• Agard, J. V., 2017: Dependence of continental severe convective instability on climatological environmental conditions. Ph.D. dissertation, Massachusetts Institute of Technology, 119 pp.

  • Agard, J. V., and K. Emanuel, 2017: Clausius–Clapeyron scaling of peak CAPE in continental convective storm environments. J. Atmos. Sci., 74, 30433054, https://doi.org/10.1175/JAS-D-16-0352.1.

    • Search Google Scholar
    • Export Citation
  • Anderson, J. L., 2001: An ensemble adjustment Kalman filter for data assimilation. Mon. Wea. Rev., 129, 28842903, https://doi.org/10.1175/1520-0493(2001)129<2884:AEAKFF>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Anderson-Frey, A. K., Y. P. Richardson, A. R. Dean, R. L. Thompson, and B. T. Smith, 2016: Investigation of near-storm environments for tornado events and warnings. Wea. Forecasting, 31, 17711790, https://doi.org/10.1175/WAF-D-16-0046.1.

    • Search Google Scholar
    • Export Citation
  • Araújo da Silva, M. P., F. Rocadenbosch, R. L. Tanamachi, and U. Saeed, 2022: Motivating a synergistic mixing-layer height retrieval method using backscatter lidar returns and microwave-radiometer temperature observations. IEEE Trans. Geosci. Remote Sens., 60, 118, https://doi.org/10.1109/TGRS.2022.3158401.

    • Search Google Scholar
    • Export Citation
  • Ashley, W. S., 2007: Spatial and temporal analysis of tornado fatalities in the United States: 1880–2005. Wea. Forecasting, 22, 12141228, https://doi.org/10.1175/2007WAF2007004.1.

    • Search Google Scholar
    • Export Citation
  • Blumberg, W. G., D. D. Turner, U. Löhnert, and S. Castleberry, 2015: Ground-based temperature and humidity profiling using spectral infrared and microwave observations. Part II: Actual retrieval performance in clear-sky and cloudy conditions. J. Appl. Meteor. Climatol., 54, 23052319, https://doi.org/10.1175/JAMC-D-15-0005.1.

    • Search Google Scholar
    • Export Citation
  • Blumberg, W. G., T. J. Wagner, D. D. Turner, and J. Correia Jr., 2017: Quantifying the accuracy and uncertainty of diurnal thermodynamic profiles and convection indices derived from the Atmospheric Emitted Radiance Interferometer. J. Appl. Meteor. Climatol., 56, 27472766, https://doi.org/10.1175/JAMC-D-17-0036.1.

    • Search Google Scholar
    • Export Citation
  • Bryan, G. H., 2008: On the computation of pseudoadiabatic entropy and equivalent potential temperature. Mon. Wea. Rev., 136, 52395245, https://doi.org/10.1175/2008MWR2593.1.

    • Search Google Scholar
    • Export Citation
  • Childs, S. J., R. S. Schumacher, and J. T. Allen, 2018: Cold-season tornadoes: Climatological and meteorological insights. Wea. Forecasting, 33, 671691, https://doi.org/10.1175/WAF-D-17-0120.1.

    • Search Google Scholar
    • Export Citation
  • Chou, M.-D., 1990: Parameterization for the absorption of solar radiation by O2 and CO2 with application to climate studies. J. Climate, 3, 209217, https://doi.org/10.1175/1520-0442(1990)003<0209:PFTAOS>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Chou, M.-D., 1992: A solar radiation model for climate studies. J. Atmos. Sci., 49, 762772, https://doi.org/10.1175/1520-0469(1992)049<0762:ASRMFU>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Chou, M.-D., and M. J. Suarez, 1994: An efficient thermal infrared radiation parameterization for use in general circulation models. NASA Tech Memo. 104606, 85 pp., https://archive.org/details/nasa_techdoc_19950009331.

  • Clough, S. A., M. W. Shephard, E. J. Mlawer, J. S. Delamere, M. J. Iacono, K. Cady-Pereira, S. Boukabara, and P. D. Brown, 2005: Atmospheric radiative transfer modeling: A summary of the AER codes. J. Quant. Spectrosc. Radiat. Transfer, 91, 233244, https://doi.org/10.1016/j.jqsrt.2004.05.058.

    • Search Google Scholar
    • Export Citation
  • Dawson, D. T., II, L. J. Wicker, E. R. Mansell, Y. Jung, and M. Xue, 2013: Low-level polarimetric radar signatures in EnKF analyses and forecasts of the May 8, 2003 Oklahoma City tornadic supercell: Impact of multimoment microphysics and comparisons with observation. Adv. Meteor., 2013, 818394, https://doi.org/10.1155/2013/818394.

    • Search Google Scholar
    • Export Citation
  • Dawson, D. T., II, E. R. Mansell, Y. Jung, L. J. Wicker, M. R. Kumjian, and M. Xue, 2014: Low-level ZDR signatures in supercell forward flanks: The role of size sorting and melting of hail. J. Atmos. Sci., 71, 276299, https://doi.org/10.1175/JAS-D-13-0118.1.

    • Search Google Scholar
    • Export Citation
  • Doswell, C. A., III, and E. Rasmussen, 1994: The effect of neglecting the virtual temperature correction on CAPE calculations. Wea. Forecasting, 9, 625629, https://doi.org/10.1175/1520-0434(1994)009<0625:TEONTV>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Emanuel, K. A., Ed., 1994: Deep convective regimes. Atmospheric Convection, Oxford University Press, 463–487.

  • Geerts, B., and Coauthors, 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.

    • Search Google Scholar
    • Export Citation
  • Hart, J., P. Marsh, and R. Thompson, 2016: SPC Mesoscale Analysis. NOAA/NWS Storm Prediction Center, accessed 25 January 2018, https://www.spc.noaa.gov/exper/mesoanalysis/.

  • Helmus, J. J., and S. M. Collis, 2016: The Python ARM radar toolkit (Py-ART), a library for working with weather radar data in the Python programming language. J. Open Res. Software, 4, e25, https://doi.org/10.5334/jors.119.

    • Search Google Scholar
    • Export Citation
  • İnce, T., S. J. Frasier, A. Muschinski, and A. L. Pazmany, 2003: An S-band frequency-modulated continuous-wave boundary layer profiler: Description and initial results. Radio Sci., 38, 1072, https://doi.org/10.1029/2002RS002753.

    • Search Google Scholar
    • Export Citation
  • Janjić, Z. I., 2003: A nonhydrostatic model based on a new approach. Meteor. Atmos. Phys., 82, 271285, https://doi.org/10.1007/s00703-001-0587-6.

    • Search Google Scholar
    • Export Citation
  • Jung, Y., M. Xue, and M. Tong, 2012: Ensemble Kalman filter analyses of the 29–30 May 2004 Oklahoma tornadic thunderstorm using one- and two-moment bulk microphysics schemes, with verification against polarimetric radar data. Mon. Wea. Rev., 140, 14571475, https://doi.org/10.1175/MWR-D-11-00032.1.

    • Search Google Scholar
    • Export Citation
  • King, J. R., M. D. Parker, K. D. Sherburn, and G. M. Lackmann, 2017: Rapid evolution of cool season, low-CAPE severe thunderstorm environments. Wea. Forecasting, 32, 763779, https://doi.org/10.1175/WAF-D-16-0141.1.

    • Search Google Scholar
    • Export Citation
  • Kis, A. K., and J. M. Straka, 2010: Nocturnal tornado climatology. Wea. Forecasting, 25, 545561, https://doi.org/10.1175/2009WAF2222294.1.

    • Search Google Scholar
    • Export Citation
  • Klein, P., and Coauthors, 2015: LABLE: A multi-institutional, student-led, atmospheric boundary layer experiment. Bull. Amer. Meteor. Soc., 96, 17431764, https://doi.org/10.1175/BAMS-D-13-00267.1.

    • Search Google Scholar
    • Export Citation
  • Knuteson, R. O., and Coauthors, 2004a: Atmospheric Emitted Radiance Interferometer. Part I: Instrument design. J. Atmos. Oceanic Technol., 21, 17631776, https://doi.org/10.1175/JTECH-1662.1.

    • Search Google Scholar
    • Export Citation
  • Knuteson, R. O., and Coauthors, 2004b: Atmospheric Emitted Radiance Interferometer. Part II: Instrument performance. J. Atmos. Oceanic Technol., 21, 17771789, https://doi.org/10.1175/JTECH-1663.1.

    • Search Google Scholar
    • Export Citation
  • Koch, S., and E. N. Rasmussen, 2016: VORTEX-Southeast: Program and activities. 28th Conf. on Severe Local Storms, Portland, OR, Amer. Meteor. Soc., 3.1, https://ams.confex.com/ams/28SLS/webprogram/Paper300782.html.

  • Lange, D., J. Tiana-Alsina, U. Saeed, S. Tomás, and F. Rocadenbosch, 2014: Atmospheric boundary layer height monitoring using a Kalman filter and backscatter lidar returns. IEEE Trans. Geosci. Remote Sens., 52, 47174728, https://doi.org/10.1109/TGRS.2013.2284110.

    • Search Google Scholar
    • Export Citation
  • Lange, D., F. Rocadenbosch, J. Tiana-Alsina, and S. Frasier, 2015: Atmospheric boundary layer height estimation using a Kalman filter and a frequency-modulated continuous-wave radar. IEEE Trans. Geosci. Remote Sens., 53, 33383349, https://doi.org/10.1109/TGRS.2014.2374233.

    • Search Google Scholar
    • Export Citation
  • Lee, T., M. Buban, and T. Meyers, 2016a: NOAA/ARL/ATDD micrometeorological tower data, version 1.0. UCAR/NCAR–Earth Observing Laboratory, accessed 25 August 2017, https://doi.org/10.5065/d6bg2mbj.

  • Lee, T., M. Buban, and T. Meyers, 2016b: NOAA/ATDD mobile radiosonde data, version 1.0. UCAR/NCAR–Earth Observing Laboratory, accessed 25 August 2017, https://doi.org/10.5065/d68k77fn.

  • Mansell, E. R., C. L. Ziegler, and E. C. Bruning, 2010: Simulated electrification of a small thunderstorm with two-moment bulk microphysics. J. Atmos. Sci., 67, 171194, https://doi.org/10.1175/2009JAS2965.1.

    • Search Google Scholar
    • Export Citation
  • May, R. M., and Coauthors, 2021: MetPy: A Python package for meteorological data. Unidata, accessed 10 February 2020, https://doi.org/10.5065/D6WW7G29.

  • Mlawer, E. J., S. J. Taubman, P. D. Brown, M. J. Iacono, and S. A. Clough, 1997: Radiative transfer for inhomogeneous atmospheres: RRTM, a validated correlated-k model for the longwave. J. Geophys. Res., 102, 16 66316 682, https://doi.org/10.1029/97JD00237.

    • Search Google Scholar
    • Export Citation
  • National Weather Service, 2016: NWS Huntsville Storm Surveys from 03/31/2016. NOAA, accessed 14 August 2016, https://www.weather.gov/hun/hunsur_2016-03-31_surveys#priceville.

  • NOAA/NWS Storm Prediction Center, 2016: Mesoscale analysis data. NOAA, accessed 13 March 2016, http://catalog.eol.ucar.edu/vortex-se_2016/129/date/2016/03/31.

  • Noilhan, J., and S. Planton, 1989: A simple parameterization of land surface processes for meteorological models. Mon. Wea. Rev., 117, 536549, https://doi.org/10.1175/1520-0493(1989)117<0536:ASPOLS>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • NWS Storm Prediction Center, 2016a: Mar 31, 2016 0600 UTC Day 1 Convective Outlook. NOAA, accessed 18 August 2016, https://www.spc.noaa.gov/products/outlook/archive/2016/day1otlk_20160331_1630.html.

  • NWS Storm Prediction Center, 2016b: Mesoscale Discussion 310. NOAA, accessed 18 August 2016, https://www.spc.noaa.gov/products/md/2016/md0310.html.

  • NWS Storm Prediction Center, 2016c: Mesoscale Discussion 315. NOAA, accessed 18 August 2016, http://www.spc.noaa.gov/products/md/2016/md0315.html.

  • NWS Storm Prediction Center, 2016d: Mesoscale Discussion 317. NOAA, accessed 18 August 2016, http://www.spc.noaa.gov/products/md/2016/md0317.html.

  • NWS Storm Prediction Center, 2016e: Tornado Watch 72. NOAA, accessed 18 August 2016, http://www.spc.noaa.gov/products/watch/2016/ww0072.html.

  • NWS Storm Prediction Center, 2016f: Mesoscale Discussion 311. NOAA, accessed 18 August 2016, https://www.spc.noaa.gov/products/md/2016/md0311.html.

  • Pleim, J. E., and A. Xiu, 1995: Development and testing of a surface flux and planetary boundary layer model for application in mesoscale models. J. Appl. Meteor., 34, 1632, https://doi.org/10.1175/1520-0450-34.1.16.

    • Search Google Scholar
    • Export Citation
  • Rasmussen, E., and S. Koch, 2016: VORTEX-Southeast: Lessons learned and early results. 28th Conf. on Severe Local Storms, Portland, OR, Amer. Meteor. Soc., 3.2, https://ams.confex.com/ams/28SLS/webprogram/Paper301782.html.

  • Rasmussen, E., and Coauthors, 2015: VORTEX-Southeast Program Overview. National Severe Storms Laboratory Rep., 36 pp., ftp://ftp.atdd.noaa.gov/pub/vortexse/ProjectOverview.pdf.

  • Sherburn, K. D., and M. D. Parker, 2014: Climatology and ingredients of significant severe convection in high-shear, low-CAPE environments. Wea. Forecasting, 29, 854877, https://doi.org/10.1175/WAF-D-13-00041.1.

    • Search Google Scholar
    • Export Citation
  • Sherburn, K. D., M. D. Parker, J. R. King, and G. M. Lackmann, 2016: Composite environments of severe and nonsevere high-shear, low-CAPE convective events. Wea. Forecasting, 31, 18991927, https://doi.org/10.1175/WAF-D-16-0086.1.

    • Search Google Scholar
    • Export Citation
  • Tanamachi, R. L., S. J. Frasier, J. Waldinger, A. LaFleur, D. D. Turner, and F. Rocadenbosch, 2019: Progress toward characterization of the atmospheric boundary layer over northern Alabama using observations by a vertically pointing, S-band profiling radar during VORTEX-Southeast. J. Atmos. Oceanic Technol., 36, 22212246, https://doi.org/10.1175/JTECH-D-18-0224.1.

    • Search Google Scholar
    • Export Citation
  • Tong, M., and M. Xue, 2005: Ensemble Kalman filter assimilation of Doppler radar data with a compressible nonhydrostatic model: OSS experiments. Mon. Wea. Rev., 133, 17891807, https://doi.org/10.1175/MWR2898.1.

    • Search Google Scholar
    • Export Citation
  • Tong, M., and M. Xue, 2008: Simultaneous estimation of microphysical parameters and atmospheric state with simulated radar data and ensemble square root Kalman filter. Part I: Sensitivity analysis and parameter identifiability. Mon. Wea. Rev., 136, 16301648, https://doi.org/10.1175/2007MWR2070.1.

    • Search Google Scholar
    • Export Citation
  • Turner, D. D., 2016: OU/NSSL CLAMPS microwave radiometer and surface meteorological data, version 1.0. UCAR/NCAR–Earth Observing Laboratory, accessed 15 October 2016, https://doi.org/10.5065/D6154FFP.

  • Turner, D. D., and U. Löhnert, 2014: Information content and uncertainties in thermodynamic profiles and liquid cloud properties retrieved from the ground-based Atmospheric Emitted Radiance Interferometer (AERI). J. Appl. Meteor. Climatol., 53, 752771, https://doi.org/10.1175/JAMC-D-13-0126.1.

    • Search Google Scholar
    • Export Citation
  • Turner, D. D., and W. G. Blumberg, 2019: Improvements to the AERIoe thermodynamic profile retrieval algorithm. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens., 12, 13391354, https://doi.org/10.1109/JSTARS.2018.2874968.

    • Search Google Scholar
    • Export Citation
  • Wagner, T. J., P. M. Klein, and D. D. Turner, 2019: A new generation of ground-based mobile platforms for active and passive profiling of the boundary layer. Bull. Amer. Meteor. Soc., 100, 137153, https://doi.org/10.1175/BAMS-D-17-0165.1.

    • Search Google Scholar
    • Export Citation
  • Weckwerth, T. M., K. J. Weber, D. D. Turner, and S. M. Spuler, 2016: Validation of a new water vapor micropulse differential absorption lidar (DIAL). J. Atmos. Oceanic Technol., 33, 23532372, https://doi.org/10.1175/JTECH-D-16-0119.1.

    • Search Google Scholar
    • Export Citation
  • Whitaker, J. S., and T. M. Hamill, 2012: Evaluating methods to account for system errors in ensemble data assimilation. Mon. Wea. Rev., 140, 30783089, https://doi.org/10.1175/MWR-D-11-00276.1.

    • Search Google Scholar
    • Export Citation
  • Wulfmeyer, V., and Coauthors, 2015: A review of the remote sensing of lower-tropospheric thermodynamic profiles and its indispensable role for the understanding and simulation of water and energy cycles. Rev. Geophys., 53, 819895, https://doi.org/10.1002/2014RG000476.

    • Search Google Scholar
    • Export Citation
  • Xue, M., J. Zong, and K. K. Droegemeier, 1996: Parameterization of PBL turbulence in a multi-scale non-hydrostatic model. Preprints, 11th Conf. on Numerical Weather Prediction, Norfolk, VA, Amer. Meteor. Soc., 363–365.

  • Xue, M., K. K. Droegemeier, and V. Wong, 2000: The Advanced Regional Prediction System (ARPS)—A multi-scale nonhydrostatic atmospheric simulation and prediction model. Part I: Model dynamics and verification. Meteor. Atmos. Phys., 75, 161193, https://doi.org/10.1007/s007030070003.

    • Search Google Scholar
    • Export Citation
  • Xue, M., and Coauthors, 2001: The Advanced Regional Prediction System (ARPS)—A multi-scale nonhydrostatic atmospheric simulation and prediction tool. Part II: Model physics and applications. Meteor. Atmos. Phys., 76, 143165, https://doi.org/10.1007/s007030170027.

    • Search Google Scholar
    • Export Citation
  • Xue, M., D.-H. Wang, J.-D. Gao, K. Brewster, and K. K. Droegemeier, 2003: The Advanced Regional Prediction System (ARPS), storm-scale numerical weather prediction and data assimilation. Meteor. Atmos. Phys., 82, 139170, https://doi.org/10.1007/s00703-001-0595-6.

    • Search Google Scholar
    • Export Citation
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Factors Affecting the Rapid Recovery of CAPE on 31 March 2016 during VORTEX-Southeast

Allison T. LaFleuraDepartment of Earth, Atmospheric, and Planetary Sciences, Purdue University, West Lafayette, Indiana

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Robin L. TanamachiaDepartment of Earth, Atmospheric, and Planetary Sciences, Purdue University, West Lafayette, Indiana

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Daniel T. Dawson IIaDepartment of Earth, Atmospheric, and Planetary Sciences, Purdue University, West Lafayette, Indiana

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David D. TurnerbNOAA/Global Systems Laboratory, Boulder, Colorado

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Abstract

In this study, we analyze various sources of CAPE in the environment and their contributions to its time tendency that will complement forecast models and operational analyses that are relatively temporally (∼1 h) coarse. As a case study, the relative roles of direct insolation and near-surface moisture advection in the recovery CAPE on 31 March 2016 in northern Alabama are examined using VORTEX-Southeast (VORTEX-SE) observations and numerical simulations. In between rounds of nontornadic morning storms and tornadic evening storms, CAPE over the VORTEX-SE domain increased from near zero to at least 500 J kg−1. A timeline of the day’s events is provided with a focus on the evolution of the lower levels of the atmosphere. We focus on its responses to solar insolation and moisture advection, which we hypothesize as the main mechanisms behind the recovery of CAPE. Data from the University of Massachusetts S-Band frequency-modulated, continuous-wave (FMCW) radar and NOAA National Severe Storms Laboratory (NSSL) Collaborative Lower Atmospheric Mobile Profiling System (CLAMPS), and high-resolution EnKF analyses from the Advanced Regional Prediction System (ARPS) are used to characterize the boundary layer evolution in the pre-tornadic storm environment. It is found that insolation-driven surface diabatic heating was the primary driver of rapid CAPE recovery on this day. The methodology developed in this case can be applied in other scenarios to diagnose the primary drivers of CAPE development.

Significance Statement

The mechanisms by which atmospheric instability recovers can vary widely and are often a source of uncertainty in forecasting. We want to understand how and why the environment destabilized enough to produce an evening tornado following morning storms on 31 March 2016. To do this, we used model data and observations from a collocated radar and profiler. It was found that heating from the sun at the surface was the primary cause of destabilization in the environment.

© 2023 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: Allison LaFleur, alafleu@purdue.edu

Abstract

In this study, we analyze various sources of CAPE in the environment and their contributions to its time tendency that will complement forecast models and operational analyses that are relatively temporally (∼1 h) coarse. As a case study, the relative roles of direct insolation and near-surface moisture advection in the recovery CAPE on 31 March 2016 in northern Alabama are examined using VORTEX-Southeast (VORTEX-SE) observations and numerical simulations. In between rounds of nontornadic morning storms and tornadic evening storms, CAPE over the VORTEX-SE domain increased from near zero to at least 500 J kg−1. A timeline of the day’s events is provided with a focus on the evolution of the lower levels of the atmosphere. We focus on its responses to solar insolation and moisture advection, which we hypothesize as the main mechanisms behind the recovery of CAPE. Data from the University of Massachusetts S-Band frequency-modulated, continuous-wave (FMCW) radar and NOAA National Severe Storms Laboratory (NSSL) Collaborative Lower Atmospheric Mobile Profiling System (CLAMPS), and high-resolution EnKF analyses from the Advanced Regional Prediction System (ARPS) are used to characterize the boundary layer evolution in the pre-tornadic storm environment. It is found that insolation-driven surface diabatic heating was the primary driver of rapid CAPE recovery on this day. The methodology developed in this case can be applied in other scenarios to diagnose the primary drivers of CAPE development.

Significance Statement

The mechanisms by which atmospheric instability recovers can vary widely and are often a source of uncertainty in forecasting. We want to understand how and why the environment destabilized enough to produce an evening tornado following morning storms on 31 March 2016. To do this, we used model data and observations from a collocated radar and profiler. It was found that heating from the sun at the surface was the primary cause of destabilization in the environment.

© 2023 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: Allison LaFleur, alafleu@purdue.edu
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