Editorial Type:
Article
Article Type:
Research Article

Rapid Sampling of Severe Storms by the National Weather Radar Testbed Phased Array Radar

Pamela L. Heinselman Cooperative Institute for Mesoscale Meteorological Studies, University of Oklahoma, and NOAA/OAR National Severe Storms Laboratory, Norman, Oklahoma

Search for other papers by Pamela L. Heinselman in
Current site
Google Scholar
PubMed Close
,
David L. Priegnitz Cooperative Institute for Mesoscale Meteorological Studies, University of Oklahoma, and NOAA/OAR National Severe Storms Laboratory, Norman, Oklahoma

Search for other papers by David L. Priegnitz in
Current site
Google Scholar
PubMed Close
,
Kevin L. Manross Cooperative Institute for Mesoscale Meteorological Studies, University of Oklahoma, and NOAA/OAR National Severe Storms Laboratory, Norman, Oklahoma

Search for other papers by Kevin L. Manross in
Current site
Google Scholar
PubMed Close
,
Travis M. Smith Cooperative Institute for Mesoscale Meteorological Studies, University of Oklahoma, and NOAA/OAR National Severe Storms Laboratory, Norman, Oklahoma

Search for other papers by Travis M. Smith in
Current site
Google Scholar
PubMed Close
, and
Richard W. Adams Cooperative Institute for Mesoscale Meteorological Studies, University of Oklahoma, and NOAA/OAR National Severe Storms Laboratory, Norman, Oklahoma

Search for other papers by Richard W. Adams in
Current site
Google Scholar
PubMed Close
Print Publication:
01 Oct 2008
DOI:
https://doi.org/10.1175/2008WAF2007071.1
Page(s):
808–824
Restricted access

Abstract

A key advantage of the National Weather Radar Testbed Phased Array Radar (PAR) is the capability to adaptively scan storms at higher temporal resolution than is possible with the Weather Surveillance Radar-1988 Doppler (WSR-88D): 1 min or less versus 4.1 min, respectively. High temporal resolution volumetric radar data are a necessity for rapid identification and confirmation of weather phenomena that can develop within minutes. The purpose of this paper is to demonstrate the PAR’s ability to collect rapid-scan volumetric data that provide more detailed depictions of quickly evolving storm structures than the WSR-88D. Scientific advantages of higher temporal resolution PAR data are examined for three convective storms that occurred during the spring and summer of 2006, including a reintensifying supercell, a microburst, and a hailstorm. The analysis of the reintensifying supercell (58-s updates) illustrates the capability to diagnose the detailed evolution of developing and/or intensifying areas of 1) low-altitude divergence and rotation and 2) rotation through the depth of the storm. The fuller sampling of the microburst’s storm life cycle (34-s updates) depicts precursors to the strong surface outflow that are essentially indiscernible in the WSR-88D data. Furthermore, the 34-s scans provide a more precise sampling of peak outflow. The more frequent sampling of the hailstorm (26-s updates) illustrates the opportunity to analyze storm structures indicative of rapid intensification, the development of hail aloft, and the onset of the downdraft near the surface.

Corresponding author address: Dr. Pamela L. Heinselman, 120 David L. Boren Blvd., Norman, OK 73072. Email: pam.heinselman@noaa.gov

Abstract

A key advantage of the National Weather Radar Testbed Phased Array Radar (PAR) is the capability to adaptively scan storms at higher temporal resolution than is possible with the Weather Surveillance Radar-1988 Doppler (WSR-88D): 1 min or less versus 4.1 min, respectively. High temporal resolution volumetric radar data are a necessity for rapid identification and confirmation of weather phenomena that can develop within minutes. The purpose of this paper is to demonstrate the PAR’s ability to collect rapid-scan volumetric data that provide more detailed depictions of quickly evolving storm structures than the WSR-88D. Scientific advantages of higher temporal resolution PAR data are examined for three convective storms that occurred during the spring and summer of 2006, including a reintensifying supercell, a microburst, and a hailstorm. The analysis of the reintensifying supercell (58-s updates) illustrates the capability to diagnose the detailed evolution of developing and/or intensifying areas of 1) low-altitude divergence and rotation and 2) rotation through the depth of the storm. The fuller sampling of the microburst’s storm life cycle (34-s updates) depicts precursors to the strong surface outflow that are essentially indiscernible in the WSR-88D data. Furthermore, the 34-s scans provide a more precise sampling of peak outflow. The more frequent sampling of the hailstorm (26-s updates) illustrates the opportunity to analyze storm structures indicative of rapid intensification, the development of hail aloft, and the onset of the downdraft near the surface.

Corresponding author address: Dr. Pamela L. Heinselman, 120 David L. Boren Blvd., Norman, OK 73072. Email: pam.heinselman@noaa.gov

Supplementary Materials

    • Supplemental Materials (ZIP 25.94 MB)
Save
Cover Weather and Forecasting
Weather and Forecasting

  • Brown, R. A., and Torgerson K. L. , 2003: Interpretation of single-Doppler radar signatures in a V-shaped hailstorm: Part 1–Evolution of reflectivity-based features. Natl. Wea. Dig., 27 , 2. 3–14.

    • Search Google Scholar
    • Export Citation

  • Brown, R. A., Steadham R. M. , Flickinger B. A. , Lee R. R. , Sirmans D. , and Wood V. T. , 2005: New WSR-88D volume coverage pattern 12: Results of field tests. Wea. Forecasting, 20 , 385–393.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Burgess, D. W., 2004: High resolution analyses of the 8 May 2003 Oklahoma City storm, Part I: Storm structure and evolution from radar data. Preprints, 22nd Conf. on Severe Local Storms, Hyannis, MA, Amer. Meteor. Soc., 12.4. [Available online at http://ams.confex.com/ams/pdfpapers/81939.pdf.].

  • Burgess, D. W., and Magsig M. A. , 1993: Evolution of the Red Rock, Oklahoma supercell of April 26, 1991. Preprints, 17th Conf. on Severe Local Storms, St. Louis, MO, Amer. Meteor. Soc., 257–261.

  • Burgess, D. W., and Magsig M. A. , 1998: Recent observations of tornado development at near range to WSR-88D radars. Preprints, 19th Conf. on Severe Local Storms, Minneapolis, MN, Amer. Meteor. Soc., 756–759.

  • Doviak, R. J., and Zrnić D. S. , 1993: Doppler Radar and Weather Observations. Academic Press, 562 pp.

  • Eilts, M. D., Johnson J. T. , Mitchell E. D. , Lynn R. J. , Spencer P. , Cobb S. , and Smith T. M. , 1996: Damaging downburst prediction and detection algorithm for the WSR-88D. Preprints, 18th Conf. on Severe Local Storms, San Francisco, CA, Amer. Meteor. Soc., 541–545.

  • Gilmore, M. S., and Wicker L. J. , 1998: The influence of midtropospheric dryness on supercell morphology and evolution. Mon. Wea. Rev., 126 , 943–958.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Lemon, L. R., 1998: The radar “three-body scatter spike”: An operational large-hail signature. Wea. Forecasting, 13 , 327–340.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Nielsen-Gammon, J. W., and Read W. L. , 1995: Detection and interpretation of left-moving severe thunderstorms using the WSR-88D: A case study. Wea. Forecasting, 10 , 127–140.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Proctor, F. H., 1988: Numerical simulations of an isolated microburst. Part I: Dynamics and structure. J. Atmos. Sci., 45 , 3137–3160.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Roberts, R. D., and Wilson J. W. , 1989: A proposed microburst nowcasting procedure using single-Doppler radar. J. Appl. Meteor., 28 , 285–303.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • ROC, 2007: WSR-88D system specification. WSR-88D Radar Operations Center Rep. OWY55, 164 pp. [Available from NOAA FOIA Office, Public Reference Facility (OFA56), 1315 East West Hwy. (SSMC3), Room 10730, Silver Spring, MD 20910.].

  • Smith, T. M., and Elmore K. L. , 2004: The use of radial velocity derivatives to diagnose rotation and divergence. Preprints, 11th Conf. on Aviation, Range, and Aerospace, Hyannis, MA, Amer. Meteor. Soc., P5.6. [Available online at http://ams.confex.com/ams/pdfpapers/81827.pdf.].

  • Smith, T. M., Elmore K. L. , and Dulin S. A. , 2004: A damaging downburst prediction and detection algorithm for the WSR-88D. Wea. Forecasting, 19 , 240–250.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Trapp, R. J., Mitchell E. D. , Tipton G. A. , Effertz D. W. , Watson A. I. , Andra D. L. Jr., and Magsig M. A. , 1999: Descending and nondescending tornadic vortex signatures detected by WSR-88Ds. Wea. Forecasting, 14 , 625–639.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Weber, M. E., Cho J. Y. N. , Herd J. S. , Flavin J. M. , Benner W. E. , and Torok G. S. , 2007: The next-generation multimission U.S. surveillance radar network. Bull. Amer. Meteor. Soc., 88 , 1739–1751.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Wilson, J. W., and Reum D. , 1988: The flare echo: Reflectivity and velocity signature. J. Atmos. Oceanic Technol., 5 , 197–205.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Wilson, J. W., Roberts R. D. , Kessinger C. , and McCarthy J. , 1984: Microburst wind structure and evaluation of Doppler radar for airport wind shear detection. J. Climate Appl. Meteor., 23 , 898–915.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Wolfson, M. M., Delanoy R. L. , Forman B. E. , Hallowell R. G. , Pawlak M. L. , and Smith P. D. , 1994: Automated microburst wind-shear prediction. Lincoln Laboratory Journal, Vol. 7 MIT Lincoln Laboratory. 399–426.

    • Search Google Scholar
    • Export Citation

  • Yu, T-Y., Orescanin M. B. , Curtis C. D. , Zrnić D. S. , and Forsyth D. E. , 2007: Beam multiplexing using the phased-array weather radar. J. Atmos. Oceanic Technol., 24 , 616–626.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Zhang, G., and Doviak R. J. , 2007: Spaced-antenna interferometry to measure crossbeam wind, shear, and turbulence: Theory and formulation. J. Atmos. Oceanic Technol., 24 , 791–805.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Zrnić, D. S., 1987: Three-body scattering produces precipitation signature of special diagnostic value. Radio Sci., 22 , 76–86.

  • Zrnić, D. S., and Coauthors, 2007: Agile-beam phased array radar for weather observations. Bull. Amer. Meteor. Soc., 88 , 1753–1766.

All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 1355 475 40
PDF Downloads 706 188 11