1. NC STATE UNIVERSITY Matthew D. Parker North Carolina State Univ. Raleigh, NC CSTAR/CIMMSE: High-shear, low-CAPE (“HSLC”) tornadoes and sig. severe project tornadoes and sig. severe project 29 October 2010

2. individual report influence - 160 km radius Mean Tornado Environment: ML CAPE & 0-6 km Shear 2004-2005: 3277 tornado reports | 344 tornado days Max MLCAPE Over Plains Lower MLCAPE and Stronger Shear courtesy: Steve Weiss ML CAPE (J/kg, color fill), 0-6 km Shear (kt, blue barbs) (year=2004-2005, month=ALL)

3. ML CAPE ≥ 2000 J kg -1 | 0-6 km Shear ≥ 35 kt | ML CIN ≥ - 100 J kg -1 Integrated 2003 - 2006 “environment hours” High CAPE; Strong Shear; Moderate CIN Integrated 2003 - 2006 “environment hours” High CAPE; High Shear; Moderate CIN Max 240 hr or 60 hr/yr courtesy: Steve Weiss

4. ML CAPE ≤ 1000 J kg -1 | 0-6 km Shear ≥ 35 kt | 0-1 km Shear ≥ 20 kt ML LCL ≤ 1000 m | ML CIN ≥ - 100 J kg -1 Integrated 2003 - 2006 “environment hours” Low CAPE; High Shear; Low LCL; Mod. CIN Max 480 hr or 120 hr/yr courtesy: Steve Weiss

5. High CAPE | Strong Shear Low CAPE | Strong Shear 26% of F2+ tornadoes local axis 30-60 hr/year 22% of F2+ tornadoes widespread 80-120 hr/year 48% of all F2+ tornadoes courtesy: Steve Weiss

6. Other tidbits gleaned from the AMS conference on Severe Local Storms earlier this month: Approx 8% of CAPE 500 J/kg tors)Approx 8% of CAPE 500 J/kg tors) Approx 42% of North Carolina tors occur with CAPE<500 J/kgApprox 42% of North Carolina tors occur with CAPE<500 J/kg

7. Separation of ingredients by pattern: diurnal vs. nocturnaldiurnal vs. nocturnal warm sector vs. cool sector (incl. CAD/coastal front days)warm sector vs. cool sector (incl. CAD/coastal front days) warm season vs. cool seasonwarm season vs. cool season marginal lapse rates vs. favorable lapse rates with marginal θ e (low level flow trajectories)marginal lapse rates vs. favorable lapse rates with marginal θ e (low level flow trajectories) strongly vs. weakly forced (convective coverage, convective mode)strongly vs. weakly forced (convective coverage, convective mode) Events vs. nulls goals

8. Separation of ingredients by radar signature: QLCSs (“squall lines”): embedded mesovorticesQLCSs (“squall lines”): embedded mesovortices “Broken-S” patterns“Broken-S” patterns Miniature supercellsMiniature supercells others?others? Events vs. nulls Does super-res 88D change anything? What is Δt from first echoes to first warning? goals

9. SPC datasets: 1.gridded dataset of SPC’s RUC mesoanalyses, allowing hour-by-hour identification of “qualifying” environments a.frequencies of particular combinations of ingredients b.identification of nulls (qualifying environments with no storms, or non- severe storms) 2.database of subjectively determined storm modes for all tornado and sig severe reports a.sort by environmental ingredients and assess typical modes assoc. w/ severe b.sort by storm mode and assess typical environmental ingredients assoc. w/ each tools

10. WFO datasets: 1.NLDN data a.Objectively determine whether convection was observed in “qualifying environments” b.A counter-point to radar (are some HSLC cells comparatively low in dBZ, or comparatively inactive electrically?) 2.Case study data that has been archived in real time (WES, etc.) a.Even if dataset is not complete, it will help to have a “first look” at each case… we can always grab any missing data later b.Forecast shifts you’ve worked: products used, lessons learned, line of thinking, etc. tools

11. Intended collaborative framework NCSU SPCWFOs CI data ops needs Would like to model after successes in the NCSU/WFO/NHC-TPC collab.! (Steve Weiss, Andy Dean, Jared Guyer) tasks

12. Intended collaborative framework NCSU SPCWFOs CI feedback coord of effort coord of effort Would like to model after successes in the NCSU/WFO/NHC-TPC collab.! (Steve Weiss, Andy Dean, Jared Guyer) tasks

13. Intended collaborative framework NCSU SPCWFOs Their data and tools are the best thing available for identifying large numbers of qualifying cases and crunching statistics They are happy to participate as more than “data support” but would like to see regional WFOs be the leaders Accumulated wisdom: provide good lists of events and nulls worth studying “Crowd sourcing” of case studies… as many down and dirty case studies as possible so that we have a reality check for statistics Identify ways to improve ops-relevance Parker Supervise M.S. student Help case studies from CIs have some uniformity in approach & end-product Synthesize end products from all participants into tools and pubs M.S. student Composite environments of nulls using mesoanalyses “Longitudinal” studies of qualifying environments and radar patterns (by time of day, season, etc.) (Steve Weiss, Andy Dean, Jared Guyer) (CIs to be identified) tasks

14. Basic timeline for HSLC project: Now: preparatory discussions with CIs from regional WFOs and SPCNow: preparatory discussions with CIs from regional WFOs and SPC Spring 2011: recruit NCSU M.S. student to work on projectSpring 2011: recruit NCSU M.S. student to work on project Summer 2011: begin acquiring needed datasets from SPC and regional WFOs; CIs begin work as ableSummer 2011: begin acquiring needed datasets from SPC and regional WFOs; CIs begin work as able Fall 2011: M.S. student work beginsFall 2011: M.S. student work begins Summer 2013: project concludesSummer 2013: project concludes tasks

15. End products: A composite environment (from RUC analyses) of HSLC events and HSLC nullsA composite environment (from RUC analyses) of HSLC events and HSLC nulls Quantification of significant differences in ingredients for events vs. nullsQuantification of significant differences in ingredients for events vs. nulls Statistical assessment of radar signatures (convective mode and any “boutique” features) associated with events vs. nullsStatistical assessment of radar signatures (convective mode and any “boutique” features) associated with events vs. nulls Sub-composites and stats for groupings of interest (day vs. night, summer vs. winter, etc.)Sub-composites and stats for groupings of interest (day vs. night, summer vs. winter, etc.) A population of thorough case studies (events and nulls) whose environments and radar signatures will be compared to the environmental composites and radar statisticsA population of thorough case studies (events and nulls) whose environments and radar signatures will be compared to the environmental composites and radar statistics tasks

16. Feedback please! what are the current HSLC approaches used at your WFO?what are the current HSLC approaches used at your WFO? what tools would be most helpful in HSLC situations?what tools would be most helpful in HSLC situations? what aspects of this project hold the most promise (or what additional angles need to be considered)?what aspects of this project hold the most promise (or what additional angles need to be considered)?

17. Thought for down the road: Tying in RENCI/NCSU/WRF tools: can we use ensemble forecasts/analyses to quantify the probability distributions of CAPE and CIN in these cases where small errors in thermo variables could make all the difference? How can we provide a heads-up when forecast CAPE=0 but uncertainty is high?Tying in RENCI/NCSU/WRF tools: can we use ensemble forecasts/analyses to quantify the probability distributions of CAPE and CIN in these cases where small errors in thermo variables could make all the difference? How can we provide a heads-up when forecast CAPE=0 but uncertainty is high?