• Adlerman, E. J., , and Drogemeier K. K. , 2005: The dependence of numerically simulated cyclic mesocyclogenesis upon environmental vertical wind shear. Mon. Wea. Rev., 133, 35953623.

    • Search Google Scholar
    • Export Citation
  • Atkins, N. T., , Arnott J. M. , , Przybylinski R. W. , , Wolf R. A. , , and Ketcham B. D. , 2004: Vortex structure and evolution within bow echoes. Part I: Single-Doppler and damage analysis of the 29 June 1998 derecho. Mon. Wea. Rev., 132, 22242242.

    • Search Google Scholar
    • Export Citation
  • Bharadwaj, N., , and Chandrasekar V. , 2005: Waveform design for CASA X-band radars. Preprints, 32nd Conf. on Radar Meteorology, Albuquerque, NM, Amer. Meteor. Soc., P10R.13. [Available online at http://ams.confex.com/ams/pdfpapers/96347.pdf.]

  • Bluestein, H. B., 1993: Observations and Theory of Weather Systems. Vol. 2, Synoptic–Dynamic Meteorology in Midlatitudes, Oxford University Press, 594 pp.

  • Bluestein, H. B., , and Jain M. H. , 1985: Formation of mesoscale lines of precipitation: Severe squall lines in Oklahoma during the spring. J. Atmos. Sci., 42, 17111732.

    • Search Google Scholar
    • Export Citation
  • Bluestein, H. B., , and Parker S. S. , 1993: Modes of isolated, severe convective storm formation along the dryline. Mon. Wea. Rev., 121, 13541372.

    • Search Google Scholar
    • Export Citation
  • Bluestein, H. B., , French M. M. , , Tanamachi R. L. , , Frasier S. , , Hardwick K. , , Junyent F. , , and Pazmany A. , 2007: Close-range observations of tornadoes in supercells made with a dual-polarization, X-band, mobile Doppler radar. Mon. Wea. Rev., 135, 15221543.

    • Search Google Scholar
    • Export Citation
  • Bringi, V. N., , and Chandrasekar V. , 2001: Polarimetric Doppler Weather Radar: Principles and Applications. Cambridge University Press, 636 pp.

  • Brock, F. V., , Crawford K. C. , , Elliott R. L. , , Cuperus G. W. , , Stadler S. J. , , Johnson H. L. , , and Eilts M. D. , 1995: The Oklahoma Mesonet: A technical overview. J. Atmos. Oceanic Technol., 12, 519.

    • Search Google Scholar
    • Export Citation
  • Carbone, R. E., 1982: A severe frontal rainband. Part I: Stormwide hydrodynamic structure. J. Atmos. Sci., 39, 258279.

  • Davies-Jones, R. P., , Burgess D. W. , , and Foster M. , 1990: Test of helicity as a forecast parameter. Preprints, 16th Conf. on Severe Local Storms, Kananaskis Park, AB, Canada, Amer. Meteor. Soc., 588–592.

  • Gilmore, M. S., , Straka J. M. , , and Rasmussen E. N. , 2004: Precipitation uncertainty due to variations in precipitation particle parameters within a simple microphysics scheme. Mon. Wea. Rev., 132, 26102627.

    • Search Google Scholar
    • Export Citation
  • Junyent, F., , Chandrasekar V. , , McLaughlin D. , , Insanic E. , , and Bharadwaj N. , 2010: The CASA Integrated Project 1 networked radar system. J. Atmos. Oceanic Technol., 27, 6178.

    • Search Google Scholar
    • Export Citation
  • Kumjian, M. R., , and Ryzhkov A. V. , 2008: Polarimetric signatures in supercell thunderstorms. J. Appl. Meteor., 47, 19401961.

  • Kumjian, M. R., , and Ryzhkov A. V. , 2009: Storm-relative helicity revealed from polarimetric radar measurements. J. Atmos. Sci., 66, 667685.

    • Search Google Scholar
    • Export Citation
  • Liu, Y., , and Bringi V. N. , 2006: Improved rain attenuation correction algorithms for radar reflectivity and differential reflectivity with adaptation to drop shape model variation. Proc. Int. Geoscience and Remote Sensing Symp., Denver, CO, IEEE, 1910–1913.

  • McLaughlin, D., and Coauthors, 2009: Short-wavelength technology and the potential for distributed networks of small radar systems. Bull. Amer. Meteor. Soc., 90, 17971817.

    • Search Google Scholar
    • Export Citation
  • McPherson, R. A., and Coauthors, 2007: Statewide monitoring of the mesoscale environment: A technical update on the Oklahoma Mesonet. J. Atmos. Oceanic Technol., 24, 301321.

    • Search Google Scholar
    • Export Citation
  • NWS Norman WFO, cited 2011: Storm data and unusual weather phenomena—April 2010. [Available online at http://www.srh.noaa.gov/images/oun/pdf/stormdata/oun201004.pdf.]

  • Przybylinski, R. W., 1995: The bow echo: Observations, numerical simulations, and severe weather detection methods. Wea. Forecasting, 10, 203218.

    • Search Google Scholar
    • Export Citation
  • Przybylinski, R. W., , Schmocker G. K. , , and Lin Y.-J. , 2000: A study of storm and vortex morphology during the intensifying stage of severe wind mesoscale convective systems. Preprints, 20th Conf. on Severe Local Storms, Orlando, FL, Amer. Meteor. Soc., 173–176.

  • Romine, G. S., , Burgess D. W. , , and Wilhelmson R. B. , 2008: A dual-polarization-radar-based assessment of the 8 May 2003 Oklahoma City area tornadic supercell. Mon. Wea. Rev., 136, 28492870.

    • Search Google Scholar
    • Export Citation
  • Ryzhkov, A. V., , Zrnic D. S. , , Hubbert J. C. , , Bringi V. N. , , Vivekanandan J. , , and Brandes E. A. , 2002: Polarimetric radar observations and interpretation of co-cross-polar correlation coefficients. J. Atmos. Oceanic Technol., 19, 340354.

    • Search Google Scholar
    • Export Citation
  • Smith, B. T., , Thompson R. L. , , Grams J. S. , , and Broyles C. , 2010: Climatology of convective modes associated with significant severe thunderstorms in the contiguous United States. Preprints, 25th Conf. on Severe Local Storms, Denver, CO, Amer. Meteor. Soc., 16B.6. [Available online at http://ams.confex.com/ams/pdfpapers/175727.pdf.]

  • Snook, N., , and Xue M. , 2008: Effects of microphysical drop size distribution on tornadogenesis in supercell thunderstorms. Geophys. Res. Lett., 35, L24803, doi:10.1029/2008GL035866.

    • Search Google Scholar
    • Export Citation
  • Snyder, J. C., , Bluestein H. B. , , Zhang G. , , and Frasier S. J. , 2010: Attenuation correction and hydrometeor classification of high-resolution, X-band, dual-polarized mobile radar measurements in severe convective storms. J. Atmos. Oceanic Technol., 27, 19792001.

    • Search Google Scholar
    • Export Citation
  • Trabal, J. M., , Chandrasekar V. , , Gorgucci E. , , and McLaughlin D. J. , 2009: Differential reflectivity (ZDR) calibration for CASA Radar Network using properties of the observed medium. Proc. Int. Geoscience and Remote Sensing Symp., Cape Town, South Africa, IEEE, II-960–II-963.

  • Trapp, R. J., , Tessendorf S. A. , , Godfrey E. S. , , and Brooks H. E. , 2005: Tornadoes from squall lines and bow echoes. Part I: Climatological distribution. Wea. Forecasting, 20, 2334.

    • Search Google Scholar
    • Export Citation
  • van den Heever, S. C., , and Cotton W. R. , 2004: The impact of hail size on simulated supercell storms, J. Atmos. Sci., 61, 15961609.

  • Wakimoto, R. M., , Murphey H. V. , , Davis C. A. , , and Atkins N. T. , 2006: High winds generated by bow echoes. Part II: The relationship between the mesovortices and damaging straightline winds. Mon. Wea. Rev., 134, 28132829.

    • Search Google Scholar
    • Export Citation
  • Weisman, M. L., , and Trapp R. J. , 2003: Low-level mesovortices within squall lines and bow echoes. Part I: Overview and sensitivity to environmental vertical wind shear. Mon. Wea. Rev., 131, 27792803.

    • Search Google Scholar
    • Export Citation
  • View in gallery

    The four CASA radar sites (stars) are located near the cities of Cyril (KCYR), Lawton (KLWE), Rush Springs (KRSP), and Chickasha (KSAO). Range rings of 40 km are shown in black around each CASA radar. The 40- and 60-km range rings are shown around each NEXRAD (squares). The nearest NEXRAD radars are KTLX and KFDR. The gray lines represent the damage paths from the mesovortices.

  • View in gallery

    The (a) 500- and (b) 850-hPa analyses from 1200 UTC 2 Apr 2010. [Courtesy of the Storm Prediction Center (SPC).]

  • View in gallery

    (left) Upper-air sounding and (top right) hodograph from KOUN at 1200 UTC 2 Apr 2010. [Courtesy of SPC.]

  • View in gallery

    Surface analysis with temperature (°C), dewpoint (°C), and wind barb (full barb ≡ 5 m s−1; half barb ≡ 2.5 m s−1) at 0800 UTC plotted using WeatherScope from the Oklahoma Climate Survey.

  • View in gallery

    KFDR WSR-88D scan at 0.5° elevation angle showing reflectivity (dBZ) at (a) 0900, (b) 0930, (c) 1000, and (d) 1030 UTC. The yellow box indicates the area of convection that eventually had the embedded mesovortices.

  • View in gallery

    KFDR WSR-88D scan at 0.5° elevation angle at (a) 1000 and (b) 1030 UTC. (left) Reflectivity (dBZ) and (right) radial velocity (m s−1). The arrow indicates the location of the strongest winds that were on the north side of a bowlike structure located on the south side of the embedded area of convection.

  • View in gallery

    KTLX WSR-88D scan at 0.5° elevation angle showing reflectivity (dBZ) at (a) 1032 and (b) 1102 UTC.

  • View in gallery

    KTLX WSR-88D scan at 0.5° elevation angle at 1102 UTC. (left) Reflectivity (dBZ) and (right) radial velocity (m s−1).

  • View in gallery

    KTLX WSR-88D scan at 0.5° elevation angle showing reflectivity (dBZ) at 1116 UTC.

  • View in gallery

    The CASA radar scan at 1.0° elevation angle from KRSP at (a) 1046 and (b) 1054 UTC. (left to right), reflectivity (dBZ), radial velocity (m s−1), and attenuation-corrected ZDR (dB). Range rings are in km. Vortices are numbered and circled in yellow on the velocity panels. Arrows point to the vortices on the reflectivity panels.

  • View in gallery

    As in Fig. 10, but at (a) 1057, (b) 1059, (c) 1101, and (d) 1103 UTC.

  • View in gallery

    As in Fig. 10, but at 1104 UTC.

  • View in gallery

    Maximum inbound velocity and average maximum ZDR in the arc vs time.

  • View in gallery

    The 1101 UTC KRSP CASA radar scan at 1.0° elevation angle. Clockwise from top left: reflectivity (dBZ), velocity (m s−1), ρhv, and ZDR (dB). Black plus sign (+) is at the same location in each panel near the center of the vortex.

  • View in gallery

    As in Fig. 14, but at 1102 UTC. The first area of low ρhv, possibly associated with a tornado, is circled in white and labeled D1.

  • View in gallery

    As in Fig. 14, but at 1103 UTC. The first area of low ρhv, possibly damage associated with a tornado, is circled in white and labeled D1. The second area of low ρhv, possibly damage associated with straight-line winds, is circled in black and labeled D2.

  • View in gallery

    The 1102 UTC KRSP CASA radar scan at 1.0° elevation angle. Clockwise from top left reflectivity: (dBZ), velocity (m s−1)ρhv, and spectrum width (m s−1). Black plus sign (+) is at the same location in each panel near the center of the vortex. The first area of low ρhv, possibly damage associated with a tornado, is circled in white and labeled D1.

  • View in gallery

    Damage survey and path associated with the second and third vortices. Vortex paths were determined by the KRSP CASA radar.

  • View in gallery

    Schematic of the approximate surface patterns for nonoccluding cyclic mesocyclogenesis. Scalloped black line indicates the surface cold-pool boundary. Red indicates area of vorticity maxima. Light blue indicates updraft areas, and dark blue indicates downdraft areas. Single yellow contour indicates the boundary of the rain area. [From Adlerman and Drogemeier (2005).]

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An Analysis of Vortices Embedded within a Quasi-Linear Convective System Using X-Band Polarimetric Radar

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  • 1 Center for Analysis and Prediction of Storms, and School of Meteorology, University of Oklahoma, Norman, Oklahoma
  • 2 Center for Analysis and Prediction of Storms, University of Oklahoma, Norman, Oklahoma
  • 3 School of Meteorology, University of Oklahoma, Norman, Oklahoma
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Abstract

On 2 April 2010, a developing quasi-linear convective system (QLCS) moved rapidly northeastward through central Oklahoma spawning at least three intense, mesoscale vortices. At least two of these vortices caused damage rated as category 0 to 1 on the enhanced Fujita scale (EF0–EF1) in and near the town of Rush Springs. Two radar networks—the National Weather Service Weather Surveillance Radar-1988 Doppler network (WSR-88D) and the Engineering Research Center for Collaborative Adaptive Sensing of the Atmosphere (CASA) radar network—collected high spatial and temporal resolution data of the event. This study is an in-depth polarimetric analysis of mesovortices within a QLCS. In this case study, the storm development and evolution of the QLCS mesovortices are examined. Significant findings include the following: 1) The damage in Rush Springs was caused by a combination of the fast translation speed and the embedded circulations associated with QLCS vortices. The vortices’ relative winds nearly negated the storm motion to the left of the vortex, but doubled the ground-relative wind to the right of the vortex. 2) A significant differential reflectivity (ZDR) arc developed along the forward flank of the first vortex. The ZDR arc propagated northeastward along the QLCS with the development of each new vortex. 3) A minimum in the copolar correlation coefficient (ρhv) in the center of the strongest vortex was observed, indicating the likely existence of a polarimetric tornado debris signature (TDS). A secondary ρhv minimum also was found just to the right of the vortex center, possibly associated with lofted debris from straight-line winds.

Corresponding author address: Vivek N. Mahale, School of Meteorology, Ste. 5900, 120 David L. Boren Blvd., Norman, OK 73072-7307. E-mail: vmahale@ou.edu

Abstract

On 2 April 2010, a developing quasi-linear convective system (QLCS) moved rapidly northeastward through central Oklahoma spawning at least three intense, mesoscale vortices. At least two of these vortices caused damage rated as category 0 to 1 on the enhanced Fujita scale (EF0–EF1) in and near the town of Rush Springs. Two radar networks—the National Weather Service Weather Surveillance Radar-1988 Doppler network (WSR-88D) and the Engineering Research Center for Collaborative Adaptive Sensing of the Atmosphere (CASA) radar network—collected high spatial and temporal resolution data of the event. This study is an in-depth polarimetric analysis of mesovortices within a QLCS. In this case study, the storm development and evolution of the QLCS mesovortices are examined. Significant findings include the following: 1) The damage in Rush Springs was caused by a combination of the fast translation speed and the embedded circulations associated with QLCS vortices. The vortices’ relative winds nearly negated the storm motion to the left of the vortex, but doubled the ground-relative wind to the right of the vortex. 2) A significant differential reflectivity (ZDR) arc developed along the forward flank of the first vortex. The ZDR arc propagated northeastward along the QLCS with the development of each new vortex. 3) A minimum in the copolar correlation coefficient (ρhv) in the center of the strongest vortex was observed, indicating the likely existence of a polarimetric tornado debris signature (TDS). A secondary ρhv minimum also was found just to the right of the vortex center, possibly associated with lofted debris from straight-line winds.

Corresponding author address: Vivek N. Mahale, School of Meteorology, Ste. 5900, 120 David L. Boren Blvd., Norman, OK 73072-7307. E-mail: vmahale@ou.edu

1. Introduction

Between approximately 1055 and 1105 UTC on 2 April 2010, a series of vortices embedded within a developing quasi-linear convective system (QLCS) caused significant damage to the town of Rush Springs, Oklahoma. The damage occurred as the QLCS was transitioning from quasi-discrete cells into a line. A postevent damage survey conducted by the National Weather Service (NWS) Weather Forecast Office (WFO) in Norman, Oklahoma, estimated wind gusts of 40–45 m s−1, where four unanchored mobile homes and a diner were destroyed. Numerous other structures were damaged, power lines were downed, and trees were uprooted; the total damage in Rush Springs was estimated at $400,000. As the QLCS moved northeastward, additional damage occurred across central Oklahoma in the towns of Blanchard, Newcastle, and Norman. The damage in these areas was predominantly to trees, tin outbuildings, and billboards (NWS Norman WFO 2011).

Quasi-linear convective systems include squall lines and bow echoes, and often evolve from short-lived, highly interacting cells (Trapp et al. 2005). These interacting cells produce a surface-based cold pool critical to the dynamics and motion of the QLCS (Weisman and Trapp 2003); however, the motion of the interacting cells may be different from that of the QLCS (Bluestein and Jain 1985). The cold pool produces a leading-edge baroclinic zone that results in strong horizontal vorticity that can be tilted to form low-level mesovortices (Weisman and Trapp 2003).

Low-level meso-γ-scale (2–40 km) vortices (e.g., Weisman and Trapp 2003) are common, and observations suggest that winds associated with QLCS mesovortices can be significant. Small-scale vortices were also found in a frontal rainband by Carbone (1982). These small-scale vortices were ~13 km apart and developed along a well-defined gust front as a result of Helmholtz-type inflectional instabilities due to strong low-level shear across the cold front.

In QLCSs, these mesovortices—not to be confused with larger-scale bookend vortices or midlevel mesovortices—tend to develop at low levels (generally 0–3 km above ground level) and north of a bow apex in a QLCS (e.g., Przybylinski 1995; Przybylinski et al. 2000; Atkins et al. 2004); however, Atkins et al. (2004) found that these mesovortices could develop south of the bow apex as well. Weisman and Trapp (2003) note that these mesovortices can have strengths comparable to mesocyclones associated with supercells. Hook echoes may even appear in these types of vortices; however, vortices in QLCSs usually develop near the surface and build upward and are not associated with long-lived, rotating updrafts at midlevels that occur with supercell mesocyclones. Weisman and Trapp (2003) found that QLCS vortices typically develop in environments characterized by strong, low-level (2.5–5.0 km) vertical wind shear (>20 m s−1), and there is enhanced damage potential from these more intense mesovortices. These embedded mesovortices increase the ground-relative wind speed where the vortex motion is in the same direction as the translation motion (Wakimoto et al. 2006). Tornadoes may also form from these embedded mesovortices. In a study of 3828 tornadoes, approximately 18% were produced by QLCSs (Trapp et al. 2005). The majority of these tornadoes were F1 (in this particular study F0 tornadoes were not included), though some are capable of F2–F4 damage. In another study by Smith et al. (2010), it was found from 2003 to 2009 that around 12.1% of tornadoes were associated with QLCSs. There was a clear distinction made in this study that tornadoes within lines of supercells were different from tornadoes within QLCSs. Not all mesovortices within QLCSs produce tornadoes, but Atkins et al. (2004) suggested based on observations that stronger, longer-lived, and deeper QLCS mesovortices were more likely to produce tornadoes.

The transition of individual discrete cells into a QLCS can make classification of such systems difficult and somewhat subjective. Trapp et al. (2005) defined a QLCS objectively (and as they note somewhat arbitrarily) as a quasi-linear region of radar reflectivity greater than or equal to 40 dBZ that is continuously distributed over a horizontal distance greater than 100 km.

The purpose of this paper is to document the evolution of a series of embedded QLCS vortices through analysis of low-level (<1 km) high spatial (96 m) and temporal (60 s) resolution, data collected by polarimetric X-band radar. This study is believed to be the first in-depth polarimetric analysis of mesovortices within a QLCS. Characteristics of the radars and data used in this study are given in section 2. Section 3 gives an overview of the synoptic and mesoscale conditions of the event leading up to the development of the vortices. In section 4, the evolution of the vortices is analyzed in detail. The reflectivity, radial velocity, and differential reflectivity data are discussed first, followed by a discussion of the copolar correlation coefficient and damage survey. Final conclusions and discussion are provided in section 5.

2. Data

The Weather Surveillance Radar 1988-Doppler network (WSR-88D) and the Engineering Research Center for Collaborative Adaptive Sensing of the Atmosphere (CASA; McLaughlin et al. 2009) radar network (Junyent et al. 2010) were collecting data during the 2 April 2010 event. A map of the radar locations and the location of the most significant damage from the mesovortices is provided (Fig. 1).

Fig. 1.
Fig. 1.

The four CASA radar sites (stars) are located near the cities of Cyril (KCYR), Lawton (KLWE), Rush Springs (KRSP), and Chickasha (KSAO). Range rings of 40 km are shown in black around each CASA radar. The 40- and 60-km range rings are shown around each NEXRAD (squares). The nearest NEXRAD radars are KTLX and KFDR. The gray lines represent the damage paths from the mesovortices.

Citation: Weather and Forecasting 27, 6; 10.1175/WAF-D-11-00135.1

The WSR-88D radars are S-band (10 cm) radars with a beamwidth of 0.925°, range resolution of 250 m, and an azimuth increment of 0.5°. The effective beamwidth is 1.23° and the maximum unambiguous range is 230 km. Data from the Oklahoma City (Norman, KTLX) and Frederick (KFDR), Oklahoma, WSR-88Ds were used in this study. Both KTLX and KFDR were scanning with volume coverage pattern (VCP) 11 during the event. In VCP 11, the radar completes one volume scan (14 elevation scans) every 5 min.

The CASA radars are X-band (3 cm) radars with a beamwidth of 1.8°, range resolution of 96 m, and azimuth increment of 1.0°. The effective beamwidth is 2.23° and the maximum unambiguous range is 40 km. The CASA radars provide both moment data (i.e., reflectivity, radial velocity, and spectrum width) and dual-polarized data. A dual pulse repetition frequency (PRF) waveform is used to provide a Nyquist velocity of ±38 m s−1 (Bharadwaj and Chandrasekar 2005). Manual dealiasing of the radar data was done using the National Center for Atmospheric Research’s (NCAR) radar-editing software package SOLOII. The CASA radar network uses an adaptive scanning strategy (McLaughlin et al. 2009). In the adaptive scanning strategy, weather algorithms are used to identify significant weather features. These weather features are placed in a feature repository and are converted into tasks for the radars to optimize their storm volume scanning for the next cycle. Each scanning cycle takes 1 min. A 360° surveillance scan is completed at 2.0° elevation angle, followed by sector scans at 1.0°, 3.0°, 5.3°, 7.4°, 9.5°, 11.6°, 13.6°, and 15.9°. The surveillance scan takes 20 s, and the remaining 40 s are used for targeted (optimized) sector scanning beginning at 1.0°. Sector scans are completed until the end of the 60 s; thus, not all the elevation angles may be completed.

CASA radars are more susceptible to attenuation owing to their short wavelength. Radar reflectivity and differential reflectivity (ZDR) were corrected for attenuation using the measured differential propagation phase in a self-consistent method (Liu and Bringi 2006), where a coefficient is found that linearly relates specific attenuation at horizontal polarization with a specific differential propagation phase (Bringi and Chandrasekar 2001). In this particular study, any attenuation-corrected data will be noted.

There is some known ZDR bias (Trabal et al. 2009), though for this study, no ZDR correction was applied. The emphasis in this case is on the relative values of ZDR in space and time, not the specific quantitative values.

In addition to radar data, surface and upper-air observations and radiosonde and damage survey data were used in this study. Surface observations were from the National Weather Service’s observation network and the Oklahoma Mesonet. The Oklahoma Mesonet is a statewide network of over 110 surface weather stations across the state of Oklahoma that provides weather data every 5 min to a variety of users (Brock et al. 1995; McPherson et al. 2007).

3. Synoptic and mesoscale overview of event

A 500-hPa trough was centered across eastern Colorado and eastern New Mexico at 1200 UTC on 2 April 2010 (Fig. 2a). This trough axis was located across the eastern Rocky Mountains and provided synoptic-scale ascent downstream from the axis across the central plains through differential vorticity advection (Bluestein 1993). A midlevel jet (~40 m s−1) was located downstream of the trough axis across the central plains (Fig. 2b). A strong low-level jet (~25 m s−1) and veering low-level winds provided (Fig. 3) additional synoptic-scale ascent through warm air advection.

Fig. 2.
Fig. 2.

The (a) 500- and (b) 850-hPa analyses from 1200 UTC 2 Apr 2010. [Courtesy of the Storm Prediction Center (SPC).]

Citation: Weather and Forecasting 27, 6; 10.1175/WAF-D-11-00135.1

Fig. 3.
Fig. 3.

(left) Upper-air sounding and (top right) hodograph from KOUN at 1200 UTC 2 Apr 2010. [Courtesy of SPC.]

Citation: Weather and Forecasting 27, 6; 10.1175/WAF-D-11-00135.1

At the surface, a cold front was located across western Oklahoma and a dryline trailed into northwest Texas at 0800 UTC (Fig. 4). Convection was rapidly developing at this time. The cold front was moderately strong, with a ~8°C temperature difference across it from central Oklahoma to the Oklahoma panhandle. The environment was very moist across central Oklahoma; dewpoints were in the midteens (°C). The cold front continued to move southeastward through 1200 UTC into central Oklahoma.

Fig. 4.
Fig. 4.

Surface analysis with temperature (°C), dewpoint (°C), and wind barb (full barb ≡ 5 m s−1; half barb ≡ 2.5 m s−1) at 0800 UTC plotted using WeatherScope from the Oklahoma Climate Survey.

Citation: Weather and Forecasting 27, 6; 10.1175/WAF-D-11-00135.1

Vertical profile information indicated low thermodynamic instability but strong low-level wind shear ahead of the cold front. The sounding closest to the Rush Springs event in space and time was the sounding taken in Norman, Oklahoma (KOUN), at 1200 UTC on 2 April 2010 (Fig. 3). Since the standard procedure at the NWS WFO in Norman is to launch the radiosonde approximately 1 h before the assigned time (e.g., 1200 UTC would be launched just after 1100 UTC), this sounding is a good representation of the environment at the time of the storm’s peak intensity. The sounding preceded thunderstorms in the area, and the winds on the sounding do not appear contaminated. The sounding was nearly saturated from the surface up to 650 hPa, but was much drier from 650 to about 400 hPa. Surface-based convective available potential energy (CAPE) was only ~450 J kg−1, while mixed-layer CAPE (MLCAPE) was ~1300 J kg−1, which is indicative of moderate mixed-layer instability available for convection.

The sounding hodograph indicated an environment characterized by significant directional and speed wind shear, especially within the lowest 1 km (see Fig. 4 insert). This directional shear is represented in the hodograph by a large amount of curvature from the surface up to 1 km. Much of the high winds in the lowest kilometer can be attributed to the low-level jet. The 0–1- and 0–3-km storm-relative environmental helicity (SREH) values were both ~350 m2 s−2 and the 0–6-km shear was ~35 m s−1. Supercells that can produce tornadoes tend to form in environments where the 0–3-km SREH is at least 150 m2 s−2 (Davies-Jones et al. 1990) or where 0–6 km shear is at least 20 m s−1 (Bluestein and Parker 1993). Observed shear values were more than sufficient to produce embedded mesovortices in a QLCS. QLCS mesovortices and supercells both tend to form in strong-low level shear; however, this event developed into a QLCS due to strong winds aloft (e.g., 500 hPa) that were nearly parallel to the surface boundary, which promotes linear storm structures over discrete storm structures. Overall, the environment during this event can be characterized as one with low instability and high shear.

4. Development and analysis of the QLCS

Rising motion associated with surface convergence along the dryline initiated convection across northwest Texas between 0600 and 0700 UTC (not shown). The convection continued to become better organized over time. By 0800 UTC, an area of quasi-discrete cells had formed, embedded within a larger area of precipitation in northwest Texas. The convection expanded in size and increased in intensity as it moved northeastward at around 25–30 m s−1 into Oklahoma.

The convection that would eventually evolve into the Rush Springs storm began just before 0900 UTC as a quasi-discrete area of convection, and rapidly intensified between 0900 and 0930 UTC. Maximum reflectivity values observed were in excess of 60 dBZ by 0930 UTC (Fig. 5b).

Fig. 5.
Fig. 5.

KFDR WSR-88D scan at 0.5° elevation angle showing reflectivity (dBZ) at (a) 0900, (b) 0930, (c) 1000, and (d) 1030 UTC. The yellow box indicates the area of convection that eventually had the embedded mesovortices.

Citation: Weather and Forecasting 27, 6; 10.1175/WAF-D-11-00135.1

By 1000 UTC, reflectivity in the core of the convection was ~70 dBZ at ~0.5 km above radar level (ARL). The storm continued to move northeastward at 25–30 m s−1 between 1000 and 1030 UTC (Figs. 5c and 5d). Between these times, maximum outbound velocity (relative to KFDR) increased markedly from ~18 m s−1 (at ~0.45 km ARL) to ~42 m s−1 (at ~0.66 km ARL). Rapid evolution had taken place within these 30 min; the strongest winds were located on the north side of a bowlike structure located on the south side of the embedded area of convection (Fig. 6).

Fig. 6.
Fig. 6.

KFDR WSR-88D scan at 0.5° elevation angle at (a) 1000 and (b) 1030 UTC. (left) Reflectivity (dBZ) and (right) radial velocity (m s−1). The arrow indicates the location of the strongest winds that were on the north side of a bowlike structure located on the south side of the embedded area of convection.

Citation: Weather and Forecasting 27, 6; 10.1175/WAF-D-11-00135.1

The bowlike structure expanded and became more pronounced as the convection raced northeastward between 1032 and 1102 UTC, as was seen by the KTLX WSR-88D (Figs. 7a and 7b). It is at this time that quasi-discrete convective cells rapidly transitioned to a QLCS. Vortices began to develop along the line as this transition occurred; three distinct low-level vortices developed within the CASA domain, as documented by the Rush Springs (KRSP) CASA radar at 1.0° elevation angle. Two of these vortices caused damage near Rush Springs. Only one of these vortices was detected by Next Generation Weather Radar (NEXRAD) at 1102 UTC (Fig. 8). The KTLX WSR-88D radar measured a maximum velocity of −37 m s−1 and a minimum velocity of −14.5 m s−1, spaced ~2.4 km apart. The WSR-88D was scanning at ~1.3 km AGL. Analysis of these vortices between 1044 and 1104 UTC using CASA radar is discussed in the next section.

Fig. 7.
Fig. 7.

KTLX WSR-88D scan at 0.5° elevation angle showing reflectivity (dBZ) at (a) 1032 and (b) 1102 UTC.

Citation: Weather and Forecasting 27, 6; 10.1175/WAF-D-11-00135.1

Fig. 8.
Fig. 8.

KTLX WSR-88D scan at 0.5° elevation angle at 1102 UTC. (left) Reflectivity (dBZ) and (right) radial velocity (m s−1).

Citation: Weather and Forecasting 27, 6; 10.1175/WAF-D-11-00135.1

The QLCS continued to become more linear over time as it moved toward the northeast across central Oklahoma (Fig. 9); additional wind damage was caused by the QLCS just to the southwest of Norman (Fig. 1).

Fig. 9.
Fig. 9.

KTLX WSR-88D scan at 0.5° elevation angle showing reflectivity (dBZ) at 1116 UTC.

Citation: Weather and Forecasting 27, 6; 10.1175/WAF-D-11-00135.1

5. Analysis of CASA radar data

Analysis of the vortices was conducted at the 1.0° elevation angle because it was the lowest scan angle available; therefore, it would provide the closest observations to the ground. The CASA adaptive scanning always focused on the area of interest for the 1.0° elevation scans. Higher-elevation scans (e.g., 2.0° and 3.0°) did not provide any additional insight because the height difference was negligible. Unfortunately, significantly higher scans were not available due to the low-level adaptive scanning strategy of CASA.

a. Reflectivity, velocity, and differential reflectivity

1) Vortex 1, 1044–1056 UTC

The first vortex entered the southwest edge of KRSP’s range at 1044 UTC. By 1046 UTC, the inbound velocity was in excess of 35 m s−1 (Fig. 10a) at ~0.65 km ARL. Storm motion at this time was estimated from 245° at ~25–30 m s−1. This particular vortex remained rather broad and was the weakest of the three vortices observed; there were no reports of damage with this vortex.

Fig. 10.
Fig. 10.

The CASA radar scan at 1.0° elevation angle from KRSP at (a) 1046 and (b) 1054 UTC. (left to right), reflectivity (dBZ), radial velocity (m s−1), and attenuation-corrected ZDR (dB). Range rings are in km. Vortices are numbered and circled in yellow on the velocity panels. Arrows point to the vortices on the reflectivity panels.

Citation: Weather and Forecasting 27, 6; 10.1175/WAF-D-11-00135.1

By 1054 UTC, an area of higher differential reflectivity (ZDR) was located to the northeast of the first vortex along the right-front flank reflectivity gradient (Fig. 10b). This zone of higher ZDR resembles the polarimetric signature referred to as a “ZDR arc” (e.g., Kumjian and Ryzhkov 2008). The copolar correlation coefficient (ρhv) in the ZDR arc was relatively high (≥0.9), verifying the arc was associated with hydrometeors and not biological scatterers (not shown). The ZDR arcs represent areas of larger drops and occur due to size sorting in which smaller drops having a slower terminal velocity are advected farther—leaving larger drops behind. Thus, a higher ZDR is observed at lower levels in the reflectivity gradient of the free-form deformation (FFD).

Romine et al. (2008) used the term ZDR shield to describe this polarimetric signature. They hypothesized that the ZDR shield could be used as a proxy for baroclinicity. In this paper, the term ZDR arc will be used for consistency. This first vortex and its associated ZDR arc continued moving northeastward until approximately 1056 UTC.

Kumjian and Ryzhkov (2008) note that ZDR arcs have been detected in both supercell and nonsupercell tornadoes. Previously, ZDR arcs have been documented by X-band radar in a supercell (Snyder et al. 2010). In that study, attenuation-corrected ZDR was used to make sure any detected ZDR arc was not an artifact due to differential attenuation. Therefore, attenuation-corrected ZDR is considered in this study as well, using the previously mentioned Liu and Bringi (2006) method. Attenuation-corrected reflectivity, however, was not used in this study because there were some rays of reflectivity that were not corrected properly due to errors in the specific differential phase (KDP) calculations. As a result, the attenuation-corrected reflectivity had an unrealistic and distracting pattern significantly upstream from the area of interest (i.e., in the back of the storm, away from the ZDR arcs and vortices). These spikes are not noticeable in the attenuation-corrected ZDR.

2) Vortex 2, 1054–1104 UTC

A second vortex developed to the north of the first vortex at about 17 km to the southwest of the radar at approximately 1054 UTC with a maximum inbound velocity of ~24 m s−1 (Fig. 10b) at ~0.30 km ARL. Rapid intensification of this vortex occurred between 1054 and 1057 UTC with a well-defined radar hook signature in reflectivity and a shear couplet visible in the radial velocity by 1057 UTC (Fig. 11a). Maximum inbound velocity had increased to ~38 m s−1 at ~0.24 km ARL. Rapid intensification continued for an additional few minutes as this vortex moved northeastward.

Fig. 11.
Fig. 11.

As in Fig. 10, but at (a) 1057, (b) 1059, (c) 1101, and (d) 1103 UTC.

Citation: Weather and Forecasting 27, 6; 10.1175/WAF-D-11-00135.1

The development of the second vortex coincided with the propagation of the ZDR arc between 1056 and 1059 UTC. The region of higher ZDR propagated around the new vortex as seen by the comma-head shape in the ZDR (Fig. 11b); in other words, an area of larger droplets was reforming around the vortex at low levels. The ZDR arc propagated northeastward as new vortices developed north along the line. A pronounced ZDR arc remained associated with the vortex through 1101 UTC.

By 1059 UTC, the maximum inbound velocity had increased to over 40 m s−1 at ~0.19 km ARL. A well-defined hook was present with this vortex in the reflectivity with a reflectivity minimum in the center (Fig. 11b). A well-defined velocity couplet was present as well. By 1100 UTC, maximum inbound velocity at this time was in excess of 50 m s−1 at ~0.16 km ARL. This second vortex caused the most significant damage to Rush Springs and was the strongest of the three vortices with a maximum-to-minimum Doppler velocity difference of just above 45 m s−1 at 1100 UTC. This maximum Doppler velocity difference from KRSP of 45 m s−1 is double the velocity difference measured by KTLX (Fig. 8), and was measured much closer to the surface (1.3 versus 0.16 km ARL). The strongest winds estimated from KRSP were on the right side of the vortex due to a storm motion of 25–30 m s−1. As a result, some radar scans show velocity measurements to the left of the vortex (with respect to its motion) were close to 0 m s−1.

3) Vortex 3, 1059–1104 UTC

A third vortex began to develop ~3 km to the north of the second vortex around 1059–1101 UTC (Figs. 11b and 11c). As the vortex intensified between 1059 and 1103 UTC (Fig. 11d), it caused minor damage to the north of Rush Springs.

The ZDR arc propagated to the north of the third vortex between 1101 and 1103 UTC. At the same time, the second vortex lost its association with a ZDR arc. The most rapid transition of the ZDR arc can be seen between 1101 (Fig. 11c) and 1103 UTC (Fig. 11d). It appears that the ZDR arc associated with the second vortex dissipated and a new ZDR arc developed to the north and intensified in conjunction with the third vortex.

At 1104 UTC, two distinct hooks can be seen within 4 km of KRSP (Fig. 12). However, after 1105 UTC the complex moved over KRSP, and the radar signal was completely attenuated after that time.

Fig. 12.
Fig. 12.

As in Fig. 10, but at 1104 UTC.

Citation: Weather and Forecasting 27, 6; 10.1175/WAF-D-11-00135.1

4) Correlation of radial velocity and differential reflectivity

An analysis of maximum inbound velocity and average maximum ZDR within the ZDR arc suggests there is a positive correlation between the trends in maximum inbound velocity and maximum ZDR in the arc (Fig. 13). The average maximum ZDR was found by computing the nine-pixel average around and including the maximum ZDR in the arc. The ZDR arc was defined visually as the area of higher ZDR located to the northeast of the vortices along the right-front flank reflectivity gradient. This visual analysis and the average maximum ZDR calculation were conducted manually (within the right-front flank of the storm) without the use of automatic algorithms.

Fig. 13.
Fig. 13.

Maximum inbound velocity and average maximum ZDR in the arc vs time.

Citation: Weather and Forecasting 27, 6; 10.1175/WAF-D-11-00135.1

In this particular study, as the average maximum ZDR increased, the maximum inbound velocity increased for vortices two and three. After the second vortex reached peak intensity, the average maximum ZDR decreased as the vortex started to lose its dominance to the newly developed third vortex. For forecasters, this finding may have operational significance in warning decision making. Forecasters may be able to deduce whether a vortex is intensifying or weakening depending on the tendency of the ZDR arc. In a numerical study by Kumjian and Ryzhkov (2009), a strong positive correlation was found between the maximum ZDR in the arc signature and the low-level storm-relative helicity (SRH). Recall that ZDR arcs represent areas of larger drops that occur due to size sorting. Kumjian and Ryzhkov (2009) found that the “vigorous size sorting” occurs because of low-level, storm-relative, inflow-enhanced veering wind shear. An increase in low-level inflow-enhanced veering wind shear is equivalent to an increase in SRH. Therefore, the degree of the size sorting is correlated with the low-level SRH.

While the upward trends in ZDR may not necessarily give a lot of new information compared to radial velocity, it may increase forecaster confidence that the storm may continue to intensify in future scans. In theory, as indicated by Kumjan and Ryzhkov’s study, a forecaster could consider that a storm has ingested inflow air associated with higher low-level SRH if the ZDR arc is intensifying. A forecaster who observes the development of a ZDR arc embedded within a QLCS should monitor for the development of mesovortices. Further observational study on the predictability of vortex intensification using the ZDR arc needs to be conducted.

b. Copolar cross correlation and debris signature

Studies have shown that a tornado debris signature (TDS) can be detected from copolar correlation coefficient (ρhv) measurements (e.g., Ryzhkov et al. 2002; Bluestein et al. 2007) since low ρhv with moderate reflectivity represents nonmeteorological targets. Bluestein et al. (2007) observed with X-band radar that ρhv values <0.50 are associated with debris. A TDS is produced when a tornado lofts debris into the updraft. The large size and random orientation of the debris cause the ρhv to decrease. The TDS is more likely to be present the closer the storm is to the radar since the debris may not be lofted very high. It also may be assumed that debris aerodynamically lifted in straight-line winds could be detected if close enough to the radar.

As the second vortex approached KRSP, two ρhv minima were detected. The first minimum developed between 1101 (Fig. 14) and 1102 UTC (Fig. 15) with a significant decrease in ρhv (<0.50) very near the center of the vortex. This low ρhv was collocated with relatively high reflectivity (>30 dBZ), which implies the low ρhv was not due to a lack of scatterers. Very low ZDR was also collocated at the vortex center, which is another radar signature associated with the TDS (e.g., Bluestein et al. 2007; Kumjian and Ryzhkov 2008). Another region of low ρhv was located to the northeast of the one located near the vortex center. This region of low ρhv was likely not representative because the velocity data were noisy and the reflectivity was weak [low signal-to-noise ratio (SNR)].

Fig. 14.
Fig. 14.

The 1101 UTC KRSP CASA radar scan at 1.0° elevation angle. Clockwise from top left: reflectivity (dBZ), velocity (m s−1), ρhv, and ZDR (dB). Black plus sign (+) is at the same location in each panel near the center of the vortex.

Citation: Weather and Forecasting 27, 6; 10.1175/WAF-D-11-00135.1

Fig. 15.
Fig. 15.

As in Fig. 14, but at 1102 UTC. The first area of low ρhv, possibly associated with a tornado, is circled in white and labeled D1.

Citation: Weather and Forecasting 27, 6; 10.1175/WAF-D-11-00135.1

A second ρhv minimum developed between 1102 (Fig. 15) and 1103 UTC (Fig. 16) slightly to the right (with respect to vortex motion) of the center of the vortex. This low ρhv was collocated with moderate reflectivity (>25 dBZ). As noted earlier, the strongest winds were on the right side of the vortex, and the radar was scanning at lower than 100 m ARL over this area. Thus, any debris would not have to be aerodynamically lifted very high for this signature to be present, so this low ρhv signature was likely caused by straight-line winds. Also, still present was low ρhv in the center of vortex where the radial winds were much weaker. The light radial winds suggest the low ρhv in the center of the vortex was associated with upward motion instead of horizontal motion. Spectrum width at 1102 UTC (Fig. 17) was relatively high in the center of the vortex. This increases confidence that a smaller-scale vortex could have been present within the mesovortex. Thus, there may have been a tornado in the center of the mesovortex where upward motion could have lofted debris. This would represent a TDS. Therefore, there appears to have been both a straight-line debris signature and a TDS present in this event. It is concluded from this case that not all debris signatures should be automatically associated with a tornado. If straight-line winds are close enough to the radar, then associated debris could be detected by low ρhv. This is an important finding because unlike tornadoes, straight-line winds are not easily observed visually. This demonstrates the importance of having a network of low-level scanning radars because of their ability to detect straight-line winds nearby using a polarimetric debris signature.

Fig. 16.
Fig. 16.

As in Fig. 14, but at 1103 UTC. The first area of low ρhv, possibly damage associated with a tornado, is circled in white and labeled D1. The second area of low ρhv, possibly damage associated with straight-line winds, is circled in black and labeled D2.

Citation: Weather and Forecasting 27, 6; 10.1175/WAF-D-11-00135.1

Fig. 17.
Fig. 17.

The 1102 UTC KRSP CASA radar scan at 1.0° elevation angle. Clockwise from top left reflectivity: (dBZ), velocity (m s−1)ρhv, and spectrum width (m s−1). Black plus sign (+) is at the same location in each panel near the center of the vortex. The first area of low ρhv, possibly damage associated with a tornado, is circled in white and labeled D1.

Citation: Weather and Forecasting 27, 6; 10.1175/WAF-D-11-00135.1

A postevent damage survey confirmed the interpretation of the ρhv measurements in that the most significant damage was just along and to the right of the vortices’ centers (Fig. 18). The damage survey indicated two distinct damage paths near Rush Spring. The most significant damage path was across the southern part of the city and a second, shorter damage path was located across the northern part of the city. These damage paths coincided with the paths of vortices two and three from the CASA radar data. The discovery of the northern track was possible because of the high-resolution data provided by the CASA radar data. The NEXRAD radar at KTLX detected only one vortex.

Fig. 18.
Fig. 18.

Damage survey and path associated with the second and third vortices. Vortex paths were determined by the KRSP CASA radar.

Citation: Weather and Forecasting 27, 6; 10.1175/WAF-D-11-00135.1

6. Conclusions

A quasi-linear convective system moved northeastward through central Oklahoma around sunrise on 2 April 2010 and at least three distinct mesovortices were embedded within the line. These three vortices were each observed by WSR-88D and CASA radars. Single, polarimetric Doppler data were analyzed. Results and implications are summarized as follow:

  • The damage in Rush Springs was caused by winds associated with translating low-level mesovortices embedded within a developing QLCS. A weak tornado was possibly embedded in one of these vortices. The damage was located right along and to the right of the vortices’ centers. The rapid storm motion decreased the damage potential on the left side of the vortices and increased the damage potential on the right side of the vortices; at times the vortices’ relative winds nearly negated the storm motion to the left of the vortex center and nearly doubled the ground-relative wind to the right of the vortex center.
  • All three vortices had a ZDR arc at one time that evolved in direct association with the vortex intensification. Analysis of maximum inbound velocity and average maximum ZDR in the arc suggests a positive correlation. As a result, the propagation of the ZDR arc may have forecast implications for severe weather operations, especially with the current upgrade of WSR-88D to polarimetric capability. If forecasters are able to diagnose whether a vortex is intensifying or weakening through the use of the ZDR arc, a more accurate warning could be provided.
  • A ρhv minimum developed within a few minutes, which gave evidence of lofted debris. In this particular case, two distinct ρhv minima were detected. One ρhv minimum was associated with a vortex, but the second ρhv minimum likely was associated with straight-line winds. Once lofted debris is suspected, an enhanced severe thunderstorm and/or tornado warning could be issued. Also, unlike tornadoes, straight-line winds are not easily observed visually. This detection of debris would be even more useful at nighttime when storm spotters are less available.

These results demonstrate the potential information that high-resolution polarimetric radar data can give in the detection and short-range prediction of mesovortices embedded within QLCS structures. In essence, the microphysical properties implied may give insight into the kinematic evolution. Further observational studies will need to be done with more case studies to determine if these results will be applicable to other QLCS events.

Modeling studies can also be conducted using this dataset to gain additional insight into the dynamics and formation of QLCS mesovortices. High-resolution polarimetric data assimilated into a model should give the model a better microphysical parameterization when compared to lower-resolution nonpolarimetric data. Many studies (e.g., Gilmore et al. 2004; van den Heever and Colton 2004; Snook and Xue 2008) have shown that the structure and dynamics of simulated convective systems are highly sensitive to the microphysical parameterizations. Therefore, it is believed the assimilation of this dataset should allow for a better understanding of how the microphysical evolution is linked to the dynamical evolution in QLCS mesovortices. In particular, an inquiry into what role the linear forcing may have had on the vortex generation could be conducted. This is an important consideration since the mesovortices were generated in the developing stages of the QLCS. Two possible mechanisms for vortex generation that could be investigated through modeling studies are barotropic instabilities similar to the Carbone (1982) case study and nonoccluding cyclic mesocyclogenesis (NOCM; Adlerman and Drogemeier 2005). The observed radar pattern compared to a schematic of NOCM (Fig. 19) indicates this theory is a possibility; however, due to a lack of complete observations, a modeling study may provide the best method for validating this hypothesis.

Fig. 19.
Fig. 19.

Schematic of the approximate surface patterns for nonoccluding cyclic mesocyclogenesis. Scalloped black line indicates the surface cold-pool boundary. Red indicates area of vorticity maxima. Light blue indicates updraft areas, and dark blue indicates downdraft areas. Single yellow contour indicates the boundary of the rain area. [From Adlerman and Drogemeier (2005).]

Citation: Weather and Forecasting 27, 6; 10.1175/WAF-D-11-00135.1

Acknowledgments

The authors thank Drs. Michael Biggerstaff and Frederick Carr of the School of Meteorology for providing feedback while this work was in the form of the first author’s M.S. thesis. The authors would also like to thank the two anonymous reviewers who provided constructive feedback on the original version of this manuscript. This work is supported by the Engineering Research Centers Program of the National Science Foundation under NSF Award 0313747. Any opinions, findings, conclusions, or recommendations expressed in this material are those of the authors and do not necessarily reflect those of the National Science Foundation.

REFERENCES

  • Adlerman, E. J., , and Drogemeier K. K. , 2005: The dependence of numerically simulated cyclic mesocyclogenesis upon environmental vertical wind shear. Mon. Wea. Rev., 133, 35953623.

    • Search Google Scholar
    • Export Citation
  • Atkins, N. T., , Arnott J. M. , , Przybylinski R. W. , , Wolf R. A. , , and Ketcham B. D. , 2004: Vortex structure and evolution within bow echoes. Part I: Single-Doppler and damage analysis of the 29 June 1998 derecho. Mon. Wea. Rev., 132, 22242242.

    • Search Google Scholar
    • Export Citation
  • Bharadwaj, N., , and Chandrasekar V. , 2005: Waveform design for CASA X-band radars. Preprints, 32nd Conf. on Radar Meteorology, Albuquerque, NM, Amer. Meteor. Soc., P10R.13. [Available online at http://ams.confex.com/ams/pdfpapers/96347.pdf.]

  • Bluestein, H. B., 1993: Observations and Theory of Weather Systems. Vol. 2, Synoptic–Dynamic Meteorology in Midlatitudes, Oxford University Press, 594 pp.

  • Bluestein, H. B., , and Jain M. H. , 1985: Formation of mesoscale lines of precipitation: Severe squall lines in Oklahoma during the spring. J. Atmos. Sci., 42, 17111732.

    • Search Google Scholar
    • Export Citation
  • Bluestein, H. B., , and Parker S. S. , 1993: Modes of isolated, severe convective storm formation along the dryline. Mon. Wea. Rev., 121, 13541372.

    • Search Google Scholar
    • Export Citation
  • Bluestein, H. B., , French M. M. , , Tanamachi R. L. , , Frasier S. , , Hardwick K. , , Junyent F. , , and Pazmany A. , 2007: Close-range observations of tornadoes in supercells made with a dual-polarization, X-band, mobile Doppler radar. Mon. Wea. Rev., 135, 15221543.

    • Search Google Scholar
    • Export Citation
  • Bringi, V. N., , and Chandrasekar V. , 2001: Polarimetric Doppler Weather Radar: Principles and Applications. Cambridge University Press, 636 pp.

  • Brock, F. V., , Crawford K. C. , , Elliott R. L. , , Cuperus G. W. , , Stadler S. J. , , Johnson H. L. , , and Eilts M. D. , 1995: The Oklahoma Mesonet: A technical overview. J. Atmos. Oceanic Technol., 12, 519.

    • Search Google Scholar
    • Export Citation
  • Carbone, R. E., 1982: A severe frontal rainband. Part I: Stormwide hydrodynamic structure. J. Atmos. Sci., 39, 258279.

  • Davies-Jones, R. P., , Burgess D. W. , , and Foster M. , 1990: Test of helicity as a forecast parameter. Preprints, 16th Conf. on Severe Local Storms, Kananaskis Park, AB, Canada, Amer. Meteor. Soc., 588–592.

  • Gilmore, M. S., , Straka J. M. , , and Rasmussen E. N. , 2004: Precipitation uncertainty due to variations in precipitation particle parameters within a simple microphysics scheme. Mon. Wea. Rev., 132, 26102627.

    • Search Google Scholar
    • Export Citation
  • Junyent, F., , Chandrasekar V. , , McLaughlin D. , , Insanic E. , , and Bharadwaj N. , 2010: The CASA Integrated Project 1 networked radar system. J. Atmos. Oceanic Technol., 27, 6178.

    • Search Google Scholar
    • Export Citation
  • Kumjian, M. R., , and Ryzhkov A. V. , 2008: Polarimetric signatures in supercell thunderstorms. J. Appl. Meteor., 47, 19401961.

  • Kumjian, M. R., , and Ryzhkov A. V. , 2009: Storm-relative helicity revealed from polarimetric radar measurements. J. Atmos. Sci., 66, 667685.

    • Search Google Scholar
    • Export Citation
  • Liu, Y., , and Bringi V. N. , 2006: Improved rain attenuation correction algorithms for radar reflectivity and differential reflectivity with adaptation to drop shape model variation. Proc. Int. Geoscience and Remote Sensing Symp., Denver, CO, IEEE, 1910–1913.

  • McLaughlin, D., and Coauthors, 2009: Short-wavelength technology and the potential for distributed networks of small radar systems. Bull. Amer. Meteor. Soc., 90, 17971817.

    • Search Google Scholar
    • Export Citation
  • McPherson, R. A., and Coauthors, 2007: Statewide monitoring of the mesoscale environment: A technical update on the Oklahoma Mesonet. J. Atmos. Oceanic Technol., 24, 301321.

    • Search Google Scholar
    • Export Citation
  • NWS Norman WFO, cited 2011: Storm data and unusual weather phenomena—April 2010. [Available online at http://www.srh.noaa.gov/images/oun/pdf/stormdata/oun201004.pdf.]

  • Przybylinski, R. W., 1995: The bow echo: Observations, numerical simulations, and severe weather detection methods. Wea. Forecasting, 10, 203218.

    • Search Google Scholar
    • Export Citation
  • Przybylinski, R. W., , Schmocker G. K. , , and Lin Y.-J. , 2000: A study of storm and vortex morphology during the intensifying stage of severe wind mesoscale convective systems. Preprints, 20th Conf. on Severe Local Storms, Orlando, FL, Amer. Meteor. Soc., 173–176.

  • Romine, G. S., , Burgess D. W. , , and Wilhelmson R. B. , 2008: A dual-polarization-radar-based assessment of the 8 May 2003 Oklahoma City area tornadic supercell. Mon. Wea. Rev., 136, 28492870.

    • Search Google Scholar
    • Export Citation
  • Ryzhkov, A. V., , Zrnic D. S. , , Hubbert J. C. , , Bringi V. N. , , Vivekanandan J. , , and Brandes E. A. , 2002: Polarimetric radar observations and interpretation of co-cross-polar correlation coefficients. J. Atmos. Oceanic Technol., 19, 340354.

    • Search Google Scholar
    • Export Citation
  • Smith, B. T., , Thompson R. L. , , Grams J. S. , , and Broyles C. , 2010: Climatology of convective modes associated with significant severe thunderstorms in the contiguous United States. Preprints, 25th Conf. on Severe Local Storms, Denver, CO, Amer. Meteor. Soc., 16B.6. [Available online at http://ams.confex.com/ams/pdfpapers/175727.pdf.]

  • Snook, N., , and Xue M. , 2008: Effects of microphysical drop size distribution on tornadogenesis in supercell thunderstorms. Geophys. Res. Lett., 35, L24803, doi:10.1029/2008GL035866.

    • Search Google Scholar
    • Export Citation
  • Snyder, J. C., , Bluestein H. B. , , Zhang G. , , and Frasier S. J. , 2010: Attenuation correction and hydrometeor classification of high-resolution, X-band, dual-polarized mobile radar measurements in severe convective storms. J. Atmos. Oceanic Technol., 27, 19792001.

    • Search Google Scholar
    • Export Citation
  • Trabal, J. M., , Chandrasekar V. , , Gorgucci E. , , and McLaughlin D. J. , 2009: Differential reflectivity (ZDR) calibration for CASA Radar Network using properties of the observed medium. Proc. Int. Geoscience and Remote Sensing Symp., Cape Town, South Africa, IEEE, II-960–II-963.

  • Trapp, R. J., , Tessendorf S. A. , , Godfrey E. S. , , and Brooks H. E. , 2005: Tornadoes from squall lines and bow echoes. Part I: Climatological distribution. Wea. Forecasting, 20, 2334.

    • Search Google Scholar
    • Export Citation
  • van den Heever, S. C., , and Cotton W. R. , 2004: The impact of hail size on simulated supercell storms, J. Atmos. Sci., 61, 15961609.

  • Wakimoto, R. M., , Murphey H. V. , , Davis C. A. , , and Atkins N. T. , 2006: High winds generated by bow echoes. Part II: The relationship between the mesovortices and damaging straightline winds. Mon. Wea. Rev., 134, 28132829.

    • Search Google Scholar
    • Export Citation
  • Weisman, M. L., , and Trapp R. J. , 2003: Low-level mesovortices within squall lines and bow echoes. Part I: Overview and sensitivity to environmental vertical wind shear. Mon. Wea. Rev., 131, 27792803.

    • Search Google Scholar
    • Export Citation
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