predicting seasonal tornado activity - confex
TRANSCRIPT
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.