Presenting the Snowflake Video Imager (SVI)

Andrew J. Newman Department of Atmospheric Sciences, University of North Dakota, Grand Forks, North Dakota

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Paul A. Kucera Department of Atmospheric Sciences, University of North Dakota, Grand Forks, North Dakota

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Larry F. Bliven Instrumentation Sciences Branch, NASA Goddard Space Flight Center, Greenbelt, Maryland

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Abstract

Herein the authors introduce the Snowflake Video Imager (SVI), which is a new instrument for characterizing frozen precipitation. An SVI utilizes a video camera with sufficient frame rate, pixels, and shutter speed to record thousands of snowflake images. The camera housing and lighting produce little airflow distortion, so SVI data are quite representative of natural conditions, which is important for volumetric data products such as snowflake size distributions. Long-duration, unattended operation of an SVI is feasible because datalogging software provides data compression and the hardware can operate for months in harsh winter conditions. Details of SVI hardware and field operation are given. Snowflake size distributions (SSDs) from a storm near Boulder, Colorado, are computed. An SVI is an imaging system, so SVI data can be utilized to compute diverse data products for various applications. In this paper, the authors present visualizations of frozen particles (i.e., snowflake aggregates as well as individual crystals), which provide insight into the weather conditions such as temperature, humidity, and winds.

* Current affiliation: Department of Atmospheric Science, Colorado State University, Fort Collins, Colorado

+ Current affiliation: National Center for Atmospheric Research, Boulder, Colorado

Corresponding author address: Andrew J. Newman, Department of Atmospheric Science, Colorado State University, Fort Collins, CO 80523. Email: anewman@atmos.colostate.edu

Abstract

Herein the authors introduce the Snowflake Video Imager (SVI), which is a new instrument for characterizing frozen precipitation. An SVI utilizes a video camera with sufficient frame rate, pixels, and shutter speed to record thousands of snowflake images. The camera housing and lighting produce little airflow distortion, so SVI data are quite representative of natural conditions, which is important for volumetric data products such as snowflake size distributions. Long-duration, unattended operation of an SVI is feasible because datalogging software provides data compression and the hardware can operate for months in harsh winter conditions. Details of SVI hardware and field operation are given. Snowflake size distributions (SSDs) from a storm near Boulder, Colorado, are computed. An SVI is an imaging system, so SVI data can be utilized to compute diverse data products for various applications. In this paper, the authors present visualizations of frozen particles (i.e., snowflake aggregates as well as individual crystals), which provide insight into the weather conditions such as temperature, humidity, and winds.

* Current affiliation: Department of Atmospheric Science, Colorado State University, Fort Collins, Colorado

+ Current affiliation: National Center for Atmospheric Research, Boulder, Colorado

Corresponding author address: Andrew J. Newman, Department of Atmospheric Science, Colorado State University, Fort Collins, CO 80523. Email: anewman@atmos.colostate.edu

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  • Barthazy, E., Göke S. , Schefold R. , and Högl D. , 2004: An optical array instrument for shape and fall velocity measurements of hydrometeors. J. Atmos. Oceanic Technol., 21 , 14001416.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Brandes, E. A., Ikeda K. , Zhang G. , Schönhuber M. , and Rasmussen R. , 2007: A statistical and physical description of hydrometeor distributions in Colorado snow storms using a video disdrometer. J. Appl. Meteor. Climatol., 46 , 634650.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bringi, V. N., and Chandrasekar V. , 2001: Polarimetric Doppler Weather Radar: Principles and Applications. Cambridge University Press, 636 pp.

    • Search Google Scholar
    • Export Citation
  • Frank, G., Härtl T. , and Tschiersch J. , 1994: The pluviospectrometer: Classification of falling hydrometeors via digital image processing. Atmos. Res., 34 , 367378.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kruger, A., and Krajewski W. F. , 2002: Two-dimensional video disdrometer: A description. J. Atmos. Oceanic Technol., 19 , 602617.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Liu, G., 2004: Approximation of single scattering properties of ice and snow particles for high microwave frequencies. J. Atmos. Sci., 61 , 24412456.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Löffler-Mang, M., and Joss J. , 2000: An optical disdrometer for measuring size and velocity of hydrometeors. J. Atmos. Oceanic Technol., 17 , 130139.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Löffler-Mang, M., and Blahak U. , 2001: Estimation of the equivalent radar reflectivity factor from measured snow size spectra. J. Appl. Meteor., 40 , 843849.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Matrosov, S. Y., Reinking R. F. , Kropfli R. A. , and Bartram B. W. , 1996: Estimation of ice hydrometeor types and shapes from radar polarization measurements. J. Atmos. Oceanic Technol., 13 , 8596.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Meirold-Mautner, I., Prigent C. , Defer E. , Pardo J. R. , Chaboureau J. P. , Pinty J. P. , Mech M. , and Crewell S. , 2007: Radiative transfer simulations using mesoscale cloud model outputs: Comparisons with passive microwave and infrared satellite observations for midlatitudes. J. Atmos. Sci., 64 , 15501568.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Newman, A. J., 2007: Surface and vertical retrievals of snowfall using a video disdrometer and a 915 MHz vertical profiler. M.S. thesis, University of North Dakota, 147 pp.

  • Newman, A. J., and Kucera P. A. , 2005: Gauging rainfall. Fluent News Fall Newsletter, No. 14. The Fluent Group, Lebanon, NH, 12–13.

  • Russ, J. C., 2002: The Image Processing Handbook. 4th ed. CRC Press, 732 pp.

  • Seul, M., O’Gorman L. , and Sammon M. J. , 2000: Practical Algorithms for Image Analysis: Description, Examples, and Code. Cambridge University Press, 295 pp.

    • Search Google Scholar
    • Export Citation
  • Wang, P. K., and Denzer S. M. , 1983: Mathematical description of the shape of plane hexagonal snow crystals. J. Atmos. Sci., 40 , 10241028.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Young, H. D., and Freedman R. A. , 1999: Sears and Zemansky’s: University Physics. 10th ed. Addison-Wesley, 1274 pp.

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