Experimental Quantification of the Sampling Uncertainty Associated with Measurements from PARSIVEL Disdrometers

Joël Jaffrain Laboratoire de Télédétection Environnementale, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland

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Alexis Berne Laboratoire de Télédétection Environnementale, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland

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Abstract

The variability of the (rain)drop size distribution (DSD) in time and space is an intrinsic property of rainfall, which is of primary importance for various environmental fields such as remote sensing of precipitation, for example. DSD observations are usually collected using disdrometers deployed at the ground level. Like any other measurement of a physical process, disdrometer measurements are affected by noise and sampling effects. This uncertainty must be quantified and taken into account in further analyses. This paper addresses this issue for the Particle Size Velocity (PARSIVEL) optical disdrometer by using a large dataset corresponding to light and moderate rainfall and collected from two collocated PARSIVELs deployed during 15 months in Lausanne, Switzerland. The relative sampling uncertainty associated with quantities characterizing the DSD—namely the total concentration of drops Nt and the median-volume diameter D0—is quantified for different temporal resolutions. Similarly, the relative sampling uncertainty associated with the estimates of the most commonly used weighted moments of the DSD (i.e., the rain-rate R, the radar reflectivity at horizontal polarization Zh, and the differential reflectivity Zdr) is quantified as well for different weather radar frequencies. The relative sampling uncertainty associated with estimates of Nt is below 13% for time steps longer than 60 s. For D0, it is below 8% for D0 values smaller than 1 mm. The associated sampling uncertainty for estimates of R is on the order of 15% at a temporal resolution of 60 s. For Zh, the sampling uncertainty is below 9% for Zh values below 35 dBZ at a temporal resolution of 60 s. For Zdr values below 0.75 dB, the sampling uncertainty is below 36% for all temporal resolutions. These analyses provide relevant information for the accurate quantification of the variability of the DSD from disdrometer measurements.

Corresponding author address: Laboratoire de Télédétection Environnementale, École Polytechnique Fédérale de Lausanne, Station 2, Lausanne CH-1015, Switzerland. E-mail: joel.jaffrain@epfl.ch

This article included in the State of the Science of Precipitation special collection.

Abstract

The variability of the (rain)drop size distribution (DSD) in time and space is an intrinsic property of rainfall, which is of primary importance for various environmental fields such as remote sensing of precipitation, for example. DSD observations are usually collected using disdrometers deployed at the ground level. Like any other measurement of a physical process, disdrometer measurements are affected by noise and sampling effects. This uncertainty must be quantified and taken into account in further analyses. This paper addresses this issue for the Particle Size Velocity (PARSIVEL) optical disdrometer by using a large dataset corresponding to light and moderate rainfall and collected from two collocated PARSIVELs deployed during 15 months in Lausanne, Switzerland. The relative sampling uncertainty associated with quantities characterizing the DSD—namely the total concentration of drops Nt and the median-volume diameter D0—is quantified for different temporal resolutions. Similarly, the relative sampling uncertainty associated with the estimates of the most commonly used weighted moments of the DSD (i.e., the rain-rate R, the radar reflectivity at horizontal polarization Zh, and the differential reflectivity Zdr) is quantified as well for different weather radar frequencies. The relative sampling uncertainty associated with estimates of Nt is below 13% for time steps longer than 60 s. For D0, it is below 8% for D0 values smaller than 1 mm. The associated sampling uncertainty for estimates of R is on the order of 15% at a temporal resolution of 60 s. For Zh, the sampling uncertainty is below 9% for Zh values below 35 dBZ at a temporal resolution of 60 s. For Zdr values below 0.75 dB, the sampling uncertainty is below 36% for all temporal resolutions. These analyses provide relevant information for the accurate quantification of the variability of the DSD from disdrometer measurements.

Corresponding author address: Laboratoire de Télédétection Environnementale, École Polytechnique Fédérale de Lausanne, Station 2, Lausanne CH-1015, Switzerland. E-mail: joel.jaffrain@epfl.ch

This article included in the State of the Science of Precipitation special collection.

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  • Andsager, K., Beard K. V. , and Laird N. F. , 1999: Laboratory measurements of axis ratios for large rain drops. J. Atmos. Sci., 56, 26732683.

    • Search Google Scholar
    • Export Citation
  • Battaglia, A., Rustemeier E. , Tokay A. , Blahak U. , and Simmer C. , 2010: PARSIVEL snow observations: A critical assessment. J. Atmos. Oceanic Technol., 27, 333344.

    • Search Google Scholar
    • Export Citation
  • Beard, K. V., 1977: Terminal velocity adjustment for cloud and precipitation drops aloft. J. Atmos. Sci., 34, 12931298.

  • Berne, A., and Uijlenhoet R. , 2005: Quantification of the radar reflectivity sampling error in non-stationary rain using paired disdrometers. Geophys. Res. Lett., 32, L19813, doi:10.1029/2005GL024030.

    • Search Google Scholar
    • Export Citation
  • Bringi, V. N., and Chandrasekar V. , 2001: Polarimetric Doppler Weather Radar. Cambridge University Press, 662 pp.

  • Campos, E., and Zawadzki I. , 2000: Instrumental uncertainties in ZR relations. J. Appl. Meteor., 39, 10881102.

  • Chandrasekar, V., and Gori E. G. , 1991: Multiple disdrometer observations of rainfall. J. Appl. Meteor., 30, 15141520.

  • Ciach, G. J., 2003: Local random errors in tipping-bucket rain gauge measurements. J. Atmos. Oceanic Technol., 20, 752759.

  • Egli, L., Jonas T. , and Meister R. , 2009: Comparison of different automatic methods for estimating snow water equivalent. Cold Reg. Sci. Technol., 57, 107115, doi:10.1016/j.coldregions.2009.02.008.

    • Search Google Scholar
    • Export Citation
  • Gage, K. S., Clark W. L. , Williams C. R. , and Tokay A. , 2004: Determining reflectivity measurement error from serial measurements using paired disdrometers and profilers. Geophys. Res. Lett., 31, L23107, doi:10.1029/2004GL020591.

    • Search Google Scholar
    • Export Citation
  • Gertzman, H. S., and Atlas D. , 1977: Sampling errors in the measurement of rain and hail parameters. J. Geophys. Res., 82, 49554966.

  • Habib, E., Krajewski W. F. , and Kruger A. , 2001: Sampling errors of tipping-bucket rain gauge measurements. J. Hydrol. Eng., 6, 159166, doi:10.1061/(ASCE)1084-0699(2001)6:2(159).

    • Search Google Scholar
    • Export Citation
  • Jameson, A. R., and Kostinski A. B. , 2001: What is a raindrop size distribution? Bull. Amer. Meteor. Soc., 82, 11691177.

  • Jones, P. D., Osborn T. J. , and Briffa K. R. , 1997: Estimating sampling errors in large-scale temperature averages. J. Climate, 10, 25482568.

    • Search Google Scholar
    • Export Citation
  • Joss, J., and Waldvogel A. , 1967: Ein spektrograph für niederschlagstropfen mit automatischer auswertung (A spectrograph for raindrops with automatic interpretation). Pure Appl. Geophys., 68, 240246.

    • Search Google Scholar
    • Export Citation
  • Joss, J., and Waldvogel A. , 1969: Raindrop size distribution and sampling size errors. J. Atmos. Sci., 26, 566569.

  • Krajewski, W. F., and Coauthors, 2006: DEVEX-disdrometer evaluation experiment: Basic results and implications for hydrologic studies. Adv. Water Resour., 29, 311325, doi:10.1016/j.advwatres.2005.03.018.

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

  • Lee, C. K., Lee G. , Zawadzki I. , and Kim K. , 2009: A preliminary analysis of spatial variability of raindrop size distributions during stratiform rain events. J. Appl. Meteor. Climatol., 48, 270283.

    • Search Google Scholar
    • Export Citation
  • Lee, G., and Zawadzki I. , 2005: Variability of drop size distributions: Time-scale dependence of the variability and its effects on rain estimation. J. Appl. Meteor., 44, 241255.

    • 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.

    • 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.

    • Search Google Scholar
    • Export Citation
  • Miriovsky, B., and Coauthors, 2004: An experimental study of small-scale variability of radar reflectivity using disdrometer observations. J. Appl. Meteor., 43, 106118.

    • Search Google Scholar
    • Export Citation
  • Peters, G., Fischer B. , Münster H. , Clemens M. , and Wagner A. , 2005: Profiles of raindrop size distributions as retrieved by Microrain Radars. J. Appl. Meteor., 44, 19301949.

    • Search Google Scholar
    • Export Citation
  • Salles, C., and Creutin J.-D. , 2003: Instrumental uncertainties in ZR relationships and raindrop fall velocities. J. Appl. Meteor., 42, 279290.

    • Search Google Scholar
    • Export Citation
  • Salles, C., Poesen J. , and Sempere-Torres D. , 2002: Kinetic energy of rain and its functional relationship with intensity. J. Hydrol., 257, 256270.

    • Search Google Scholar
    • Export Citation
  • Sevruk, B., Ondrás M. , and Chvíla B. , 2009: The WMO precipitation measurement intercomparisons. Atmos. Res., 92, 376380, doi:10.1016/j.atmosres.2009.01.016.

    • Search Google Scholar
    • Export Citation
  • Sheppard, B. E., and Joe P. I. , 1994: Comparison of raindrop size distribution measurements by a Joss–Waldvogel disdrometer, a PMS 2DG spectrometer, and a POSS Doppler radar. J. Atmos. Oceanic Technol., 11, 874887.

    • Search Google Scholar
    • Export Citation
  • Sieck, L. C., Burges S. J. , and Steiner M. , 2007: Challenges in obtaining reliable measurements of point rainfall. Water Resour. Res., 43, W01420, doi:10.1029/2005WR004519.

    • Search Google Scholar
    • Export Citation
  • Smith, P. L., Liu Z. , and Joss J. , 1993: A study of sampling-variability effects in raindrop size observations. J. Appl. Meteor., 32, 12591269.

    • Search Google Scholar
    • Export Citation
  • Steiner, M., Bell T. L. , Zhang Y. , and Wood E. F. , 2003: Comparison of two methods for estimating the sampling-related uncertainty of satellite rainfall averages based on a large radar dataset. J. Climate, 16, 37593778.

    • Search Google Scholar
    • Export Citation
  • Stuart, A., and Ord J. K. , 1994: Kendall’s Advanced Theory of Statistics. Vol. 1. 6th ed. Arnold and Oxford University Press, 676 pp.

  • Testik, F. Y., and Barros A. P. , 2007: Toward elucidating the microstructure of warm rainfall: A survey. Rev. Geophys., 45, RG2003, doi:10.1029/2005RG000182.

    • Search Google Scholar
    • Export Citation
  • Thurai, M., and Bringi V. N. , 2005: Drop axis ratios from a 2D video disdrometer. J. Atmos. Oceanic Technol., 22, 966978.

  • Tokay, A., and Short D. A. , 1996: Evidence from tropical raindrop spectra of the origin of rain from stratiform versus convective clouds. J. Appl. Meteor., 35, 355371.

    • Search Google Scholar
    • Export Citation
  • Tokay, A., Kruger A. , and Krajewski W. F. , 2001: Comparison of drop size distribution measurements by impact and optical disdrometers. J. Appl. Meteor., 40, 20832097.

    • Search Google Scholar
    • Export Citation
  • Tokay, A., Wolff D. B. , Wolff K. R. , and Bashor P. , 2003: Rain gauge and disdrometer measurements during the Keys Area Microphysics Project (KAMP). J. Atmos. Oceanic Technol., 20, 14601477.

    • Search Google Scholar
    • Export Citation
  • Tokay, A., Bashor P. G. , and Wolff K. R. , 2005: Error characteristics of rainfall measurements by collocated Joss–Waldvogel disdrometers. J. Atmos. Oceanic Technol., 22, 513527.

    • Search Google Scholar
    • Export Citation
  • Tokay, A., and Coauthors, 2007: Disdrometer derived ZS relations in South Central Ontario, Canada. Extended Abstracts, 33rd Conf. on Radar Meteorology, Cairns, Australia, Amer. Meteor. Soc., 8A.8. [Available online at http://ams.confex.com/ams/33Radar/techprogram/paper_123455.htm.]

    • Search Google Scholar
    • Export Citation
  • Uijlenhoet, R., Steiner M. , and Smith J. A. , 2003: Variability of raindrop size distributions in a squall line and implications for radar rainfall estimation. J. Hydrometeor., 4, 4361.

    • Search Google Scholar
    • Export Citation
  • Uijlenhoet, R., Porrà J. M. , Sempere-Torres D. , and Creutin J.-D. , 2006: Analytical solutions to sampling effects in drop size distribution measurements during stationary rainfall: Estimation of bulk rainfall variables. J. Hydrol., 328, 6582, doi:10.1016/j.jhydrol.2005.11.043.

    • Search Google Scholar
    • Export Citation
  • WMO, 2008: WMO guide to meteorological instruments and methods of observation. World Meteorological Organization Rep. WMO-08 (7th ed.), 681 pp. [Available online at http://www.wmo.int/pages/prog/www/IMOP/publications/CIMO-Guide/CIMO_Guide-7th_Edition-2008.html.]

    • Search Google Scholar
    • Export Citation
  • Yuter, S. E., Kingsmill D. E. , Nance L. B. , and Löffler-Mang M. , 2006: Observations of precipitation size and fall speed characteristics within coexisting rain and wet snow. J. Appl. Meteor. Climatol., 45, 14501464.

    • Search Google Scholar
    • Export Citation
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