How To Build a Seasonal Tornado Model

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1 How To Build a Seasonal Tornado Model James B. Elsner (@JBElsner) Department of Geography, Florida State University Tallahassee, FL March 10, 2016 SC&C Workshop, Columbia University, New York, NY Help: Thomas Jagger, Tyler Fricker, Holly M. Widen Money: RPI2.0 (Mark Guishard, John Wardman)

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4 Why are we doing this? Dynamical models can t predict tornadoes They can predict conditions necessary But necessary does not imply sufficient Statistical models are needed, but how should they be made? Here I show you a way that is quite flexible Spatial model (county level) Space-time model (grid level)

5 Model must be spatial Annual Number of Tornadoes [ ] El Nino Neutral La Nina Kansas Tennessee

6 Model must deal with pathological tornado records Systematic Tabulation of Tornado Data Tornado Watch and Warning Program Tornado Spotter Network Fujita Scale Discovery of Microbursts Doppler Radar Mobile Radar Enhanced Fujita Scale Year

7 Annual number of tornado reports (Kansas) 200 Number of EF0+ Tornadoes Year

8 Cheyenne Rawlins Decatur Norton Phillips Smith Jewell Republic Washington Marshall Brown Nemaha Doniphan Sherman Thomas Sheridan Graham Rooks Wallace Logan Gove Trego Ellis Greeley Wichita Scott Lane Ness Rush Pawnee Hamilton Kearny Finney Hodgeman Edwards Gray Ford Stanton Grant Haskell Kiowa Morton Stevens Seward Meade Clark Comanche Cloud Atchison Osborne Mitchell Clay Pottawatomie Jackson Riley Jefferson Leavenworth Ottawa Wyandott Lincoln Geary Shawnee Russell Wabaunsee Dickinson Douglas Johnson Saline Ellsworth Morris Osage Franklin Miami Barton Lyon Rice McPherson Marion Chase Coffey Anderson Linn Stafford Pratt Barber Harvey Reno GreenwoodWoodson Allen Bourbon Butler Sedgwick Kingman Wilson Neosho Crawford Elk Harper Sumner Cowley Chautauqua MontgomeryLabette Cherokee Population Density [persons per square km]

9 Number of EF0+ Tornadoes ( )

10 Observed Poisson 30 Number of Counties Number of Tornadoes

11 The number of tornado reports in each cell (T s ) is assumed to follow a negative binomial distribution (NegBin) with mean (µ s ) and parameter r s. T s µ s, r s NegBin(µ s, r s ) µ s = exp(a s ν s ) ν s = β 0 + β 1 lpd s + β 2 (t t 0 ) + β 3 lpd s (t t 0 ) + u s r s = A s n where the mean of the distribution is linked to a structured additive response ν s and the county area (A s ). The base-two log of county population density is lpd s, t is the year, t 0 is the base year set to 1991 (middle year of the record), n is the dispersion parameter, and u s is the random effects term.

12 To account for spatial correlation the random effects term follows an intrinsic Besag formulation with a sum-to-zero constraint. u i {u j,j i, τ} N 1 1 u j, τ, m i m i where N is the normal distribution with mean 1/m i i j u j and variance 1/m i 1/τ where m i is the number of neighbors of cell i and τ is the precision; i j indicates cells i and j are neighbors. Neighboring cells are determined by contiguity (queen s rule). i j

13 Tornado (EF0+) Occurrence Rate [% Difference from Statewide Avg]

14 The model was used to show that tornadoes are significantly more likely to occur over smooth terrain at a rate of 23%/10 m decrease in roughness. 1 a b Posterior Density Posterior Density % increase in tornado reports per doubling of population % increase in tornado reports per meter decrease in elevation roughness 1 Elsner, J. B., T. Fricker, H. M. Widen, et al., The relationship between elevation roughness and tornado activity: A spatial statistical model fit to data from the central Great Plains, Journal of Applied Meteorology and Climatology, in the press.

15 Number of EF0+ Tornadoes ( )

16 Number of EF0+ Tornado Days ( )

17 Expected Annual Tornado (EF0+) Occurrence Rate [per 100 km square region]

18 A space-time model

19 44 N 42 N 40 N 38 N 36 N 34 N 32 N N 42 N 40 N 38 N 36 N 34 N 32 N W 90 W 85 W 100 W 90 W 85 W Number of Tornadoes

20 A B 44 N 44 N 42 N 42 N 40 N 40 N 38 N 38 N 36 N 36 N 34 N 34 N 32 N 32 N 100 W 95 W 90 W 85 W 100 W 95 W 90 W 85 W ENSO Effect on Tornadoes [% Change in Rate/S.D. Increase in ENSO Index] Signficance Level

21 40 N 35 N 30 N 100 W 95 W 90 W 85 W Effect on Tornadoes [% Change in Rate/S.D. Increase in NAO]

22 40 N 35 N 30 N 100 W 95 W 90 W 85 W Effect on Tornadoes [% Change in Rate/Deg. C Increase in WCA Temp]

23 Elsner, J. B., and H. M. Widen, 2014: Predicting spring tornado activity in the central Great Plains by March 1st. Monthly Weather Review, 142,

24 According to Aon, the costliest U.S. thunderstorm outbreak on record occurred in late April 2011 across the Lower Mississippi Valley and cost insurers $7.7 bn in today s dollars. 40 N 35 N 30 N 100 W 95 W 90 W 85 W 80% 90% 100% 110% 120% 130% 140% 150% 2011 Forecast (% of normal) enso = 2 nao = 0.5 td = 1.2

25 40 N 35 N 30 N 100 W 95 W 90 W 85 W Hindcast (% of normal) enso = 0.8 nao = 0.25 td = 0.56

26

27 Annual Tennessee Tornado Fatalities by ENSO Phase [ ] El Nino Neutral La Nina 10 1

28 Preliminary forecast for N 35 N 30 N 100 W 95 W 90 W 85 W Forecast (% of normal) enso = 0 nao = 0 td = 0.54

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