2004 rms june 7-8 boston a collaborative design for catastrophe management the impact of new science...

2004 RMS June 7-8 Boston 2004 RMS June 7-8 Boston

A Collaborative Design for Catastrophe ManagementA Collaborative Design for Catastrophe Management

The impact of new Science on The impact of new Science on Catastrophe ModelingCatastrophe Modeling

CARe Boston CARe Boston June 7June 7thth & 8 & 8thth 2004 2004

Dr. Robert Muir-WoodDr. Robert Muir-Wood

Chief Research Office & EVPChief Research Office & EVP

Copyright RMS 2004Copyright RMS 2004

Today’s questionsToday’s questions

1) What has been learnt that is new from recent smaller Catastrophes – such as Hurricane Isabel - or the San Simeon Earthquake - that has more general application to understanding the future losses from larger Catastrophes?

2) What are the new scientific findings that have emerged since the 1990s to change how Hurricane Catastrophe risk should be modeled in the Northeast?

© 2004 Risk Management Solutions, Inc.© 2004 Risk Management Solutions, Inc.ConfidentialConfidential

Hurricane CAT modeling is 12-14 years old - Hurricane CAT modeling is 12-14 years old -

Model T 1909Model T 1909

REAC 400, 1960REAC 400, 1960

Some other examples of 12 year old technologies:


1) Something about Isabel1) Something about Isabel

Isabel at Cat 5 – NE of Puerto RicoIsabel at Cat 5 – NE of Puerto Rico


VRG Windfield and Recorded WindspeedsVRG Windfield and Recorded Windspeeds

2626: Complete record: Complete record2626 : Incomplete – i.e. peak missing: Incomplete – i.e. peak missing

Peak Gust mphPeak Gust mph

Permanent stationsPermanent stations


Post-event insured mean loss estimates for IsabelPost-event insured mean loss estimates for Isabel

SeptSept OctOct NovNov DecDec JanJan FebFeb

$1Bn$1Bn

$2Bn$2Bn

PCSPCS

I I II I I

AIRAIR

EQEEQE

RMSRMS

http://www.willisre.com/html/reports/catastrophe.htm


A forensic investigation of IsabelA forensic investigation of Isabel


Tree damage close to the coastTree damage close to the coast

TreefallTreefall


Urban tree damageUrban tree damageRichmond VA (65mph)Richmond VA (65mph)


Treefall deep inland: Montgomery County <50mphTreefall deep inland: Montgomery County <50mph


American Association of Wind Engineers perspective on American Association of Wind Engineers perspective on Isabel Isabel ((www.aawe.org/Isabel_2003/Damaging%20Winds_Isabel.pdf)

‘‘the overall wind velocities were below the ASCE 7 the overall wind velocities were below the ASCE 7 recommendations’recommendations’

‘‘the damage that resulted was not of a type that might have been the damage that resulted was not of a type that might have been expected for the average winds that occurred and in fact there was expected for the average winds that occurred and in fact there was relatively limited direct structural damage.’ relatively limited direct structural damage.’

‘‘there was a large amount of structural damage resulting from fallen there was a large amount of structural damage resulting from fallen trees striking structures.’trees striking structures.’

‘‘the types of failures and damage that occurred in Isabel the types of failures and damage that occurred in Isabel indicate indicate that there is a whole new area of research that should be pursued that there is a whole new area of research that should be pursued by wind engineers.’ by wind engineers.’


Comparative woodframe residential ZIP level %lossesComparative woodframe residential ZIP level %losses

0%

5%

10%

15%

20%

50 60 70 80 90 100 110

Peak gust (mph)

MD

R

Vulnerability FunctionAndrewHugoErinGeorgesBobFran Isabel

RMS has analyzed RMS has analyzed

c 15% of all claimsc 15% of all claims


Comparative Mean Damage Ratios v Windspeed BandComparative Mean Damage Ratios v Windspeed Band

0.0%

0.1%

0.2%

0.3%

0.4%

0.5%

0.6%

0.7%

0.8%

1 2 3 4

Ave

rag

e o

f Z

IP D

amag

e R

atio

(%

)

Isabel Hugo - NC Fran -NC

< 50 mph 50-60 mph 70-80 mph60-70 mph

The ‘Isabel Effect’ is only seen below 70mph.The ‘Isabel Effect’ is only seen below 70mph.


Exposure Values in the path of Isabel’s windfieldExposure Values in the path of Isabel’s windfield

Falling windspeeds

Falling windspeeds

Rising exposure values

Rising exposure values


Comparative Industry Exposure Value by Windspeed BandComparative Industry Exposure Value by Windspeed Band

0

200

400

600

800

1000

1200

<30 30-40 40-50 50-60 60-70 70-80 80-90 90-100 100-110 110-120 120-130 130-140 140-150 150-160

Peak Gust (mph)

Ind

ust

ry E

xpo

sure

($B

)

Fran

Hugo

Isabel


Why did trees blow down at lower windspeeds than ‘normal’ Why did trees blow down at lower windspeeds than ‘normal’ in Isabel? in Isabel?

Generic effectsGeneric effects

A) Exceptionally wet antecedent conditions?A) Exceptionally wet antecedent conditions?

B) Lack of high winds for many years?B) Lack of high winds for many years?

C) Inadequate tree maintenance – of older trees?C) Inadequate tree maintenance – of older trees?

D) Higher density of urban trees in this region?D) Higher density of urban trees in this region?

E) Poorer soils, influence of tree species? E) Poorer soils, influence of tree species?

Regional EffectsRegional Effects


Summer Rainfall before IsabelSummer Rainfall before Isabel


Overturning from saturated soil: Foxhall (50mph)Overturning from saturated soil: Foxhall (50mph)

Many mature trees fell away from the roadway:Many mature trees fell away from the roadway:

- asymmetric rootballasymmetric rootball

- shallow urban soil layershallow urban soil layer

- runoff concentratedrunoff concentrated


Highest windspeeds in MidAtlantic region since 1970Highest windspeeds in MidAtlantic region since 1970

Area impacted by IsabelArea impacted by Isabel

has been untouched by has been untouched by

Hurricane winds for >30 yearsHurricane winds for >30 years


C) Tree maintenanceC) Tree maintenance

Pruning rigor Pruning rigor

correlates with recentcorrelates with recent

damagedamage

Resistance to tree Resistance to tree

maintenance tends to maintenance tends to

correlate with affluencecorrelate with affluence

as mature trees as mature trees

increase property valuesincrease property values


D) Percentage of urban areas covered by trees versus D) Percentage of urban areas covered by trees versus population density (by State)population density (by State)

0

1

10

100

0 1000 2000 3000 4000 5000 6000 7000

Population density (people/km2)

Tre

e p

erce

nta

ge

area

co

vere

d

Virginia Florida Texas North Carolina

Virginia has high treeVirginia has high tree

density even in citiesdensity even in cities


Generic effectsGeneric effects

Exceptionally wet antecedent conditions?Exceptionally wet antecedent conditions?

Lack of high winds for many yearsLack of high winds for many years

Inadequate tree maintenance – of older trees?Inadequate tree maintenance – of older trees?

Higher density of urban trees in this regionHigher density of urban trees in this region

Poorer soils, influence of tree species Poorer soils, influence of tree species

Regional EffectsRegional Effects

Why did trees blow down at lower windspeeds than ‘normal’:Why did trees blow down at lower windspeeds than ‘normal’:- allocating the causes - allocating the causes

??

InterrelatedInterrelated


‘Trees are more of a threat to the U.S. power grid than terrorists’,

Michael Gent, President of the North American Electric Reliability CouncilOct 27th 2003


Isabel’s power outagesIsabel’s power outages

5.8 million customers lost power (largest ever in a natural disaster)

‘Not a single transmission line went out of service where there were no trees’. (Ken DeFontes, VP of T&D: Baltimore Gas and Electric)

‘Many utilities have fired their in-house tree trimming crews and hired cheaper contractors’, (Robert Burns, a senior researcher with the National Regulatory Research Institute).

28% of residential claims and 10% of losses freezer-contents related

– average claim value $950

– payments sometimes made outside the terms of the original insurance

Higher proportion of commercial claims relate to power outage:


Implications for Hurricane CAT loss modeling Implications for Hurricane CAT loss modeling


Role of Trees in affecting Residential vulnerabilities (for Role of Trees in affecting Residential vulnerabilities (for typical inventory) typical inventory)

Loss%Loss%

windspeedwindspeed50mph50mph 100mph100mph

proportion of total lossproportion of total loss

from tree damagefrom tree damage

Building onlyBuilding only

CombinedCombined

Tree onlyTree only


Impact of reducing the windspeed threshold for treefallImpact of reducing the windspeed threshold for treefall

Loss%Loss%

windspeedwindspeed50mph50mph 100mph100mph

proportion of total lossproportion of total loss

from tree damagefrom tree damage

c 10mph reduction in start ofc 10mph reduction in start of

tree damage vulnerabilitiestree damage vulnerabilities

Inland WindspeedsInland Windspeeds

Building onlyBuilding only

CombinedCombined

Tree onlyTree only

baselinebaseline


Implications for modeling the riskImplications for modeling the risk

Proportion of buildingsProportion of buildings

with high tree proximity with high tree proximity

Impact of highImpact of high

antecedent rainfallantecedent rainfall

Windspeeds at whichWindspeeds at which

loss is most sensitiveloss is most sensitive

to excess treefallto excess treefall

ISABELISABEL


Lessons from Isabel: Tree Proximity in Building Inventory Lessons from Isabel: Tree Proximity in Building Inventory Isabel demonstrates that at low windspeeds, treefall can be the Isabel demonstrates that at low windspeeds, treefall can be the

principal determinant of residential building damage principal determinant of residential building damage

Damage and loss underestimated by whole wind engineering Damage and loss underestimated by whole wind engineering community (and all Cat modelers) community (and all Cat modelers)

‘‘Tree proximity’ is currently only implicitly included in building Tree proximity’ is currently only implicitly included in building inventoryinventory

– In future likely to be included explicitlyIn future likely to be included explicitly

Richmond, VARichmond, VA

Low tree proximityLow tree proximityHigh tree proximityHigh tree proximity


© 2004 Risk © 2004 Risk Management Solutions, Management Solutions, Inc.Inc.

ConfidentialConfidential2004 RMS2004 RMS

2) Application of New Science in modeling hurricane 2) Application of New Science in modeling hurricane risk in the Northeast US risk in the Northeast US

The Puzzle of the “Missing Loss” in Northeast The Puzzle of the “Missing Loss” in Northeast “Hurricanes”: Top 5 Storms After 1930“Hurricanes”: Top 5 Storms After 1930

19381938

19441944

1985 (Gloria)1985 (Gloria)

1999 (Floyd*)1999 (Floyd*)

Modeled as a Modeled as a Hurricane With DefaultsHurricane With Defaults

Yr2000 LossYr2000 Loss

Exposure/InflationExposure/InflationUpdated Yr 2000 Loss Updated Yr 2000 Loss

(Collins and Lowe, 2001)(Collins and Lowe, 2001)

$9.4 Bn$9.4 Bn

$2.1 Bn$2.1 Bn

$1.4 Bn$1.4 Bn

$2.0 Bn$2.0 Bn

$10.1 Bn$10.1 Bn

$18.4 Bn$18.4 Bn

$5.0 Bn$5.0 Bn

$2.31 Bn$2.31 Bn

1954 (Carol)1954 (Carol) $6.1 Bn$6.1 Bn $10.6 Bn$10.6 Bn

X 2.0X 2.0

X 4.8X 4.8

X 1.6X 1.6

X 1.7X 1.7

X 2.5X 2.5

TotalTotal $21.0 Bn$21.0 Bn $46.4 Bn$46.4 Bn X 2.2X 2.2

**Loss also in Mid Atlantic and NCLoss also in Mid Atlantic and NC

MultipleMultiple

##

##

##

## Default RMax and windfield parametersDefault RMax and windfield parameters


Development of ‘Anomalies’ in the 1990s paradigm Development of ‘Anomalies’ in the 1990s paradigm for hurricane modeling in the North East USfor hurricane modeling in the North East US

Standard hurricane windfields exaggerate updated Standard hurricane windfields exaggerate updated historical losses in the North East by an average of x2 historical losses in the North East by an average of x2

– identified in RMS 1997 work on ‘historical updated’ identified in RMS 1997 work on ‘historical updated’ losses losses

– updated historical losses confirmed and refined by updated historical losses confirmed and refined by Collins and Lowe (2001) CAS studyCollins and Lowe (2001) CAS study

Response to the anomalies:Response to the anomalies:

– 1997 RMS IRAS3.6 (RiskLink4.2) model employed 1997 RMS IRAS3.6 (RiskLink4.2) model employed ‘NorthEast region hurricane windfield parameters’ to get ‘NorthEast region hurricane windfield parameters’ to get consistent empirical match with historical lossesconsistent empirical match with historical losses

– however at the time there was no physical explanation however at the time there was no physical explanation for these empirical modificationsfor these empirical modifications


Extratropical Transition (Typhoon Kochi June 2003) Extratropical Transition (Typhoon Kochi June 2003)


The surge in Extratropical Transition ResearchThe surge in Extratropical Transition Research

Extratropical Transition Research

05

10152025

30354045501

93

0s

19

40

s

19

50

s

19

60

s

19

70

s

19

80

s

19

90

19

91

19

92

19

93

19

94

19

95

19

96

19

97

19

98

19

99

20

00

20

01

20

02

20

03

Year

Nu

mb

er

of

Art

icle

s/P

res

en

ati

on

s

Journal Articles Conference Presentations

Key Hart and Evans papers Key Hart and Evans papers on transitioning of historical on transitioning of historical US hurricanes using US hurricanes using ECMWF reanalysis dataECMWF reanalysis data


Historical Transitioning Storms (around 45% of all Historical Transitioning Storms (around 45% of all Atlantic hurricanes undergo transition)Atlantic hurricanes undergo transition)

OpalOpal

HugoHugoHazelHazel

BobBob

19381938

CarolCarol

GloriaGloria

FloydFloyd


Deeper pressureDeeper pressure

after transitioningafter transitioning

925925 950950 10001000975975Hurricane pressure mbHurricane pressure mb

925925

950950

975975

10001000

Tra

ns

itio

nin

g p

res

su

reT

ran

sit

ion

ing

pre

ss

ure

Deeper pressureDeeper pressure

before transitioningbefore transitioning Minimum transitioning Minimum transitioning pressure v minimum pressure v minimum hurricane pressure hurricane pressure within 6hrs before within 6hrs before transitioning initiatedtransitioning initiated

How often do transitioning storms reintensify? How often do transitioning storms reintensify?

Cat 1Cat 1


Transitioning & Forward SpeedTransitioning & Forward Speed

00

1010

2020

3030

4040

5050

6060

7070

8080

9090

100100

00 1010 2020 3030 4040 5050 6060 7070 8080 9090

Forward Speed (mph)Forward Speed (mph)

% o

f S

yste

ms

in

So

me

Sta

ge

of

Tra

ns

itio

n%

of

Sys

tem

s i

n S

om

e S

tag

e o

f T

ran

sit

ion NHC Data: 1979-1999NHC Data: 1979-1999

Systems transition when they are Systems transition when they are picked up by the jet stream; the picked up by the jet stream; the interaction with the jet alters the interaction with the jet alters the system’s structuresystem’s structure

The forward speed of storms is The forward speed of storms is therefore strongly linked with therefore strongly linked with transitiontransition

Transitioning storms in the Transitioning storms in the stochastic model are identified by stochastic model are identified by assessing forward speedsassessing forward speeds

All storms above All storms above 40mph transition 40mph transition within next 6-12hrswithin next 6-12hrs


Sustainable Hurricane Central Pressures in U.S. Sustainable Hurricane Central Pressures in U.S. Atlantic MarginAtlantic Margin

960mb960mb

900mb900mb

930mb930mb

Lowest sustainable SeptemberLowest sustainable Septemberpressures from Emanuel et al.pressures from Emanuel et al.

closest approachclosest approach to Long Islandto Long Island


19381938

GloriaGloria

BobBob

18691869

Role of forward speed in affecting transitioning status Role of forward speed in affecting transitioning status of North East Storms at landfallof North East Storms at landfall

900900

920920

940940

960960

980980

10001000

10201020

00 1010 2020 3030 4040 5050 6060 7070

Cen

tral

pre

ssu

re (

mb

)C

entr

al p

ress

ure

(m

b)

Forward Speed (mph)Forward Speed (mph)

Cat 4Cat 4

Cat 3Cat 3

Cat 2Cat 2

EstimatedEstimatedProbability ofProbability ofTransitioningTransitioning

75-95%75-95%

>95%>95%

<75%<75%

95%95%

75%75%

Theoretical limitTheoretical limit


950

955

960

965

970

975

40 50 60 70 80 90 100 110 120 130 140 150Wind Speed (mph))

Ce

ntr

al P

res

su

re (

mb

)

Tropical Systems

Transitioning Systems

Transitioned Systems

Maximum Windspeeds of Transitioning StormsMaximum Windspeeds of Transitioning Storms

Mean wind speed (mph) in the 950-975mb rangeMean wind speed (mph) in the 950-975mb rangeTransitioned: Transitioned: 69.469.4 Transitioning: Transitioning: 81.681.6 Tropical: Tropical: 102.9102.9

NHC DataNHC Data1979-19991979-1999


The 1938 “Hurricane” Damage FootprintThe 1938 “Hurricane” Damage Footprint

Combined detailedforestry damage with Fujita scale property damage data (from Boose et al, 2000)


Modeling the 1938 Storm: #2 Hurricane Windfield Modeling the 1938 Storm: #2 Hurricane Windfield with Rmax 2 SDs larger than averagewith Rmax 2 SDs larger than average

$26bn LossWindfield as a hurricane with RMax increased by 2 standard deviations to fit damage geography on the right hand side

Significant over Significant over prediction of prediction of windspeeds and windspeeds and damage on the damage on the left hand sideleft hand side


Modeling the 1938 Storm: #3 With a Transitioning Modeling the 1938 Storm: #3 With a Transitioning Storm windfieldStorm windfield

$14bn Loss

Windfield as a transitioning storm


The Puzzle of the “Missing Loss” in Northeast The Puzzle of the “Missing Loss” in Northeast “Hurricanes”: Top 5 Storms After 1930“Hurricanes”: Top 5 Storms After 1930

19381938

19441944

1985 (Gloria)1985 (Gloria)

1999 (Floyd*)1999 (Floyd*)

Modeled as TransitioningModeled as Transitioning with Defaultswith Defaults

2000 Loss2000 Loss

Exposure/InflationExposure/InflationUpdated Yr 2000 Loss Updated Yr 2000 Loss

(Collins and Lowe, 2001)(Collins and Lowe, 2001)

$9.4 Bn$9.4 Bn

$2.1 Bn$2.1 Bn

$1.4 Bn$1.4 Bn

$2.0 Bn$2.0 Bn

$1.5 Bn$1.5 Bn

$14.4 Bn$14.4 Bn

$2.0 Bn$2.0 Bn

$1.87 Bn$1.87 Bn

1954 (Carol)1954 (Carol) $6.1 Bn$6.1 Bn $4.4 Bn$4.4 Bn

X 1.5X 1.5

X 0.71X 0.71

X 1.3X 1.3

X 0.72X 0.72

X 1.0X 1.0

TotalTotal $21.0 Bn$21.0 Bn $24.2 Bn$24.2 Bn X 1.1X 1.1

**Loss also in Mid Atlantic and NCLoss also in Mid Atlantic and NC

MultipleMultiple

##

##

## Default transitioning time, RMax Default transitioning time, RMax and windfield parametersand windfield parameters


The Paradigm Shift in North-East Hurricane Loss The Paradigm Shift in North-East Hurricane Loss ModelingModeling

Florida HurricaneFlorida Hurricane

North-East HurricaneNorth-East Hurricane

‘‘Old 1990s Paradigm’Old 1990s Paradigm’

%%

Rmax kmRmax km

FloridaFlorida

NE HurricaneNE HurricaneNE TransitioningNE Transitioning

New 2003 ParadigmNew 2003 Paradigm

Rmax kmRmax km

%%

Using uncorrected Using uncorrected hurricane windfields hurricane windfields

gives $60-80Bn gives $60-80Bn 1000yr loss1000yr loss

With separate With separate hurricane and hurricane and transitioning transitioning

windfields gives windfields gives $35Bn 1000yr loss$35Bn 1000yr loss


Conclusions for North-East Hurricane risk Conclusions for North-East Hurricane risk

The ‘Old Paradigm’ (with North-East risk beyond 100 year The ‘Old Paradigm’ (with North-East risk beyond 100 year return periods driven by major Cat 4 storms with pure return periods driven by major Cat 4 storms with pure hurricane windfields) is no longer scientifically credible hurricane windfields) is no longer scientifically credible

Additional cost to the US Insurance Industry of the Additional cost to the US Insurance Industry of the ‘old paradigm’ model =‘old paradigm’ model = c $140M per yearc $140M per year


© 2004 Risk © 2004 Risk Management Solutions, Management Solutions, Inc.Inc.

ConfidentialConfidential2004 RMS2004 RMS

Session 17D

tropical cyclone extratropical transition II

Chairperson: Jenni L Evans, Penn State University, University Park, PA

10:15 AM

17D.1

The impact of extra tropical transition on hurricane risk in the Northeast US Robert Muir-Wood, Risk Management Solutions Ltd., London, United Kingdom; and R. Dixon and A. Boissonnade

10:30 AM

17D.2

The extratropical transitions of eastern Pacific Hurricane Lester (1992) and Atlantic Hurricane Andrew (1992): A comparison. Michael Dickinson, Univ. at Albany/SUNY, Albany, NY; and L. F. Bosart, K. L. Corbosiero, S. Hopsch, K. Lombardo, M. J. Novak, B. Smith, and A. C. Wasula

10:45 AM

17D.3

Synoptic composites of the extratropical transition lifecycle of North Atlantic TCs as defined within cyclone phase space Robert E. Hart, Florida State University, Tallahassee, FL; and J. L. Evans

11:00 AM

17D.4

Predictability Associated with Extratropical Transition of Tropical Cyclones as defined by Operational Ensemble Prediction Systems Patrick A. Harr, NPS, Monterey, CA; and D. Anwender and S. C. Jones

26th Conference on Hurricanes and Tropical

Meteorology3—7 May 2004

Miami, FL

Deauville

Beach Resort, 6701 Collins Avenue, Miami, FL 33141

305-865-8511

RMS ET Research:RMS ET Research:at the 2004 AMSat the 2004 AMSmeetingmeeting

May 7May 7

The Science is new The Science is new

and only in the RMS model!and only in the RMS model!

2004 rms june 7-8 boston a collaborative design for catastrophe management the impact of new science...

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