Explicit Forecasts of Low-Level Rotation from Convection-Allowing Models for Next-Day Tornado Prediction

Ryan A. Sobash National Center for Atmospheric Research, Boulder, Colorado

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Glen S. Romine National Center for Atmospheric Research, Boulder, Colorado

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Craig S. Schwartz National Center for Atmospheric Research, Boulder, Colorado

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David J. Gagne II National Center for Atmospheric Research, Boulder, Colorado, and Center for Analysis and Prediction of Storms/School of Meteorology, University of Oklahoma, Norman, Oklahoma

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Morris L. Weisman National Center for Atmospheric Research, Boulder, Colorado

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Abstract

Three diagnostic fields were examined to assess their ability to act as surrogates for tornadoes in a convection-allowing ensemble system run during the spring of 2015. The diagnostics included midlevel (2–5 km AGL) updraft helicity (UH25), low-level (0–3 km AGL) updraft helicity (UH03), and low-level (1 km AGL) vertical relative vorticity (RVORT1). RVORT1 was used as a direct measure of low-level rotation strength. Each storm’s RVORT1 magnitude and near-storm environment properties were extracted from each hour’s forecasts using an object-based approach. The near-storm environments of storm objects with large magnitudes of RVORT1 were very similar to the environments identified as conducive for the development of tornadic supercells in previous proximity sounding-based studies (e.g., low lifted condensation levels and strong low-level shear). This motivated the use of RVORT1 as a direct surrogate for tornadoes, without the need to filter forecasts with environmental information. The relationship between UH25 and UH03 was also explored among the simulated storms; UH03 only exceeded UH25 in storms occurring within low-CAPE/high-shear environments, while UH03 rarely exceeded UH25 in traditional supercell environments. Next-day ensemble surrogate severe probability forecasts (E-SSPFs) for tornadoes were generated using these diagnostics for 92 forecasts, with thresholds based on the number of observed tornado reports. E-SSPFs for tornadoes using RVORT1 and UH03 were more skillful than E-SSPFs using UH25. The UH25 E-SSPFs possessed little skill, regardless of threshold or smoothing length scale. All E-SSPFs suffered from poor sharpness at skillful scales, with few forecast probabilities greater than 40%.

Corresponding author address: Dr. Ryan A. Sobash, NCAR/MMM, P.O. Box 3000, Boulder, CO 80307. E-mail: sobash@ucar.edu

Abstract

Three diagnostic fields were examined to assess their ability to act as surrogates for tornadoes in a convection-allowing ensemble system run during the spring of 2015. The diagnostics included midlevel (2–5 km AGL) updraft helicity (UH25), low-level (0–3 km AGL) updraft helicity (UH03), and low-level (1 km AGL) vertical relative vorticity (RVORT1). RVORT1 was used as a direct measure of low-level rotation strength. Each storm’s RVORT1 magnitude and near-storm environment properties were extracted from each hour’s forecasts using an object-based approach. The near-storm environments of storm objects with large magnitudes of RVORT1 were very similar to the environments identified as conducive for the development of tornadic supercells in previous proximity sounding-based studies (e.g., low lifted condensation levels and strong low-level shear). This motivated the use of RVORT1 as a direct surrogate for tornadoes, without the need to filter forecasts with environmental information. The relationship between UH25 and UH03 was also explored among the simulated storms; UH03 only exceeded UH25 in storms occurring within low-CAPE/high-shear environments, while UH03 rarely exceeded UH25 in traditional supercell environments. Next-day ensemble surrogate severe probability forecasts (E-SSPFs) for tornadoes were generated using these diagnostics for 92 forecasts, with thresholds based on the number of observed tornado reports. E-SSPFs for tornadoes using RVORT1 and UH03 were more skillful than E-SSPFs using UH25. The UH25 E-SSPFs possessed little skill, regardless of threshold or smoothing length scale. All E-SSPFs suffered from poor sharpness at skillful scales, with few forecast probabilities greater than 40%.

Corresponding author address: Dr. Ryan A. Sobash, NCAR/MMM, P.O. Box 3000, Boulder, CO 80307. E-mail: sobash@ucar.edu
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  • Anderson, J. L., 2001: An ensemble adjustment Kalman filter for data assimilation. Mon. Wea. Rev., 129, 28842903, doi:10.1175/1520-0493(2001)129<2884:AEAKFF>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Anderson, J. L., 2003: A local least squares framework for ensemble filtering. Mon. Wea. Rev., 131, 634642, doi:10.1175/1520-0493(2003)131<0634:ALLSFF>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Anderson, J. L., Hoar T. , Raeder K. , Liu H. , Collins N. , Torn R. , and Avellano A. , 2009: The Data Assimilation Research Testbed: A community facility. Bull. Amer. Meteor. Soc., 90, 12831296, doi:10.1175/2009BAMS2618.1.

    • Search Google Scholar
    • Export Citation
  • Brooks, H. E., 2006: A global view of severe thunderstorms: Estimating the current distribution and possible future changes. Preprints, Symp. on the Challenges of Severe Convective Storms, Atlanta, GA, Amer. Meteor. Soc., J4.2. [Available online at https://ams.confex.com/ams/Annual2006/techprogram/paper_102202.htm.]

  • Brooks, H. E., Doswell C. A. III, and Cooper J. , 1994: On the environments of tornadic and nontornadic mesocyclones. Wea. Forecasting, 9, 606618, doi:10.1175/1520-0434(1994)009<0606:OTEOTA>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Clark, A. J., Kain J. S. , Marsh P. T. , Correia J. Jr., Xue M. , and Kong F. , 2012: Forecasting tornado pathlengths using a three-dimensional object identification algorithm applied to convection-allowing forecasts. Wea. Forecasting, 27, 10901113, doi:10.1175/WAF-D-11-00147.1.

    • Search Google Scholar
    • Export Citation
  • Clark, A. J., Gao J. , Marsh P. , Smith T. , Kain J. , Correia J. , Xue M. , and Kong F. , 2013: Tornado pathlength forecasts from 2010 to 2011 using ensemble updraft helicity. Wea. Forecasting, 28, 387407, doi:10.1175/WAF-D-12-00038.1.

    • Search Google Scholar
    • Export Citation
  • Craven, J. P., and Brooks H. E. , 2004: Baseline climatology of sounding derived parameters associated with deep, moist convection. Natl. Wea. Dig., 28, 1324.

    • Search Google Scholar
    • Export Citation
  • Davis, C., Brown B. , and Bullock R. , 2006: Object-based verification of precipitation forecasts. Part I: Methodology and application to mesoscale rain areas. Mon. Wea. Rev., 134, 17721784, doi:10.1175/MWR3145.1.

    • Search Google Scholar
    • Export Citation
  • Dawson, D. T., II, Wicker L. J. , Mansell E. R. , and Tanamachi R. L. , 2012: Impact of the environmental low-level wind profile on ensemble forecasts of the 4 May 2007 Greensburg, Kansas, tornadic storm and associated mesocyclones. Mon. Wea. Rev., 140, 696716, doi:10.1175/MWR-D-11-00008.1.

    • Search Google Scholar
    • Export Citation
  • Eastin, M. D., and Link M. C. , 2009: Miniature supercells in an offshore outer rainband of Hurricane Ivan (2004). Mon. Wea. Rev., 137, 20812104, doi:10.1175/2009MWR2753.1.

    • Search Google Scholar
    • Export Citation
  • Edwards, R., Dean A. R. , Thompson R. L. , and Smith B. T. , 2012: Convective modes for significant severe thunderstorms in the contiguous United States. Part III: Tropical cyclone tornadoes. Wea. Forecasting, 27, 15071519, doi:10.1175/WAF-D-11-00117.1.

    • Search Google Scholar
    • Export Citation
  • Fawbush, E. J., and Miller R. C. , 1954: The types of air masses in which North American tornadoes form. Bull. Amer. Meteor. Soc., 35, 154166.

    • Search Google Scholar
    • Export Citation
  • Gagne, D. J., II, McGovern A. , Snook N. , Sobash R. , Labriola J. , Williams J. K. , Haupt S. E. , and Xue M. , 2016: Hagelslag: Scalable object-based severe weather analysis and forecasting. Proc. Sixth Symp. on Advances in Modeling and Analysis Using Python, New Orleans, LA, Amer. Meteor. Soc., 447. [Available online at https://ams.confex.com/ams/96Annual/webprogram/Paper280723.html.]

  • Gallo, B. T., Clark A. J. , and Dembek S. R. , 2016: Forecasting tornadoes using convection-permitting ensembles. Wea. Forecasting, 31, 273295, doi:10.1175/WAF-D-15-0134.1.

    • Search Google Scholar
    • Export Citation
  • Gallus, W. A., Jr., Snook N. A. , and Johnson E. V. , 2008: Spring and summer severe weather reports over the Midwest as a function of convective mode: A preliminary study. Wea. Forecasting, 23, 101113, doi:10.1175/2007WAF2006120.1.

    • Search Google Scholar
    • Export Citation
  • Jirak, I. L., Melick C. J. , and Weiss S. J. , 2014: Combining probabilistic ensemble information from the environment with simulated storm attributes to generate calibrated probabilities of severe weather hazards. Proc. 27th Conf. on Severe Local Storms, Madison, WI, Amer. Meteor. Soc., 2.5. [Available online at https://ams.confex.com/ams/27SLS/webprogram/Paper254649.html.]

  • Johnson, A., Wang X. , Kong F. , and Xue M. , 2011: Hierarchical cluster analysis of a convection-allowing ensemble during the Hazardous Weather Testbed 2009 Spring Experiment. Part I: Development of the object-oriented cluster analysis method for precipitation fields. Mon. Wea. Rev., 139, 36733693, doi:10.1175/MWR-D-11-00015.1.

    • Search Google Scholar
    • Export Citation
  • Kain, J. S., and Coauthors, 2008: Some practical considerations regarding horizontal resolution in the first generation of operational convection-allowing NWP. Wea. Forecasting, 23, 931952, doi:10.1175/WAF2007106.1.

    • Search Google Scholar
    • Export Citation
  • Kain, J. S., Dembek S. R. , Weiss S. J. , Case J. L. , Levit J. J. , and Sobash R. A. , 2010: Extracting unique information from high-resolution forecast models: Monitoring selected fields and phenomena every time step. Wea. Forecasting, 25, 15361542, doi:10.1175/2010WAF2222430.1.

    • Search Google Scholar
    • Export Citation
  • Lakshmanan, V., 2012: Automating the Analysis of Spatial Grids: A Practical Guide to Data Mining Geospatial Images for Human and Environmental Applications. Geotechnologies and the Environment, Vol. 6, Springer, 320 pp.

  • Lakshmanan, V., Hondl K. , and Rabin R. , 2009: An efficient, general-purpose technique for identifying storm cells in geospatial images. J. Atmos. Oceanic Technol., 26, 523537, doi:10.1175/2008JTECHA1153.1.

    • Search Google Scholar
    • Export Citation
  • Markowski, P. M., Straka J. M. , and Rasmussen E. N. , 2002: Direct surface thermodynamic observations within the rear-flank downdrafts of nontornadic and tornadic supercells. Mon. Wea. Rev., 130, 16921721, doi:10.1175/1520-0493(2002)130<1692:DSTOWT>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Markowski, P. M., and Coauthors, 2012: The pretornadic phase of the Goshen County, Wyoming, supercell of 5 June 2009 intercepted by VORTEX2. Part II: Intensification of low-level rotation. Mon. Wea. Rev., 140, 29162938, doi:10.1175/MWR-D-11-00337.1.

    • Search Google Scholar
    • Export Citation
  • McCaul, E. W., Jr., 1991: Buoyancy and shear characteristics of hurricane-tornado environments. Mon. Wea. Rev., 119, 19541978, doi:10.1175/1520-0493(1991)119<1954:BASCOH>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • McCaul, E. W., Jr., and Weisman M. L. , 1996: Simulations of shallow supercell storms in landfalling hurricane environments. Mon. Wea. Rev., 124, 408429, doi:10.1175/1520-0493(1996)124<0408:SOSSSI>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Mittermaier, M., and Roberts N. , 2010: Intercomparison of spatial forecast verification methods: Identifying skillful spatial scales using the fractions skill score. Wea. Forecasting, 25, 343354, doi:10.1175/2009WAF2222260.1.

    • Search Google Scholar
    • Export Citation
  • Naylor, J., Gilmore M. S. , Thompson R. L. , Edwards R. , and Wilhelmson R. B. , 2012: Comparison of objective supercell identification techniques using an idealized cloud model. Mon. Wea. Rev., 140, 20902102, doi:10.1175/MWR-D-11-00209.1.

    • Search Google Scholar
    • Export Citation
  • Rasmussen, E. N., 2003: Refined supercell and tornado forecast parameters. Wea. Forecasting, 18, 530535, doi:10.1175/1520-0434(2003)18<530:RSATFP>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Rasmussen, E. N., and Blanchard D. O. , 1998: A baseline climatology of sounding-derived supercell and tornado forecast parameters. Wea. Forecasting, 13, 11481164, doi:10.1175/1520-0434(1998)013<1148:ABCOSD>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Roberts, N. M., and Lean H. W. , 2008: Scale-selective verification of rainfall accumulations from high-resolution forecasts of convective events. Mon. Wea. Rev., 136, 7897, doi:10.1175/2007MWR2123.1.

    • Search Google Scholar
    • Export Citation
  • Rotunno, R., 1979: A study in tornado-like vortex dynamics. J. Atmos. Sci., 36, 140155, doi:10.1175/1520-0469(1979)036<0140:ASITLV>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Schenkman, A. D., Xue M. , and Hu M. , 2014: Tornadogenesis in a high-resolution simulation of the 8 May 2003 Oklahoma City supercell. J. Atmos. Sci., 71, 130154, doi:10.1175/JAS-D-13-073.1.

    • Search Google Scholar
    • Export Citation
  • Schwartz, C. S., Romine G. S. , Sobash R. A. , Fossell K. R. , and Weisman M. L. , 2015: NCAR’s experimental real-time convection-allowing ensemble prediction system. Wea. Forecasting, 30, 16451654, doi:10.1175/WAF-D-15-0103.1.

    • Search Google Scholar
    • Export Citation
  • Skamarock, W. C., and Coauthors, 2008: A description of the Advanced Research WRF version 3. NCAR Tech. Note NCAR/TN-475+STR, 113 pp., doi:10.5065/D6DZ069T.

  • Skinner, P. S., Weiss C. C. , French M. M. , Bluestein H. B. , Markowski P. M. , and Richardson Y. P. , 2014: VORTEX2 observations of a low-level mesocyclone with multiple internal rear-flank downdraft momentum surges in the 18 May 2010 Dumas, Texas, supercell. Mon. Wea. Rev., 142, 29352960, doi:10.1175/MWR-D-13-00240.1.

    • Search Google Scholar
    • Export Citation
  • Smith, B. T., Thompson R. L. , Dean A. R. , and Marsh P. T. , 2015: Diagnosing the conditional probability of tornado damage rating using environmental and radar attributes. Wea. Forecasting, 30, 914932, doi:10.1175/WAF-D-14-00122.1.

    • Search Google Scholar
    • Export Citation
  • Sobash, R. A., Kain J. S. , Bright D. R. , Dean A. R. , Coniglio M. C. , and Weiss S. J. , 2011: Probabilistic forecast guidance for severe thunderstorms based on the identification of extreme phenomena in convection-allowing model forecasts. Wea. Forecasting, 26, 714728, doi:10.1175/WAF-D-10-05046.1.

    • Search Google Scholar
    • Export Citation
  • Sobash, R. A., Schwartz C. S. , Romine G. S. , Fossell K. R. , and Weisman M. L. , 2016: Severe weather prediction using storm surrogates from an ensemble forecasting system. Wea. Forecasting, 31, 255271, doi:10.1175/WAF-D-15-0138.1.

    • Search Google Scholar
    • Export Citation
  • Stensrud, D. J., and Coauthors, 2009: Convective-scale warn-on-forecast system: A vision for 2020. Bull. Amer. Meteor. Soc., 90, 14871499, doi:10.1175/2009BAMS2795.1.

    • Search Google Scholar
    • Export Citation
  • Thompson, R. L., Edwards R. , and Hart J. A. , 2002: Evaluation and interpretation of the supercell composite and significant tornado parameters at the Storm Prediction Center. Preprints, 21st Conf. on Severe Local Storms/19th Conf. on Weather Analysis and Forecasting/15th Conf. on Numerical Weather Prediction, San Antonio, TX, Amer. Meteor. Soc., J3.2. [Available online at https://ams.confex.com/ams/pdfpapers/46942.pdf.]

  • Thompson, R. L., Edwards R. , Hart J. A. , Elmore K. L. , and Markowski P. , 2003: Close proximity soundings within supercell environments obtained from the Rapid Update Cycle. Wea. Forecasting, 18, 12431261, doi:10.1175/1520-0434(2003)018<1243:CPSWSE>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Thompson, R. L., Smith B. T. , Grams J. S. , Dean A. R. , and Broyles C. , 2012: Convective modes for significant severe thunderstorms in the contiguous United States. Part II: Supercell and QLCS tornado environments. Wea. Forecasting, 27, 11361154, doi:10.1175/WAF-D-11-00116.1.

    • Search Google Scholar
    • Export Citation
  • Trapp, R. J., and Weisman M. L. , 2003: Low-level mesovortices within squall lines and bow echoes. Part II: Their genesis and implications. Mon. Wea. Rev., 131, 28042823, doi:10.1175/1520-0493(2003)131<2804:LMWSLA>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Trapp, R. J., Mitchell E. D. , Tipton G. A. , Effertz D. W. , Watson A. I. , Andra D. L. Jr., and Magsig M. A. , 1999: Descending and nondescending tornadic vortex signatures detected by WSR-88Ds. Wea. Forecasting, 14, 625639, doi:10.1175/1520-0434(1999)014<0625:DANTVS>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Trapp, R. J., Stumpf G. J. , and Manross K. L. , 2005: A reassessment of the percentage of tornadic mesocyclones. Wea. Forecasting, 20, 680687, doi:10.1175/WAF864.1.

    • Search Google Scholar
    • Export Citation
  • Wakimoto, R. M., Cai H. , and Murphey H. V. , 2004: The Superior, Nebraska, supercell during BAMEX. Bull. Amer. Meteor. Soc., 85, 10951106, doi:10.1175/BAMS-85-8-1095.

    • Search Google Scholar
    • Export Citation
  • Weisman, M. L., and Klemp J. B. , 1982: The dependence of numerically simulated convective storms on vertical wind shear and buoyancy. Mon. Wea. Rev., 110, 504520, doi:10.1175/1520-0493(1982)110<0504:TDONSC>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Weisman, M. L., and Klemp J. B. , 1984: The structure and classification of numerically simulated convective storms in directionally varying wind shears. Mon. Wea. Rev., 112, 24792498, doi:10.1175/1520-0493(1984)112<2479:TSACON>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Weiss, S. J., and Vescio M. D. , 1998: Severe local storm climatology 1955–1996: Analysis of reporting trends and implications for NWS operations. Preprints, 18th Conf. on Severe Local Storms, Minneapolis, MN, Amer. Meteor. Soc., 536–539.

  • Wheatley, D. M., Knopfmeier K. H. , Jones T. A. , and Creager G. J. , 2015: Storm-scale data assimilation and ensemble forecasting with the NSSL Experimental Warn-on-Forecast System. Part I: Radar data experiments. Wea. Forecasting, 30, 17951817, doi:10.1175/WAF-D-15-0043.1.

    • Search Google Scholar
    • Export Citation
  • Wilks, D. S., 2006: Statistical Methods in the Atmospheric Sciences: An Introduction. 2nd ed. Academic Press, 627 pp.

  • Yussouf, N., Dowell D. C. , Wicker L. J. , Knopfmeier K. H. , and Wheatley D. M. , 2015: Storm-scale data assimilation and ensemble forecasts for the 27 April 2011 severe weather outbreak in Alabama. Mon. Wea. Rev., 143, 30443066, doi:10.1175/MWR-D-14-00268.1.

    • Search Google Scholar
    • Export Citation
  • Zhang, J., and Coauthors, 2016: Multi-radar multi-sensor (MRMS) quantitative precipitation estimation: Initial operating capabilities. Bull. Amer. Meteor. Soc., 97, 621638, doi:10.1175/BAMS-D-14-00174.1.

    • Search Google Scholar
    • Export Citation
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