2. Statistical Severe Convective Risk Assessment Model (SSCRAM) SPC Mesoanalysis Data every hour from 2006-2014 (Bothwell et al. 2002) + CG NLDN Lightning Data 2006-2014 + Severe Reports 2006-2014 SSCRAM – NWA 2015 System that relates current meteorological conditions near thunderstorms to short-term forecasts of severe weather (2006- 2014).

3. SSCRAM Design – Gathering The Data SfcOA grid (40-km RUC-2/RAP)

4. + + + + + + + + + Lightning strikes in the initial hour. SSCRAM Design – Gathering The Data

5. + + + + + + + + + Grid the lightning strikes. 1 3 4 1 SSCRAM Design – Gathering The Data

6. + + + + + + + + + 1 3 4 1 -Example documentation: Top of hour at gridbox "4" -Date/Time -Centerpoint (lat,lon) -# strikes -Bunkers storm motion -Environmental info from SPC Mesoanalysis system SSCRAM Design – Gathering The Data

7. + + + + + + + + + 1 3 4 1 Bunkers et al. (2000) right-moving supercell motion. x represents top-of-hour grid center point SSCRAM Design – Gathering The Data

8. + + + + + + + + + 1 3 4 1 forecast point midway through first hour; gather all reports in area in first hour (40-km search radius) forecast point midway through second hour; gather all reports in area in the second hour (40-km search radius) 40 km SSCRAM Design – Gathering The Data 40 km

9. SSCRAM Objectives Produce probability distributions for numerous parameters. One of the most comprehensive datasets for severe forecasting. Sample size of over 3.8 million lightning/environment grid-hours. Relate downstream severe reports with diagnostic parameters and lightning, and consider null events. Explore use of diagnostic parameters in a prognostic manner – motivated by Doswell and Schultz (2006). Base future impacts from present atmospheric state. Incorporation of ongoing environmental information can contribute to “warn- on-forecast” (Stensrud et al. 2009) and “Forecasting a Continuum of Environmental Threats” initiatives (Rothfusz et al. 2013). Produce probability distributions for numerous parameters. One of the most comprehensive datasets for severe forecasting. Sample size of over 3.8 million lightning/environment grid-hours. Relate downstream severe reports with diagnostic parameters and lightning, and consider null events. Explore use of diagnostic parameters in a prognostic manner – motivated by Doswell and Schultz (2006). Base future impacts from present atmospheric state. Incorporation of ongoing environmental information can contribute to “warn- on-forecast” (Stensrud et al. 2009) and “Forecasting a Continuum of Environmental Threats” initiatives (Rothfusz et al. 2013). SSCRAM – NWA 2015

10. How to Read The Charts SSCRAM – NWA 2015 Conditional probability component – Probability of severe report within parameter range... given lightning in grid box and other constraints.  Incorporate null set. 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 Prob (%)

11. How to Read The Charts SSCRAM – NWA 2015 Relative frequency component (RF) – Proportion of severe-producing environments associated with parameter range (with listed constraints/lightning occurrence).  Strictly requires reports. RF (%) 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 RF (%) 0

12. How to Read The Charts SSCRAM – NWA 2015 Combined Prob (%) RF (%) 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

13. Most Unstable CAPE SSCRAM – NWA 2015 Prob (%) RF (%) 0 1 2 3 4 5 MUCAPE/1000 0 1 2 3 4 5  Increasing svr probabilities with increasing MUCAPE for wind/hail.  Most severe occurring for MUCAPE < 3000 J kg -1.  Probabilities flat-line for tornadoes.

14. Effective Bulk Shear SSCRAM – NWA 2015  Increasing svr probabilities with increasing effective shear (especially above 10-15 kt). Thompson et al. (2007) Prob (%) RF (%) 0 25 50 75 100

15. SVR WIND – Effective Bulk Shear (Vary MUCAPE) SSCRAM – NWA 2015 Thompson et al. (2007)  With increasing MUCAPE, bulk of severe-wind reports occur at weaker effective shear, and severe-wind probabilities increase.

16. Significant Tornado Parameter (STP) SSCRAM – NWA 2015  Increase in tor probabilities with increase in STP.  STP < 1 accounts for around 60% of weak-tor-linked grid boxes around 37% of sigtor-linked grid boxes. Thompson et al. (2012) Prob (%) RF (%) 0 5 10 15 20

17. Mixed-Layer Lifting Condensation Level Height (MLLCL) SSCRAM – NWA 2015 Surface-based, organized convection  Nearly flat-line probabilities.  MLLCL of 0.5-1.1 km account for bulk of tor-linked grid boxes. Prob (%) RF (%) 0.0 0.5 1.0 1.5 2.0 MLLCL/1000

18. Conclusions We have… Created and explained a system for pairing environmental information with severe-storm occurrences in the future (2 hours). Incorporates severe-report and null occurrences. Investigated ability for diagnostic parameters to provide probabilistic guidance for future severe-storm report occurrences. Useful for warn-on-forecast initiatives/FACETS. MUCAPE – utility in anticipating severe hail/wind. Effective shear – utility in anticipating all severe hazards. Bulk of severe wind occurs at decreasing effective shear for increasing MUCAPE. STP – utility in anticipating tornado occurrences. Tornadoes occur within an optimal range of MLLCL values, although MLLCL offers similar probabilities of tornado occurrences across multiple parameter ranges. We can apply this system to analyzing season variability of parameter utility for assessing tornado potential. SSCRAM – NWA 2015

19. References Bothwell, P. D., J. A. Hart, and R. L. Thompson, 2002: An integrated three-dimensional objective analysis scheme in use 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., JP3.1. [Available online at https://ams.confex.com/ams/pdfpapers/47482.pdf.] Bunkers, M. J., B. A. Klimowski, J. W. Zeitler, R. L. Thompson, and M. L. Weisman, 2000: Predicting supercell motion using a new hodograph technique. Wea. Forecasting, 15, 61–79. Doswell, C. A., III, and D. M. Schultz, 2006: On the use of indices and parameters in forecasting severe storms. Electronic J. Severe Storms Meteor., 1(3), 1–22. Rothfusz, L. P., E. Jacks, J. T. Ferree, G. J. Stumpf, and T. M. Smith, 2013: Next-Generation Warning Concept: Forecasting a Continuum of Environmental Threats (FACETs). 2nd Conf. on Weather Warnings and Communication, Nashville, TN, Amer. Meteor. Soc., 3.4. Stensrud, D. J., and Coauthors, 2009: Convective-scale warn-on-forecast system: A vision for 2020. Bull. Amer. Meteor. Soc., 90, 1487–1499. Thompson, R. L., C. M. Mead, and R. Edwards, 2007: Effective storm-relative helicity and bulk shear in supercell thunderstorm environments. Wea. Forecasting, 22, 102–115. Thompson, R. L., B. T. Smith, J. S. Grams, A. R. Dean, and C. Broyles, 2012: Convective modes for significant severe thunderstorms in the contiguous United States. Part II: Supercell and QLCS tornado environments. Wea. Forecasting, 27, 1136–1154. SSCRAM – NWA 2015