• Achtemeier, G. L., 1991: The use of insects as tracers for “clear-air” boundary-layer studies by Doppler radar. J. Atmos. Oceanic Technol., 8, 746765, https://doi.org/10.1175/1520-0426(1991)008<0746:TUOIAT>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ahijevych, D., G. H. Bryan, M. L. Weisman, S. B. Trier, C. A. Davis, and D. Dowell, 2006: Composite bow echo observed during BAMEX. 23rd Conf. on Severe Local Storms, St. Louis, MO, Amer. Meteor. Soc., 7.3, https://ams.confex.com/ams/23SLS/techprogram/paper_115151.htm.

  • Barnes, S. L., 1964: A technique for maximizing details in numerical weather map analysis. J. Appl. Meteor., 3, 396409, https://doi.org/10.1175/1520-0450(1964)003<0396:ATFMDI>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bechini, R., and V. Chandrasekar, 2015: A semisupervised robust hydrometeor classification method for dual-polarization radar applications. J. Atmos. Oceanic Technol., 32, 2247, https://doi.org/10.1175/JTECH-D-14-00097.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Benjamin, T., 1968: Gravity currents and related phenomena. J. Fluid Mech., 31, 209248, https://doi.org/10.1017/S0022112068000133.

  • Borque, P., E. Luke, and P. Kollias, 2016: On the unified estimation of turbulence eddy dissipation rate using Doppler cloud radars and lidars. J. Geophys. Res. Atmos., 120, 59725989, https://doi.org/10.1002/2015JD024543.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Brandes, E. A., 1977: Gust front evolution and tornado genesis as viewed by Doppler radar. J. Appl. Meteor., 16, 333338, https://doi.org/10.1175/1520-0450(1977)016<0333:GFEATG>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Brock, F. V., K. C. Crawford, R. L. Elliott, G. W. Cuperus, S. J. Stadler, H. L. Johnson, and M. D. Eilts, 1995: The Oklahoma mesonet: A technical overview. J. Atmos. Oceanic Technol., 12, 519, https://doi.org/10.1175/1520-0426(1995)012<0005:TOMATO>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bryan, G. H., and M. D. Parker, 2010: Observations of a squall line and its near environment using high-frequency rawinsonde launches during VORTEX2. Mon. Wea. Rev., 138, 40764097, https://doi.org/10.1175/2010MWR3359.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bryan, G. H., D. Ahijevych, C. Davis, S. Trier, and M. Weisman, 2005: Observations of cold pool properties in mesoscale convective systems during BAMEX. 11th Conf. on Mesoscale Processes/32nd Conf. on Radar Meteorology, Albuquerque, NM, Amer. Meteor. Soc., JP5J.12, https://ams.confex.com/ams/32Rad11Meso/techprogram/paper_96718.htm.

  • Byers, H. R., and R. R. Braham Jr., 1949: The Thunderstorm: Report of the Thunderstorm Project. U.S. Government Printing Office, 282 pp.

    • Search Google Scholar
    • Export Citation
  • Cazenave, F., M. Gosset, M. Kacou, M. Alcoba, E. Fontaine, C. Duroure, and B. Dolan, 2016: Characterization of hydrometeors in Sahelian convective systems with an X-band radar and comparison with in situ measurements. Part I: Sensitivity of polarimetric radar particle identification retrieval and case study evaluation. J. Appl. Meteor. Climatol., 55, 231249, https://doi.org/10.1175/JAMC-D-15-0013.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Charba, J., 1974: Application of gravity current model to analysis of squall-line gust front. Mon. Wea. Rev., 102, 140156, https://doi.org/10.1175/1520-0493(1974)102<0140:AOGCMT>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Cifelli, R., V. Chandrasekar, S. Lim, P. C. Kennedy, Y. Wang, and S. A. Rutledge, 2011: A new dual-polarization radar rainfall algorithm: Application in Colorado precipitation events. J. Atmos. Oceanic Technol., 28, 352364, https://doi.org/10.1175/2010JTECHA1488.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Coniglio, M. C., S. F. Corfidi, and J. S. Kain, 2011: Environment and early evolution of the 8 May 2009 derecho-producing convective system. Mon. Wea. Rev., 139, 10831102, https://doi.org/10.1175/2010MWR3413.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Dawson, D. T., M. Xue, J. A. Milbrandt, and M. K. Yau, 2010: Comparison of evaporation and cold pool development between single-moment and multimoment bulk microphysics schemes in idealized simulations of tornadic thunderstorms. Mon. Wea. Rev., 138, 11521171, https://doi.org/10.1175/2009MWR2956.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Dolan, B., S. A. Rutledge, S. Lim, V. Chandrasekar, and M. Thurai, 2013: A robust C-band hydrometeor identification algorithm and application to a long-term polarimetric radar dataset. J. Appl. Meteor. Climatol., 52, 21622186, https://doi.org/10.1175/JAMC-D-12-0275.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Doviak, R. J., and D. S. Zrnić, 1993: Doppler Radar and Weather Observations. 2nd ed. Academic Press, 562 pp.

  • Duvernoy, J., and J. L. Gaumet, 1996: Precipitating hydrometeor characterization by a CW Doppler radar. J. Atmos. Oceanic Technol., 13, 620629, https://doi.org/10.1175/1520-0426(1996)013<0620:PHCBAC>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Engerer, N. A., D. J. Stensrud, and M. C. Coniglio, 2008: Surface characteristics of observed cold pools. Mon. Wea. Rev., 136, 48394849, https://doi.org/10.1175/2008MWR2528.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Erlingis, J. M., and A. P. Barros, 2014: A study of the role of daytime land–atmosphere interactions on nocturnal convective activity in the Southern Great Plains during CLASIC. J. Hydrometeor., 15, 19321953, https://doi.org/10.1175/JHM-D-14-0016.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Feng, Z., S. Hagos, A. K. Rowe, C. D. Burleyson, M. N. Martini, and S. P. de Szoeke, 2015: Mechanisms of convective cloud organization by cold pools over tropical warm ocean during the AMIE/DYNAMO field campaign. J. Adv. Model. Earth Syst., 7, 357381, https://doi.org/10.1002/2014MS000384.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Geerts, B., and et al. , 2017: The 2015 Plains Elevated Convection at Night Field Project. Bull. Amer. Meteor. Soc., 98, 767786, https://doi.org/10.1175/BAMS-D-15-00257.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gentine, P., A. Garelli, S.-B. Park, J. Nie, G. Torri, and Z. Kuang, 2016: Role of surface heat fluxes underneath cold pools. Geophys. Res. Lett., 43, 874883, https://doi.org/10.1002/2015GL067262.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gilmore, M. S., J. M. Straka, and E. N. Rasmussen, 2004: Precipitation uncertainty due to variations in precipitation particle parameters within a simple microphysics scheme. Mon. Wea. Rev., 132, 26102627, https://doi.org/10.1175/MWR2810.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Grant, L. D., and S. C. van den Heever, 2014: Microphysical and dynamical characteristics of low-precipitation and classic supercells. J. Atmos. Sci., 71, 26042624, https://doi.org/10.1175/JAS-D-13-0261.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Grant, L. D., and S. C. van den Heever, 2016: Cold pool dissipation. J. Geophys. Res. Atmos., 121, 11381155, https://doi.org/10.1002/2015JD023813.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Grant, L. D., and S. C. van den Heever, 2018: Cold pool-land surface interactions in a dry continental environment. J. Adv. Model. Earth Syst., 10, 15131526, https://doi.org/10.1029/2018MS001323.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hardin, J., D. Nelson, I. Lindenmaier, B. Isom, S. Collis, A. Matthews, and N. Bharadwaj, 1990: ARM: C-SAPR Range Height Indicator scan. Atmospheric Radiation Measurement (ARM) Climate Research Facility Data Archive, accessed 1 January 2017, https://doi.org/10.5439/1025168.

    • Crossref
    • Export Citation
  • Hitchcock, S. M., R. S. Schumacher, G. R. Herman, M. C. Coniglio, M. D. Parker, and C. L. Ziegler, 2019: Evolution of pre- and postconvective environmental profiles from mesoscale convective systems during PECAN. Mon. Wea. Rev., 147, 23292354, https://doi.org/10.1175/MWR-D-18-0231.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Jensen, M. P., and et al. , 2015: The Midlatitude Continental Convective Clouds Experiment (MC3E) sounding network: Operations, processing and analysis. Atmos. Meas. Tech., 8, 421434, https://doi.org/10.5194/amt-8-421-2015.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Jensen, M. P., and et al. , 2016: The Midlatitude Continental Convective Clouds Experiment (MC3E). Bull. Amer. Meteor. Soc., 97, 16671686, https://doi.org/10.1175/BAMS-D-14-00228.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Johnson, D. E., P. K. Wang, and J. M. Straka, 1993: Numerical simulations of the 2 August 1981 CCOPE supercell storm with and without ice microphysics. J. Appl. Meteor., 32, 745759, https://doi.org/10.1175/1520-0450(1993)032<0745:NSOTAC>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lang, T. J., S. A. Rutledge, and J. L. Smith, 2004: Observations of quasi-symmetric echo patterns in clear air with the CSU-CHILL polarimetric radar. J. Atmos. Oceanic Technol., 21, 11821189, https://doi.org/10.1175/1520-0426(2004)021<1182:OOQEPI>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lee, J. T., J. Stokes, Y. Sasaki, and T. Baxter, 1978: Thunderstorm Oust Fronts—Observations and modeling. FAA Rep. FAA-RD-78-145, Dept. of Transportation, 100 pp.

  • Lesage, A. T., 2012: Frontal passage and cold pool detection using Oklahoma Mesonet observations. M.S. thesis, Dept. of Atmospheric Sciences, University of Utah, 76 pp., http://home.chpc.utah.edu/~u0652833/Lesage_thesis/Lesage_Thesis.pdf.

  • Lhermitte, R. M., 1963: Motions of scatterers and the variance of the mean intensity of weather radar signals. Rep. SRRC-RR-63-57, Sperry Rand Research Center, 43 pp.

  • Linden, P. F., and J. E. Simpson, 1985: Microbursts: A hazard for aircraft. Nature, 317, 601602, https://doi.org/10.1038/317601a0.

  • Liu, H., and V. Chandrasekar, 2000: Classification of hydrometeors based on polarimetric radar measurements: Development of fuzzy logic and neuro-fuzzy systems, and in situ verification. J. Atmos. Oceanic Technol., 17, 140164, https://doi.org/10.1175/1520-0426(2000)017<0140:COHBOP>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Maddox, R. A., 1980: An objective technique for separating macroscale and mesoscale features in meteorological data. Mon. Wea. Rev., 108, 11081121, https://doi.org/10.1175/1520-0493(1980)108<1108:AOTFSM>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Mallinson, H. M., and S. G. Lasher-Trapp, 2019: An investigation of hydrometeor latent cooling upon convective cold pool formation, sustainment, and properties. Mon. Wea. Rev., 147, 32053222, https://doi.org/10.1175/MWR-D-18-0382.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Marion, G. R., and R. J. Trapp, 2019: The dynamical coupling of convective updrafts, downdrafts, and cold pools in simulated supercell thunderstorms. J. Geophys. Res. Atmos., 124, 664683, https://doi.org/10.1029/2018JD029055.

    • Crossref
    • 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, https://doi.org/10.1175/BAMS-D-11-00218.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • McCumber, M., W. Tao, J. Simpson, R. Penc, and S. Soong, 1991: Comparison of ice-phase microphysical parameterization schemes using numerical simulations of tropical convection. J. Appl. Meteor., 30, 9851004, https://doi.org/10.1175/1520-0450-30.7.985.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • McPherson, R. A., and et al. , 2007: Statewide monitoring of the mesoscale environment: A technical update on the Oklahoma Mesonet. J. Atmos. Oceanic Technol., 24, 301321, https://doi.org/10.1175/JTECH1976.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Melnikov, V. M., R. J. Doviak, D. S. Zrnić, and D. J. Stensrud, 2011: Mapping Bragg scatter with a polarimetric WSR-88D. J. Atmos. Oceanic Technol., 28, 12731285, https://doi.org/10.1175/JTECH-D-10-05048.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Mueller, E. G., and R. P. Larkin, 1985: Insects observed using dual-polarization radar. J. Atmos. Oceanic Technol., 2, 4954, https://doi.org/10.1175/1520-0426(1985)002<0049:IOUDPR>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • North, K., M. Oue, P. Kollias, S. E. Giangrande, S. M. Collis, and C. K. Potvin, 2017: Vertical air motion retrievals in deep convective clouds using the ARM scanning radar network in Oklahoma during MC3E. Atmos. Meas. Tech., 10, 27852806, https://doi.org/10.5194/amt-10-2785-2017.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • NWS, 1991: NOAA Next Generation Radar (NEXRAD) Level II base data. NOAA/NCEI, accessed 1 January 2017, https://doi.org/10.7289/V5W9574V.

    • Crossref
    • Export Citation
  • Ogura, Y., and Y. Chen, 1977: A life history of an intense mesoscale convective storm in Oklahoma. J. Atmos. Sci., 34, 14581476, https://doi.org/10.1175/1520-0469(1977)034<1458:ALHOAI>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ogura, Y., and M.-T. Liou, 1980: The structure of a midlatitude squall line: A case study. J. Atmos. Sci., 37, 553567, https://doi.org/10.1175/1520-0469(1980)037<0553:TSOAMS>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Oue, M., P. Kollias, A. Shapiro, A. Tatarevic, and T. Matsui, 2019: Investigation of observational error sources in multi-Doppler-radar three-dimensional variational vertical air motion retrievals. Atmos. Meas. Tech., 12, 19992018, https://doi.org/10.5194/amt-12-1999-2019.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Oye, R., C. Mueller, and S. Smith, 1995: Software for radar translation, visualization, editing, and interpolation. Preprints, 27th Conf. on Radar Meteorology, Vail, CO, Amer. Meteor. Soc., 359361.

  • Park, S.-U., and D. N. Sikdar, 1982: Evolution of the dynamics and thermodynamics of a mesoscale convective system: A case study. Mon. Wea. Rev., 110, 10241040, https://doi.org/10.1175/1520-0493(1982)110<1024:EOTDAT>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Parker, M. D., and R. H. Johnson, 2004: Simulated convective lines with leading precipitation. Part II: Evolution and maintenance. J. Atmos. Sci., 61, 16561673, https://doi.org/10.1175/1520-0469(2004)061<1656:SCLWLP>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Potvin, C. K., D. Betten, L. J. Wicker, K. L. Elmore, and M. I. Biggerstaff, 2012: 3DVAR versus traditional dual-Doppler wind retrievals of a simulated supercell thunderstorm. Mon. Wea. Rev., 140, 34873494, https://doi.org/10.1175/MWR-D-12-00063.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Pritchard, M. S., M. W. Moncrieff, and R. C. Somerville, 2011: Orogenic propagating precipitation systems over the United States in a global climate model with embedded explicit convection. J. Atmos. Sci., 68, 18211840, https://doi.org/10.1175/2011JAS3699.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Purdom, J. F. W., 1976: Some uses of high-resolution GOES imagery in the mesoscale forecasting of convection and its behavior. Mon. Wea. Rev., 104, 14741483, https://doi.org/10.1175/1520-0493(1976)104<1474:SUOHRG>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Randall, D. A., and et al. , 2003: Confronting models with data: The GEWEX cloud systems study. Bull. Amer. Meteor. Soc., 84, 455470, https://doi.org/10.1175/BAMS-84-4-455.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Rauber, R. M., and S. W. Nesbitt, 2018: Radar Meteorology: A First Course. Wiley, 488 pp.

  • Rotunno, R., J. B. Klemp, and M. L. Weisman, 1988: A theory for strong, long-lived squall lines. J. Atmos. Sci., 45, 463485, https://doi.org/10.1175/1520-0469(1988)045<0463:ATFSLL>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Roux, F., 1988: The West African squall line observed on 23 June 1981 during COPT81: Kinematics and thermodynamics of the convective region. J. Atmos. Sci., 45, 406426, https://doi.org/10.1175/1520-0469(1988)045<0406:TWASLO>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Russell, R. W., and J. W. Wilson, 1997: Radar-observed fine lines in the optically clear boundary layer: Reflectivity contributions from aerial plankton and its predators. Bound.-Layer Meteor., 82, 235262, https://doi.org/10.1023/A:1000237431851.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ryzhkov, A. V., T. J. Schuur, D. W. Burgess, P. L. Heinselman, S. E. Giangrande, and D. S. Zrnić, 2005: The Joint Polarization Experiment: Polarimetric rainfall measurements and hydrometeor classification. Bull. Amer. Meteor. Soc., 86, 809824, https://doi.org/10.1175/BAMS-86-6-809.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sanders, F., and K. A. Emanuel, 1977: The momentum budget and temporal evolution of a mesoscale convective system. J. Atmos. Sci., 34, 322330, https://doi.org/10.1175/1520-0469(1977)034<0322:TMBATE>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Simpson, J. E., 1969: A comparison between laboratory and atmospheric density currents. Quart. J. Roy. Meteor. Soc., 95, 758765, https://doi.org/10.1002/qj.49709540609.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Snively, D. V., and W. A. Gallus, 2014: Prediction of convective morphology in near-cloud-permitting WRF Model simulations. Wea. Forecasting, 29, 130149, https://doi.org/10.1175/WAF-D-13-00047.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Straka, J. M., D. S. Zrnić, and A. V. Ryzhkov, 2000: Bulk hydrometeor classification and quantification using polarimetric radar data: Synthesis of relations. J. Appl. Meteor., 39, 13411372, https://doi.org/10.1175/1520-0450(2000)039<1341:BHCAQU>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Thorpe, A. J., M. J. Miller, and M. W. Moncrieff, 1982: Two-dimensional convection in non-constant shear: A model of mid-latitude squall lines. Quart. J. Roy. Meteor. Soc., 108, 739762, https://doi.org/10.1002/qj.49710845802.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Trapp, R. J., and C. A. Doswell, 2000: Radar data objective analysis. J. Atmos. Oceanic Technol., 17, 105120, https://doi.org/10.1175/1520-0426(2000)017<0105:RDOA>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Trapp, R. J., and J. M. Woznicki, 2017: Convectively induced stabilizations and subsequent recovery with supercell thunderstorms during the Mesoscale Predictability Experiment (MPEX). Mon. Wea. Rev., 145, 17391754, https://doi.org/10.1175/MWR-D-16-0266.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Troxel, S. W., and R. L. Delanoy, 1994: Machine intelligent approach to automated gust-front detection for Doppler weather radars. Proc. SPIE, 2220, https://doi.org/10.1117/12.179602.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Troyan, D., S. Giangrande, and T. Toto, 1996: Merged Sounding (MERGESONDE1MACE): 23–24 May 2011. Southern Great Plains (SGP) Central Facility, Lamont, OK (C1), Atmospheric Radiation Measurement (ARM) Climate Research Facility Data Archive, Oak Ridge, TN, accessed 1 January 2017, https://doi.org/10.5439/1034922.

    • Crossref
    • Export Citation
  • van den Heever, S. C., and W. R. Cotton, 2004: The impact of hail size on simulated supercell storms. J. Atmos. Sci., 61, 15961609, https://doi.org/10.1175/1520-0469(2004)061<1596:TIOHSO>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • von Kármán, T., 1940: The engineer grapples with nonlinear problems. Bull. Amer. Math. Soc., 46, 615683, https://doi.org/10.1090/S0002-9904-1940-07266-0.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wakimoto, R. M., 1982: The life cycle of thunderstorm gust fronts as viewed by Doppler radar and rawinsonde data. Mon. Wea. Rev., 110, 10601082, https://doi.org/10.1175/1520-0493(1982)110<1060:TLCOTG>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wilson, J. W., and W. E. Schreiber, 1986: Initiation of convective storms at radar-observed boundary layer convergence lines. Mon. Wea. Rev., 114, 25162536, https://doi.org/10.1175/1520-0493(1986)114<2516:IOCSAR>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wilson, J. W., T. M. Weckwerth, J. Vivekanandan, R. M. Wakimoto, and R. W. Russel, 1994: Boundary layer clear-air radar echoes: Origin of echoes and accuracy of deriving winds. J. Atmos. Oceanic Technol., 11, 11841206, https://doi.org/10.1175/1520-0426(1994)011<1184:BLCARE>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Xue, M., 2002: Density currents in shear flows: Effects of rigid lid and cold-pool internal circulation, and application to squall line dynamics. Quart. J. Roy. Meteor. Soc., 128, 4773, https://doi.org/10.1256/00359000260498789.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zipser, E. J., 1977: Mesoscale and convective-scale downdrafts as distinct components of squall-line structure. Mon. Wea. Rev., 105, 15681589, https://doi.org/10.1175/1520-0493(1977)105<1568:MACDAD>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zrnić, D. S., and J. T. Lee, 1983: Investigation of the detectability and lifetime of gust fronts and other weather hazards to aircraft. FAA Rep. DOT/FAA/PM-83/33, 58 pp.

  • Zrnić, D. S., and A. V. Ryzhkov, 1998: Observations of insects and birds with a polarimetric radar. IEEE Trans. Geosci. Remote Sens., 36, 661668, https://doi.org/10.1109/36.662746.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • View in gallery

    Location of mesonet stations (red dots), C-SAPR RHI scans (dotted black line), 3DVAR domain (black and white dashed square), soundings (blue star), NEXRAD network (white triangles), and NEXRAD radius of influence of Doppler and polarimetric radars (black and purple rings, respectively).

  • View in gallery

    Height of the beam axis as a function of distance from the radar for the KVNX radar scan strategy. Each scan strategy elevation angle is shown at the border of the plot with the respective color. The estimated height of the LECP (hLECP) and its uncertainty due to the radar beamwidth (ΔhLECP) at 50 km from the radar location (rLECP) are also shown in the figure.

  • View in gallery

    (a)–(d) Topography (shaded, bottom color bar) and reflectivity composite at 500 m above ground overlaid (shaded, top color bar) from combined KICT, KVNX, KTLX, KDFR, and KINX radars (shaded) every 2 h from 2000 UTC 23 May to 0200 UTC 24 May. Location of all the radars used in this work is also shown (red dots). White arrows denote the location of LECP. Black dashed line in (b) denotes the location of the C-SAPR RHI scan at the closest time of the scan used for validating the 3DVAR results shown in the appendix.

  • View in gallery

    (a)–(f) Interpolated temperature field from mesonet stations (shaded), wind at each mesonet station (vector), 30 and 50 dBZ reflectivity at ground level (red dashed contour), 15 dBZ reflectivity at 10 km height (black dashed contour), and location of the leading edge of the cold pool (green lines) hourly from 2000 UTC 23 May to 0100 UTC 24 May.

  • View in gallery

    Normalized probability distribution functions of (a) spectrum width (SW), (b) reflectivity (Ze), (c) differential reflectivity (ZDR), and (d) correlation coefficient (ρhv), for all the observations sampled by the KVNX radar (blue) and for the LECP subset (orange).

  • View in gallery

    Position of the LECP extracted from the lowest elevation scan of the KVNX radar every 30 min. Transect connecting mesonet stations (labeled) is color-coded to correspond with the time series for each station shown in Fig. 7. Yellow stars denote location of the maximum height of the LECP at each time.

  • View in gallery

    Time series of (top) temperature, (middle) wind speed, and (bottom) wind direction from Cherokee [CHER (black)], Alva [ALV2 (green)], Freedom [FREE (red)], and Buffalo [BUFF (blue)] mesonet stations. Location of each station is marked in Fig. 6. Time of the temperature decrease associated with passage of the cold pool leading edge is denoted by vertical line (colors black, green, red, and blue correspond to the color labels for each station).

  • View in gallery

    Time series of LECP height maxima (solid blue line) and mean (solid green line) from 2100 UTC 23 May to 0100 UTC 24 May. Shaded regions denote their uncertainty as defined in section 3c. The number at each time corresponds to the number of gates where the cold pool was detected by the KVNX radar.

  • View in gallery

    (a) Location of the LECP echoes as detected by the KVNX radar, (b) highest elevation angle where the cold pool signature was detected, (c) height of the LECP estimated following the 4/3 Earth radius approximation, and (d) highest elevation angle that the KVNX radar should have detected the LECP signature if it had a uniform height equal to the maximum height detected at 2230 UTC 23 May. Rings denote the distance from the radar every 10 km and the black triangle indicates the radar location.

  • View in gallery

    Vertical profiles of (a) temperature, (b) wind speed, and (c) wind direction from the Pratt, KS, sonde launched at 2330 UTC 23 May (blue) and at 0230 UTC 24 May (red).

  • View in gallery

    Area covered by hail (shaded) as a function of time and height, and time series of area covered by downdraft and updraft (blue dashed and solid lines, respectively) and downdraft and updraft maximum intensity (light blue dashed and solid lines, respectively) at 5 km height over the domain shown in Fig. 1.

  • View in gallery

    As in Fig. 11, but for graupel area.

  • View in gallery

    Doppler velocity from the C-SAPR RHI measurements at 2330 UTC 23–24 May 2011 (shaded and gray contours) and estimated radial velocity from the 3DVAR retrieval in the direction of the C-SAPR RHI scan (black contours).

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Observational Study of the Thermodynamics and Morphological Characteristics of a Midlatitude Continental Cold Pool Event

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  • 1 Department of Atmospheric Sciences, University of Illinois at Urbana–Champaign, Urbana, Illinois
  • | 2 School of Marine and Atmospheric Sciences, Stony Brook University, State University of New York, Stony Brook, New York
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Abstract

Convectively generated cold pools are important to the Earth system as they exert strong controls on deep convective-storm initiation, intensity, and life cycle. Despite their importance, efforts to introduce such cold pool controls into weather and climate models lack guidance and/or physical constraints from cold pool observations. This work presents a detailed, purely observational analysis of a cold pool event that took place on 23–24 May 2011 in north-central Oklahoma. The characteristics of the cold pool, and the spatiotemporal evolution of the hydrometeors and dynamics in the proximity of the cold pool, are studied with high-resolution observations. The unprecedented dataset used in this work to study cold pool characteristics includes an enhanced network of surface weather stations, a high-temporal-frequency sounding array, and the NEXRAD and Atmospheric Radiation Measurement (ARM) Southern Great Plains radar networks. The potential use of NEXRAD surveillance scans to estimate height and propagation speed of the leading edge of the cold pool (LECP) is presented in this work. Manual identification and tracking of the LECP from NEXRAD imagery shows a spatial and temporal heterogeneity of the LECP properties. Surprisingly, over its detected life cycle, the LECP speed remains almost constant, even though the strength of the cold pool diminishes in time and its height varies. Radar analysis shows that pulses of graupel and hail within downdrafts in the convective system generating the cold pool appeared to be related to temporary increases in the LECP height.

© 2020 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Paloma Borque, paloma@illinois.edu

Abstract

Convectively generated cold pools are important to the Earth system as they exert strong controls on deep convective-storm initiation, intensity, and life cycle. Despite their importance, efforts to introduce such cold pool controls into weather and climate models lack guidance and/or physical constraints from cold pool observations. This work presents a detailed, purely observational analysis of a cold pool event that took place on 23–24 May 2011 in north-central Oklahoma. The characteristics of the cold pool, and the spatiotemporal evolution of the hydrometeors and dynamics in the proximity of the cold pool, are studied with high-resolution observations. The unprecedented dataset used in this work to study cold pool characteristics includes an enhanced network of surface weather stations, a high-temporal-frequency sounding array, and the NEXRAD and Atmospheric Radiation Measurement (ARM) Southern Great Plains radar networks. The potential use of NEXRAD surveillance scans to estimate height and propagation speed of the leading edge of the cold pool (LECP) is presented in this work. Manual identification and tracking of the LECP from NEXRAD imagery shows a spatial and temporal heterogeneity of the LECP properties. Surprisingly, over its detected life cycle, the LECP speed remains almost constant, even though the strength of the cold pool diminishes in time and its height varies. Radar analysis shows that pulses of graupel and hail within downdrafts in the convective system generating the cold pool appeared to be related to temporary increases in the LECP height.

© 2020 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Paloma Borque, paloma@illinois.edu

1. Introduction

Surface-based, convectively generated cold pools are one of the most prominent low-level features of convective storms. Cold pools from first-generation storms exert strong controls on the initiation, intensity, and life cycle of second-generation storms, thus controlling cloud macrostructure and microstructure. Cold pools form via latently cooled convective-scale and mesoscale downdrafts that spread horizontally once they reach the surface underneath the storm (Zipser 1977). The cooler, denser outflow within the cold pool displaces the relatively warmer, less dense environmental air up and over the outflow boundary. This intrusion of denser air has been identified as gravity, or density, currents (e.g., von Kármán 1940; Benjamin 1968; Simpson 1969; Charba 1974). The leading edge of the cold pool (LECP) is the boundary between the horizontally moving outflow and the surrounding environmental air. Cold pools are important to cloud formation, organization, and maintenance mechanisms for a number of reasons. First, they are a key mechanism for maintaining and reinvigorating mesoscale convective systems (MCSs) and influence their organization and structure (e.g., Thorpe et al. 1982; Rotunno et al. 1988; Parker and Johnson 2004). Second, as environmental air is lifted over the cold pool, the LECP can potentially trigger new convection (e.g., Byers and Braham 1949; Purdom 1976; Wilson and Schreiber 1986; Feng et al. 2015; Grant and van den Heever 2014). Third, quasi-stationary storms can occur where there is a balance between the environment flow and the cold pool motion, leading to longer periods of intense rainfall on a fixed location and thus to high probability of flash flood events (e.g., Maddox 1980). Fourthly, they represent a severe hazard for aviation as the LECP is likely to be associated with intense wind gusts (e.g., Zrnić and Lee 1983; Linden and Simpson 1985; Troxel and Delanoy 1994). Fifthly, cold pools modulate land–atmosphere and ocean–atmosphere exchanges of heat, moisture, and momentum within them, which can exert strong controls on planetary boundary layer conditions (e.g., Gentine et al. 2016; Grant and van den Heever 2016; Grant and van den Heever 2018). Finally, surface-based convection is likely suppressed over the area influenced by the cold pool due to its strong stabilization effect in the lower level of the atmosphere (e.g., Trapp and Woznicki 2017). Thus, it is important to understand the mechanisms governing cold pool dynamics and to be able to represent them in weather and climate models.

Over the past decades, several numerical studies aimed to understand the characteristics and governing mechanisms associated with cold pools (e.g., von Kármán 1940; Benjamin 1968). The importance of representing convective cold pools has also been recognized by developers of multiscale modeling frameworks (e.g., Randall et al. 2003; Pritchard et al. 2011), as well as by the growing community of users of convection-permitting models (e.g., Erlingis and Barros 2014; Snively and Gallus 2014). Numerous studies showed that latent cooling by particles melting, sublimating, or evaporating is likely to strengthen the downdrafts, and thus affects the convective cool outflow near the surface (e.g., McCumber et al. 1991; van den Heever and Cotton 2004). However, the strong coupling between the updraft, downdraft, and surface-based cool outflow as shown recently in an idealized framework by Marion and Trapp (2019) is not yet well represented in the weather and climate prediction models, especially in those models in which convection is parameterized. Questions remain regarding the microphysical processes affecting the dynamical processes that are most important for determining cold pool horizontal extent, depth, and strength. In particular, some disagreement still remains on whether cold pool strength and size is predominantly influenced by graupel, hail, or rain processes (e.g., Johnson et al. 1993; Gilmore et al. 2004; van den Heever and Cotton 2004; Dawson et al. 2010; Mallinson and Lasher-Trapp 2019). However, and despite their importance, numerical modeling studies are often done in absence of guidance and/or physical constraints provided by detailed observational datasets. A proper characterization of cold pools and their impact on convective system life cycle from observations is yet to be accomplished. This is in part because the range of scales over which cloud processes occur presents significant challenges for any observational network that needs to be able to resolve and properly sample both mesoscale and microscale processes during the entire life cycle of cold pools.

The near-surface structure of cold pools can be observed from surface measurements. Engerer et al. (2008) analyzed high-temporal-resolution observations from the Oklahoma Mesonet during the warm season and studied the changes in surface properties (temperature, surface pressure, wind speed, and direction) given by the passage of a cold pool. They noted that the vertical extent of cold pools remained largely unknown. Radiosonde information is usually used to observe the vertical dependency of cold pool properties (e.g., Wakimoto 1982; Bryan and Parker 2010). Owing to the high temporal (on order of minutes) and spatial (on order of kilometers) variability in cold pools, targeted data collection is needed to resolve detailed vertical structures of cold pools. Examples of such observations, either serendipitous or by design, are the Northern Illinois Meteorological Research On Downburst (NIMROD) project (Wakimoto 1982), the Thunderstorm Project in southwest Ohio (Byers and Braham 1949), several special field experiments from the National Severe Storms Laboratory (Sanders and Emanuel 1977; Ogura and Chen 1977; Ogura and Liou 1980; Park and Sikdar 1982), the Bow Echo and Mesoscale Convective Vortex Experiment (BAMEX) (Bryan et al. 2005; Ahijevych et al. 2006), the Second Verification of the Origins of Rotation in Tornadoes Experiment (VORTEX2) (Bryan and Parker 2010), and more recently the Plains Elevated Convection At Night (PECAN) field campaign (Geerts et al. 2017; Hitchcock et al. 2019). However, due to the lack of synergy between different observational networks or limited radar observations (e.g., low-spatiotemporal-resolution, limited high-quality polarimetric radar measurements), the detection and analysis of cold pool properties (intensity, height, and speed) throughout its life cycle have not been exhaustively studied from a purely observational stand point.

The work presented herein shows the advantages of using the synergistic observations collected during the Midlatitude Continental Convective Clouds Experiment (MC3E; Jensen et al. 2016) in combination with the routine observations that take place in Oklahoma. MC3E was a field campaign conducted in April–June 2011 and its goal was to provide a better characterization of convective cloud systems and their environment and to improve the rainfall estimates over land. For this, additional observations over central Oklahoma given by the deployment of more weather stations, radars, and soundings by the Atmospheric Radiation Measurement (ARM; Mather and Voyles 2013) program provided a higher temporal and spatial resolution to this already densely observed region. This enhanced observational network, complemented by the observations from the Next Generation Weather Radar (NEXRAD) network and the Oklahoma Mesonet (Brock et al. 1995; McPherson et al. 2007), allowed the detection and analysis of the intensity, height, and speed of the cold pool as it evolved. Here, a detailed and purely observational description and analysis of a cold pool event that took place in north-central Oklahoma is presented. The main goal of this study is to use this unprecedented dataset to study, and quantify where possible, the evolution of cold pool characteristics over a time period exceeding several hours. In particular, a new methodology is presented to estimate the height and speed of the leading edge of the cold pool from the NEXRAD surveillance scans. Given the vast amount of publicly available NEXRAD data over the United States, this work serves as a proof of concept for a technique to create a future extensive observational dataset of cold pool height and speed. Additionally, the analysis of the combined NEXRAD and MC3E observations provides a unique glimpse at the spatiotemporal coevolution of hydrometeors and kinematic structures (up/downdraft properties) in the proximity of the evolving cold pool.

2. Datasets

Observations presented herein correspond to a case study of convective systems and cold pools that took place on 23–24 May 2011 in north-central Oklahoma during MC3E. MC3E was the result of a collaborative effort between the National Aeronautics and Space Administration (NASA) and the U.S. Department of Energy (DOE) ARM program. The vast majority of the instrumentation for this field campaign was deployed around the ARM Southern Great Plains–Central Facility (SGP CF) (Fig. 1). ARM observations included radar deployments, surface weather stations that enhanced the already existing Oklahoma Mesonet, disdrometers, and a high temporal frequency sounding array. The case on 23–24 May 2011 studied herein represents the best case of a cold pool event that was observed by these deployed sensors over a period lasting more than 4 h during MC3E.

Fig. 1.
Fig. 1.

Location of mesonet stations (red dots), C-SAPR RHI scans (dotted black line), 3DVAR domain (black and white dashed square), soundings (blue star), NEXRAD network (white triangles), and NEXRAD radius of influence of Doppler and polarimetric radars (black and purple rings, respectively).

Citation: Monthly Weather Review 148, 2; 10.1175/MWR-D-19-0068.1

a. Radar observations

Much of the instrumentation and sampling during MC3E focused around a multiscale remote sensing network. Scanning precipitation radars deployed as part of MC3E included the dual-polarization 5.4-GHz (5.5-cm wavelength) C-band Scanning ARM Precipitation Radar (C-SAPR, Hardin et al. 1990). The C-SAPR sampling strategy adopted for this event mostly included range–height indicator scans (RHIs). The NEXRAD network also provided good coverage for tracking evolutions of cold pool and dynamical structure (e.g., updraft) of convective systems over central Oklahoma. Of the radars in the NEXRAD network, the Vance Air Force Base (KVNX), Oklahoma City (KTLX), Tulsa (KINX), and Frederick (KFDR) radars in Oklahoma, and the Wichita (KICT) radar in Kansas are of particular interest to this study (Fig. 1; NWS 1991). These S-Band radars operated continuously during MC3E, and provided surveillance scans every roughly 6 min over a range of 230 km from each radar. Those NEXRAD radar measurements had the range-gate spacing of 250 m and azimuth spacing of 0.5°. The KVNX radar was upgraded to dual-polarization technology in March 2011 and thus, provided information about the microphysical structure of the observed convection.

b. Surface meteorological observations

The Oklahoma Mesonet, operated by the University of Oklahoma and Oklahoma State University, provides 5-min standard meteorological observations at 120 stations across Oklahoma (Fig. 1). ARM weather stations deployed during MC3E enhanced this network through the addition of 9 surface stations with 1-min temporal sampling. The resulting mean distance between surface stations of this observationally enhanced network was about 35 km. Combined, this dense network of surface weather stations provided invaluable information for identifying and documenting the temporal evolution of cold pool surface properties with high time and space resolution.

c. Sounding observations

MC3E conducted extensive sounding measurements. Sondes were launched eight times per day at six sites in Oklahoma and southern Kansas (Fig. 1). Sonde data were processed and quality controlled, including calibration and humidity bias correction (Jensen et al. 2015). These soundings are fundamental for understanding the environment of convective systems observed on this day, as well as documenting the cold pool passage. Of particular interest to the cold pool studied in this work is the sonde station S1 located 160 km NW of the SGP-CF in Pratt, Kansas (Fig. 1). This sonde station was located in the region affected by the cold pool. Thus, the sonde launched from S1 observed the environment after the passage of the LECP and provides an estimate of the depth of the cold pool behind the LECP.

3. Methods

a. Vertical velocity retrievals

In this work, observations from the KICT, KTLX, and KVNX radars and radiosonde information are used in conjunction with the three-dimensional variational (3DVAR) algorithm developed by North et al. (2017) to retrieve the 3D wind field over the domain shown in Fig. 1. This wind retrieval algorithm inputs the Cartesian coordinate radar reflectivity and Doppler velocity fields from each radar, which were converted from the observed radar reflectivity and Doppler velocity with the radar polar coordinate using a single-pass isotropic Barnes distance-dependent weight (Barnes 1964; see also Trapp and Doswell 2000). In the 3DVAR technique, the optimal wind field solution is obtained at the minimum of a cost function which consists of the physical constraints of radar radial velocity observations, anelastic mass continuity, surface impermeability, background wind field, and spatial smoothness (Potvin et al. 2012). The background wind fields were obtained from the ARM Merged Sounding value-added product that combined the observations from radiosonde soundings at the SGP Central Facility (available every 3 h during MC3E), microwave radiometers, surface meteorological instruments, and European Centre for Medium-Range Weather Forecasts (ECMWF) model output to produce a dataset at 1 min intervals and at 266 altitude levels (Troyan et al. 1996). The horizontal and vertical air motions from this algorithm were retrieved in a Cartesian coordinate grid having 250 m horizontal and vertical grid spacings, over a 150 km × 150 km horizontal domain from the surface to 15 km height.

The 3DVAR wind retrieval could include uncertainties which increased with height. Oue et al. (2019) investigated the overall uncertainties in the wind retrievals. The significant uncertainties appeared above 6 km AGL, where updraft regions tended to be overestimated and the mean updraft values also tended to be underestimated. These errors are mainly due to sparse radar data points and time periods needed to complete a volume scan by radar measurements. A more detailed look at the ability of the 3DVAR to adequately represent the 3D wind field for this case is provided in the appendix.

b. Hydrometeor identification

Hydrometeor identification algorithms (HID) have been widely used to investigate cloud microphysics (e.g., Duvernoy and Gaumet 1996; Straka et al. 2000; Liu and Chandrasekar 2000; Ryzhkov et al. 2005; Cifelli et al. 2011; Dolan et al. 2013; Bechini and Chandrasekar 2015; Cazenave et al. 2016). Dolan et al. (2013) developed a C-Band HID algorithm valid for warm season convection that uses the radar reflectivity (Ze), differential reflectivity (ZDR), specific differential phase (KDP), and correlation coefficient (ρhv), along with the temperature profile to diagnose 10 different hydrometeor categories: drizzle, rain, wet snow, dry snow, ice crystals, vertically aligned ice, high-density graupel, low-density graupel, hail, and big drops (>5 mm)/melting hail. The algorithm incorporates fuzzy logic weighting functions at C- and S- Band wavelengths, and thus the dual-polarization capability of the KVNX radars can be used to retrieve the most probable type of hydrometeor for each radar range gate. Here, the Dolan et al. (2013) HID algorithm is applied in polar coordinates, and gridded in the same domain as the 3DVAR, and with the same horizontal and vertical resolution (using the nearest neighbor approach) to provide comparable retrievals of in-storm bulk microphysics and kinematics.

Vertical velocity retrievals and HID algorithms are powerful tools to understand the kinematic and microphysical coevolution of the convective storms from an observational analysis. The enhanced network of surface weather stations and the surveillance volume scans from KVNX allow a further extension of the observational study into a detailed characterization of cold pool properties (such as, intensity, speed, and depth). The connection between the observed changes in convection properties and cold pool characteristics is further explored in this work.

c. Cold pool detection, and estimations of its height and speed

Following previous studies (e.g., Brandes 1977; Lee et al. 1978; Wakimoto 1982), the observed LECP is identified by “thin line” echoes in KVNX reflectivity fields. Radar reflectivity data were then edited using the National Center for Atmospheric Research (NCAR) Solo-II radar data processing software (Oye et al. 1995) to manually isolate the thin line from the rest of the radar echoes in every plan position indicator (PPI) scan where it was visible. This allows the analysis of cold pool radar-dependent characteristics (such as reflectivity, spectrum width, and differential reflectivity), and derived characteristics (such as speed and cold pool depth), independently of surrounding echoes.

The speed of the LECP was determined through the mean distance traveled by the LECP between two consecutive PPI scans at 0.5° elevations. The height of the LECP was determined in association with the maximum elevation angle at which the thin line echoes in KVNX reflectivity was detected by the radar. Figure 2 shows the KVNX PPI scan strategy for the case analyzed in this work. The height of the LECP is measured to beam center above ground following the 4/3 Earth radius model (e.g., Doviak and Zrnić 1993; Rauber and Nesbitt 2018). For example, if the cold pool feature was detected in the reflectivity field 50 km away from the radar (rLECP in Fig. 2) by the first 5 elevation angles (0.5°, 0.9°, 1.3°, 1.8°, and 2.4°) but not by the following elevation angle (i.e., 3.1°) then, the estimated height of the LECP (hLECP in Fig. 2) is 2.1 km, note that this estimate required detection of the thin line over at least 2 elevation angles. The uncertainty associated with hLECP was defined by the height of half the radar beamwidth (0.5°) above and below the elevation angle that detected the highest LECP-associated signal. For the example shown in Fig. 2, this uncertainty was 440 m around hLECPhLECP in Fig. 2). Note that the uncertainty associated with the radar beamwidth increases with distance from the radar. For the previous example, if this feature was detected 20 km away from the radar by the scan at the same elevation angle then ΔhLECP < 180m, but if it was detected 90 km away from the radar then, ΔhLECP > 780 m (Fig. 2).

Fig. 2.
Fig. 2.

Height of the beam axis as a function of distance from the radar for the KVNX radar scan strategy. Each scan strategy elevation angle is shown at the border of the plot with the respective color. The estimated height of the LECP (hLECP) and its uncertainty due to the radar beamwidth (ΔhLECP) at 50 km from the radar location (rLECP) are also shown in the figure.

Citation: Monthly Weather Review 148, 2; 10.1175/MWR-D-19-0068.1

4. Results

Figure 3 depicts the overall evolution of the convective storms on 23–24 May 2011. This includes convection initiation along a dryline in western Oklahoma through northern Texas (Fig. 3a), the subsequent development of supercells and multicellular clusters along a southwest to northeast oriented line (Figs. 3a,b), and the eventual organization of these clusters (and perhaps supercells) into an MCS (Fig. 3d).

Fig. 3.
Fig. 3.

(a)–(d) Topography (shaded, bottom color bar) and reflectivity composite at 500 m above ground overlaid (shaded, top color bar) from combined KICT, KVNX, KTLX, KDFR, and KINX radars (shaded) every 2 h from 2000 UTC 23 May to 0200 UTC 24 May. Location of all the radars used in this work is also shown (red dots). White arrows denote the location of LECP. Black dashed line in (b) denotes the location of the C-SAPR RHI scan at the closest time of the scan used for validating the 3DVAR results shown in the appendix.

Citation: Monthly Weather Review 148, 2; 10.1175/MWR-D-19-0068.1

Figures 3b and 3c also reveal the development of a westward-moving thin line, and thus apparent LECP (shown by white arrow in Figs. 3a and 3b), in association with the multicells/MCS. This LECP is therefore comprised of outflow from the multiple convective cells that developed during this day. As also revealed by Figs. 3b and 3c, the LECP does not become associated with (i.e., initiate) new deep convection, possibly due to the relatively drier environment into which it moved.

Figure 4 shows hourly linearly interpolated temperature from the surface weather stations, the surface wind vector at each station, and the edge of the precipitation near the surface and in the upper level given by the NEXRAD network from 2100 UTC 23 May to 0200 UTC 24 May. Note that this analysis fails to show a cold pool in association with the supercell that developed in southwest Oklahoma (135 km west and 160 km south of SGP-CF at 2200 UTC 23 May; Figs. 3b and 3c); this supercell is therefore excluded from further analysis. Figure 4 indicates that the LECP remained in a precipitation free region for the entirety of the analyzed time (Fig. 4). Therefore, the variations in the different state variables observed by the surface weather stations mostly correspond to intrinsic characteristics of the cold pool, and are not influenced by new convection in the area or by anvil shadowing. These variations include a decrease in the temperature with the passage of the LECP, a corresponding shift in the low-level flow to a more distinct easterly component, and an increase in the wind speed (Fig. 4).

Fig. 4.
Fig. 4.

(a)–(f) Interpolated temperature field from mesonet stations (shaded), wind at each mesonet station (vector), 30 and 50 dBZ reflectivity at ground level (red dashed contour), 15 dBZ reflectivity at 10 km height (black dashed contour), and location of the leading edge of the cold pool (green lines) hourly from 2000 UTC 23 May to 0100 UTC 24 May.

Citation: Monthly Weather Review 148, 2; 10.1175/MWR-D-19-0068.1

Further evidence of this characterization is provided in Fig. 5 through a comparison of several radar-variable values associated with the LECP and those over the entire radar domain (ERD). ERD refers to the region covered by the KVNX radar lowest elevation PPI scan with 230 km radius. Spectrum width within both datasets (LECP and ERD) have a similar distribution, with one maximum near 0 m s−1 and another around 2 m s−1 (Fig. 5a). However, the relative importance of these peaks depends on the dataset analyzed. The significance of the spectrum width in the LECP subset is not its absolute values but rather its comparison to the surrounding values, as the LECP is a turbulent region (mostly due to the expected lifting of the environmental air and mixing that occurs at the LECP) embedded in a relatively calm environment (e.g., Zrnić and Lee 1983). In this case, as the LECP moved away from the generating convection without initiating new cells nor reinvigorating old ones on its path, it is expected that the increase in spectrum width is mostly due to turbulence and not to other contributions associated with convection (e.g., strong shear, and broad drop size distribution – Lhermitte 1963; Wakimoto 1982; Doviak and Zrnić 1993; Borque et al. 2016). This aspect results in the highest peak centered near 2 m s−1 within the LECP but near 0 m s−1 over the ERD. The larger tail of the spectrum width distribution within the ERD is most likely associated with higher turbulence associated with convective echoes, as these convective cells are separated from the analyzed cold pool and thus, not included in the LECP subset (Fig. 5a).

Fig. 5.
Fig. 5.

Normalized probability distribution functions of (a) spectrum width (SW), (b) reflectivity (Ze), (c) differential reflectivity (ZDR), and (d) correlation coefficient (ρhv), for all the observations sampled by the KVNX radar (blue) and for the LECP subset (orange).

Citation: Monthly Weather Review 148, 2; 10.1175/MWR-D-19-0068.1

Radar reflectivity factor in the LECP has a much narrower distribution than over the ERD (Fig. 5b). Within the LECP, the mean reflectivity is 8 dBZ with a standard deviation of 2 dBZ, but over the ERD, the mean and standard deviation are 15 and 16 dBZ, respectively. Spectrum width and reflectivity values associated with the LECP agree with values commonly observed in radar returns from insects (e.g., Achtemeier 1991; Russell and Wilson 1997).

Polarimetric data provide further confirmation of the likely presence of insects in the LECP. The distribution of the ρhv in the LECP subset shows a much greater frequency of smaller values than for the ERD (Fig. 5c). Considering that ρhv is expected to be larger than 0.98 for raindrops, the low ρhv values present in the LECP indicate that the echoes do not likely contain precipitation particles carried by the outflow. Furthermore, the distribution of ZDR within the LECP is skewed toward large values, with a median value of 6.3 dB and a skewness of −1.02, especially when compared to the ZDR distribution of the ERD, which has a median of 1.5 dB with a skewness of 0.06 (Fig. 5d). The observed high ZDR and low ρhv associated with the LECP are the opposite of what is expected by Bragg scattering of clear air due to gradients in the refractive index (e.g., Melnikov et al. 2011; Mueller and Larkin 1985; Wilson et al. 1994; Zrnić and Ryzhkov 1998; Lang et al. 2004). Thus, the combination of radar variables shown in Fig. 5 suggests that the signal of the LECP detected by the radar in this case is due to biota being carried away by the outflow.

a. LECP and cold pool characteristics

1) Cold pool intensity

Figure 6 shows the locations of the LECP over a 4.5 h period. This figure is the result of applying the cold pool identification methodology described in section 3c to the radar’s lowest elevation angle PPI scan at times when the LECP was visible (i.e., from 2030 UTC 23 May to 0130 UTC 24 May; note that the cold pool did not necessarily dissipate after 0130 UTC but was no longer detected by the KVNX radar). Surface weather stations located along the path of the cold pool confirm the timing shown by the radar analysis. In particular, the time when the LECP moves through the Oklahoma Mesonet stations Cherokee (CHER), Alva (ALV2), Freedom (FREE), and Buffalo (BUFF) (which were aligned perpendicular to the orientation of the LECP) agrees with the timing estimated from the radar analysis (Figs. 6 and 7). At the time of the cold pool passage, the mesonet stations CHER, ALV2, FREE, and BUFF were located at a distance of about 34, 50, 84, and 126 km from the parent convection. As the cold pool moves westward, the magnitude of the temperature decrease diminishes: the mesonet station CHER records a temperature decrease of 6°C as it is overtaken by the cold pool, but at the stations ALV2, FREE, and BUFF temperature decreases by only 5°, 3°, and 2°C, respectively (Fig. 7a). Similarly, there is a notable decrease of the cold pool impact upon the measured wind speed. Prior to the passage of the LECP the average environmental wind speed is around 4 m s−1 at these stations; after its passage the wind speed almost doubled at CHER—reaching wind speeds slightly above 10 m s−1—but only increased by 2 m s−1 at BUFF—reaching wind speeds slightly above 6 m s−1 (Fig. 7b). The prevailing undisturbed wind field at these stations has a marked NE component, and as the LECP moves through the mesonet sites the wind direction changes to the southeast at CHER and ALV2, and to the east at FREE and BUFF. This wind shift agrees with the expected change in wind direction given the orientation of the LECP at the time of its passage through these stations (Figs. 6 and 7c). The timing of the different atmospheric variables affected by passage of the LECP shown in Fig. 7 agrees with that described in Charba (1974); a shift in wind direction, followed by a drop in temperature, and then the attainment of maximum wind speed. Furthermore, the overall changes in magnitude of the kinematic and thermodynamic variables observed are within the range of values previously observed in previous studies for this region (e.g., Lesage 2012; Engerer et al. 2008).

Fig. 6.
Fig. 6.

Position of the LECP extracted from the lowest elevation scan of the KVNX radar every 30 min. Transect connecting mesonet stations (labeled) is color-coded to correspond with the time series for each station shown in Fig. 7. Yellow stars denote location of the maximum height of the LECP at each time.

Citation: Monthly Weather Review 148, 2; 10.1175/MWR-D-19-0068.1

Fig. 7.
Fig. 7.

Time series of (top) temperature, (middle) wind speed, and (bottom) wind direction from Cherokee [CHER (black)], Alva [ALV2 (green)], Freedom [FREE (red)], and Buffalo [BUFF (blue)] mesonet stations. Location of each station is marked in Fig. 6. Time of the temperature decrease associated with passage of the cold pool leading edge is denoted by vertical line (colors black, green, red, and blue correspond to the color labels for each station).

Citation: Monthly Weather Review 148, 2; 10.1175/MWR-D-19-0068.1

2) Cold pool height

Figure 8 shows the retrieved maximum and mean height of the LECP over the time period that the associated thin line echoes were visible in more than one elevation angle in the KVNX imagery. The height of the LECP varied considerably, with maximum height ranging from slightly above 2.5 to 4 km and higher (Fig. 8). These high values agree with previous studies of cold pool depths observed with sounding data over the tropics and midlatitudes (e.g., Roux 1988; Bryan and Parker 2010). On the other hand, the mean height of the LECP has a more gradual overall increase, from just below 1 km to close to 3 km (Fig. 8). Further indication of the heterogeneity of the height of the LECP can be seen from a horizontal view of the LECP as detected by the KVNX radar at a given time. As an example, Fig. 9 shows the location of the LECP echoes, the highest elevation angle where the signature was detected, and the height of the LECP estimated following the 4/3 Earth radius approximation at 2230 UTC 23 May (Figs. 9a–c, respectively). It can be seen from this figure that the height of the LECP at this time varies from 1 to 2 km. This change in height is not clearly linked with distance to the radar (Fig. 9). Also shown in Fig. 9 is the highest elevation angle at which the KVNX radar should have detected the LECP signature if the LECP had a uniform height equal to the maximum height detected at this time (2 km, Figs. 8 and 9d). Farther from the radar location (ranges >50 km), the discrepancy between the elevation angle where the radar detected the LECP, and the expected detection angle if the LECP had a homogenous height throughout its horizontal extension, is close to 1 elevation angle (Figs. 9b,d). However, closest to the radar location, this discrepancy increases to 2 or 3 elevation angles: at ranges closest to the radar location (<25 km), the LECP signature should have been detected by the PPI scan performed at 5.1° and at ranges slightly further from the radar location (<35 km) by the scan at 4° (Fig. 9d). Instead, over this entire region (closer than 35 km to the radar location) the scan at 2.4° was the highest PPI scan that detected the LECP signature (Fig. 9b). There is a ±1 elevation angle error associated with the discretization of the elevation angles in the radar scan strategy and there is no physical reason for biota to not be uniformly carried away by the LECP. Thus, the observed 2 or 3 tilt discrepancy between the elevation angle where the radar detected the LECP and the expected detection angle if the LECP had a homogenous height further shows the heterogeneity of the height of the LECP. The simulated cold pool for this case by Mallinson and Lasher-Trapp (2019, their Fig. 12) also shows significant heterogeneity in its height at any one time, and as it evolves.

Fig. 8.
Fig. 8.

Time series of LECP height maxima (solid blue line) and mean (solid green line) from 2100 UTC 23 May to 0100 UTC 24 May. Shaded regions denote their uncertainty as defined in section 3c. The number at each time corresponds to the number of gates where the cold pool was detected by the KVNX radar.

Citation: Monthly Weather Review 148, 2; 10.1175/MWR-D-19-0068.1

Fig. 9.
Fig. 9.

(a) Location of the LECP echoes as detected by the KVNX radar, (b) highest elevation angle where the cold pool signature was detected, (c) height of the LECP estimated following the 4/3 Earth radius approximation, and (d) highest elevation angle that the KVNX radar should have detected the LECP signature if it had a uniform height equal to the maximum height detected at 2230 UTC 23 May. Rings denote the distance from the radar every 10 km and the black triangle indicates the radar location.

Citation: Monthly Weather Review 148, 2; 10.1175/MWR-D-19-0068.1

The minimal difference between the mean and maximum of the LECP height with relatively small uncertainties between 2230 and 2330 UTC is suggestive of a more uniform LECP height at these times (Fig. 8). The uncertainly of the mean LECP height (shaded green in Fig. 8) increases with time, from 0.17 km at 2100 UTC to 0.90 km at 0100 UTC (Fig. 8). As described in section 3c, the uncertainty of the mean LECP height is associated with the volume sampled by the radar. Moreover, the volume covered by the radar increases with distance from the radar. Thus, given that in the case analyzed here, the LECP moves away from the radar, it is expected that this measurement of uncertainty increases with time or distance from the radar. On the other hand, the uncertainty associated with the maximum LECP height (shaded blue in Fig. 8) is more variable with time, further confirming that the radar-relative location of the LECP is not biasing the results presented here. Finally, the uncertainty associated with the mean and maximum LECP height do not overlap, thus providing further evidence of the spatial heterogeneity of the height of the LECP (Fig. 8).

According to the KVNX analysis the LECP passed by the Pratt sounding site (37.7°N, 98.75°W) before 0130 UTC 24 May (Figs. 1 and 6). Given the path taken by the sonde launched from this site at 0230 UTC 24 May—it ascended the first 3 km of the atmosphere while it drifted 5.11 km to the northwest from its launching location—this sounding provides thermodynamic and kinematic observations of the vertical structure within and above the cold pool (Figs. 7 and 10a). On the other hand, the sonde launched from this site at 2330 UTC 23 May observed the environment before the passage of the LECP. Therefore, a comparison between both sondes can provide an estimate of the cold pool depth. The temperature vertical profile from the 0230 UTC sonde is colder from surface to 1.4 km than at 2230 UTC (Fig. 10a). The horizontal wind has a maximum speed around the middle of this layer, showing a jet-like profile with the maximum speed of 12.3 m s−1 at around 1 km height with an easterly prevailing wind direction (Figs. 10b,c). This wind direction is coincident with the surface observations registered by the Oklahoma Mesonet after the passage of the LECP (Fig. 7). Comparing this wind vertical profile with the one observed at 2330 UTC 23 May, note the change in wind direction from NE to E in this layer over the 3 h period when the cold pool passed over the location (Figs. 10c). Thus, a comparison of the thermodynamic variables between the 2330 and 0230 UTC Pratt sondes suggests a height of the cold pool between 1.4 and 1.6 km in this region at 0230 UTC 24 May. The fact that this depth estimate is shallower than the estimation of the mean height of the LECP from the KVNX analysis (Figs. 6, 8, and 10) can be explained given the geometry of the cold pool. Past studies have shown that the general shape of the cold pool has a vertical bulge present near the LECP, called the “head,” which is followed by a relative flatness and shallowness of the upper surface upstream of the head (e.g., Charba 1974; Benjamin 1968; Xue 2002). Considering that the 0230 UTC Pratt sounding sampled the air 1.5 h after the passage of the LECP over this location, it can then be hypothesized that the 0230 UTC Pratt sonde sampled the shallower portion of the cold pool behind the LECP.

Fig. 10.
Fig. 10.

Vertical profiles of (a) temperature, (b) wind speed, and (c) wind direction from the Pratt, KS, sonde launched at 2330 UTC 23 May (blue) and at 0230 UTC 24 May (red).

Citation: Monthly Weather Review 148, 2; 10.1175/MWR-D-19-0068.1

3) Cold pool propagation speed

It can be inferred from Fig. 6 that the LECP has an almost-constant speed. Following the methodology described in section 3c, the mean propagation speed estimated from KVNX’s observations is 6.7 m s−1 (Fig. 6). However, there are small but noticeable variations in the LECP propagation speed over time and along the LECP, with a standard deviation smaller than 1.12 m s−1 over the detected period. Several factors can impact the cold pool resulting in variations in the propagation speed of the LECP; among these are the direction of the prevailing winds at the height of the LECP that might oppose the propagation of the LECP at certain places, heterogeneity in the surface characteristics that can affect different regions along the LECP, and the effect of discrete pulses of air from the different convective cells that can impact the LECP at different times. However, despite these many factors that can impact the LECP it is interesting that the variations in the propagation speed do not seem have a systematic acceleration or deceleration with time or region and thus, the LECP speed remains almost constant over the detected period.

The radar-based estimation of the LECP propagation speed agrees with the timing of several atmospheric variables at different mesonet stations affected by passage of the LECP (Fig. 7). Propagation speed can also be estimated following gravity current theory. However, comparing the observed estimates of propagation speed with the theoretical counterpart is not straightforward. Gravity current theory assumes, among other things, homogeneous and quiescent conditions, and given the spatial and temporal heterogeneity of the LECP properties shown in the previous section, these assumptions are not met in this case.

b. Interaction between convection and cold pool

Time-dependent changes in cold pool height and intensity shown in the previous subsections (e.g., Fig. 8) could be associated with changes in the characteristics of the parent convection (e.g., Coniglio et al. 2011). Here, convection is characterized by the vertical motion associated with the convective cells that generated the cold pool (following the 3DVAR retrievals described in section 3a) and by the algorithm-derived hydrometeor classification in each radar-sampled volume (following methodology present in section 3b).

Figures 11 and 12 show the time evolution of the volume covered with HID-based hail and graupel retrieved from the KVNX observations along with the draft area and intensity over the KVNX domain. Draft area is defined as the enclosed area with vertical velocities exceeding a threshold of 8 m s−1 (positive for updrafts and negative for downdrafts) at 5 km height. The intensity of the updraft (downdraft) was associated with the 95th (5th) percentile of the estimated vertical velocities at the same height. The selected 5 km height for quantifying the draft area does not have a major impact on the qualitative analysis presented here; the time evolution of the draft area and intensity do not present a significant variation with height. During this time period, there seems to be two distinct convective pulses, as defined by independent relative maxima in hail and graupel volumes, and also by downdraft and updraft area. The first pulse occurs around 2200 UTC, when both algorithm-identified hail and graupel volumes reach their maximum; this time also corresponds with the cold pool reaching its maximum height (Figs. 8, 11, and 12). Consistent with the theoretical arguments and modeling results of Marion and Trapp (2019), peaks in downdraft and updraft area and updraft intensity occur slightly earlier (approximately by 30 min) than those for cold pool height and hail and graupel areas (Figs. 8 and 11). The second pulse in convection is slightly broader and with a different time dependency; maxima in graupel volume and draft intensity and area occur first (at 2345 UTC), followed by a maximum in cold pool height (at 2400 UTC) and then a maximum in the hail volume at (2415 UTC; Figs. 8, 11, and 12). Thus, the first pulse could be related to the first maximum in height of the LECP, but as time passes, it becomes more difficult to link both quantities in time given the many factors that play a role in their coevolution, especially since the LECP moves farther away from the generating convection.

Fig. 11.
Fig. 11.

Area covered by hail (shaded) as a function of time and height, and time series of area covered by downdraft and updraft (blue dashed and solid lines, respectively) and downdraft and updraft maximum intensity (light blue dashed and solid lines, respectively) at 5 km height over the domain shown in Fig. 1.

Citation: Monthly Weather Review 148, 2; 10.1175/MWR-D-19-0068.1

Fig. 12.
Fig. 12.

As in Fig. 11, but for graupel area.

Citation: Monthly Weather Review 148, 2; 10.1175/MWR-D-19-0068.1

5. Summary and conclusions

This study presents the largest synergistic data analysis known to the authors of a purely observational case study of a midlatitude continental cold pool. Observations analyzed here correspond to convective systems and a cold pool that took place on 23–24 May 2011 in north-central Oklahoma (Figs. 3 and 4). An enhanced network of surface weather stations, a high temporal frequency sounding array, and surveillance volume scans from the NEXRAD network present a high time and space resolution to document the intensity, speed, and height of the cold pool. This work presents a successful proof-of-concept of measuring the height and propagation speed of the leading edge of the cold pool from the NEXRAD surveillance scans. Given the vast amount of publicly available NEXRAD data over the United States, this work paves the way toward creating an extensive observational dataset and the generation of observational analyses to constrain numerical modeling studies.

From the analyzed radar imagery, signatures of the leading edge of the cold pool (LECP) were detected from 2030 UTC 23 May to 0130 UTC 24 May with surface weather stations located along the path of the LECP confirming the location and timing (Figs. 37). The passage of the cold pool modified the temperature, wind direction, and wind speed as expected (with a considerably decrease in temperature, a clear shift in the low-level flow and an acceleration of the wind speed) but the temperature deficit and wind speed decreased as the LECP moved westward (Figs. 4 and 7). The LECP moved away from the generating convection without generating new cells or reinvigorating old ones in its path and thus, the variations analyzed corresponded to intrinsic characteristics of the analyzed cold pool.

Manual identification of the LECP from KVNX reflectivity fields allowed an analysis of its radar-based (spectrum width, correlation coefficient, and differential reflectivity) and radar-derived (cold pool height, and speed) characteristics. The LECP was associated with relatively large spectrum width, small correlation coefficient, and a ZDR distribution skewed toward very large values (Fig. 4). This relatively high spectrum width, high ZDR, and low ρhv associated with the LECP strongly suggest that the ability of the NEXRAD network to detect the cold pool signal in this case is most likely due to biota being carried away by the outflow. The height of the LECP retrieved from the KVNX subset varied quite considerably over its detected life cycle (from 2100 UTC 23 May to 0100 UTC 24 May) suggesting a large spatial and temporal heterogeneity of the LECP (Figs. 7 and 8). Cold pool height estimates from a sounding launched within the cold pool were shallower than the LECP estimates from the radar analysis (Fig. 10). This could be related with the geometry of the cold pool, with the height of LECP representing the height of the head of the cold pool, and the sounding launched 1.5 h after the passage of the LECP measuring the relatively shallower cold pool body. A final interesting feature that can be inferred from the radar imagery is that, despite the many factors that can impact the speed of the cold pool, the propagation speed of the LECP is almost constant throughout the entire time period it was detected (Fig. 6). Following density current theory, it would then appear that the cold pool density and depth are compensating for each other, maintaining thus an almost constant LECP speed. However, the assumptions in the calculation of the theoretical density current speed (e.g., unlimited source of dense fluid, frictionless surface, and homogeneous environment) do not hold in this case. This feature could be analyzed in more depth with high-resolution modeling studies.

Radar-based hydrometeor classification and vertical velocity analysis shows prevalent graupel and hail in the convective core. Over the entire domain, the volume covered with hail or graupel, as well as draft area and intensity, show two distinct pulses in time (Figs. 11 and 12). The earliest pulse shows peak draft intensity occurring around 30 min before the highest LECP height with concurrent maximum in hail or graupel volume (Figs. 8, 11, and 12). However, the second convective pulse is more asynchronous: the maximum in graupel volume precedes that in cold pool height, and then a maximum in hail volume follows. The numerical simulations of this case by Mallinson and Lasher-Trapp (2019) suggested that multiple downdrafts can be contributing to an expansive cold pool from multicellular convection such as this, and thus graupel or hail signatures evident in the observations here might not have been the sole contributors, in part obfuscating the effects of other important contributions like evaporating rain. Thus, it is extremely difficult to link the outflow characteristics (such as, depth, intensity, and speed) with changes in convection internal structure (such as, intensity and size of draft, and amount of graupel or hail or even rain) from a purely observational analysis, given the many different factors that can play a role in their evolution. This shows the clear need for synergetic work combining detailed cold pool observations (like those presented here) with high-resolution modeling to advance our understanding of continental midlatitude cold pools. Model simulations allow for an isolation of the different factors that can affect the properties of continental cold pools individually, while heavily observed cases help to anchor the simulations in reality. The most recent example of such work is Mallinson and Lasher-Trapp (2019), where the influence of hydrometeor latent cooling was examined in the cold pool life cycle. Similar approaches to the analysis presented here but with more cases will help evaluate the fidelity of cold pool representation in analytic theory and convection-permitting model configurations.

Acknowledgments

The authors thank Pam Heinselman, and three anonymous reviewers for their helpful comments and suggestions that helped improve the manuscript. This research was supported by the Department of Energy through Grant DE-SC0014101 and Subcontract 415431 from Pacific Northwest National Laboratory. We thank all the participants of MC3E for collecting the data used in this study. Data can be obtained from the ARM data archive at http://www.archive.arm.gov and NEXRAD data at https://data.nodc.noaa.gov/cgi-bin/iso?id=gov.noaa.ncdc:C00345.

APPENDIX

Comparison of 3DVAR-Retrieved Wind Fields to an Independent RHI Sweep

Independent higher spatiotemporal measurements within the 3DVAR domain such as Doppler velocity observed by the C-SAPR’s RHI scans (Fig. 3) provide a framework for validating the 3DVAR results. The along-beam component of the wind as would be seen by the C-SAPR can be calculated from the 3DVAR retrieval, and compared with the observed Doppler velocities from the C-SAPR. Figure A1 shows a vertical cross section of Doppler velocity from the C-SAPR RHI scan at 2230 UTC 23 May and the estimated radial velocity from the 3DVAR retrieval in the direction of the C-SAPR RHI scan. The Doppler velocity at radar ranges greater than 40 km and altitudes below 5 km shows a kinematical structure with a convergence/divergence pattern that resembles that of a cold pool. This feature is well captured by the 3DVAR estimates. In the upper levels, there is divergent flow centered around 80 km away from the radar, with Doppler velocities larger than 20 m s−1. This structure is also well depicted by the 3DVAR retrieved radial velocities. Therefore, the 3DVAR technique is able to efficiently resolve the structure and magnitudes of the C-SAPR observed Doppler velocity and thus, provides evidence that the 3DVAR methodology is able to retrieve the relevant structures of the storms analyzed in this work.

Fig. A1.
Fig. A1.

Doppler velocity from the C-SAPR RHI measurements at 2330 UTC 23–24 May 2011 (shaded and gray contours) and estimated radial velocity from the 3DVAR retrieval in the direction of the C-SAPR RHI scan (black contours).

Citation: Monthly Weather Review 148, 2; 10.1175/MWR-D-19-0068.1

REFERENCES

  • Achtemeier, G. L., 1991: The use of insects as tracers for “clear-air” boundary-layer studies by Doppler radar. J. Atmos. Oceanic Technol., 8, 746765, https://doi.org/10.1175/1520-0426(1991)008<0746:TUOIAT>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ahijevych, D., G. H. Bryan, M. L. Weisman, S. B. Trier, C. A. Davis, and D. Dowell, 2006: Composite bow echo observed during BAMEX. 23rd Conf. on Severe Local Storms, St. Louis, MO, Amer. Meteor. Soc., 7.3, https://ams.confex.com/ams/23SLS/techprogram/paper_115151.htm.

  • Barnes, S. L., 1964: A technique for maximizing details in numerical weather map analysis. J. Appl. Meteor., 3, 396409, https://doi.org/10.1175/1520-0450(1964)003<0396:ATFMDI>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bechini, R., and V. Chandrasekar, 2015: A semisupervised robust hydrometeor classification method for dual-polarization radar applications. J. Atmos. Oceanic Technol., 32, 2247, https://doi.org/10.1175/JTECH-D-14-00097.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Benjamin, T., 1968: Gravity currents and related phenomena. J. Fluid Mech., 31, 209248, https://doi.org/10.1017/S0022112068000133.

  • Borque, P., E. Luke, and P. Kollias, 2016: On the unified estimation of turbulence eddy dissipation rate using Doppler cloud radars and lidars. J. Geophys. Res. Atmos., 120, 59725989, https://doi.org/10.1002/2015JD024543.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Brandes, E. A., 1977: Gust front evolution and tornado genesis as viewed by Doppler radar. J. Appl. Meteor., 16, 333338, https://doi.org/10.1175/1520-0450(1977)016<0333:GFEATG>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Brock, F. V., K. C. Crawford, R. L. Elliott, G. W. Cuperus, S. J. Stadler, H. L. Johnson, and M. D. Eilts, 1995: The Oklahoma mesonet: A technical overview. J. Atmos. Oceanic Technol., 12, 519, https://doi.org/10.1175/1520-0426(1995)012<0005:TOMATO>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bryan, G. H., and M. D. Parker, 2010: Observations of a squall line and its near environment using high-frequency rawinsonde launches during VORTEX2. Mon. Wea. Rev., 138, 40764097, https://doi.org/10.1175/2010MWR3359.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bryan, G. H., D. Ahijevych, C. Davis, S. Trier, and M. Weisman, 2005: Observations of cold pool properties in mesoscale convective systems during BAMEX. 11th Conf. on Mesoscale Processes/32nd Conf. on Radar Meteorology, Albuquerque, NM, Amer. Meteor. Soc., JP5J.12, https://ams.confex.com/ams/32Rad11Meso/techprogram/paper_96718.htm.

  • Byers, H. R., and R. R. Braham Jr., 1949: The Thunderstorm: Report of the Thunderstorm Project. U.S. Government Printing Office, 282 pp.

    • Search Google Scholar
    • Export Citation
  • Cazenave, F., M. Gosset, M. Kacou, M. Alcoba, E. Fontaine, C. Duroure, and B. Dolan, 2016: Characterization of hydrometeors in Sahelian convective systems with an X-band radar and comparison with in situ measurements. Part I: Sensitivity of polarimetric radar particle identification retrieval and case study evaluation. J. Appl. Meteor. Climatol., 55, 231249, https://doi.org/10.1175/JAMC-D-15-0013.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Charba, J., 1974: Application of gravity current model to analysis of squall-line gust front. Mon. Wea. Rev., 102, 140156, https://doi.org/10.1175/1520-0493(1974)102<0140:AOGCMT>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Cifelli, R., V. Chandrasekar, S. Lim, P. C. Kennedy, Y. Wang, and S. A. Rutledge, 2011: A new dual-polarization radar rainfall algorithm: Application in Colorado precipitation events. J. Atmos. Oceanic Technol., 28, 352364, https://doi.org/10.1175/2010JTECHA1488.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Coniglio, M. C., S. F. Corfidi, and J. S. Kain, 2011: Environment and early evolution of the 8 May 2009 derecho-producing convective system. Mon. Wea. Rev., 139, 10831102, https://doi.org/10.1175/2010MWR3413.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Dawson, D. T., M. Xue, J. A. Milbrandt, and M. K. Yau, 2010: Comparison of evaporation and cold pool development between single-moment and multimoment bulk microphysics schemes in idealized simulations of tornadic thunderstorms. Mon. Wea. Rev., 138, 11521171, https://doi.org/10.1175/2009MWR2956.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Dolan, B., S. A. Rutledge, S. Lim, V. Chandrasekar, and M. Thurai, 2013: A robust C-band hydrometeor identification algorithm and application to a long-term polarimetric radar dataset. J. Appl. Meteor. Climatol., 52, 21622186, https://doi.org/10.1175/JAMC-D-12-0275.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Doviak, R. J., and D. S. Zrnić, 1993: Doppler Radar and Weather Observations. 2nd ed. Academic Press, 562 pp.

  • Duvernoy, J., and J. L. Gaumet, 1996: Precipitating hydrometeor characterization by a CW Doppler radar. J. Atmos. Oceanic Technol., 13, 620629, https://doi.org/10.1175/1520-0426(1996)013<0620:PHCBAC>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Engerer, N. A., D. J. Stensrud, and M. C. Coniglio, 2008: Surface characteristics of observed cold pools. Mon. Wea. Rev., 136, 48394849, https://doi.org/10.1175/2008MWR2528.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Erlingis, J. M., and A. P. Barros, 2014: A study of the role of daytime land–atmosphere interactions on nocturnal convective activity in the Southern Great Plains during CLASIC. J. Hydrometeor., 15, 19321953, https://doi.org/10.1175/JHM-D-14-0016.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Feng, Z., S. Hagos, A. K. Rowe, C. D. Burleyson, M. N. Martini, and S. P. de Szoeke, 2015: Mechanisms of convective cloud organization by cold pools over tropical warm ocean during the AMIE/DYNAMO field campaign. J. Adv. Model. Earth Syst., 7, 357381, https://doi.org/10.1002/2014MS000384.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Geerts, B., and et al. , 2017: The 2015 Plains Elevated Convection at Night Field Project. Bull. Amer. Meteor. Soc., 98, 767786, https://doi.org/10.1175/BAMS-D-15-00257.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gentine, P., A. Garelli, S.-B. Park, J. Nie, G. Torri, and Z. Kuang, 2016: Role of surface heat fluxes underneath cold pools. Geophys. Res. Lett., 43, 874883, https://doi.org/10.1002/2015GL067262.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gilmore, M. S., J. M. Straka, and E. N. Rasmussen, 2004: Precipitation uncertainty due to variations in precipitation particle parameters within a simple microphysics scheme. Mon. Wea. Rev., 132, 26102627, https://doi.org/10.1175/MWR2810.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Grant, L. D., and S. C. van den Heever, 2014: Microphysical and dynamical characteristics of low-precipitation and classic supercells. J. Atmos. Sci., 71, 26042624, https://doi.org/10.1175/JAS-D-13-0261.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Grant, L. D., and S. C. van den Heever, 2016: Cold pool dissipation. J. Geophys. Res. Atmos., 121, 11381155, https://doi.org/10.1002/2015JD023813.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Grant, L. D., and S. C. van den Heever, 2018: Cold pool-land surface interactions in a dry continental environment. J. Adv. Model. Earth Syst., 10, 15131526, https://doi.org/10.1029/2018MS001323.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hardin, J., D. Nelson, I. Lindenmaier, B. Isom, S. Collis, A. Matthews, and N. Bharadwaj, 1990: ARM: C-SAPR Range Height Indicator scan. Atmospheric Radiation Measurement (ARM) Climate Research Facility Data Archive, accessed 1 January 2017, https://doi.org/10.5439/1025168.

    • Crossref
    • Export Citation
  • Hitchcock, S. M., R. S. Schumacher, G. R. Herman, M. C. Coniglio, M. D. Parker, and C. L. Ziegler, 2019: Evolution of pre- and postconvective environmental profiles from mesoscale convective systems during PECAN. Mon. Wea. Rev., 147, 23292354, https://doi.org/10.1175/MWR-D-18-0231.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Jensen, M. P., and et al. , 2015: The Midlatitude Continental Convective Clouds Experiment (MC3E) sounding network: Operations, processing and analysis. Atmos. Meas. Tech., 8, 421434, https://doi.org/10.5194/amt-8-421-2015.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Jensen, M. P., and et al. , 2016: The Midlatitude Continental Convective Clouds Experiment (MC3E). Bull. Amer. Meteor. Soc., 97, 16671686, https://doi.org/10.1175/BAMS-D-14-00228.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Johnson, D. E., P. K. Wang, and J. M. Straka, 1993: Numerical simulations of the 2 August 1981 CCOPE supercell storm with and without ice microphysics. J. Appl. Meteor., 32, 745759, https://doi.org/10.1175/1520-0450(1993)032<0745:NSOTAC>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lang, T. J., S. A. Rutledge, and J. L. Smith, 2004: Observations of quasi-symmetric echo patterns in clear air with the CSU-CHILL polarimetric radar. J. Atmos. Oceanic Technol., 21, 11821189, https://doi.org/10.1175/1520-0426(2004)021<1182:OOQEPI>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lee, J. T., J. Stokes, Y. Sasaki, and T. Baxter, 1978: Thunderstorm Oust Fronts—Observations and modeling. FAA Rep. FAA-RD-78-145, Dept. of Transportation, 100 pp.

  • Lesage, A. T., 2012: Frontal passage and cold pool detection using Oklahoma Mesonet observations. M.S. thesis, Dept. of Atmospheric Sciences, University of Utah, 76 pp., http://home.chpc.utah.edu/~u0652833/Lesage_thesis/Lesage_Thesis.pdf.

  • Lhermitte, R. M., 1963: Motions of scatterers and the variance of the mean intensity of weather radar signals. Rep. SRRC-RR-63-57, Sperry Rand Research Center, 43 pp.

  • Linden, P. F., and J. E. Simpson, 1985: Microbursts: A hazard for aircraft. Nature, 317, 601602, https://doi.org/10.1038/317601a0.

  • Liu, H., and V. Chandrasekar, 2000: Classification of hydrometeors based on polarimetric radar measurements: Development of fuzzy logic and neuro-fuzzy systems, and in situ verification. J. Atmos. Oceanic Technol., 17, 140164, https://doi.org/10.1175/1520-0426(2000)017<0140:COHBOP>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Maddox, R. A., 1980: An objective technique for separating macroscale and mesoscale features in meteorological data. Mon. Wea. Rev., 108, 11081121, https://doi.org/10.1175/1520-0493(1980)108<1108:AOTFSM>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Mallinson, H. M., and S. G. Lasher-Trapp, 2019: An investigation of hydrometeor latent cooling upon convective cold pool formation, sustainment, and properties. Mon. Wea. Rev., 147, 32053222, https://doi.org/10.1175/MWR-D-18-0382.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Marion, G. R., and R. J. Trapp, 2019: The dynamical coupling of convective updrafts, downdrafts, and cold pools in simulated supercell thunderstorms. J. Geophys. Res. Atmos., 124, 664683, https://doi.org/10.1029/2018JD029055.

    • Crossref
    • 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, https://doi.org/10.1175/BAMS-D-11-00218.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • McCumber, M., W. Tao, J. Simpson, R. Penc, and S. Soong, 1991: Comparison of ice-phase microphysical parameterization schemes using numerical simulations of tropical convection. J. Appl. Meteor., 30, 9851004, https://doi.org/10.1175/1520-0450-30.7.985.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • McPherson, R. A., and et al. , 2007: Statewide monitoring of the mesoscale environment: A technical update on the Oklahoma Mesonet. J. Atmos. Oceanic Technol., 24, 301321, https://doi.org/10.1175/JTECH1976.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Melnikov, V. M., R. J. Doviak, D. S. Zrnić, and D. J. Stensrud, 2011: Mapping Bragg scatter with a polarimetric WSR-88D. J. Atmos. Oceanic Technol., 28, 12731285, https://doi.org/10.1175/JTECH-D-10-05048.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Mueller, E. G., and R. P. Larkin, 1985: Insects observed using dual-polarization radar. J. Atmos. Oceanic Technol., 2, 4954, https://doi.org/10.1175/1520-0426(1985)002<0049:IOUDPR>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • North, K., M. Oue, P. Kollias, S. E. Giangrande, S. M. Collis, and C. K. Potvin, 2017: Vertical air motion retrievals in deep convective clouds using the ARM scanning radar network in Oklahoma during MC3E. Atmos. Meas. Tech., 10, 27852806, https://doi.org/10.5194/amt-10-2785-2017.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • NWS, 1991: NOAA Next Generation Radar (NEXRAD) Level II base data. NOAA/NCEI, accessed 1 January 2017, https://doi.org/10.7289/V5W9574V.

    • Crossref
    • Export Citation
  • Ogura, Y., and Y. Chen, 1977: A life history of an intense mesoscale convective storm in Oklahoma. J. Atmos. Sci., 34, 14581476, https://doi.org/10.1175/1520-0469(1977)034<1458:ALHOAI>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ogura, Y., and M.-T. Liou, 1980: The structure of a midlatitude squall line: A case study. J. Atmos. Sci., 37, 553567, https://doi.org/10.1175/1520-0469(1980)037<0553:TSOAMS>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Oue, M., P. Kollias, A. Shapiro, A. Tatarevic, and T. Matsui, 2019: Investigation of observational error sources in multi-Doppler-radar three-dimensional variational vertical air motion retrievals. Atmos. Meas. Tech., 12, 19992018, https://doi.org/10.5194/amt-12-1999-2019.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Oye, R., C. Mueller, and S. Smith, 1995: Software for radar translation, visualization, editing, and interpolation. Preprints, 27th Conf. on Radar Meteorology, Vail, CO, Amer. Meteor. Soc., 359361.

  • Park, S.-U., and D. N. Sikdar, 1982: Evolution of the dynamics and thermodynamics of a mesoscale convective system: A case study. Mon. Wea. Rev., 110, 10241040, https://doi.org/10.1175/1520-0493(1982)110<1024:EOTDAT>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Parker, M. D., and R. H. Johnson, 2004: Simulated convective lines with leading precipitation. Part II: Evolution and maintenance. J. Atmos. Sci., 61, 16561673, https://doi.org/10.1175/1520-0469(2004)061<1656:SCLWLP>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Potvin, C. K., D. Betten, L. J. Wicker, K. L. Elmore, and M. I. Biggerstaff, 2012: 3DVAR versus traditional dual-Doppler wind retrievals of a simulated supercell thunderstorm. Mon. Wea. Rev., 140, 34873494, https://doi.org/10.1175/MWR-D-12-00063.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Pritchard, M. S., M. W. Moncrieff, and R. C. Somerville, 2011: Orogenic propagating precipitation systems over the United States in a global climate model with embedded explicit convection. J. Atmos. Sci., 68, 18211840, https://doi.org/10.1175/2011JAS3699.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Purdom, J. F. W., 1976: Some uses of high-resolution GOES imagery in the mesoscale forecasting of convection and its behavior. Mon. Wea. Rev., 104, 14741483, https://doi.org/10.1175/1520-0493(1976)104<1474:SUOHRG>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Randall, D. A., and et al. , 2003: Confronting models with data: The GEWEX cloud systems study. Bull. Amer. Meteor. Soc., 84, 455470, https://doi.org/10.1175/BAMS-84-4-455.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Rauber, R. M., and S. W. Nesbitt, 2018: Radar Meteorology: A First Course. Wiley, 488 pp.

  • Rotunno, R., J. B. Klemp, and M. L. Weisman, 1988: A theory for strong, long-lived squall lines. J. Atmos. Sci., 45, 463485, https://doi.org/10.1175/1520-0469(1988)045<0463:ATFSLL>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Roux, F., 1988: The West African squall line observed on 23 June 1981 during COPT81: Kinematics and thermodynamics of the convective region. J. Atmos. Sci., 45, 406426, https://doi.org/10.1175/1520-0469(1988)045<0406:TWASLO>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Russell, R. W., and J. W. Wilson, 1997: Radar-observed fine lines in the optically clear boundary layer: Reflectivity contributions from aerial plankton and its predators. Bound.-Layer Meteor., 82, 235262, https://doi.org/10.1023/A:1000237431851.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ryzhkov, A. V., T. J. Schuur, D. W. Burgess, P. L. Heinselman, S. E. Giangrande, and D. S. Zrnić, 2005: The Joint Polarization Experiment: Polarimetric rainfall measurements and hydrometeor classification. Bull. Amer. Meteor. Soc., 86, 809824, https://doi.org/10.1175/BAMS-86-6-809.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sanders, F., and K. A. Emanuel, 1977: The momentum budget and temporal evolution of a mesoscale convective system. J. Atmos. Sci., 34, 322330, https://doi.org/10.1175/1520-0469(1977)034<0322:TMBATE>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Simpson, J. E., 1969: A comparison between laboratory and atmospheric density currents. Quart. J. Roy. Meteor. Soc., 95, 758765, https://doi.org/10.1002/qj.49709540609.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Snively, D. V., and W. A. Gallus, 2014: Prediction of convective morphology in near-cloud-permitting WRF Model simulations. Wea. Forecasting, 29, 130149, https://doi.org/10.1175/WAF-D-13-00047.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Straka, J. M., D. S. Zrnić, and A. V. Ryzhkov, 2000: Bulk hydrometeor classification and quantification using polarimetric radar data: Synthesis of relations. J. Appl. Meteor., 39, 13411372, https://doi.org/10.1175/1520-0450(2000)039<1341:BHCAQU>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Thorpe, A. J., M. J. Miller, and M. W. Moncrieff, 1982: Two-dimensional convection in non-constant shear: A model of mid-latitude squall lines. Quart. J. Roy. Meteor. Soc., 108, 739762, https://doi.org/10.1002/qj.49710845802.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Trapp, R. J., and C. A. Doswell, 2000: Radar data objective analysis. J. Atmos. Oceanic Technol., 17, 105120, https://doi.org/10.1175/1520-0426(2000)017<0105:RDOA>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Trapp, R. J., and J. M. Woznicki, 2017: Convectively induced stabilizations and subsequent recovery with supercell thunderstorms during the Mesoscale Predictability Experiment (MPEX). Mon. Wea. Rev., 145, 17391754, https://doi.org/10.1175/MWR-D-16-0266.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Troxel, S. W., and R. L. Delanoy, 1994: Machine intelligent approach to automated gust-front detection for Doppler weather radars. Proc. SPIE, 2220, https://doi.org/10.1117/12.179602.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Troyan, D., S. Giangrande, and T. Toto, 1996: Merged Sounding (MERGESONDE1MACE): 23–24 May 2011. Southern Great Plains (SGP) Central Facility, Lamont, OK (C1), Atmospheric Radiation Measurement (ARM) Climate Research Facility Data Archive, Oak Ridge, TN, accessed 1 January 2017, https://doi.org/10.5439/1034922.

    • Crossref
    • Export Citation
  • van den Heever, S. C., and W. R. Cotton, 2004: The impact of hail size on simulated supercell storms. J. Atmos. Sci., 61, 15961609, https://doi.org/10.1175/1520-0469(2004)061<1596:TIOHSO>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • von Kármán, T., 1940: The engineer grapples with nonlinear problems. Bull. Amer. Math. Soc., 46, 615683, https://doi.org/10.1090/S0002-9904-1940-07266-0.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wakimoto, R. M., 1982: The life cycle of thunderstorm gust fronts as viewed by Doppler radar and rawinsonde data. Mon. Wea. Rev., 110, 10601082, https://doi.org/10.1175/1520-0493(1982)110<1060:TLCOTG>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wilson, J. W., and W. E. Schreiber, 1986: Initiation of convective storms at radar-observed boundary layer convergence lines. Mon. Wea. Rev., 114, 25162536, https://doi.org/10.1175/1520-0493(1986)114<2516:IOCSAR>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wilson, J. W., T. M. Weckwerth, J. Vivekanandan, R. M. Wakimoto, and R. W. Russel, 1994: Boundary layer clear-air radar echoes: Origin of echoes and accuracy of deriving winds. J. Atmos. Oceanic Technol., 11, 11841206, https://doi.org/10.1175/1520-0426(1994)011<1184:BLCARE>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Xue, M., 2002: Density currents in shear flows: Effects of rigid lid and cold-pool internal circulation, and application to squall line dynamics. Quart. J. Roy. Meteor. Soc., 128, 4773, https://doi.org/10.1256/00359000260498789.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zipser, E. J., 1977: Mesoscale and convective-scale downdrafts as distinct components of squall-line structure. Mon. Wea. Rev., 105, 15681589, https://doi.org/10.1175/1520-0493(1977)105<1568:MACDAD>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zrnić, D. S., and J. T. Lee, 1983: Investigation of the detectability and lifetime of gust fronts and other weather hazards to aircraft. FAA Rep. DOT/FAA/PM-83/33, 58 pp.

  • Zrnić, D. S., and A. V. Ryzhkov, 1998: Observations of insects and birds with a polarimetric radar. IEEE Trans. Geosci. Remote Sens., 36, 661668, https://doi.org/10.1109/36.662746.

    • Crossref
    • Search Google Scholar
    • Export Citation
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