severe convective storms: a reinsurer’s perspective - tornado … · 2019-09-09 · 14% may jun...

Severe Convective Storms: A Reinsurer’s Perspective

Kirsten Orwig, Ph.D.Atmospheric Perils Specialist

Kenneth SlackSenior Treaty Underwriter

Tornado Summit, March 2 , 2 016

2016 Tornado Summit | Dr. Kirsten Orwig and Ken Slack

Severe Convective Storm Risk

• Risk dependent on:

– Frequency and severity of hazard

– Location re lative to hazard

– Structural vulnerability

– Portfolio s tructure and conditions

2


0

200

400

600

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Num

ber o

f Occ

urre

nces

per

Yea

r

F/EF5

F/EF4

F/EF3

F/EF2

F/EF1

F/EF0

US Severe Convective StormsTornado Statis tics

Doppler radar era

12 27

Jan3% Feb

3%Mar6%

Apr14%

May22%Jun

19%

Jul10%

Aug6%

Sep6%

Oct5%

Nov4%

Dec2%

3


US Convective StormsHail Statis tics

0

50

100

150

200

250

300

1990

1991

1992

1993

1994

1995

1996

1997

1998

1999

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2011

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2014

Num

ber o

f Day

s

<1 inch 1-2 inches 2-3 inches 3-4 inches 4-5 inches >5 inchesObserved max hail size

Feb2%

Mar6%

Apr13%

May19%

Jun20%

Jul16%

Aug12%

Sep6%

Oct3%

Nov – Jan3%

236

4


US Annual Insured Losses

$-

$5

$10

$15

$20

$25

$3019

91

1993

1994

1995

1996

1997

1998

1999

2000

2001

2002

2003

2004

2005

2006

2007

2008

2009

2010

2011

2012

2013

2014

2015

Billi

ons

(USD

201

5)

Annual Losses

$ 0 .5 B increase per yearon average

5


US Average Annual Insured Loss by State19 9 0 -2 015

Millions (2 0 1 5 USD)

6


Tornadoes and HailWorldwide Statis tics

0

300

600

900

1200

US Canada

0

300

600

900

1200

SouthAmerica

0

300

600

900

1200

Europe

0

300

600

900

1200

Africa

0

300

600

900

1200

Asia

0

300

600

900

1200

Australia/New Zealand

CombinedTornadoesHailSource : Swiss Re CatNet®.

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Worldwide Insured Losses2 014 Swiss Re Sigma Catastrophes

2 015 Sigma Reportcoming soon!

Source: Swiss Re http:/ / media.swissre .com/ documents/ sigma2 _2 0 1 5 _en_final.pdf

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http://media.swissre.com/documents/sigma2_2015_en_final.pdf


Severe Convective Storm Risk ModelingWhy is it so difficult?

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Basic Cat Modeling MethodologyThe four box model approach

Hazard Exposure Vulnerability Economics

Where, how often and how strong?

What damage degree? What is covered?

• Property value• Contents• Coverage type• Expected loss

Where, what and characteristics?


• Likelihood of tornado hitting any one point is extremely remote due to localized nature

• Hail swath and tornado path characteris tics

• Hail and tornado his torical records contain many biases

Hazard Considerations

11


• Spatial corre lation of damage

• Large wind speed gradient from outer to inner circulation

• Building orientation

Exposure ConsiderationsDamage re lative to location and time

Lower Damage Zone

Moderate Damage Zone

Peak Damage Zone

Moderate Damage Zone

Lower Damage Zone

Building

1 km

12


Example: Joplin Tornado Source : US Army Corps of Engineers

13


• 2 5 0 m wide tornado

• EF2 —winds to 13 5 mph (6 0 m/ s)

• Moving from WSW to ENE

• Debris impact would be low-moderate

Hypothetical Scenario

~265m

14


• The most vulnerable areas for this s ite :

– upwind-facing windows coinciding with peak pressures on roof

– lee-s ide overhead doors

• If envelope compromised then:

– uplift on roof from inside and out

– significant content damage

– greater like lihood of total loss

windows

peak negative pressures

on roof

overheaddoors

+

+ windows

15


Exposure ConsiderationsBuilding Contents

Source : NIST (2 0 1 4 ) NIST Technical Inves tigation of the May 2 2 2 0 1 1 Tornado in Joplin, MO

16

http://nvlpubs.nist.gov/nistpubs/NCSTAR/NIST.NCSTAR.3.pdf


Exposure ConsiderationsBusiness Interruption

Emergency Generator Building

Source : NIST (2 0 1 4 ) NIST Technical Inves tigation of the May 2 2 2 0 1 1 Tornado in Joplin, MO

Source : NOAA"Ten days after …restored power to all customers who were able to receive service ."

• 2 substations , 4 ,0 0 0 dis tribution poles and transmission towers , 1,5 0 0 transformers , and 110 miles of transmission/ dis tribution lines

• 5 0 ce ll towers downed or destroyed• 4 ,0 0 0 leaking water lines , 2 5 broken fire-service lines• 3 ,5 0 0 gas meters and 5 5 ,0 0 0 ft of gas main damaged

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2016 Tornado Summit | Dr. Kirsten Orwig and Ken SlackSource : Orwig PhD Dissertation,

Examining Strong Winds from a Time-Varying Perspective

• Building codes and s tandards do not include loads from tornado winds

• Stationary vs non-stationary

• Strength of wall-to-roof and wall-to-floor connections

Vulnerability ConsiderationsTornadoes vs Straight-line Winds

Source : NIST report on Joplin tornadohttp:/ / nvlpubs .nis t.gov/ nis tpubs/ NCSTAR/ NIST.NCSTAR.3 .pdf

Modeled Tornadowind speed and direction

Win

d sp

eed

(m/s

)W

ind

Dire

ctio

n

0 6 0 0 1 2 0 0 1 8 0 0 2 4 0 0 3 0 0 0 3 6 0 0Time (s)

0 6 0 0 1 2 0 0 1 8 0 0 2 4 0 0 3 0 0 0 3 6 0 0Time (s)

Measured in Hurricane Katrina

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• Not only vulnerable in EQ

• FEMA observations:

– Catastrophic collapse in several locations– Some failures from overload on long span roof system– Most failures occurred at wall-to-roof connections

Structural Performance in Tornado WindsTilt-Up Panel Walls

Puddle weld failures common

Source : FEMA (2 0 1 2 ) http:/ / www.fema.gov/ media-library-data/ 2 0 1 3 0 7 2 6 -1 8 2 7 -2 5 0 4 5 -3 8 3 0 / tornado_mat_chapter5 _5 0 8 .pdf

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http://www.fema.gov/media-library-data/20130726-1827-25045-3830/tornado_mat_chapter5_508.pdf


• Tilt-Up Concrete Association (TCA) task force report after Joplin

– Initial failures occurred in s tee l jois t roof system

– Panels themselves were very robust (were generally intact despite collapse)

– "Tilt-Up construction played no role in the failure ."

– Recommendations:

o Develop procedure for more predictable collapse performance

o Establish roof system design criteria "that has ultimate failure capacities…similar to over-s trength requirements for certain e lements in the se ismic design…"

o Send recommendations to ICC, FEMA, Steel Deck Institute , Stee l Jois t Institute , and others to develop s tandard codes, procedures , and products

Structural Performance in Tornado WindsTilt-Up Panel Walls

Source : TCA (2 0 1 2 ) http:/ / tilt-up.org/ tilt-uptoday/ wp-content/ uploads/ 2 0 1 2 / 0 3 / TCA-Report-2 0 1 -1 2 -0 1 .pdf

2 0

http://tilt-up.org/tilt-uptoday/wp-content/uploads/2012/03/TCA-Report-201-12-01.pdf


Source : NOAA/ NWS

“Missiles”

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• Highly localized, short-lived phenomena

• Large model domain coupled with high frequency makes probabilis tic modeling computationally intensive

– Most of southern Canadian provinces and entire eastern 2 / 3 of the United States is vulnerable to severe thunderstorms

– Millions of years of s imulation required for s ingle location convergence

• Hail data are based on subjective reports (e .g. pea-, penny-, baseball-s ized), and are mostly along roadways

• Tornado wind measurements are scarce , damage serves as a proxy for wind speed

– Rating based on maximum damage observed, which is often a small fraction of total path area– Under-classification common in rural areas– Original intensity scale was the Fujita scale , Enhanced Fujita scale adopted in 2 0 07 in US and

2 013 in Canada

Severe Convective Storm ModelingChallenges and Obstacles

2 2


• Treaty perspective

– Tropical Cyclones are major driver of loss in United States for national accounts

– Tornado/ Hail exposure drives losses for regional accounts

– Only large outbreaks or severe urban tornadoes penetrate cat treaties

– Cedent loss experience common

• Single risk perspective

– Damage paths of severe thunderstorms are very localized

– Probability of direct s trike by tornado VERY low

– Direct hit by s ingle tornado can result in a large loss , regardless of industry impact

– Highly corre lated losses over small areas

– Cedent loss experience is scarce

Tornado/ Hail RiskReinsurer’s Perspective

RISK PERSPECTIVE MATTERS!

2 3


Modelling results

• What results to the models produce

• How do we interpret them

• Art over Science?

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• Subject income US$ 12 5 m +

• State exposure Gulf

• Line of Business 10 0 % Homeowner

Sample company 1

2 5


Model 1 Model 2

2 5 0 yrs 2 9 ,4 34 ,411 13 9 ,13 6 ,5 0 6

10 0 yrs 21,511,4 8 3 9 2 ,2 0 3 ,74 3

5 0 yrs 16 ,4 3 0 ,8 0 0 76 ,2 9 0 ,5 3 2

2 5 yrs 12 ,8 8 6 ,6 2 0 6 6 ,2 3 6 ,4 9 9

10 yrs 9 ,316 ,34 5 2 8 ,16 2 ,14 6

5 yrs 6 ,9 5 0 ,018 17,5 91,7 0 6

AAL 16 ,5 3 8 ,5 5 3 3 0 ,175 ,0 0 9

• Actual Tornado Hail Loss experience

• 2 015 18 .0 4 m, 5 .47 m, 3 .24 m

• 2 014 16 .01 m, 10 .16 m, 8 .4 9 m

• 2 013 16 .87 m, 10 .01 m, 9 .15 m

• 2 012 2 0 .2 8 m, 5 .3 5 m, 2 .3 6 m

• 2 011 13 .8 0 m, 3 .4 5 m, 2 .5 5 m

• 2 010 3 .64 m, 2 .5 9 m, 1.67 m

• 2 0 0 9 10 .8 0 m, 6 .8 2 m, 4 .3 3 m

• 2 0 0 8 6 .012 m, 4 .24 m 2 .4 4 m

What results do the models produce?

What does a Tornado Hail Exceedance Probability curve actually look like

2 6


• Model 1

• looks very light throughout curve

• 10 yr return period is just 9 .31 m yet company has incurred 9 events exceeding that number in past 8 years

• Model 2

• looks appropriate up to the 10 year period - 10 yr return period is 2 8 .16 m and largest loss in period is 2 0 .2 8 m

• How credible is curve excess of 10 years?? – the 2 5 year return number infers a very s teep increase in expected loss at $ 6 6 .2 3 m

• Much higher losses reflected at higher return periods

Observations

27


• Blend the models?

• Might make the upper return period numbers reflect a reasonable number

• 2 5 0 year return period number would be $ 8 4 .5 0 m

• However - 10 year RP number would be just $ 18 .5 m – still looks light

-------------------------------------------------------------------------------------------

• Ignore model 2 and load model 1 by how much?

• Load the whole curve by 2 5 0 % ?

• 10 year return period loss is $ 2 3 .2 9 m

• 2 5 0 year return period number $ 73 .5 8 m

Possible actions

2 8


• Loading the whole curve produced by model 1 by a factor of 2 5 0 % gives us a viewpoint which seems to match our knowledge of the company and their recent experience.

• Company purchases 16 0 m of vertical protection

– close to a 2 5 0 return period event for Hurricane

– excess of a 2 5 0 return period (no matter what model) event for TH

Conclusion

2 9


• Subject income US$ 275 m +

• State exposure Upper Mid West


10 % Commercial,

5 % Auto

Sample company 2

3 0


Model 1 Model 2

2 5 0 yrs 94 ,6 8 5 ,0 8 5 14 0 ,074 ,3 97

10 0 yrs 6 6 ,19 2 ,9 6 0 10 5 ,011,78 3

5 0 yrs 4 8 ,131,517 72 ,191,2 6 0

2 5 yrs 3 3 ,17 9 ,415 5 5 ,47 0 ,6 0 9

10 yrs 2 0 ,711,94 8 3 8 ,0 4 9 ,5 5 0

5 yrs 14 ,3 2 2 ,4 57 2 8 ,2 9 5 ,2 8 2

AAL 2 0 ,572 ,3 6 5 2 6 ,16 5 ,19 2


• 2 015 12 .3 5 m, 8 .84 m, 8 .0 6 m

• 2 014 24 .9 6 m, 17.9 9 m, 9 .2 2 m

• 2 013 10 .74 m, 6 .61 m, 4 .9 5 m

• 2 012 8 .71 m, 4 .41 m

• 2 011 10 .0 0 m, 4 .12 m

• 2 010 18 .6 5 m, 6 .51 m,

• 2 0 0 9 nil

• 2 0 0 8 11.34 m, 8 .2 9 m,



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• Model 1

• Once again lighter through curve

• closer to experience -10 yr return period is 2 0 .7 0 m with only one loss greater than in within the period.

• Model 2

• Appears penal when compared to experience - 10 yr return period is 3 8 .0 4 m and largest loss in period is 24 .9 8 m

• Once again the curve accelerates s teeply - the 2 5 year return number infers an RP loss of 5 5 .4 5 m

Observations

3 2


• Load a model or blend them?

• Blending 5 0 / 5 0 might make sense – or you may wish to load model 1 by 5 0 %

5 0 / 5 0 Model 1 * 5 0 %

2 5 0 117.37 m 14 2 .0 2 m

10 0 8 5 .5 0 m 9 9 .2 8 m

5 0 6 0 .15 m 72 .19 m

2 5 4 4 .3 2 m 4 9 .75 m

10 2 9 .3 8 m 31.0 6 m

5 21.3 0 m 21.4 9 m

Possible actions

3 3


• Either approach produces a return period result up to 10 years which makes sense but blending does provide some re lief to the higher return period loss estimates .

• Company purchases 13 5 m of vertical protection

– just in excess of a 2 5 0 return period event for TH on a blended approach

– Just within the 2 5 0 return period for TH on a loaded approach

In this instance a blended approach may be considered appropriate

Conclusion

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• Subject income US$ 7 0 0 m

• State exposure Broad Central and Mid West Footprint


2 % Commercial,

8 % Auto

Sample company 3

3 5


Model 1 Model 2

2 5 0 yrs 278 ,412 ,24 5 3 3 9 ,9 91,24 9

10 0 yrs 2 0 9 ,2 3 5 ,5 2 0 2 51,9 8 6 ,847

5 0 yrs 16 9 ,94 5 ,778 19 6 ,8 37,3 94

2 5 yrs 13 5 ,2 61,74 9 14 4 ,6 61,3 0 8

10 yrs 9 3 ,78 0 ,417 9 3 ,74 5 ,5 6 2

5 yrs 6 6 ,2 5 5 ,19 9 6 5 ,8 6 6 ,2 01

AAL 16 2 ,3 3 6 ,2 6 9 14 3 ,5 8 3 ,3 67


• 2 015 12 .3 5 m, 8 .84 m, 8 .0 6 m

• 2 014 2 2 .41 m, 19 .37 m, 16 .19 m

• 2 013 67.3 2 m, 27.9 6 m, 2 2 .8 6 m

• 2 012 41.67 m, 3 6 .9 6 m 2 9 .0 0 m

• 2 011 101.27 m, 3 6 .6 3 m, 3 0 .5 2 m

• 2 010 41.5 6 m, 2 0 .71 m,

• 2 0 0 9 2 9 .6 0 m, 2 3 .17 m, 2 3 .0 9 m

• 2 0 0 8 6 0 .61 m, 17.0 8 m, 16 .5 6 m



3 6


• Both models far closer together

• Both models reflect reasonable losses vs experience for period

• 5 yr losses of $ 6 5 m and 10 yr loss of $ 9 3 m

• Two 6 0 m losses and one loss Xs 10 0 m within period

• Standard loadings for growth and ALAE should be applied, no need to further manipulate model curves

• Slight difference at high return periods

Observations

37


• Overall both models produce credible curves

• You may wish to blend the two to get a consensus view at the higher return periods.

• Selecting one curve over another is justifiable

• The much larger footprint and exposure base produced results far closer in agreement from the two models .

Conclusion

3 8

2016 Tornado Summit | Dr. Kirsten Orwig and Ken Slack 3 9


Thank you



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severe convective storms: a reinsurer’s perspective - tornado … · 2019-09-09 · 14% may jun...

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