 16, 311–330, https://doi.org/10.5194/amt-16-311-2023,https://doi.org/10.5194/amt-16-311-2023, 2023
Short summary
Inferring surface energy fluxes using drone data assimilation in large eddy simulations
Norbert Pirk, Kristoffer Aalstad, Sebastian Westermann, Astrid Vatne, Alouette van Hove, Lena Merete Tallaksen, Massimo Cassiani, and Gabriel Katul
Atmos. Meas. Tech., 15, 7293–7314, https://doi.org/10.5194/amt-15-7293-2022,https://doi.org/10.5194/amt-15-7293-2022, 2022
Short summary
Raindrop size distribution (DSD) during the passage of tropical cyclone Nivar: effect of measuring principle and wind on DSDs and retrieved rain integral and polarimetric parameters from impact and laser disdrometers
Basivi Radhakrishna
Atmos. Meas. Tech., 15, 6705–6722, https://doi.org/10.5194/amt-15-6705-2022,https://doi.org/10.5194/amt-15-6705-2022, 2022
Short summary
Automatic quality control of telemetric rain gauge data providing quantitative quality information (RainGaugeQC)
Katarzyna Ośródka, Irena Otop, and Jan Szturc
Atmos. Meas. Tech., 15, 5581–5597, https://doi.org/10.5194/amt-15-5581-2022,https://doi.org/10.5194/amt-15-5581-2022, 2022
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.