• Benjamin, S. G., and Coauthors, 2004: An hourly assimilation–forecast cycle: The RUC. Mon. Wea. Rev., 132 , 495518.

  • Bergot, T., D. Carrer, J. Noilhan, and P. Bougeault, 2005: Improved site-specific numerical prediction of fog and low clouds: A feasibility study. Wea. Forecasting, 20 , 627646.

    • Search Google Scholar
    • Export Citation
  • Côté, J., S. Gravel, A. Methot, A. Patoine, M. Roach, and A. Staniforth, 1998: The operational CMC-MRB Global Environmental Multiscale (GEM) model. Part I: Design considerations and formulation. Mon. Wea. Rev., 126 , 13731395.

    • Search Google Scholar
    • Export Citation
  • Doran, J. A., P. J. Roohr, D. J. Beberwyk, G. R. Brooks, G. A. Gayno, R. T. Williams, J. M. Lewis, and R. J. Lefevre, 1999: The MM5 at the AF Weather Agency: New products to support military operations. Preprints, Eighth Conf. on Aviation, Range, and Aerospace Meteorology, Dallas, TX, Amer. Meteor. Soc., 115–119.

    • Search Google Scholar
    • Export Citation
  • Glickman, T. S., Ed.,. 2000: AMS Glossary of Meteorology. 2nd ed. Amer. Meteor. Soc., 855 pp.

  • Gultepe, I., and G. A. Isaac, 2006: Visibility versus precipitation rate and relative humidity. Preprints, 12th Cloud Physics Conf., Madison, WI, Amer. Meteor. Soc. P2.55. [Available online at http://ams.confex.com/ams/Madison2006/techprogram/paper_113177.htm].

    • Search Google Scholar
    • Export Citation
  • Gultepe, I., M. D. Müller, and Z. Boybeyi, 2006: A new visibility parameterization for warm fog applications in numerical weather prediction models. J. Appl. Meteor. Climatol., 45 , 14691480.

    • Search Google Scholar
    • Export Citation
  • Gultepe, I., and Coauthors, 2007: Fog research: A review of past achievements and future perspectives. J. Pure Appl. Geophys., 164 , 11211159.

    • Search Google Scholar
    • Export Citation
  • Gultepe, I., and Coauthors, 2009: The Fog Remote Sensing and Modeling (FRAM) field project. Bull. Amer. Meteor. Soc., 90 , 341359.

  • Haij, M. D., 2007: Automated discrimination of precipitation type using the FD12P present weather sensor: Evaluation and opportunities. KNMI Tech. Note 297, 73 pp.

    • Search Google Scholar
    • Export Citation
  • Knapp, D., 1998: An advanced algorithm to diagnose atmospheric turbulence using numerical model output. Preprints, 16th Conf. on Weather Analysis and Forecasting. Phoenix, AZ, Amer. Meteor. Soc., 79–81.

    • Search Google Scholar
    • Export Citation
  • Kunkel, B. A., 1984: Parameterization of droplet terminal velocity and extinction coefficient in fog models. J. Climate Appl. Meteor., 23 , 3441.

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

    • Search Google Scholar
    • Export Citation
  • Marshall, J. S., and W. Mc K. Palmer, 1948: The distribution of raindrops with size. J. Meteor., 17 , 10541061.

  • Rasmussen, R. M., J. Hallett, R. Purcell, J. Cole, and M. Tryhane, 2002: The hot plate snow gauge. Preprints, 11th Conf. on Cloud Physics, Ogden, UT, Amer. Meteor. Soc., P1.6. [Available online at http://ams.confex.com/ams/11AR11CP/techprogram/paper_42751.htm].

    • Search Google Scholar
    • Export Citation
  • Roquelaure, S., and T. Bergot, 2007: Seasonal sensitivity on COBEL-ISBA local forecast system for fog and low clouds. J. Pure Appl. Geophys., 164 , 12831303.

    • Search Google Scholar
    • Export Citation
  • Seagraves, M. A., 1984: Precipitation rate and extinction in falling snow. J. Atmos. Sci., 41 , 18271835.

  • Sekhon, R. S., and R. C. Srivastava, 1971: Doppler radar observations of drop-size distributions in a thunderstorm. J. Atmos. Sci., 28 , 983994.

    • Search Google Scholar
    • Export Citation
  • Smirnova, T. G., S. G. Benjamin, and J. M. Brown, 2000: Case study verification of RUC/MAPS fog and visibility forecasts. Preprints, Ninth Conf. on Aviation, Range, and Aerospace Meteorology, Orlando, FL, Amer. Meteor. Soc., 2.3.

    • Search Google Scholar
    • Export Citation
  • Stallabrass, J. R., 1985: Measurements of the concentration of falling snow. Proc. Snow Property Measurements Workshop (Tech. Memo. 140). Lake Louise, AB, Canada, National Research Council Canada, 389–410.

    • Search Google Scholar
    • Export Citation
  • Stoelinga, M. T., and T. T. Warner, 1999: Nonhydrostatic, mesobeta-scale model simulations of cloud ceiling and visibility for an East Coast winter precipitation event. J. Appl. Meteor., 38 , 385404.

    • Search Google Scholar
    • Export Citation
  • Tardif, R., 2007: The impact of vertical resolution in the explicit numerical forecasting of radiation fog: A case study. J. Pure Appl. Geophys., 164 , 12211241.

    • Search Google Scholar
    • Export Citation
  • Ulbrich, C. W., and D. Atlas, 1985: Extinction of visible and infrared radiation in rain: Comparison of theory and experiment. J. Atmos. Oceanic Technol., 2 , 331339.

    • Search Google Scholar
    • Export Citation
  • Vaisala, Inc., 2002: Weather sensor FD12P user’s guide. Vaisala, Inc., 65 pp.

All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 29 29 29
PDF Downloads 27 27 27

Probabilistic Parameterizations of Visibility Using Observations of Rain Precipitation Rate, Relative Humidity, and Visibility

View More View Less
  • 1 Cloud Physics and Severe Weather Research Section, Science and Technology Branch, Meteorological Research Division, Environment Canada, Toronto, Ontario, Canada
  • | 2 Numerical Weather Prediction Research Section, Meteorological Research Division, Environment Canada, Dorval, Quebec, Canada
Restricted access

Abstract

This study analyzes the occurrence of the visibility (Vis) versus precipitation rates (PR) for rain and versus relative humidity (RH) from surface observations that were collected during the Fog Remote Sensing and Modeling (FRAM) field project, which was conducted near Toronto, Ontario, Canada, during the winter of 2005/06 and in Lunenburg, Nova Scotia, during the summers of 2006 and 2007. The main observations used in the analysis were PR and Vis for rain episodes from the Vaisala, Inc., FD12P present-weather sensor and RH and temperature from the Campbell Scientific Instruments, Inc., HMP45 sensor. The PR is compared with those from a total precipitation sensor to check the accuracy of the FD12P measurements. Vis parameterizations related to precipitation type have been previously studied by many other researchers and showed large variability in Vis (up to 1 order of magnitude) for a fixed PR. The results from the work presented here suggest that 1) significant differences exist among the various parameterizations of Vis (deterministic approach) and 2) statistical relationships obtained using fits applied to percentiles (probabilistic approach) can be a feasible alternative for model applications. Comparisons of previous parameterizations with the new Vis relationships suggest that simulated Vis values based on probabilistic approaches could be used in extreme-weather applications.

Corresponding author address: Ismail Gultepe, Cloud Physics and Severe Weather Research Section, Science and Technology Branch, MRD, Environment Canada, Toronto, ON M3H 5T4, Canada. Email: ismail.gultepe@ec.gc.ca

Abstract

This study analyzes the occurrence of the visibility (Vis) versus precipitation rates (PR) for rain and versus relative humidity (RH) from surface observations that were collected during the Fog Remote Sensing and Modeling (FRAM) field project, which was conducted near Toronto, Ontario, Canada, during the winter of 2005/06 and in Lunenburg, Nova Scotia, during the summers of 2006 and 2007. The main observations used in the analysis were PR and Vis for rain episodes from the Vaisala, Inc., FD12P present-weather sensor and RH and temperature from the Campbell Scientific Instruments, Inc., HMP45 sensor. The PR is compared with those from a total precipitation sensor to check the accuracy of the FD12P measurements. Vis parameterizations related to precipitation type have been previously studied by many other researchers and showed large variability in Vis (up to 1 order of magnitude) for a fixed PR. The results from the work presented here suggest that 1) significant differences exist among the various parameterizations of Vis (deterministic approach) and 2) statistical relationships obtained using fits applied to percentiles (probabilistic approach) can be a feasible alternative for model applications. Comparisons of previous parameterizations with the new Vis relationships suggest that simulated Vis values based on probabilistic approaches could be used in extreme-weather applications.

Corresponding author address: Ismail Gultepe, Cloud Physics and Severe Weather Research Section, Science and Technology Branch, MRD, Environment Canada, Toronto, ON M3H 5T4, Canada. Email: ismail.gultepe@ec.gc.ca

Save