SEVERE CONVECTIVE STORM MODELING
Meghan Purdy
Kay Cleary
Associate Manager, Model Solutions
Director, Regulatory Practice
©2013 Risk Management Solutions, Inc.
A RECENT
SURVEY
What peril
concerns you on a
day-to-day basis?
Has your company
made changes to
your severe weather
ratemaking
methodology in the
last 3 years?
©2013 Risk Management Solutions, Inc.
In your opinion,
what is the biggest
threat regarding
climate change?
2
A RECENT
SURVEY
#1: SCS
#2: Flood
#3: Hurricane
SCS, Flood, and
Storm Surge/
Hurricane
~80% yes!
©2013 Risk Management Solutions, Inc.
3
RISK OVERVIEW
Loss
Historical Losses
Accounts for 1/3 of all US peril AAL
(~11 billion USD)
Several events in last 15 years exceed
$2 billion in loss
– 3 events in 2011
– 1 so far in 2013
Risk
Challenges
High risk to aggregate covers, auto
lines, and large single location risks
Eats at profit, as most risk is retained
Event frequency not well captured in
statistical data
SCS annual losses can be
volatile/non-stable
©2013 Risk Management Solutions, Inc.
4
OUTLINE
•
•
•
©2013 Risk Management Solutions, Inc.
Intro to the RMS Severe Convective Storm model
Applications and considerations
Resilient risk management
5
SEVERE CONVECTIVE
STORM MODELING
FOUR PERILS
OF SEVERE
CONVECTIVE
STORMS
©2013 Risk Management Solutions, Inc.
•
Hail
–
–
–
Most frequent of SCS perils
Auto and Residential lines most at risk
Smaller damage ratios, over large areas
•
Tornadoes
– Rarest of the SCS perils
– Highest damage ratios
•
Straight-line winds
– Largest footprints of SCS perils
– Treefall an issue for residential and auto
•
Lightning
– Frequent, but least damaging
– Losses to electrical equipment (power surge)
7
FRAMEWORK FOR SCS MODELING
Generate
Events
©2013 Risk Management Solutions, Inc.
Assess
Hazard
Calculate
Damage
Quantify
Financial
Loss
8
EVENT GENERATION
CHALLENGE:
DEFINE THE
PERIL
©2013 Risk Management Solutions, Inc.
Image via
foldedstory.com
10

CHALLENGE:
DEFINE THE
PERIL
Tornadoes are #1 driver for loss of life
–
–

324 deaths in April 2011 outbreak
Last death due to hail in US was 12 years ago; ~1,000 deaths due
to tornadoes in same period
Hail storms are #1 driver for insurance loss
–
–
Aggregate loss: hail is dominant, 60% of all claims
Tail loss: hail & tornado are ≈ 40%
Annual Losses
Large Event Losses
Straight-line
Wind
Hail
Tornado
Straight-line
Wind
Tornado
Hail
Based on Claims Data
©2013 Risk Management Solutions, Inc.
11
CHALLENGE: BIASED HISTORICAL RECORDS
Records and Observations
(PCS) are limited to and biased
by observation location and
damage.
Low
Cat Models can provide
physically-based frequency and
severity distributions with
complete coverage.
Hazar
High
12
Step 1
Step 2
Step 3
Step 4
SCS EVENT GENERATION
Simulate stochastic years of atmospheric conditions
•
•
Resample events from the North
American Regional Reanalysis
(NARR)
– Reanalysis data from 19792005
Create “stochastic” years
– 3-day blocks within 3 month
periods
– Over 27 years of data
– Preserve seasonality
– Preserve temporal and spatial
correlations
Stochastic Year 1
Stochastic Year 2
NARR
Stochastic
Day
Day
Year 12, Day 12
Day 1
Year 12, Day 13
Day 2
Year 12, Day 14
Day 3
NARR
Stochastic
Day
Day
Year 22, Day 12
Day 1
Year 22, Day 13
Day 2
Year 22, Day 34
Day 3
Year 03, Day 20
Year 03, Day 21
Year 03, Day 22
Year 05, Day 20
Year 05, Day 21
Year 05, Day 22
Day 4
Day 5
Day 6
Year 26, Day 33
Year 26, Day 34
Year 26, Day 35
Day 7
Day 8
Day 9
Year 14, Day 323 Day 365
Day 4
Day 5
Day 6
Year 27, Day 33
Year 27, Day 34
Year 27, Day 35
Day 7
Day 8
Day 9
Year 16, Day 311 Day 365
13
13
Step 1
Step 2
Step 3
Step 4
SCS EVENT GENERATION
Create probability surface based on atmospheric conditions for an event
•
Combine NARR atmospheric conditions
and historical observations
– Create probability of specific perils
occurring at a location
– Climatology of risk
Surface shear/CAPE triggers event initial
location
– CAPE (Convective Available
Potential Energy, a measure of
energy to feed storm development)
– Wind Shear (to provide rotation for
updrafts and tornadogenesis)
– Size/intensity event as function
shear/CAPE
CAPE or shear
atmospheric
conditions
# Historical
Occurrences
•
CAPE or Shear
14
SCS EVENT GENERATION
Perils & intensity based on probability surface and CAPE/Shear values
•
Contents of event modeled as
function shear/CAPE
independently
•
Individual peril intensity
relationships
– Derived from observations
• Wind: anemometer
network
• Tornado: F-Scale
• Hail: radar
Straight
Line Wind
Intensity
Hail
Swath and
Intensity
Tornado
Frequency &
Intensity
correlated
Step 1
Step 2
Step 3
Step 4
Lightning
Footprint
15
SCS EVENT GENERATION
Apply peril footprints for event given probability and intensity information
Step 1
Step 2
Step 3
Step 4
Hail
Intensity1 / Intensity 2
Lightning
Tornado Path
•
Some events will contain
single, multiple, or all perils
16
SCS EVENT
GENERATION:
PUTTING IT ALL
TOGETHER
Shear
©2013 Risk Management Solutions, Inc.
A hybrid model that unites statistics with
numerical modeling
• Numerical modeling provides thousands of years of
large-scale, 3D meteorological “ingredients” for storms
• Statistics are used to place tornado, hail, and straightline winds in each cell using probability distributions and
historical data
• Result is verified and calibrated against historical
observations and damage surveys where appropriate
CAPE
17
CHALLENGE: HIGH-FREQUENCY EVENTS
State
% AAL HF
Alabama
9%
Oklahoma
10%
Texas
14%
Louisiana
16%
Wyoming
24%
New York
28%
Massachusetts
45%
Nevada
77%
Washington
82%
©2013 Risk Management Solutions, Inc.
•
•
•
High-frequency events can contribute over 50% of the annual
AAL in some regions, particularly in the West
Impractical to model as individual events
SCS model’s solution:
– Determine percentage of claims from high-frequency
events, verify with CAPE as proxy for thunderstorms
– 1 pseudo-event per state
– Model as an annual occurrence (frequency = 1) for the
aggregate contribution of high-frequency events to the
location AAL
2011 IED, All Lines,
All Subperils
18
CHALLENGE: HIGH-FREQUENCY EVENTS
High-frequency event: Isolated t-storms/wind
Low-frequency event: Major severe weather outbreak
©2013 Risk Management Solutions, Inc.
Low-Frequency
Events
High-Frequency
Events
Storm Type
Cat events
Non-Cat events
Examples
Thunderstorms
Straight-line winds
Tornadoes
Lightning
Isolated Thunderstorms
Downbursts
Hailstorms
Storm size
Large-scale
(1000s of sq mi or km)
Small-scale
(10s of sq mi or km)
RiskLink Stochastic
footprint?
Yes
No
Regional Impact
Dominant in
Midwestern Plains
Dominant in West
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HAZARD
HAIL
Intensity 2 Hail Pad
•
Hailstorms
-
Intensity 1 Hail Pad
Stochastic Hail Swath
•
Many hail swaths per day possible
Calibrated with 50 years of observations
Hail swaths often occur in clusters
-
Modeled at two intensity levels
Intensity related to hail stone size and
density
Intensity distribution varies geographically
Number of hail swaths, size, and intensity
distribution dependent on storm size
Footprint morphology calibrated on
historical and radar data
Ellipses fitted to the SPC points for the event of 3
May 1999, along with the WDT polygons from radar.
©2013 Risk Management Solutions, Inc.
21
STRAIGHT-LINE WINDS
•
Wind Surface Grid
•
•
•
•
•
MPH
Ranging from microburst to derecho (1 mn/yr vs. 25
year)
Derecho – widespread, long-lived convective
windstorm
Size: 3 miles to 100+ miles wide
Duration: minutes to 24 hr
Wind speeds: up to 100 mph gust
Methods of reconstructing straight-line winds
– Storm Prediction Center historical reports
– Airport locations, mesonet stations, Global Summary
of the Day
– Examine roughness
©2013 Risk Management Solutions, Inc.
22
TORNADO
•
•
•
•
Outbreak modeled by maximum F-intensity
tornado
Historical tornado reports are clustered into larger
outbreaks (similar to hail)
Intensity size distributions based on Rankine
vortex model
Adjusted with high-resolution damage surveys
(from scientific literature, consultants)
Vmax
Tornado intensity based on
Rankine vortex model.
Goshen County WY: June 5th, 2009
©2013 Risk Management Solutions, Inc.
23
LIGHTNING
•
•
Losses from lightning strikes
(non-fire)
Two main damage modes:
–
–
•
•
©2013 Risk Management Solutions, Inc.
Damage at point of entry
(singe or burn marks)
Electrical system
(electronics that are plugged in)
Typically low damage ratios
Highly correlated with hail
hazard so modeled on top
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VULNERABILITY
PERIL-SPECIFIC VULNERABILITY FUNCTIONS
•
•
Photos from RMS (Matthew Nielsen)
©2013 Risk Management Solutions, Inc.
•
•
•
Distinct functions for Hail, Tornado, and Wind
Hail  kinetic energy
– Key vulnerability components:
• General roof shape (e.g. steep, low slope)
• Roof cover (e.g. asphalt, shake, tile, built-up, single-ply)
• Roof age (critical age ~10-15 years for most types)
Tornado  F-rating
– Relates damage to approximate wind speed range
Straight-line winds  peak gust
– Dominant range of wind speeds < 80 mph
– Tree damage
Use of claims data and consultants for calibration/validation
26
FUTURE
MODEL
UPDATES:
RISKLINK
©2013 Risk Management Solutions, Inc.
• Interim update of SCS model in January 2014
• Fundamentals of event generation module still
strong
• 2008-2012 taught us new lessons that we wish to
integrate
– Add information on tail events and EPs from
2008-2012 SCS seasons
– Integrate new client data to further refine
hazard and vulnerability
27
FUTURE
MODEL
UPDATES:
RMS(ONE)
EXPOSURE
EVENT
VULNERABILITY
RATES
Spring 2014: SCS translated
for use on RMS(one)
More powerful platform to
make the model work for you:
•
•
•
Conduct sensitivity tests
Leverage your own claims
data and research
Gain competitive
advantage
HAZARD
PLA
LOSS
©2013 Risk Management Solutions, Inc.
28
SCS APPLICATIONS &
CONSIDERATIONS
IMPLICATIONS
AND
APPLICATIONS
•
•
•
©2013 Risk Management Solutions, Inc.
Ratemaking (primary companies)
– Statewide level
– Territorial
– Class Plans
– Policy Terms
Transfer of Risk (e.g., reinsurance)
Concentration of Risk
30
HOW EVENTS
ARE DEFINED
SPC Risk Map
*synoptic = large scale
• Any vertically developed thunderstorm that
produces damage due to hail, tornado, and/or a
straight-line wind
• Can occur in all states and provinces in the U.S.
and Canada any time during the year
• Peril model and catastrophe model
• Event can be
– Synoptic* system
– Used in RiskLink to capture high-frequency
losses
atmospheric phenomenon
©2013 Risk Management Solutions, Inc.
31
EXPERIENCE
DATA
•
Low frequency
– PCS definition
•
•
•
•
>=$25M industrywide, and
>=$5M for any state
Gross loss
Lifetime of synoptic system
– Company ID
– ~$Ms
•
High frequency
– Remainder – “follows” low freq
– One “event” per year for each state
– $10,000s to $100,000s
©2013 Risk Management Solutions, Inc.
32
HIGH FREQUENCY AND LOW FREQUENCY SCS LOSSES
Contributes
to AAL
EP curve
Discrete Events
Low
Freq
Yes
AEP / OEP
Yes
High
Freq
Yes
Becomes
meaningful when
combined with lowfrequency losses
Thousands of actual
occurrences every year.
©2013 Risk Management Solutions, Inc.
One event each year per
state/province with
varying hazard at more
granular level.
33
RATES WITHIN
A STATE OR
REGION
•
•
©2013 Risk Management Solutions, Inc.
Does geographic location within a
state matter for SCS? Do you need
to have territorial differentials?
What about other characteristics?
34
AAL BY PRIMARY
CHARACTERISTICS
•
Reference Structure: 200k structure, 150k
contents, 40k ALE ($250 deductible)
•
Selected location in Midwest
Scenario
Construction
Occupancy
Yr Built
# of Stories
AAL
CV
1
Unknown
Unknown
Unknown
Unknown
$82
32.7
2
Wood
Unknown
Unknown
Unknown
$107
27.0
3
Wood
SFD
Unknown
Unknown
$123
23.6
4
Wood
SFD
1995
Unknown
$113
25.0
5
Wood
SFD
1995
2
$97
27.2
©2013 Risk Management Solutions, Inc.
35
AAL BY PRIMARY
CHARACTERISTICS
Scenario
Construction
Occupancy
Yr Built
# of Stories
AAL
CV
6
Wood
SFD
2005
2
$95
27.8
7
Wood
SFD
1965
2
$107
25.5
Scenario
Construction
Occupancy
Yr Built
# of Stories
AAL
CV
6
Wood
SFD
2005
2
$95
27.8
8
Wood
SFD
2005
1
$115
24.7
©2013 Risk Management Solutions, Inc.
36
PRIMARY
CHARACTERISTICS:
NUMBER OF
STORIES
13% Damage Ratio
Risk is primarily determined by the roof system covering and its
value relative to the remainder of the structure
– Brick veneer structure example
– $100,000 per story replacement cost
– $15,000 for roof
7% Damage Ratio
5% Damage Ratio
37
SECONDARY MODIFIERS
Secondary modifiers are invoked only when sufficient
primary characteristics are known: occupancy, construction
class, year of construction, and building height
Hail
Tornado
Straight-line Wind
• Roof System Covering
• Cladding Type
• Roof Age
• Mechanical and Electrical Systems
• Foundation System
• Roof Anchor
• Wind Missiles
• Tree Density
• Cladding
• Tree Density
• Roof System Covering
• Roof Sheathing Attachment
38
Return period of an F2 or greater tornado at a point in 1,000’s of years
HOW DO YOU
THINK ABOUT
RISK AT A
LOCATION
BASIS?
100,000
50,000
20,000
10,000
5,000
2,000
•
Location-level risk is
fundamentally different
for SCS than for other
perils like hurricane
•
The RP of hurricane
winds at a location is
generally less than 100
years in high risk areas
•
The RP of an F2 at a
location is measured in
the THOUSANDS of
years in high risk areas
©2013 Risk Management Solutions, Inc.
Source: Meyer et al. 2002
39
DEDUCTIBLES
•
•
Given that AAL is
driven in large part by
hail, damage ratios for
SCS tend to be on the
smaller side (5-10%)
These types of loss
ratios can be very
sensitive to the
deductible chosen
when modeling SCS
©2013 Risk Management Solutions, Inc.
Real-world case study:
• Take a book of business for a particular state, and
change the deductible from $250 to 1% of the limit
• Determine the change to AAL and RP losses as a
result
Loss Metric /
Return Period
Change
AAL
-25%
5
-20%
10
-20%
50
-15%
100
-15%
250
-10%
500
-10%
43
RISK TRANSFER
CONSIDERATIONS
US TH AEP
US HU AEP
HU OEP
US HUUSAEP
US HU OEP
40%
Exceedance Probability
40%
Hurricane
45%
US TH OEP
US SCS AEP
US SCS OEP
45%
Tail of distribution
Aggregate EP
~80% less than 72 hour duration
50%
Severe Convective Storm
50%
Exceedance Probability
•
•
•
35%
30%
25%
20%
15%
10%
35%
30%
25%
20%
15%
10%
5%
5%
0%
0%
0
10
20
30
40
50
Gross Loss
©2013 Risk Management Solutions, Inc.
60
70
80
90
100
0
10
20
30
40
50
60
70
80
90
100
Gross Loss
44
Looking at
accumulation using
a sample tornado
footprint may be
more helpful for
understanding the
amount of
exposure at risk for
‘the big one’
EXPOSURE
ACCUMULATION
FOR
TORNADOES
Given the small
footprint size for
tornadoes, what are
some ways to examine
the worst-case
scenarios?
Develop a sample
tornado ellipse using
size parameters for
long-lived/large
tornadoes
Place over hotspots of
exposure and use
simple damage ratios to
calculate sample loss
45
MAY NEED
ADDITIONAL
EXPECTED $$
•
Included
- Tree fall
- Debris removal
- Power outage if there is direct damage to the
location
•
Nonmodeled losses
- Flood
- Fire following
- Power outage off premises unless there is
direct damage to the location
• Can model auto
©2013 Risk Management Solutions, Inc.
46
MORE
INFORMATION
•
•
•
©2013 Risk Management Solutions, Inc.
RMS document in response to ASOP #38: Using
Models Outside the Actuary’s Area of
Expertise (Property and Casualty)
Provides basic understanding of the model
Non-proprietary – just ask
47
THE FUTURE: RESILIENT
RISK MANAGEMENT
RESILIENT
RISK
MANAGEMENT
©2013 Risk Management Solutions, Inc.
Models aren’t
perfect
Resiliency in
principal
Resiliency in
practice
Catastrophe risk
is characterized
by deep
uncertainty
Understanding
implied bets
Diagnostic views
and sensitivity
tests
Learning is
ongoing
Adapting
quickly to new
information
Agile updates,
post-event and
interim views
One size
doesn’t always
fit all
Owning a view
of risk
Adjustments,
alternatives,
open platform
49
BENEFITS OF
OWNING YOUR
VIEW OF RISK
More
profitable
and agile
underwriting
Improved
capital
allocation
Take
control of
cat models
Own View
of Risk
Reflect
your
unique
portfolio
©2013 Risk Management Solutions, Inc.
Leverage
your
experience
and claims
Stable view
of risk over
time
Manage
internal and
external
stakeholders
50
QUESTIONS?
©2013 Risk Management Solutions, Inc.
•
Kay.Cleary@rms.com | 850-386-5292
•
Meghan.Purdy@rms.com | 510-608-3884
51
APPENDIX
LA NIÑA AND ENHANCED TORNADO ACTIVITY
El Niño
The lack of strong jet stream energy
generally leads to lower activity
Sample analog years:
1992, 1998, 2003
La Niña
A strong jet stream and warm moist air
over the Southeast combine to create
good conditions for storms
Sample analog years:
1965, 1974, 2008, 2011
Maps courtesy of Climate Prediction Center
53
LOCATION OF
EVENTS
MATTERS
Traditional ‘Tornado Alley’ is
sparsely populated compared to
areas of the South and East
Tornadoes tend to happen earlier
in the year in the South and East
(Jan-April) and leads to higher
fatality rates and increased losses
©2013 Risk Management Solutions, Inc.
Source: Weather Channel
54
IS CLIMATE
CHANGE A
FACTOR IN
SCS RISK?
The effect of climate change on storms is difficult to discern for two
reasons:
• Historical record is not well resolved
• Favorable SCS conditions are more tied to geography
Storms to this point have not been proven to be more violent or more
intense
• EF4 and 5 tornado frequencies haven’t increased over time
• EF0 and 1 tornadoes have seen increases, but most likely from
historical underreporting than from any physical mechanism
Your perception of the influence of climate change depends on how
you trend historical data
©2013 Risk Management Solutions, Inc.
55
IS CLIMATE
CHANGE A
FACTOR IN
SCS RISK?
It is unclear how a warming climate will influence SCS behavior
• Increase in warm, moist air should increase thunderstorms
• Decrease in wind shear due to decrease in temperature gradient
from equator to poles should lead to a decrease in hail and
tornadoes
• Strength and location of forcing mechanisms may lead to
increases/drops in activity regionally
Human Impacts
• Outbreaks and severe weather peak months may shift to be
earlier in the year
• More people in harms way, as winter tornadoes tend to be more
fatal
©2013 Risk Management Solutions, Inc.
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