Predicting Seasonal Tornado Activity

James B. Elsner (@JBElsner)

Department of Geography, Florida State UniversityTallahassee, FL, USA

January 11, 2016AMS Annual Meeting

New Orleans, LA

Tyler Fricker, Holly M. Widen, Thomas H. Jagger, Victor Mesev

Motivation

I Climate models do not predict tornadoes

I Models can be used to predict necessary conditions

I But necessary does not imply sufficient

I Needed are evidence-based analysis and models

I Establish a baseline level of skill then a forecast model

I Outline:Spatial model (county level) ⇒ Space-time model (grid level)

Climate effects on tornadoes vary spatially

Kansas Tennessee

1

10

100

El Nino Neutral La Nina

Annual Number of Tornadoes [1954−2014]

Reliability of tornado records vary over time

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 2025Year

Annual number of tornado reports (Kansas)

50

100

150

200

1970 1980 1990 2000 2010Year

Num

ber

of E

F0+

Tor

nado

es

2012 Population Density [persons per square km]

Clark

Lyon

Gray

MarshallSmith

Rice

Thomas

Scott

Kiowa

Wichita

Clay

Cheyenne

Meade

Osborne

Franklin

Linn

Harvey

Sumner

Wilson

Cloud

Kearny

Sedgwick

Osage

Lincoln

Rush

RussellGove

Jewell

Mitchell

Republic

Trego

Stevens

Jackson

Stanton

Chautauqua

Stafford

McPhersonChase

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

HodgemanFinneyHamilton

Comanche

Edwards

Riley

MortonBarber

Miami

Sherman

Reno

Crawford

Woodson

Doniphan

Wabaunsee

Pratt

BrownPhillips

Leavenworth

Ellis

Kingman

Decatur

Lane

Norton

Cherokee

Atchison

Allen

Geary

Cowley

Ness

Morris

Rawlins

Greeley

Ford

Pottawatomie

Dickinson

Harper

JeffersonWyandotte

Bourbon

Seward

Nemaha

1 3 10 40 158 631

Number of EF0+ Tornadoes (1970−2014)0 10 20 30 40 50 60 70 80

Number of EF0+ Tornado Days (1970−2014)0 10 20 30 40 50

Observed Poisson

0

10

20

30

0 20 40 60 80 0 20 40 60 80Number of Tornadoes

Num

ber

of C

ount

ies

The number of tornado reports in each cell (Ts) is assumed tofollow a negative binomial distribution with mean (µs)

Ts |µs , rs ∼ NegBin(µs , rs)

µs = As exp(νs)

νs = β0 + β1 lpds + β2 (t − t0) + β3 lpds(t − t0) + us + vt

rs = As n

where NegBin(µs , rs) indicates that the conditional tornado counts(Ts |µs , rs) are described by a negative binomial distribution withmean µs and size rs , lpds represents the base two logarithm of thepopulation density during 2012 for each county, and t0 is the baseyear set to 1991 (middle year of the record).

The spatially correlated random effects us follows an intrinsicBesag formulation with a sum-to-zero constraint.

ui |{uj ,j 6=i , τ} ∼ N

1

mi

∑i∼j

uj ,1

miτ

,

where N is the normal distribution with mean 1/mi ·∑

i∼j uj andvariance 1/mi · 1/τ where mi is the number of neighbors of cell iand τ is the precision; i ∼ j indicates cells i and j are neighbors.Neighboring cells are determined by contiguity (queen’s rule). Theannual uncorrelated random effect, vt , is modeled as a sequence ofnormally distributed random variables, with mean zero andvariance 1/τ ′.

Number of EF0+ Tornadoes (1970−2014)0 10 20 30 40 50 60 70 80

Expected Annual Per County Tornado (EF0+) Rate0.5 0.75 1 1.25 1.5 1.75

Tornado (EF0+) Occurrence Rate [% Difference from Statewide Avg]−50 −40 −30 −20 −10 0 10 20 30 40 50

Oklahoma

Number of EF0+ Tornadoes (1970−2014)

0 100 km

10 20 30 40 50 60 70 80

Expected Annual Tornado (EF0+) Rate

0 100 km

0.7 0.8 0.9 1 1.1 1.2

A space-time model

Number of Tornadoes

32°N34°N36°N38°N40°N42°N44°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°N34°N36°N38°N40°N42°N44°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

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

1

10

El Nino Neutral La Nina

Annual Tennessee Tornado Fatalities by ENSO Phase [1954−2014]

Other predictors

I PNA

I Global wind oscillation

I NAO

Government efforts toward seasonal prediction of severeweather in the United States

Last March scientists held a workshop in D.C. to assess thefeasibility of developing severe weather outlooks on sub-seasonal &seasonal timescales. Two notable recommendations were made:

1. Experimental seasonal weather outlook will be issuedsometime this spring, and

2. Research activities for improving outlooks should besupported.