Role of Convective Events in Wind Design

1. Role of Convective Events in Wind Design Franklin T. Lombardo University of Illinois at Urbana-Champaign National Wind Technology Center Uncertainty and Risk in the Design Process for Wind July 12, 2016

2. Introduction • Windstorms (e.g., hurricane, thunderstorm, nor’easter, tornado) cause most loss of any natural hazard in the U.S. (NOAA) - ~80% damage due to convective events (thunderstorm, tornado) in 2014. • Perhaps surprisingly, relatively little is known about these types of events any how they affect structures – not explicitly designed for in codes and standards (incl. IEC) – same problems continue Damage Mitigation Loss Resistance • Thunderstorm and tornado wind fields affect each link of the chain differently than is currently assumed (and hence the risk) – uncertainty propagates through each link of the chain

3. Wind Design Needs • Improved classification of atmospheric conditions • Better reflection of actual site-specific, location-based conditions (stability, turbulence, wind shear) • Estimation of load conditions (single turbine, large wind plants) • Resistance and target reliability and associated risk with cognizance of cost • All of these steps (or links) come with some measure of uncertainty • Improvements Needed: measurement capabilities, probabilistic models, physical models

4. For tornadoes and thunderstorms, occurrence is relatively rare but cause a disproportionate amount of damage • Limited full-scale data – much is unknown and uncertainties are large – especially close to the surface • Probabilistic and physical differences than what is prescribed for design (and hence uncertainty and associated risk) Wind Design Challenges

5. Probabilistic Characterization - Thunderstorm • Percent annual maxima from thunderstorm (non-tropical)  3-s gust (surface data set) • Need to classify by storm type Highest recorded gust ~37 m/s (10m) from thunderstorm (“valid outlier”) • Thunderstorms dominate frequency and/or magnitude for a lot of U.S.

6. Probabilistic Characterization - Thunderstorm Location Uncertainty Single Station Uncertainty Akin to γL for same load, β will different

7. Probabilistic Characterization - Thunderstorm • When fit to GEV (c = 0 or c = c, thunderstorms display more variability) • ~ 800 U.S. surface stations (mainly airports)  location uncertainty “Tail Behavior” “Dispersion” • Approximate resolution of airport-based surface stations

8. Leads to the belief that these “outliers” are more common than previously thought (risk assessments are incorrect) • Could be the “design” events associated with Vref • What are the physical characteristics? Physical Characterization - Thunderstorm SWERF 10 m tower – Rantoul, IL (since June 2nd) – will measure loading

10. TTU 50 m, 200 m tower – Lubbock, TX Physical Characterization - Thunderstorm Eight (8) ramp-up events • How do we convert this information to Vref (e.g., hub height, 10 min - can’t use theory) • How do gust structure, turbulence compare to IEC “extreme” conditions? Do they change by location (site-specific)? • How do these events load structures differently?

11. Risk is generally assumed low – left out codes and standards • Recent events have challenged that thinking (e.g., Joplin, 2011). • Two major issues here with respect to wind plants • Plant risk > ‘point’ (damage area) risk (analysis is currently done) • Many areas where plants reside suffer from low population density (hence low damage indicator density) – tornadoes hard to “rate” Probabilistic Characterization - Tornado

12. If at some point, tornadoes are included partially or fully – need to understand physical characteristics • Very few records available – October 6, 2010 in Arizona Physical Characterization - Tornado • Sonic anemometer at 2.5 m AGL • Peak wind speed – 83 m/s

13. Arizona record has very unique aspects that likely will “load” a structure differently (will challenge current wind design): • Rapid changes in wind speed and direction (> 70 m/s2 acceleration) • Significant vertical component (±20-25 m/s @ 2.5 m) • Possibly due to multiple vortices Physical Characterization - Tornado

14. Climate Change • Convective events can’t be simulated in models • “Crude” downscaling using large-scale climate projections • Above: Modification of extreme value pdf • Right: Uncertainty in present (black) and future (gray) climate for n-year MRI

15. Convective Events Overview (Thunderstorm, Tornado) • Cause most wind damage annually • Rare by nature; not measured or understood adequately for wind loading • Need to address in wind design • Convective Events Conclusions • Probabilistic and physical nature different than currently assumed in wind design, literature • Clear location-based differences • Likely to induce different loading than prescribed • Significant uncertainties in estimation – what is a “design event”? • More measurements, models needed Conclusions/Future Work

16. How do we estimate Vref at a wind plant site with short records (1-4 yr.) from on-site met mast where thunderstorm-generated winds may dominate? • Combination of surface stations and reanalysis data (“extend” time series) • Develop probability distribution of observed thunderstorm events correlate to large-scale parameters (e.g., wind shear, instability)) Dual Characterization - Thunderstorm

17. Multi-Hazard Analysis (Wind-Ice) R~500 yr.