This content is not included in
your SAE MOBILUS subscription, or you are not logged in.
Snow Particle Characterization. Part B: Morphology Dependent Study of Snow Crystal 3D Properties Using a Convolutional Neural Network (CNN)
Technical Paper
2023-01-1486
ISSN: 0148-7191, e-ISSN: 2688-3627
Annotation ability available
Sector:
Language:
English
Abstract
This study presents the results of the ICE GENESIS 2021 Swiss Jura Flight Campaign in a way that is readily usable for ice accretion modelling and aims at improving the description of snow particles for model inputs. 2D images from two OAP probes, namely 2D-S and PIP, have been used to extract 3D shape parameters in the oblate spheroid assumption, as there are the diameter of the sphere of equivalent volume as ellipsoid, sphericity, orthogonal sphericity, and an estimation of bulk density of individual ice crystals through a mass-geometry parametrization. Innovative shape recognition algorithm, based on Convolutional Neural Network, has been used to identify ice crystal shapes based on these images and produce shape-specific mass particle size distributions to describe cloud ice content quantitatively in details. 3D shape descriptors and bulk density have been extracted for all the data collected in cloud environments described in the regulation as icing conditions. They are presented under the form of composite size distributions and gathered in size classes, representative of fixed portions of the total mass encountered during the field campaign. The examination of the data shows high discrepancies between crystals of identical size. To solve this issue shape parameters are combined with the morphological analysis to provide comprehensive explanations for the observed snow descriptor variabilities. Finally, the results are summarized under the form of simple habit-specific parametrizations for 3D shape descriptors and bulk density, as functions of crystal size.
Authors
Topic
Citation
JAFFEUX, L., Coutris, P., Schwarzenboeck, A., and Dezitter, F., "Snow Particle Characterization. Part B: Morphology Dependent Study of Snow Crystal 3D Properties Using a Convolutional Neural Network (CNN)," SAE Technical Paper 2023-01-1486, 2023, https://doi.org/10.4271/2023-01-1486.Also In
References
- Aguilar , B. et al. Ice Crystal Drag Model Extension to Snowflakes: Experimental and Numerical Investigations AIAA Journal 2022 1 14 https://doi.org/10.2514/1.J062122
- Baker, B. and R.P. Lawson, Improvement in Determination of Ice Water Content from Two-Dimensional Particle Imagery. Part I: Image-to-Mass Relationships J. Appl. Meteor. Climatol. 45 2006 1282 1290 10.1175/JAM2398.1
- Anne-Claire , B.-R. , Ghiggi , G. , Jaffeux , L. , Martini , A. et al. Dual-Frequency Spectral Radar Retrieval of Snowfall Microphysics: A Physics-Driven Deep-Learning Approach Atmospheric Measurement Techniques 16 4 2023 911 940 https://doi.org/10.1175/BAMS-D-21-0184.1
- Jaffeux , L. , Schwarzenboeck , A. , Coutris , P. , and Duroure , C. Ice Crystals Images from Optical Array Probes: Classification with Convolutional Neural Networks Atmos. Meas. Tech. Discuss 2022 https://doi.org/10.5194/amt-2022-72
- Leroy , D. , Fontaine , E. , Schwarzenboeck , A. , and Strapp , J.W. Ice Crystal Sizes in High Ice Water Content Clouds. Part I: On the Computation of Median Mass Diameter from In Situ Measurements Journal of Atmospheric and Oceanic Technology 33 11 2016 2461 2476
- Magono , C. and Chung , W.L. Meteorological Classification of Natural Snow Crystals Journal of the Faculty of Science, Hokkaido University. Series 7, Geophysics 2 4 1966 321 335
- Schwarzenboeck , A. and Heintzenberg , J. Cut Size Minimization and Cloud Element Break-Up in a Ground-Based CVI J. Aerosol Sci. 31 4 2000 477 489
- Schwarzenboeck , A. , Heintzenberg , J. , and Mertes , S. Incorporation of Aerosol Particles between 25 and 850 Nanometers into Cloud Elements: Measurement with a New Complementary Sampling System Atmos. Res. 52 4 2000 241 260
- Stewart , R.E. , Marwitz , J.D. , Pace , J.C. , and Carbone , R.E. Characteristics through the Melting Layer of Stratiform Clouds Journal of Atmospheric Sciences 41 22 1984 3227 3237 https://doi.org/10.1175/1520-0469
- Vaillant de Guélis , T. et al. "Study of the diffraction pattern of cloud particles and the respective responses of optical array probes." Atmospheric Measurement Techniques 12 4 2019 2513 2529