• Apke, J. M., J. R. Mecikalski, and C. P. Jewett, 2016: Analysis of mesoscale atmospheric flows above mature deep convection using super rapid scan geostationary satellite data. J. Appl. Meteor. Climatol., 55, 18591887, https://doi.org/10.1175/JAMC-D-15-0253.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bedka, K. M., and J. R. Mecikalski, 2005: Application of satellite-derived atmospheric motion vectors for estimating mesoscale flows. J. Appl. Meteor., 44, 17611772, https://doi.org/10.1175/JAM2264.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bedka, K. M., and K. Khlopenkov, 2016: A probabilistic multispectral pattern recognition method for detection of overshooting cloud tops using passive satellite imager observations. J. Appl. Meteor. Climatol., 55, 19832005, https://doi.org/10.1175/JAMC-D-15-0249.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bedka, K. M., C. S. Velden, R. A. Petersen, W. F. Feltz, and J. R. Mecikalski, 2009: Comparisons of satellite-derived atmospheric motion vectors, rawinsondes, and NOAA wind profiler observations. J. Appl. Meteor. Climatol., 48, 15421561, https://doi.org/10.1175/2009JAMC1867.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bedka, K. M., J. Brunner, R. Dworak, W. Feltz, J. Otkin, and T. Greenwald, 2010: Objective satellite-based detection of overshooting tops using infrared window channel brightness temperature gradients. J. Appl. Meteor. Climatol., 49, 181202, https://doi.org/10.1175/2009JAMC2286.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bedka, K. M., R. Dworak, J. Brunner, and W. Feltz, 2012: Validation of satellite-based objective overshooting cloud-top detection methods using CloudSat cloud profiling radar observations. J. Appl. Meteor. Climatol., 51, 18111822, https://doi.org/10.1175/JAMC-D-11-0131.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bedka, K. M., C. Wang, R. Rogers, L. D. Carey, W. Feltz, and J. Kanak, 2015: Examining deep convective cloud evolution using total lightning, WSR-88D, and GOES-14 super rapid scan datasets. Wea. Forecasting, 30, 571590, https://doi.org/10.1175/WAF-D-14-00062.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bedka, K. M., E. Murillo, C. R. Homeyer, B. Scarino, and H. Mersiovski, 2018: The above anvil cirrus plume: An important severe weather indicator in visible and infrared satellite imagery. Wea. Forecasting, 33, 11591181, https://doi.org/10.1175/WAF-D-18-0040.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Blair, S. F., D. R. Deroche, J. M. Boustead, J. W. Leighton, B. L. Barjenbruch, and W. P. Gargan, 2011: A radar-based assessment of the detectability of giant hail. Electron. J. Severe Storms Meteor., 6 (7), http://www.ejssm.org/ojs/index.php/ejssm/article/viewArticle/87.

    • Search Google Scholar
    • Export Citation
  • Boustead, J. M., 2008: Using maximum storm-top divergence and the vertical freezing level to forecast hail size. 24th Conf. on Severe Local Storms, Savannah, GA, Amer. Meteor. Soc., P6.6, https://ams.confex.com/ams/pdfpapers/142145.pdf.

  • Bresky, W. C., and J. Daniels, 2006: The feasibility of an optical flow algorithm for estimating atmospheric motion. Proc. Eighth Int. Winds Workshop, Beijing, China, EUMETSAT, 24–28.

  • Bresky, W. C., J. Daniels, A. A. Bailey, and S. T. Wanzong, 2012: New methods toward minimizing the slow speed bias associated with atmospheric motion vectors. J. Appl. Meteor. Climatol., 51, 21372151, https://doi.org/10.1175/JAMC-D-11-0234.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bruning, E., N. Wang, R. Albrecht, and K. Gopalan, 2011: A Lightning Mapping Array for West Texas: Deployment and research plans. Fifth Conf. on Meteorological Applications of Lightning Data, Seattle, WA, Amer. Meteor. Soc., 6.2, https://ams.confex.com/ams/91Annual/webprogram/Paper184224.html.

  • Brunner, J. C., S. A. Ackerman, A. S. Bachmeier, and R. M. Rabin, 2007: A quantitative analysis of the enhanced-V feature in relation to severe weather. Wea. Forecasting, 22, 853872, https://doi.org/10.1175/WAF1022.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Carletta, N. D., G. L. Mullendore, M. Starzec, B. Xi, Z. Feng, and X. Dong, 2016: Determining the best method for estimating the observed level of maximum detrainment based on radar reflectivity. Mon. Wea. Rev., 144, 29152926, https://doi.org/10.1175/MWR-D-15-0427.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Cooney, J. W., K. P. Bowman, C. R. Homeyer, and T. M. Fenske, 2018: Ten year analysis of tropopause-overshooting convection using GridRad data. J. Geophys. Res. Atmos., 123, 329343, https://doi.org/10.1002/2017JD027718.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Crum, T. D., and R. L. Alberty, 1993: The WSR-88D and the WSR-88D operational support facility. Bull. Amer. Meteor. Soc., 74, 16691687, https://doi.org/10.1175/1520-0477(1993)074<1669:TWATWO>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Deierling, W., and W. A. Petersen, 2008: Total lightning activity as an indicator of updraft characteristics. J. Geophys. Res., 113, D16210, https://doi.org/10.1029/2007JD009598.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Emersic, C., P. L. Heinselman, D. R. MacGorman, and E. C. Bruning, 2011: Lightning activity in a hail-producing storm observed with phased-array radar. Mon. Wea. Rev., 139, 18091825, https://doi.org/10.1175/2010MWR3574.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Frame, J., P. Markowski, Y. Richardson, J. Straka, and J. Wurman, 2009: Polarimetric and dual-Doppler radar observations of the Lipscomb County, Texas, supercell thunderstorm on 23 May 2002. Mon. Wea. Rev., 137, 544561, https://doi.org/10.1175/2008MWR2425.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Fujita, T. T., 1981: Tornadoes and downbursts in the context of generalized planetary scales. J. Atmos. Sci., 38, 15111534, https://doi.org/10.1175/1520-0469(1981)038<1511:TADITC>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Fujita, T. T., 1982: Principle of stereoscopic height computations and their applications to stratospheric cirrus over severe thunderstorms. J. Meteor. Soc. Japan, 60, 355368.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Griffin, S. M., K. M. Bedka, and C. S. Velden, 2016: A method for calculating the height of overshooting convective cloud tops using satellite-based IR imager and CloudSat cloud profiling radar observations. J. Appl. Meteor. Climatol., 55, 479491, https://doi.org/10.1175/JAMC-D-15-0170.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hasler, A. F., K. Palaniappan, C. Kambhammetu, P. Black, E. Uhlhorn, and D. Chesters, 1998: High-resolution wind fields within the inner core and eye of a mature tropical cyclone from GOES 1-min images. Bull. Amer. Meteor. Soc., 79, 24832496, https://doi.org/10.1175/1520-0477(1998)079<2483:HRWFWT>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hayden, C. M., and R. J. Purser, 1995: Recursive filter objective analysis of meteorological fields: Applications to NESDIS operational processing. J. Appl. Meteor. Climatol., 34, 315, https://doi.org/10.1175/1520-0450-34.1.3.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Holmlund, K., 1998: The utilization of statistical properties of satellite-derived atmospheric motion vectors to derive quality indicators. Wea. Forecasting, 13, 10931104, https://doi.org/10.1175/1520-0434(1998)013<1093:TUOSPO>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Homeyer, C. R., and K. P. Bowman, 2017: Algorithm description document for version 3.1 of the three-dimensional gridded NEXRAD WSR-88D radar (GridRad) dataset. University of Oklahoma/Texas A&M University, 23 pp., http://gridrad.org/pdf/GridRad-v3.1-Algorithm-Description.pdf.

  • Homeyer, C. R., J. D. McAuliffe, and K. M. Bedka, 2017: On the development of above-anvil cirrus plumes in extratropical convection. J. Atmos. Sci., 74, 16171633, https://doi.org/10.1175/JAS-D-16-0269.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kalina, E. A., and Coauthors, 2017: The ice water paths of small and large ice species in Hurricanes Arthur (2014) and Irene (2011). J. Appl. Meteor. Climatol., 56, 13831404, https://doi.org/10.1175/JAMC-D-16-0300.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Knupp, K. R., 1996: Structure and evolution of a long-lived, microburst-producing storm. Mon. Wea. Rev., 124, 27852806, https://doi.org/10.1175/1520-0493(1996)124<2785:SAEOAL>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Knupp, K. R., and Coauthors, 2014: Meteorological overview of the devastating 27 April 2011 tornado outbreak. Bull. Amer. Meteor. Soc., 95, 10411062, https://doi.org/10.1175/BAMS-D-11-00229.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Koshak, W. J., and Coauthors, 2004: North Alabama Lightning Mapping Array (LMA): VHF source retrieval algorithm and error analyses. J. Atmos. Oceanic Technol., 21, 543558, https://doi.org/10.1175/1520-0426(2004)021<0543:NALMAL>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Krehbiel, P. R., R. J. Thomas, W. Rison, T. Hamlin, J. Harlin, and M. Davis, 2000: GPS-based mapping system reveals lightning inside storms. Eos, Trans. Amer. Geophys. Union, 81, 2125, https://doi.org/10.1029/00EO00014.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Krehbiel, P. R., W. Rison, and R. Thomas, 2012: Lightning mapping observations during DC3 in northern Colorado. 2012 Fall Meeting, San Francisco, CA, Amer. Geophys. Union, Abstract AE12A-05.

  • Lakshmanan, V., T. Smith, G. Stumpf, and K. Hondl, 2007: The Warning Decision Support System–Integrated Information. Wea. Forecasting, 22, 596612, https://doi.org/10.1175/WAF1009.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lang, T. J., S. A. Rutledge, B. Dolan, P. Krehbiel, W. Rison, and D. T. Lindsey, 2014: Lightning in wildfire smoke plumes observed in Colorado during summer 2012. Mon. Wea. Rev., 142, 489507, https://doi.org/10.1175/MWR-D-13-00184.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lemon, L. R., and C. A. Doswell, 1979: Severe thunderstorm evolution and mesocyclone structure as related to tornadogenesis. Mon. Wea. Rev., 107, 11841197, https://doi.org/10.1175/1520-0493(1979)107<1184:STEAMS>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lemon, L. R., D. W. Burgess, and R. A. Brown, 1978: Tornadic storm airflow and morphology derived from single-Doppler radar measurements. Mon. Wea. Rev., 106, 4860, https://doi.org/10.1175/1520-0493(1978)106<0048:TSAAMD>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Line, W. E., T. J. Schmit, D. T. Lindsey, and S. J. Goodman, 2016: Use of geostationary super rapid scan satellite imagery by the Storm Prediction Center. Wea. Forecasting, 31, 483494, https://doi.org/10.1175/WAF-D-15-0135.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • McCann, D. W., 1983: The Enhanced-V: A satellite observable severe storm signature. Mon. Wea. Rev., 111, 887894, https://doi.org/10.1175/1520-0493(1983)111<0887:TEVASO>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • McCaul, E. W., Jr., J. C. Bailey, J. Hall, S. J. Goodman, R. J. Blakeslee, and D. E. Buechler, 2005: A flash clustering algorithm for North Alabama Lightning Mapping Array data. Conf. on Meteorological Applications of Lightning Data, San Diego, CA, Amer. Meteor. Soc., 5.3, https://ams.confex.com/ams/Annual2005/techprogram/paper_84373.htm.

  • Mohr, C. G., L. J. Miller, R. L. Vaughan, and H. W. Frank, 1986: The merger of mesoscale datasets into a common Cartesian format for efficient and systematic analyses. J. Atmos. Oceanic Technol., 3, 143161, https://doi.org/10.1175/1520-0426(1986)003<0143:TMOMDI>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • NCEI, 2018: Storm events database. National Centers for Environmental Information, accessed 10 August 2016, https://www.ncdc.noaa.gov/stormevents/.

  • Nieman, S. J., J. Schmetz, and W. P. Menzel, 1993: A comparison of several techniques to assign heights to cloud tracers. J. Appl. Meteor., 32, 15591568, https://doi.org/10.1175/1520-0450(1993)032<1559:ACOSTT>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Nieman, S. J., W. P. Menzel, C. M. Hayden, D. Gray, S. T. Wanzong, C. S. Velden, and J. Daniels, 1997: Fully automatic cloud drift winds in NESDIS operations. Bull. Amer. Meteor. Soc., 78, 11211133, https://doi.org/10.1175/1520-0477(1997)078<1121:FACDWI>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Orf, L., R. Wilhelmson, B. Lee, C. Finley, and A. Houston, 2017: Evolution of a long-track violent tornado within a simulated supercell. Bull. Amer. Meteor. Soc., 98, 4568, https://doi.org/10.1175/BAMS-D-15-00073.1.

    • 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., 359–361.

  • Pruppacher, H. R., and J. D. Klett, 1997: Microphysics of Clouds and Precipitation. Kluwer Academic Publishers, 954 pp.

  • Purser, R. J., and R. McQuigg, 1982: A successive correction analysis scheme using recursive numerical filters. Met. O 11, Tech. Note 154, British Meteorological Service, 17 pp.

  • Ray, P. S., 1976: Vorticity and divergence fields within tornadic storms from dual-Doppler observations. J. Appl. Meteor., 15, 879890, https://doi.org/10.1175/1520-0450(1976)015<0879:VADFWT>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ray, P. S., K. K. Wagner, K. W. Johnson, J. J. Stephens, W. C. Bumgarner, and E. A. Mueller, 1978: Triple-Doppler observations of a convective storm. J. Appl. Meteor., 17, 12011212, https://doi.org/10.1175/1520-0450(1978)017<1201:TDOOAC>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ray, P. S., C. L. Ziegler, W. Bumgarner, and R. J. Serafin, 1980: Single- and multiple-Doppler radar observations of tornadic storms. Mon. Wea. Rev., 108, 16071625, https://doi.org/10.1175/1520-0493(1980)108<1607:SAMDRO>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Rison, W., R. J. Thomas, P. R. Krehbiel, T. Hamlin, and J. Harlin, 1999: A GPS-based three-dimensional lightning mapping system: Initial observations in central New Mexico. Geophys. Res. Lett., 26, 35733576, https://doi.org/10.1029/1999GL010856.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Schmit, T. J., and Coauthors, 2013: Geostationary Operational Environmental Satellite (GOES)-14 super rapid scan operations to prepare for GOES-R. J. Appl. Remote Sens., 7, 073462, https://doi.org/10.1117/1.JRS.7.073462.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Schultz, C. J., W. A. Petersen, and L. D. Carey, 2009: Preliminary development and evaluation of lightning jump algorithms for the real-time detection of severe weather. J. Appl. Meteor. Climatol., 48, 25432563, https://doi.org/10.1175/2009JAMC2237.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Schultz, C. J., W. A. Petersen, and L. D. Carey, 2011: Lightning and severe weather: A comparison between total and cloud-to-ground lightning trends. Wea. Forecasting, 26, 744755, https://doi.org/10.1175/WAF-D-10-05026.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Schultz, C. J., and Coauthors, 2012: Dual-polarization tornadic debris signatures. Part I: Examples and utility in an operational setting. Electron. J. Oper. Meteor., 13, 120137.

    • Search Google Scholar
    • Export Citation
  • Schultz, C. J., L. D. Carey, E. V. Schultz, and R. J. Blakeslee, 2015: Insight into the kinematic and microphysical processes that control lightning jumps. Wea. Forecasting, 30, 15911621, https://doi.org/10.1175/WAF-D-14-00147.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Schultz, C. J., L. D. Carey, E. V. Schultz, and R. J. Blakeslee, 2017: Kinematic and microphysical significance of lightning jumps versus nonjump increases in total flash rate. Wea. Forecasting, 32, 275288, https://doi.org/10.1175/WAF-D-15-0175.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Setvák, M., and Coauthors, 2010: Satellite-observed cold-ring-shaped features atop deep convective clouds. Atmos. Res., 97, 8096, https://doi.org/10.1016/j.atmosres.2010.03.009.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Starzec, M., C. R. Homeyer, and G. L. Mullendore, 2017: Storm Labeling in Three Dimensions (SL3D): A volumetric radar echo and dual-polarization updraft classification algorithm. Mon. Wea. Rev., 145, 11271145, https://doi.org/10.1175/MWR-D-16-0089.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Velden, C. S., C. M. Hayden, S. J. Nieman, W. P. Menzel, S. Wanzong, and J. S. Goerss, 1997: Upper-tropospheric winds derived from geostationary satellite water vapor observations. Bull. Amer. Meteor. Soc., 78, 173195, https://doi.org/10.1175/1520-0477(1997)078<0173:UTWDFG>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Velden, C. S., T. L. Olander, and S. Wanzong, 1998: The impact of multispectral GOES-8 wind information on Atlantic tropical cyclone track forecasts in 1995. Part II: NOGAPS forecasts. Mon. Wea. Rev., 126, 12021218, https://doi.org/10.1175/1520-0493(1998)126<1202:TIOMGW>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Velden, C. S., and Coauthors, 2005: Recent innovations in deriving tropospheric winds from meteorological satellites. Bull. Amer. Meteor. Soc., 86, 205223, https://doi.org/10.1175/BAMS-86-2-205.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wang, P. K., 2003: Moisture plumes above thunderstorm anvils and their contributions to cross-tropopause transport of water vapor in midlatitudes. J. Geophys. Res., 108, 4194, https://doi.org/10.1029/2002JD002581.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wang, P. K., 2007: The thermodynamic structure atop a penetrating convective thunderstorm. Atmos. Res., 83, 254262, https://doi.org/10.1016/j.atmosres.2005.08.010.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Witt, A., and S. P. Nelson, 1991: The use of single-Doppler radar for estimating maximum hailstone size. J. Appl. Meteor., 30, 425431, https://doi.org/10.1175/1520-0450(1991)030<0425:TUOSDR>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wu, Q., H.-Q. Wang, Y.-J. Lin, Y.-Z. Zhuang, and Y. Zhang, 2016: Deriving AMVs from geostationary satellite images using optical flow algorithm based on polynomial expansion. J. Atmos. Oceanic Technol., 33, 17271747, https://doi.org/10.1175/JTECH-D-16-0013.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Yost, C. R., and Coauthors, 2018: A prototype method for diagnosing high ice water content probability using satellite imager data. Atmos. Meas. Tech., 11, 16151637, https://doi.org/10.5194/amt-11-1615-2018.

    • Crossref
    • Search Google Scholar
    • Export Citation
All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 22 22 22
PDF Downloads 17 17 17

Relationships between Deep Convection Updraft Characteristics and Satellite-Based Super Rapid Scan Mesoscale Atmospheric Motion Vector–Derived Flow

View More View Less
  • 1 Atmospheric Sciences Department, University of Alabama in Huntsville, Huntsville, Alabama
  • | 2 NASA Langley Research Center, Hampton, Virginia
  • | 3 Universities Space Research Association, Huntsville, Alabama
  • | 4 School of Meteorology, University of Oklahoma, Norman, Oklahoma
  • | 5 Earth Systems Science Center, University of Alabama in Huntsville, Huntsville, Alabama
Restricted access

Abstract

Rapid acceleration of cloud-top outflow near vigorous storm updrafts can be readily observed in Geostationary Operational Environmental Satellite-14 (GOES-14) super rapid scan (SRS; 60 s) mode data. Conventional wisdom implies that this outflow is related to the intensity of updrafts and the formation of severe weather. However, from an SRS satellite perspective, the pairing of observed expansion and updraft intensity has not been objectively derived and documented. The goal of this study is to relate GOES-14 SRS-derived cloud-top horizontal divergence (CTD) over deep convection to internal updraft characteristics, and document evolution for severe and nonsevere thunderstorms. A new SRS flow derivation system is presented here to estimate storm-scale (<20 km) CTD. This CTD field is coupled with other proxies for storm updraft location and intensity such as overshooting tops (OTs), total lightning flash rates, and three-dimensional flow fields derived from dual-Doppler radar data. Objectively identified OTs with (without) matching CTD maxima were more (less) likely to be associated with radar-observed deep convection and severe weather reports at the ground, suggesting that some OTs were incorrectly identified. The correlation between CTD magnitude, maximum updraft speed, and total lightning was strongly positive for a nonsupercell pulse storm, and weakly positive for a supercell with multiple updraft pulses present. The relationship for the supercell was nonlinear, though larger flash rates are found during periods of larger CTD. Analysis here suggests that combining CTD with OTs and total lightning could have severe weather nowcasting value.

© 2018 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: Jason Apke, jason.apke@gmail.com

Abstract

Rapid acceleration of cloud-top outflow near vigorous storm updrafts can be readily observed in Geostationary Operational Environmental Satellite-14 (GOES-14) super rapid scan (SRS; 60 s) mode data. Conventional wisdom implies that this outflow is related to the intensity of updrafts and the formation of severe weather. However, from an SRS satellite perspective, the pairing of observed expansion and updraft intensity has not been objectively derived and documented. The goal of this study is to relate GOES-14 SRS-derived cloud-top horizontal divergence (CTD) over deep convection to internal updraft characteristics, and document evolution for severe and nonsevere thunderstorms. A new SRS flow derivation system is presented here to estimate storm-scale (<20 km) CTD. This CTD field is coupled with other proxies for storm updraft location and intensity such as overshooting tops (OTs), total lightning flash rates, and three-dimensional flow fields derived from dual-Doppler radar data. Objectively identified OTs with (without) matching CTD maxima were more (less) likely to be associated with radar-observed deep convection and severe weather reports at the ground, suggesting that some OTs were incorrectly identified. The correlation between CTD magnitude, maximum updraft speed, and total lightning was strongly positive for a nonsupercell pulse storm, and weakly positive for a supercell with multiple updraft pulses present. The relationship for the supercell was nonlinear, though larger flash rates are found during periods of larger CTD. Analysis here suggests that combining CTD with OTs and total lightning could have severe weather nowcasting value.

© 2018 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: Jason Apke, jason.apke@gmail.com
Save