• Ackerman, T. A., and G. M. Stokes, 2003: The Atmospheric Radiation Measurement Program. Phys. Today, 56, 3845, doi:10.1063/1.1554135.

    • Search Google Scholar
    • Export Citation
  • Boer, E., and V. Ramanathan, 1997: Lagrangian approach for deriving cloud characteristics from satellite observations and its implications to cloud parameterizations. J. Geophys. Res., 102, 21 38321 399, doi:10.1029/97JD00930.

    • Search Google Scholar
    • Export Citation
  • Burnet, F., and J.-L. Brenguier, 2010: The onset of precipitation in warm cumulus clouds: An observational case-study. Quart. J. Roy. Meteor. Soc., 136, 374381, doi:10.1002/qj.552.

    • Search Google Scholar
    • Export Citation
  • Chen, S. S., R. A. Houze Jr., and B. E. Mapes, 1996: Multiscale variability of deep convection in relation to large-scale circulation in TOGA COARE. J. Atmos. Sci., 53, 13801409, doi:10.1175/1520-0469(1996)053<1380:MVODCI>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Dixon, M., and G. Wiener, 1993: TITAN: Thunderstorm Identification, Tracking, Analysis, and Nowcasting—A radar-based methodology. J. Atmos. Oceanic Technol., 10, 785797, doi:10.1175/1520-0426(1993)010<0785:TTITAA>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Feingold, G., and H. Siebert, 2009: Cloud–aerosol interactions from the micro to the cloud scale. Clouds in the Perturbed Climate System: Their Relationship to Energy Balance, Atmospheric Dynamics, and Precipitation. J. Heintzenberg, and R. J. Charlson, Eds., Strüngmann Forum Rep. 2, MIT Press, 319–338.

  • French, J. R., G. Vali, and R. D. Kelly, 1999: Evolution of small cumulus clouds in Florida: Observations of pulsating growth. Atmos. Res., 52, 143165, doi:10.1016/S0169-8095(99)00024-1.

    • Search Google Scholar
    • Export Citation
  • Futyan, J. M., and A. D. Del Genio, 2007: Deep convective system evolution over Africa and the tropical Atlantic. J. Climate, 20, 50415060, doi:10.1175/JCLI4297.1.

    • Search Google Scholar
    • Export Citation
  • Ghan, S. J., L. R. Leung, and J. McCaa, 1999: A comparison of three different modeling strategies for evaluating cloud and radiation parameterizations. Mon. Wea. Rev., 127, 19671984, doi:10.1175/1520-0493(1999)127<1967:ACOTDM>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Göke, S., H. T. Ochs, and R. M. Rauber, 2007: Radar analysis of precipitation initiation in maritime versus continental clouds near the Florida coast: Inferences concerning the role of CCN and giant nuclei. J. Atmos. Sci., 64, 36953707, doi:10.1175/JAS3961.1.

    • Search Google Scholar
    • Export Citation
  • Grenier, H., and C. S. Bretherton, 2001: A moist PBL parameterization for large-scale models and its application to subtropical cloud-topped marine boundary layers. Mon. Wea. Rev., 129, 357377, doi:10.1175/1520-0493(2001)129<0357:AMPPFL>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Illingworth, A. J., and Coauthors, 2007: Cloudnet: Continuous evaluation of cloud profiles in seven operational models using ground-based observations. Bull. Amer. Meteor. Soc., 88, 883898, doi:10.1175/BAMS-88-6-883.

    • Search Google Scholar
    • Export Citation
  • Johnson, J. T., P. L. MacKeen, A. Witt, E. D. Mitchell, G. J. Stumpf, M. D. Eilts, and K. W. Thomas, 1998: The storm cell identification and tracking algorithm: An enhanced WSR-88D algorithm. Wea. Forecasting, 13, 263276, doi:10.1175/1520-0434(1998)013<0263:TSCIAT>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Jorgensen, D. P., E. J. Zipser, and M. A. LeMone, 1985: Vertical motions in intense hurricanes. J. Atmos. Sci., 42, 839856, doi:10.1175/1520-0469(1985)042<0839:VMIIH>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Knight, C. A., and L. J. Miller, 1993: First radar echoes from cumulus clouds. Bull. Amer. Meteor. Soc., 74, 179188, doi:10.1175/1520-0477(1993)074<0179:FREFCC>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Knight, C. A., J. Vivekanandan, and S. G. Lasher-Trapp, 2002: First radar echoes and the early ZDR history of Florida cumulus. J. Atmos. Sci., 59, 14541472, doi:10.1175/1520-0469(2002)059<1454:FREATE>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Kollias, P., E. E. Clothiaux, A. A. Miller, B. A. Albrecht, G. L. Stephens, and T. P. Ackerman, 2007: Millimeter-wavelength radars: New frontier in atmospheric cloud and precipitation research. Bull. Amer. Meteor. Soc., 88, 16081624, doi:10.1175/BAMS-88-10-1608.

    • Search Google Scholar
    • Export Citation
  • Kollias, P., N. Bharadwaj, K. Widener, I. Jo, and K. Johnson, 2014a: Scanning ARM cloud radars. Part I: Operational sampling strategies. J. Atmos. Oceanic Technol.,31, 569–582, doi:10.1175/JTECH-D-13-00044.1.

  • Kollias, P., and Coauthors, 2014b: Scanning ARM cloud radars. Part II: Data quality control and processing. J. Atmos. Oceanic Technol.,31, 583–598, doi:10.1175/JTECH-D-13-00045.1.

  • Lu, M.-L., G. Feingold, H. H. Jonsson, P. Y. Chuang, H. Gates, R. C. Flagan, and J. H. Seinfeld, 2008: Aerosol–cloud relationships in continental shallow cumulus. J. Geophys. Res., 113, D15201, doi:10.1029/2007JD009354.

    • Search Google Scholar
    • Export Citation
  • Machado, L. A., W. B. Rossow, R. L. Guedes, and A. W. Walker, 1998: Life cycle variations of mesoscale convective systems over the Americas. Mon. Wea. Rev., 126, 16301654, doi:10.1175/1520-0493(1998)126<1630:LCVOMC>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Maddox, R. A., 1980: Mesoscale convective complexes. Bull. Amer. Meteor. Soc., 61, 13741387, doi:10.1175/1520-0477(1980)061<1374:MCC>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Mather, J. H., and J. W. Voyles, 2013: The ARM Climate Research Facility: A review of structure and capabilities. Bull. Amer. Meteor. Soc., 94, 377392, doi:10.1175/BAMS-D-11-00218.1.

    • Search Google Scholar
    • Export Citation
  • Park, S., and C. S. Bretherton, 2009: The University of Washington shallow convection and moist turbulence schemes and their impact on climate simulations with the Community Atmosphere Model. J. Climate, 22, 34493469, doi:10.1175/2008JCLI2557.1.

    • Search Google Scholar
    • Export Citation
  • Rosenfeld, D., 1987: Objective method for analysis and tracking of convective cells as seen by radar. J. Atmos. Oceanic Technol., 4, 422434, doi:10.1175/1520-0426(1987)004<0422:OMFAAT>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Stephens, G. L., 2005: Cloud feedbacks in the climate system: A critical review. J. Climate, 18, 237273, doi:10.1175/JCLI-3243.1.

  • Stevens, B., and G. Feingold, 2009: Untangling aerosol effects on clouds and precipitation in a buffered system. Nature, 461, 607613, doi:10.1038/nature08281.

    • Search Google Scholar
    • Export Citation
  • Stokes, G. M., and S. E. Schwartz, 1994: The Atmospheric Radiation Measurement (ARM) Program: Programmatic background and design of the Cloud and Radiation Testbed. Bull. Amer. Meteor. Soc., 75, 12011221, doi:10.1175/1520-0477(1994)075<1201:TARMPP>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Velasco, I., and J. M. Fritsch, 1987: Mesoscale convective complexes in the Americas. J. Geophys. Res., 92, 95919613, doi:10.1029/JD092iD08p09591.

    • Search Google Scholar
    • Export Citation
  • Williams, M., and R. A. Houze, 1987: Satellite-observed characteristics of winter monsoon cloud clusters. Mon. Wea. Rev., 115, 505519, doi:10.1175/1520-0493(1987)115<0505:SOCOWM>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 211 102 5
PDF Downloads 207 104 2

First Observations of Tracking Clouds Using Scanning ARM Cloud Radars

View More View Less
  • 1 Department of Atmospheric and Oceanic Sciences, McGill University, Montreal, Quebec, Canada
  • | 2 Brookhaven National Laboratory, Upton, New York
Restricted access

Abstract

Tracking clouds using scanning cloud radars can help to document the temporal evolution of cloud properties well before large-drop formation (weather radar “first echo”). These measurements also complement cloud and precipitation tracking using geostationary satellites and weather radars. Here, two-dimensional (2D) along-wind range–height indicator observations of a population of shallow cumuli (with and without precipitation) from the 35-GHz scanning Atmospheric Radiation Measurement Program (ARM) cloud radar (SACR) at the U.S. Department of Energy (DOE)–ARM Southern Great Plains (SGP) site are presented. Observations from the ARM SGP network of scanning precipitation radars are used to provide the larger-scale context of the cloud field and to highlight the advantages of the SACR to detect the numerous small nonprecipitating cloud elements. A new cloud identification and tracking algorithm (CITA) is developed to track cloud elements. In CITA, a cloud element is identified as a region having a contiguous set of pixels exceeding a preset reflectivity and size threshold. The high temporal resolution of the SACR 2D observations (30 s) allows for an area superposition criteria algorithm to match cloud elements at consecutive times. Following CITA, the temporal evolution of cloud-element properties (number, size, and maximum reflectivity) is presented. The vast majority of the designated elements during this cumulus event were short-lived nonprecipitating clouds having an apparent life cycle shorter than 15 min. The advantages and disadvantages of cloud tracking using an SACR are discussed.

Corresponding author address: Paloma Borque, Department of Atmospheric and Oceanic Sciences, Room 945, Burnside Hall, 805 Sherbrooke Street West, Montreal QC H3A 2K6, Canada. E-mail: paloma.borque@mail.mcgill.ca

Abstract

Tracking clouds using scanning cloud radars can help to document the temporal evolution of cloud properties well before large-drop formation (weather radar “first echo”). These measurements also complement cloud and precipitation tracking using geostationary satellites and weather radars. Here, two-dimensional (2D) along-wind range–height indicator observations of a population of shallow cumuli (with and without precipitation) from the 35-GHz scanning Atmospheric Radiation Measurement Program (ARM) cloud radar (SACR) at the U.S. Department of Energy (DOE)–ARM Southern Great Plains (SGP) site are presented. Observations from the ARM SGP network of scanning precipitation radars are used to provide the larger-scale context of the cloud field and to highlight the advantages of the SACR to detect the numerous small nonprecipitating cloud elements. A new cloud identification and tracking algorithm (CITA) is developed to track cloud elements. In CITA, a cloud element is identified as a region having a contiguous set of pixels exceeding a preset reflectivity and size threshold. The high temporal resolution of the SACR 2D observations (30 s) allows for an area superposition criteria algorithm to match cloud elements at consecutive times. Following CITA, the temporal evolution of cloud-element properties (number, size, and maximum reflectivity) is presented. The vast majority of the designated elements during this cumulus event were short-lived nonprecipitating clouds having an apparent life cycle shorter than 15 min. The advantages and disadvantages of cloud tracking using an SACR are discussed.

Corresponding author address: Paloma Borque, Department of Atmospheric and Oceanic Sciences, Room 945, Burnside Hall, 805 Sherbrooke Street West, Montreal QC H3A 2K6, Canada. E-mail: paloma.borque@mail.mcgill.ca
Save