Introduction

Particle Flux Analytics has pioneered the technology of precipitation measurement and puts forward its one of a kind Multi-Angle Snowflake Camera (MASC), a product which is at the forefront of the current state of the art technology. The Multi-Angle Snowflake Camera takes 30 micron resolution photographs of hydrometeors in free fall from three angles while simultaneously measuring their fallspeed. Current customers include government, military and university agencies in the Canada, Korea, Switzerland and the United States.

Multi-Angle Snowflake Camera

The Multi-Angle Snowflake Camera (MASC) takes 10 to 30 micron resolution photographs of hydrometeors from three angles while simultaneously measuring their fallspeed. The cameras are triggered by a vertically stacked bank of sensitive infrared motion sensors designed to filter out slow variations in ambient light. The triggering is auto-calibrated, and sensitive to snowflake sizes ranging from 100 micrometers to 3 cm (30,000 micrometers). Fallspeed is derived from successive triggers along the fall path. Photographs are obtained at a speed of up to 1/40,000th of a second and the hydrometeors are illuminated by three 40 W LEDs rated at 2700 lumens each.

The instrument is robust to cold and weather and runs unattended. Calibration is limited to occasional camera alignment and lens focusing using a calibration tool that attaches to the instrument. Software is included for image and fallspeed acquisition and display on PC platforms and for creating a live internet feed from the installation site. The executables include lossless (png) image compression to facilitate with data management. Tens of thousands of images might be obtained in a single day. Scientific analysis scripts are available for detailed post-processing.

Technical details are available in the specifications PDF.

For more information or to request a quote, please reach out to us at info@particleflux.net.

PUBLICATIONS
  • Garrett, T. J. and Yuter, S. E: Observed influence of riming, temperature, and turbulence on the fallspeed of solid precipitation Geophys. Res. Lett. doi:10.1002/2014GL061016
  • Gergely M. and T. J. Garrett, 2016: Impact of the natural variability in snowflake diameter, aspect ratio, and orientation on modeled snowfall radar reflectivity. J. Geophys. Research, doi: 10.1002/2016JD025192
  • Mathias Gergely, Steven J. Cooper, and Timothy J. Garrett, 2017: Using snowflake surface-area-to-volume ratio to model and interpret snowfall triple-frequency radar signatures, Atmos. Chem. Phys., 17, 12011-12030, 2017, doi: 10.5194/acp-17-12011-2017
  • Oue, M., Kollias, P., Ryzhkov, A., & Luke,E. P. (2018). Toward exploring the synergy between cloud radar polarimetry and Doppler spectral analysis in deep cold precipitating systems in the Arctic. Journal of Geophysical Research:Atmospheres, 123. https://doi.org/10.1002/2017JD027717
  • Besic, N., Gehring, J., Praz, C., Figueras i Ventura, J., Grazioli, J., Gabella, M., Germann, U., and Berne, A.: Unraveling hydrometeor mixtures in polarimetric radar measurements, Atmos. Meas. Tech. Discuss., https://doi.org/10.5194/amt-2018-58, in review, 2018.
  • Patrick Kennedy, Merhala Thurai, Christophe Praz, V. N. Bringi, Alexis Berne, and Branislav M. Notaroš, 2018: Variations in Snow Crystal Riming and ZDR: A Case Analysis. J. Appl. Met. Clim., doi: 10.1175/JAMC-D-17-0068.1
  • Matrosov, S.Y., C.G. Schmitt, M. Maahn, and G. de Boer, 2017: Atmospheric Ice Particle Shape Estimates from Polarimetric Radar Measurements and In Situ Observations. J. Atmos. Oceanic Technol., 34, 2569–2587, https://doi.org/10.1175/JTECH-D-17-0111.1
  • Huang, G-J, Kleinkort, C. Bringi, and V.N. Notaroš, B.M.: Winter precipitation particle size distribution measurement by Multi-Angle Snowflake Camera. Atmos. Res. doi.org/10.1016/j.atmosres.2017.08.005, 2017
  • Bair EH, Dozier J, Davis RE, Colee MT and Claffey KJ, 2015: CUES—a study site for measuring snowpack energy balance in the Sierra Nevada. Front. Earth Sci. 3:58. doi: 10.3389/feart.2015.00058
  • Praz, C., Roulet, Y.-A., and Berne, A.: Solid hydrometeor classification and riming degree estimation from pictures collected with a Multi-Angle Snowflake Camera, Atmos. Meas. Tech., 10, 1335-1357, https://doi.org/10.5194/amt-10-1335-2017, 2017
  • Cooper, S. J., Wood, N. B. and L’Ecuyer, T. S.: A variational technique to estimate snowfall rate from coincident radar, snowflake, and fall-speed observations, Atmos. Meas. Tech., 10, 2557-2571, 2017, doi:10.5194/amt-10-2557-2017
  • Grazioli, J., Genthon, C., Boudevillain, B., Duran-Alarcon, C., Del Guasta, M., Madeleine, J.-B., and Berne, A.: Measurements of precipitation in Dumont d'Urville, Adélie Land, East Antarctica, The Cryosphere, 11, 1797-1811, https://doi.org/10.5194/tc-11-1797-2017, 2017
  • Garrett, T. J., Yuter, S.E., Fallgatter, C., Shkurko, K., Rhodes, S. R. and Endries, J. L., 2015: Orientations and aspect ratios of falling snow. Geophys. Res. Lett., 42, 4617-4622, doi:10.1002/2015GL06404
  • Garrett, T. J., Fallgatter, C., Shkurko, K., and Howlett, D., 2012: Fallspeed measurement and high-resolution multi-angle photography of hydrometeors in freefall. Atmos. Meas. Tech., 5, 2625-2633, doi:10.5194/amt-5-2625-2012
  • Notaroš, B. M., Bringi, V. N., Kleinkort, C., Kennedy, P., Huang, G. J., Thurai, M., ... & Lee, G., 2016: Accurate characterization of winter precipitation using multi-angle snowflake camera, visual hull, advanced scattering methods and polarimetric radar. Atmosphere, 7(6), 81.
  • C. Kleinkort, G.-J. Huang, V. N. Bringi, and B. M. Notaros, 2016: Visual Hull Method for Realistic 3D Particle Shape Reconstruction Based on High-Resolution Photographs of Snowflakes in Freefall from Multiple Views, Journal of Atmospheric and Oceanic Technology, doi:10.1175/JTECH-D-16-0099.1.
  • V. N. Bringi, P. C. Kennedy, G.-J. Huang, C. Kleinkort, M. Thurai, and B. M. Notaros, 2016: Dual-polarized radar and surface observations of a winter graupel shower with negative Zdr column, Journal of Applied Meteorology and Climatology, doi:10.1175/JAMC-D-16-0197.1

Media

Images shown below were captured using a MASC in Salt Lake City, UT. Further media coverage.

Our Team

Timothy Garrett, PhD

Professor Tim Garrett is the Chief Atmospheric Scientist at Particle Flux Analytics, and Professor of Atmospheric Sciences at the University of Utah. His research focuses on aerosols, clouds, precipitation, radiation, and climate from theoretical, numerical and observational perspectives, including field work from the high Arctic to the tropical tropopause and high mountain environments. Professor Garrett is the author of 74 peer-reviewed journal articles and book chapters and a patent.

CONTACT

Mailing Address

Particle Flux Analytics Inc.
6300 N. Sagewood Drive
STE. H#623
Park City, UT 84098
United States

Email

info@particleflux.net