How to Build a Seasonal Tornado Forecast Model

Severe Convective Storms & Climate Workshop, Columbia University: How to build a seasonal tornado forecast model.

Transcript

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)
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 Kansas Tennessee 1 10 100 El Nino Neutral La Nina Annual Number of Tornadoes [1954−2014]
6. Model must deal with pathological tornado records Enhanced Fujita Scale Mobile Radar Doppler Radar Discovery of Microbursts Fujita Scale Tornado Spotter Network Tornado Watch and Warning Program Systematic Tabulation of Tornado Data 1900 1925 1950 1975 2000 2025 Year
7. Annual number of tornado reports (Kansas) 50 100 150 200 1970 1980 1990 2000 2010 Year Number of EF0+ Tornadoes
8. 2012 Population Density [persons per square km] Clark Lyon Gray Marshall Smith Rice Thomas Scott Kiowa Wichita Clay Cheyenne Meade Osborne Franklin Linn Harvey Sumner Wilson Cloud Kearny Sedgwick Osage Lincoln Rush Russell Gove Jewell Mitchell Republic Trego Stevens Jackson Stanton Chautauqua Stafford McPherson Chase Coffey Ottawa Saline Butler Wallace Montgomery Grant Washington Shawnee Sheridan Johnson Haskell Greenwood Barton Neosho Rooks Douglas Elk Labette Pawnee Graham Ellsworth Marion Anderson Logan Hodgeman Finney Hamilton Comanche Edwards Riley Morton Barber Miami Sherman Reno Crawford Woodson Doniphan Wabaunsee Pratt Brown Phillips Leavenworth Ellis Kingman Decatur Lane Norton Cherokee Atchison Allen Geary Cowley Ness Morris Rawlins Greeley Ford Pottawatomie Dickinson Harper Jefferson Wyandotte Bourbon Seward Nemaha 1 3 10 40 158 631
9. Number of EF0+ Tornadoes (1970−2014) 0 10 20 30 40 50 60 70 80
10. Observed Poisson 0 10 20 30 0 20 40 60 80 0 20 40 60 80 Number of Tornadoes Number of Counties
11. The number of tornado reports in each cell (Ts) is assumed to follow a negative binomial distribution (NegBin) with mean (µs) and parameter rs. Ts|µs, rs ∼ NegBin(µs, rs) µs = exp(Asνs) νs = β0 + β1 lpds + β2 (t − t0) + β3 lpds (t − t0) + us rs = As n where the mean of the distribution is linked to a structured additive response νs and the county area (As). The base-two log of county population density is lpds , t is the year, t0 is the base year set to 1991 (middle year of the record), n is the dispersion parameter, and us 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. ui |{uj,j=i , τ} ∼ N   1 mi i∼j uj , 1 mi τ   , where N is the normal distribution with mean 1/mi · i∼j uj and variance 1/mi · 1/τ where mi 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).
13. Tornado (EF0+) Occurrence Rate [% Difference from Statewide Avg] −50 −40 −30 −20 −10 0 10 20 30 40 50
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 0.0 0.1 0.2 0.3 0 5 10 15 % increase in tornado reports per doubling of population Posterior Density a 0.0 0.3 0.6 0.9 0 1 2 3 % increase in tornado reports per meter decrease in elevation roughness Posterior Density b 1Elsner, 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 (1970−2014) 0 40 80 120 160 200 240
16. Number of EF0+ Tornado Days (1970−2014) 0 20 40 60 80 100 120 140
17. Expected Annual Tornado (EF0+) Occurrence Rate [per 100 km square region] 0.03 0.06 0.12 0.25 0.5 1 2 4 8
18. A space-time model
19. Number of Tornadoes 32°N 34°N 36°N 38°N 40°N 42°N 44°N 0 500 km 1999 0 500 km 2000 0 500 km 2001 0 500 km 2002 0 500 km 2003 0 500 km 2004 0 500 km 2005 0 500 km 2006 32°N 34°N 36°N 38°N 40°N 42°N 44°N 0 500 km 2007 0 500 km 2008 0 500 km 2009 0 500 km 2010 100°W 90°W 85°W 0 500 km 2011 0 500 km 2012 100°W 90°W 85°W 0 500 km 2013 0 500 km 2014 1 2 4 8 16 32 64 128
20. A ENSO Effect on Tornadoes [% Change in Rate/S.D. Increase in ENSO Index] 32°N 34°N 36°N 38°N 40°N 42°N 44°N 100°W 95°W 90°W 85°W 0 500 km −20 −15 −10 −5 0 5 10 15 20 B Signficance Level 32°N 34°N 36°N 38°N 40°N 42°N 44°N 100°W 95°W 90°W 85°W 0 500 km 0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5
21. Effect on Tornadoes [% Change in Rate/S.D. Increase in NAO] 30°N 35°N 40°N 100°W 95°W 90°W 85°W −20 −15 −10 −5 0 5 10 15 20
22. Effect on Tornadoes [% Change in Rate/Deg. C Increase in WCA Temp] 30°N 35°N 40°N 100°W 95°W 90°W 85°W −30 −20 −10 0 10 20 30
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, 259–267.
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. 2011 Forecast (% of normal) enso = −2 nao = −0.5 td = 1.2 30°N 35°N 40°N 100°W 95°W 90°W 85°W 80% 90% 100% 110% 120% 130% 140% 150%
25. 2015 Hindcast (% of normal) enso = 0.8 nao = 0.25 td = −0.56 30°N 35°N 40°N 100°W 95°W 90°W 85°W 85 90 95 100 105 110 115
27. 1 10 El Nino Neutral La Nina Annual Tennessee Tornado Fatalities by ENSO Phase [1954−2014]
28. Preliminary forecast for 2016 2016 Forecast (% of normal) enso = 0 nao = 0 td = 0.54 30°N 35°N 40°N 100°W 95°W 90°W 85°W 100 101 102 103 104