GRENIER 4PM TUES VEN V.pdf
Transcript of GRENIER 4PM TUES VEN V.pdf
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Fill in the Blank:
y re a as rop e o e s so __________
C o m p l i c a t e d Wrong
Different
Expensive Cr i t ica l
Useful
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Discussion Topics
n ers an ng o e omponen s
Hazard: If models use the same data, how are they so different?
Engineering: How well do we understand building behavior?
Financial: Its just math. Is it that important?
Understanding Model Results
When can I trust the models?
Sanity checks on results
Dealing with uncertainty
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Catastrophe Model Components
HAZARD
EventGeneration ENGINEERING
Dama e
IntensityCalculation
Estimation
ExposureInformation LossCalculation
PolicyConditions
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US Hurricane
Where Do We Start? Historical Data
National Hurricane Centermaintains the HURDAT2database
Historical hurricane datafrom 1851 2012
Over 1700 individual events
ar qua e USGS develops National Seismic
Hazard Maps
Incorporates a wide array ofstudies to provide a scientificconsensus on earthquake hazard
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,planning, etc.
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Simulate Parameters To Create a Catalog of Synthetic Events
n e parame ers es ma efrom historical data
Parameters used inana y ca w n emodel
Windfield propagatedover exposures to
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If the Sources are the Same, Why are Results Different?
HURDAT2 goes back to 1851, but is farfrom complete and accurate over theentire history Storms were missed and intensities
- reconnaissance (ca. 1944) and pre-satellite (ca. late 1960s) eras
Techniques have improved dramatically
Ship observations
primary data sourcesfor the first half of thehistorical record
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Early Years in the Historical Data are Missing Information
See Chris Landsea (NHC/NOAA) for an excellent summary:
http://www.aoml.noaa.gov/hrd/Landsea/gw_hurricanes/
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Coverage Gaps are Especially Evident in Eastern Atlantic
http://www.aoml.noaa.gov/hrd/Landsea/gw_hurricanes/
i i l
1933
i i lPicture comparing 2005 and 1933 seasons
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Historical Events are Sparse in Time and Space
www.weather.com
On average, roughly 12 storms form inthe Atlantic Basin each year -
hurricanes (CAT 3-5) Fewer than 2 landfalls, with only 0.6
major landfalls per year
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Earthobservatory.nasa.gov
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How Do the Modelers Handle the Data?
Is the risk lowerhere?
Smoothing techniques applied tohistorical data
and intensity Smoothing methodologies can be
different
Results in different risk profilesalong the coast
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AIR Worldwide
model parameters
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Climate Conditioned Model Catalogs Add to Differences
AIR and RMS each have two different catalo s Standard/Long Term rates assume future activity follows the long termpattern captured in HURDAT2
WSST/Medium Term assumes we are currently in a period (since 1995) of
Significant Differences in Methodology:
AIR Separates historical record in warm years and cool years
Conditions their sampling methodology to consider only warm years ,
RMS Developed models to estimate sea surface temperatures and uses
multiple models to estimate landfall rates Developed regionalization model to distribute landfalls More complicated approach, but relies on a sequence of individual
models
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What is the Range of Landfall Frequencies?
Hurricanes (CAT1-2) and Major Hurricanes (CAT3-5) in 6 Regions Lines re resent difference in landfall rate com ared to historical
AIR and RMS long term and WSST/medium term views
WSST/Medium Term drive most (though not all)
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Sources of Uncertainty in Earthquake Models
n nown ear qua e au s 1994 M6.7 Northridge, CA earthquake previously unknown fault 9
miles below surface . ,
before Variability in deformation assumptions
Assumptions have significant impact on energy release
Uncertainty in earthquake rates and probabilities ow muc energy s s ore n a spec c reg on Which faults will trigger? How many? With what probability? 2011 M9.0 Tohoku Earthquake was stronger than previously though
Dealing with uncertainty leads to different assumptions, models andcatalogs
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Catastrophe Model Components
HAZARD
EventGeneration ENGINEERING
Dama e
IntensityCalculation
Estimation
Exposure
InformationLoss
Calculation
PolicyConditions
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Intensity Module Adjusts Windspeed for Local Conditions
Roughness Length Z 0 is a
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Loss Estimates are Very Sensitive to Roughness Assumptions
A few standard references exist However, no consensus view on estimates of roughness by land use
,
estimates by experienced practitioners can vary by a factor of 2 e.g. Simiu and Scanlan, (1996): Z 0 = 0.2 0.4 for suburbs
Assuming a standard log wind speed profile
Changing Z 0 from 0.2 to 0.3 for hurricane wind profilecan chan e surface winds b 5-7%
80 mph wind speed damage ratio = 2.4% 84 mph wind speed damage ratio = 3.3%
Assume 2000 structures, $500K per Loss estimates = $24M $33M
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Catastrophe Model Components
HAZARD
EventGeneration ENGINEERING
Dama e
IntensityCalculation
Estimation
Exposure
InformationLoss
Calculation
PolicyConditions
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Vulnerability Module Converts Intensity to Damage
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How Are Damage Functions Developed?
Claims Data Majority of claims are for residential policies
Mostly for recent events (Florida, Texas) where wind was a significantcause of loss
Each modeler interprets the results differently Published engineering studies
Post disaster surveys
Large scale model tests arebecoming more common
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Damageability Varies by Geography
AIR Building code zones (left)and RMS zones (below)
Includes modifiers for inland vs
Reflects differences in codesand code enforcement
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Variability Within and Across Occupancies Presents a Challenge
Office
Hotels
Retail
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Special Occupancies are Even More Challenging
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There is Significant Variability around the mean Damage Ratio
Similar buildings subjectedto same intensity can havedifferent amounts of loss
Modelers account for thisusin a distribution around the
mean damage ratio
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Financial Model is an Underappreciated Source of Differences
an o cons er uncer a n y n e mean amage ra o Secondary uncertainty to distinguish it from other sources of uncertainty Uncertainty in the loss, given an event
However, they use different assumptions:
AIR: uses customs r u on y per w
numerical convolution
RMS: assumes a betamean
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For Excess Policies, Small Differences in Shape can Have A LargeInfluence on Loss
Area between attachment (A)and limit (L)=
A L
a b i l i t y
P r o
.
Loss
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Detailed, Accurate Data is Useful
Address Geocode Building Contents Time Limits DeductiblesReplacement ValuesLocation Policy Terms
Construction Occupancy Age Height Shutters Roof Shape Foundation Type
Building Attributes Building Details
significant simplification
of reality
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Generally, Results are Less Reliable for Small, Complex Exposures
NationwideHomeowners QS
Floridaomeowners
QS
l i o s i z e
Commercial
CountrywideCommercial
Excess i n g p o r t
f
D e c r e a
Complex Commercial XPR
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Increasing portfolio complexity
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Interpreting the Exceedance Probability Curve
EV 4575 641
10 841 e r t a
i n t y As a general rule, uncertaintyincreases as the number of
events decreases25 1,093
50 1,315100 1,554
a s
i n g u n
With increasing geographicdetail,
500 2,3921000 3,090 I
n c r
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Increasing uncertainty
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Sanity Checks
s or ca esu s Modelers provide historical event catalogs to allow as-if analyses Understand exposure change to interpret results + - reasona ty t res o ; oo or as
Market share of loss Compute market share of industry loss (vs exposure/premium) Aggregate models are useful for this purpose
Old-fashioned aggregates
Useful on their own, and are a good benchmark for model assessment PML/Limit ratios, regional and historical trends become good methods
for tracking results over time
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Industry Trend: Deeper Analytics, Greater Transparency
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