• Alter, J. C., 1937: Shielded storage precipitation gauges. Mon. Wea. Rev., 65, 262265, doi:10.1175/1520-0493(1937)65<262:SSPG>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Colli, M., , L. G. Lanza, , R. Rasmussen, , and J. M. Thériault, 2015a: The collection efficiency of shielded and unshielded precipitation gauges. Part I: CFD airflow modeling. J. Hydrometeor., doi:10.1175/JHM-D-15-0010.1, in press.

    • Search Google Scholar
    • Export Citation
  • Colli, M., , L. G. Lanza, , R. Rasmussen, , and J. M. Thériault, 2015b: The collection efficiency of shielded and unshielded precipitation gauges. Part II: Modeling particle trajectories. J. Hydrometeor., doi:10.1175/JHM-D-15-0011.1, in press.

    • Search Google Scholar
    • Export Citation
  • Colli, M., , R. Rasmussen, , J. M. Thériault, , L. G. Lanza, , C. B. Baker, , and J. Kochendorfer, 2015c: An improved trajectory model to evaluate the collection performance of snow gauges. J. Appl. Meteor. Climatol., 54, 1826–1836, doi:10.1175/JAMC-D-15-0035.1.

    • Search Google Scholar
    • Export Citation
  • Goodison, B. E., , P. Y. T. Louie, , and D. Yang, 1998: WMO solid precipitation measurement intercomparison. Instruments and Observing Methods Rep. No. 67 and WMO/TD No. 872, 318 pp. [Available online at http://www.wmo.int/pages/prog/www/reports/WMOtd872.pdf.]

  • Groisman, P. Ya., , and D. R. Legates, 1994: The accuracy of United States precipitation data. Bull. Amer. Meteor. Soc., 75, 215227, doi:10.1175/1520-0477(1994)075<0215:TAOUSP>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Groisman, P. Ya., , V. V. Koknaeva, , T. A. Belokrylova, , and T. R. Karl, 1991: Overcoming biases of precipitation measurement: A history of the USSR experience. Bull. Amer. Meteor. Soc., 72, 17251733, doi:10.1175/1520-0477(1991)072<1725:OBOPMA>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Houze, R. A., , P. V. Hobbs, , and P. H. Herzegh, 1979: Size distributions of precipitation particles in frontal clouds. J. Atmos. Sci., 36, 156162, doi:10.1175/1520-0469(1979)036<0156:SDOPPI>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Khvorostyanov, V., , and J. Curry, 2005: Fall velocities of hydrometeors in the atmosphere: Refinements to a continuous analytical power law. J. Atmos. Sci., 62, 43434357, doi:10.1175/JAS3622.1.

    • Search Google Scholar
    • Export Citation
  • Marshall, J. S., , and W. M. Palmer, 1948: The distribution of raindrops with size. J. Meteor., 5, 165166, doi:10.1175/1520-0469(1948)005<0165:TDORWS>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Nespor, V., , and B. Sevruk, 1999: Estimation of wind-induced error of rainfall gauge measurements using a numerical simulation. J. Atmos. Oceanic Technol., 16, 450464, doi:10.1175/1520-0426(1999)016<0450:EOWIEO>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Nitu, R., and et al. , 2012: WMO intercomparison of instruments and methods for the measurement of solid precipitation and snow on the ground: Organization of the experiment. Preprints, TECO-2012: WMO Technical Conf. on Meteorological and Environmental Instruments and Methods of Observations, Brussels, Belgium, WMO, 10 pp. [Available online at https://www.wmo.int/pages/prog/www/IMOP/publications/IOM-109_TECO-2012/Session1/O1_01_Nitu_SPICE.pdf.]

  • Pruppacher, H. R., , and J. D. Klett, 1997: Microphysics of Clouds and Precipitation. 2nd ed. Kluwer Academic Publishers, 954 pp.

  • Rasmussen, R. M., , J. Vivekanandan, , J. Cole, , B. Meyers, , and C. Masters, 1999: The estimation of snowfall rate using visibility. J. Appl. Meteor., 38, 15421563, doi:10.1175/1520-0450(1999)038<1542:TEOSRU>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Rasmussen, R. M., and et al. , 2001: Weather support to deicing decision making WSDDM: A winter weather nowcasting system. Bull. Amer. Meteor. Soc., 82, 579595, doi:10.1175/1520-0477(2001)082<0579:WSTDDM>2.3.CO;2.

    • Search Google Scholar
    • Export Citation
  • Rasmussen, R. M., and et al. , 2012: How well are we measuring snow: The NOAA/FAA/NCAR winter precipitation test bed. Bull. Amer. Meteor. Soc., 93, 811829, doi:10.1175/BAMS-D-11-00052.1.

    • Search Google Scholar
    • Export Citation
  • Rogers, D. C., 1974: The aggregation of natural ice crystals. M.S. thesis, Dept. of Atmospheric Resources, College of Engineering, University of Wyoming, 91 pp.

  • Smith, C., 2009: The relationship between snowfall catch efficiency and wind speed for the Geonor T-200B precipitation gauge utilizing various wind shield configurations. Proc. 77th Western Snow Conf., Canmore, AB, Canada, 115221.

  • Sugiura, K., , T. Ohata, , and D. Yang, 2006: Catch characteristics of precipitation gauges in high-latitude regions with high winds. J. Hydrometeor., 7, 984994, doi:10.1175/JHM542.1.

    • Search Google Scholar
    • Export Citation
  • Thériault, J. M., , R. Rasmussen, , K. Ikeda, , and S. Landolt, 2012: Dependence of snow gauge collection efficiency on snowflake characteristics. J. Appl. Meteor. Climatol., 51, 745762, doi:10.1175/JAMC-D-11-0116.1.

    • Search Google Scholar
    • Export Citation
  • Yang, D., 2014: Double fence intercomparison reference (DFIR) vs. bush gauge for “true” snowfall measurement. J. Hydrol., 509, 94100, doi:10.1016/j.jhydrol.2013.08.052.

    • Search Google Scholar
    • Export Citation
  • Yang, D., and et al. , 1995: Accuracy of Tretyakov precipitation gauge: Result of WMO intercomparison. Hydrol. Processes, 9, 877895, doi:10.1002/hyp.3360090805.

    • Search Google Scholar
    • Export Citation
  • Yang, D., , D. Kane, , Z. Zhang, , D. Legates, , and B. Goodison, 2005: Bias corrections of long-term (1973–2004) daily precipitation data over the northern regions. Geophys. Res. Lett., 32, L19501, doi:10.1029/2005GL024057.

    • Search Google Scholar
    • Export Citation
All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 345 345 13
PDF Downloads 285 285 10

Impact of Wind Direction, Wind Speed, and Particle Characteristics on the Collection Efficiency of the Double Fence Intercomparison Reference

View More View Less
  • 1 Department of Earth and Atmospheric Sciences, Université du Québec à Montréal, Montreal, Quebec, Canada
  • | 2 Research Application Laboratory, National Center for Atmospheric Research,* Boulder, Colorado
  • | 3 Department of Mechanical Engineering, École Polytechnique Montréal, Montreal, Quebec, Canada
  • | 4 Department of Civil, Chemical and Environmental Engineering, University of Genoa, and WMO/CIMO Lead Centre “B. Castelli” on Precipitation Intensity, Genoa, Italy
© Get Permissions Rent on DeepDyve
Restricted access

Abstract

The accurate measurement of snowfall is important in various fields of study such as climate variability, transportation, and water resources. A major concern is that snowfall measurements are difficult and can result in significant errors. For example, collection efficiency of most gauge–shield configurations generally decreases with increasing wind speed. In addition, much scatter is observed for a given wind speed, which is thought to be caused by the type of snowflake. Furthermore, the collection efficiency depends strongly on the reference used to correct the data, which is often the Double Fence Intercomparison Reference (DFIR) recommended by the World Meteorological Organization. The goal of this study is to assess the impact of weather conditions on the collection efficiency of the DFIR. Note that the DFIR is defined as a manual gauge placed in a double fence. In this study, however, only the double fence is being investigated while still being called DFIR. To address this issue, a detailed analysis of the flow field in the vicinity of the DFIR is conducted using computational fluid dynamics. Particle trajectories are obtained to compute the collection efficiency associated with different precipitation types for varying wind speed. The results show that the precipitation reaching the center of the DFIR can exceed 100% of the actual precipitation, and it depends on the snowflake type, wind speed, and direction. Overall, this study contributes to a better understanding of the sources of uncertainty associated with the use of the DFIR as a reference gauge to measure snowfall.

The National Center for Atmospheric Research is sponsored by the National Science Foundation.

Corresponding author address: Julie M. Thériault, Dept. of Earth and Atmospheric Sciences, Université du Québec à Montréal, P.O. Box 8888, Succursale Centre-Ville, Montreal, QC H3C 3P8, Canada. E-mail: theriault.julie@.uqam.ca

Abstract

The accurate measurement of snowfall is important in various fields of study such as climate variability, transportation, and water resources. A major concern is that snowfall measurements are difficult and can result in significant errors. For example, collection efficiency of most gauge–shield configurations generally decreases with increasing wind speed. In addition, much scatter is observed for a given wind speed, which is thought to be caused by the type of snowflake. Furthermore, the collection efficiency depends strongly on the reference used to correct the data, which is often the Double Fence Intercomparison Reference (DFIR) recommended by the World Meteorological Organization. The goal of this study is to assess the impact of weather conditions on the collection efficiency of the DFIR. Note that the DFIR is defined as a manual gauge placed in a double fence. In this study, however, only the double fence is being investigated while still being called DFIR. To address this issue, a detailed analysis of the flow field in the vicinity of the DFIR is conducted using computational fluid dynamics. Particle trajectories are obtained to compute the collection efficiency associated with different precipitation types for varying wind speed. The results show that the precipitation reaching the center of the DFIR can exceed 100% of the actual precipitation, and it depends on the snowflake type, wind speed, and direction. Overall, this study contributes to a better understanding of the sources of uncertainty associated with the use of the DFIR as a reference gauge to measure snowfall.

The National Center for Atmospheric Research is sponsored by the National Science Foundation.

Corresponding author address: Julie M. Thériault, Dept. of Earth and Atmospheric Sciences, Université du Québec à Montréal, P.O. Box 8888, Succursale Centre-Ville, Montreal, QC H3C 3P8, Canada. E-mail: theriault.julie@.uqam.ca
Save