Weathering the Storm - Aon Benfield Events Center

Weathering the Storm
Steve Drews, Impact Forecasting & Paul Eaton, Aon Benfield Analytics
2014 Analytics Insights Conference
July 22 - 24
Agenda
Section 1
IF’s New Historical Severe Thunderstorm Model: STS RePlay
Section 2
Experience Analysis
Section 3
Enhanced Tornado Viewing Guide
Aon Benfield | Analytics
Proprietary & Confidential | July 2014
2
Agenda Tracker
Section 1
IF’s New Historical Severe Thunderstorm Model: STS RePlay
Section 2
Experience Analysis
Section 3
Enhanced Tornado Viewing Guide
Aon Benfield | Analytics
Proprietary & Confidential | July 2014
3
Why…
the current
severe
Not…are
enough
emphasis
thunderstorm stochastic
on
or
a
lack
of
recent
catastrophe models so far off
historical
hazard
data
with their AAL
values????
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4
Annual Severe Weather Frequency By Different Periods
Average Annual SPC Reports: All Tornadoes
Average Annual SPC Reports: All STS Perils
35,000
30,000
26,931
28,642
29,638
1,283
1,336
924
500
0
15,000
20,000
Reports / Year
Reports / Year
1,000
1,284
Average Annual SPC Reports: All Hail
25,000
15,000
Reports / Year
1,500
11,271
10,000
12,588
13,461
13,858
10,000
5,000
5,206
0
5,000
Average Annual SPC Reports: All Convective Wind
0
1996-2013
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2000-2013
2004-2013
Reports / Year
1950-2013
20,000
13,059
10,000
5,941
0
5
13,898
14,444
Annual Severe Weather Frequency: Tornadoes
The vast majority of tornado frequency increase
stems from weaker (and not as easily detectable)
tornadoes
Note: 2013 lowest tornado total since 1989
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6
Annual Severe Weather Frequency: Hail
The vast majority of hail frequency increase stems
from smaller hail occurrences
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7
Annual Severe Weather Frequency: Wind
Both weaker AND stronger thunderstorm-produced
winds continue to show frequency increases
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8
What if…
…there was a severe thunderstorm
catastrophe model that would more
accurately predict my AAL?
Well, now there is…
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9
New IF ELEMENTS Severe Thunderstorm Tool: STS RePlay
 Based on recent historical data obtained from the Storm Prediction Center
– Contains last 10 years of severe thunderstorm activity
– Contains tornado, hail and convective wind occurrences
– Recent historic experience contains a richer catalog of activity based on better data capture using
Doppler radar and other technological advancements
 ELEMENTS STS RePlay event set
– Can be used for a replay of a large catalog of historical daily outbreaks to better understand loss
behavior
– A replay of historical event catalog against current exposures allows a recast to current loss
expectations
– RePlay catalog can be used for obtaining a view of average yearly losses as an alternative to
Average Annual Losses produced by stochastic models
– Note: the scenario set is not a replacement for stochastic models, but is an alternative recast view
using actual historical experience
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10
STS RePlay Source Data
 Source data: Storm Prediction Center
– www.spc.noaa.gov
 Local Storm Reports (LSRs)
– Gathered from trained storm spotters, emergency
managers, law enforcement, automated weather
stations and Doppler Radar
– Three types of severe weather data gathered
• All tornadoes
• Hail 1” or greater
• Convectively-induced (thunderstorm-produced)
winds of 58 mph or greater
– Reports characterize severe weather occurrences
• Date and time, location (lat/long), etc.
• Intensity, damage, etc.
 Data used
– Total of 260,610 LSRs analyzed and converted into
ELEMENTS format after initial filtering (hail below
1”, winds below 58 mph, reports with incomplete
data)
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STS RePlay Input Data
 Occurrence Counts
– Tornadoes: 13,764 (average 1,376/year)
– Hail: 125,924 (average 12,592/year)
– Wind: 120,922 (average 12,092/year)
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STS RePlay Event Set Generation
Hail and Wind
 Data usage within SPC LSR database files
– Start coordinates
– Hail Size
– Wind (knots)
 Directionality calculated as a random number due to the uncertainty of the direction (i.e. no end
coordinates)
 Path Length and Path Width calculated as a random number based on average, lower bound, upper
bound, and standard deviation values given within the ELEMENTS Severe Thunderstorm Model
stochastic event set for each hail size bin (0-5) and wind intensity bin (0-3)
 Hail Size calculated using hail size provided by SPC and converted from inches to millimeters
– If hail size is less than 1” (25.4 mm), data is not used
 Wind intensity calculated using wind speed provided by SPC and assigned to Fujita Scale
– If wind intensity is less than 50 knots (58 mph) data is not used
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13
Special Considerations For Wind and Hail SPC Reports
 Limited to point estimates
– No end coordinate reported in SPC
data
– No data on swath shape (length,
width)
– SOLUTION: Implement current inplace Impact Forecasting research on
characterization of hail and wind
swaths
Multiple Hail Reports
 Multiple reports could represent portions
of same event swath
– Overlapping swaths over-count losses
– SOLUTION: Bin events by report time
(5 minute increment) and spatial
proximity (0.6 mile increment) and
treat similar reports as duplicates of
the previous reports
Single Hail Swath
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14
STS RePlay Event Set Generation
Tornadoes
 Data usage within SPC LSR database files
– Start and end coordinates
– Intensity (Fujita Scale/Enhanced Fujita Scale)
– Path Length (verification only)
– Path Width
 Directionality calculated by utilizing the start and end coordinates given by SPC data
 Path Length calculated by utilizing the start and end coordinates given by SPC data and converted
into kilometers
– Output verified by SPC data values
 Path Width (maximum) taken directly from SPC data and converted into meters (given in yards)
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15
Special Considerations For EF3+ Tornado SPC Reports
 Strong to violent tornadoes (EF3+) often long
lived with complex track shape and varying
intensity
– Example: Joplin, MO 5/22/2011 EF5
tornado
 SPC LSR data characterizes start and end
coordinates and single intensity rating
– Example: Joplin EF5 as straight line
between SPC start and end coordinates
– 490 EF3+ events in 2003-2012 data set
– All 490 EF3+ events researched
– Majority didn’t have detailed information
 28 EF3+ tornado tracks modified
– Ad hoc research for event shape and
intensity (NWS-sourced official track data)
– Re-parameterized events as necessary for
proper representation within event set
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16
Hazard Uncertainty
 Two mile tolerance for coordinate accuracy creates uncertainty in LSR event location
– Street-level addressing not available – cross-street coordinate assignments most likely
– SOLUTION: Sample every LSR 25 times, adding position uncertainty
• 5% position uncertainty for tornadoes (more accurate reporting)
• 20% position uncertainty for hail and wind
• 6.5 million event set generated in ELEMENTS from 260,610 individual LSRs
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17
STS RePlay Results Verification
Comparison of Severe Convective Storm Models To Actual Client Historical AALs
Client
Client
Size*
IF STS
RePlay
to Actual
Stochastic
Model A
to Actual
Stochastic
Model B
to Actual
Stochastic
Model C
to Actual
A
Large
0.90
0.51
0.54
0.40
B
Large
1.00
0.49
0.58
0.40
C
Small
1.78
0.87
2.30
0.95
D
Small
1.33
2.06
1.54
1.16
E
Large
1.04
0.85
0.84
n/a
F
Large
0.90
0.65
0.61
n/a
0.93
0.54
0.58
0.41
Weighted Average
* Large client actual loss approx. 10x small client actual losses
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18
IF ELEMENTS STS RePlay Summary
 Designed only as an alternative AAL view using rich data due to technological improvements in data
capture and an overall active severe convective weather period
 Contains historical SPC occurrences of tornadoes, hail and convective winds from active 10-year
period
 Each severe weather occurrence is sampled 25 times to account for hazard position uncertainty
 Reports were analyzed spatially and temporally to eliminate duplicate reports
 Significant tornadoes were analyzed and modified (where information existed) to better represent the
overall track and intensity fluctuations that EF3+ tornadoes often exhibit
 Over 6.5 million severe weather occurrences are contained within the STS RePlay event set
 Official Release: September 2014 (in coordination with new IF STS Stochastic Model)
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Agenda Tracker
Section 1
IF’s New Historical Severe Thunderstorm Model: STS RePlay
Section 2
Experience Analysis
Section 3
Enhanced Tornado Viewing Guide
Aon Benfield | Analytics
Proprietary & Confidential | July 2014
20
What if I…
Instant
Have
Need
Want
Don’t
Don’t
noisy
ato
ahave
realistic
quantifiable
have
data
experience
credible
that
assessment
makes
basis
loss
that
for
trend
of
Want
see
Derrick
Rose
fast
experience
selections
my
comparing
represents
average
and
because
stochastic
annual
on-leveling
mythe
current
I’ve
loss
model
grown
from
very
break
through
entire
underwriting
severe
output
into new
thunderstorms?
difficult?
todefense
experience?
regions?
paradigm?
Mavericks
again?
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21
Severe Weather Experience Templates
Superior Trend Diagnostics
Daily Incurred Losses
Log Scale
100,000
Loss
Thousands
10,000
1,000
100
10
1
Jan-98
Trend: 8.2%
Jan-00
Jan-02
Jan-04
Jan-06
Jan-08
Jan-10
Jan-12
Date
Daily Average Severity
Log Scale
Trend: 9.1%
Daily Claim Counts
Log Scale
100,000
Trend: 0.0%
10,000
Claims
Severity
10,000
1,000
1,000
100
10
Trend: 0.2%
Trend: 8.1%
100
Jan-98
Jan-00
Jan-02
Jan-04
Jan-06
Jan-08
Jan-10
Jan-12
1
Jan-98
Jan-00
Jan-02
Date
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Jan-04
Jan-06
Date
22
Jan-08
Jan-10
Jan-12
Severe Weather Experience Templates
Analysis Of Daily Loss Data
Automated Aggregator of Events
Per Occurrence XOL & Aggregate Structure Analysis
Cat XOL
Co-Part
5%
Limit 210,000,000
Retention 20,000,000
Trended
IF RePlay
Selected
Incurred
AccidentDate
31,438
07-Jun-92
18,838
08-Jun-92
86,403
09-Jun-92
588,810
10-Jun-92
169,952
11-Jun-92
32,875
12-Jun-92
33,038
13-Jun-92
7,286,350
14-Jun-92
104,998
15-Jun-92
67,508
16-Jun-92
62,157
17-Jun-92
61,605
18-Jun-92
2,402,488
19-Jun-92
23,295
20-Jun-92
22,837
21-Jun-92
54,423
22-Jun-92
40,066
23-Jun-92
52,521
24-Jun-92
524,827
25-Jun-92
37,133
26-Jun-92
29,800
27-Jun-92
15,283
28-Jun-92
60,824
29-Jun-92
DOY
96 Hour Total
158
152,342
159
164,301
160
207,116
161
725,489
162
864,003
163
878,039
164
824,675
165
7,522,215
166
7,457,260
167
7,491,894
168
7,521,013
169
296,268
170
2,593,758
171
2,549,545
172
2,510,226
173
2,503,044
174
140,622
175
169,848
176
671,837
177
654,547
178
644,281
179
607,043
180
143,040
Losses by day tracking
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Regular XOL
-
Occ Ceded
Loss
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
Aggregate
Per Event Deductible 2,000,000
Deductible Type Standard
Per Event Limit 18,000,000
Regular XOL Franchise XOL Ceded Loss
0
0
0
0
0
0
0
5,522,215
7,522,215 5,522,215
5,457,260
7,457,260 5,457,260
5,491,894
7,491,894 5,491,894
5,521,013
7,521,013 5,521,013
0
593,758
2,593,758
593,758
549,545
2,549,545
549,545
510,226
2,510,226
510,226
503,044
2,503,044
503,044
0
0
0
0
0
0
0
Optimization of Program
Total Ceded
0
0
0
0
0
0
0
5,522,215
5,457,260
5,491,894
5,521,013
0
593,758
549,545
510,226
503,044
0
0
0
0
0
0
0
Initial step to aggregate into 3 day
step cumulative totals before and
after an aggregate deductible
Max ceded
through day n
assuming an
event ends on
Max ceded Events, Gross
day n
through day n of Reinsurance
3,128,895
3,128,895
0
3,128,895
3,128,895
0
3,128,895
3,128,895
0
3,128,895
3,128,895
0
3,128,895
3,128,895
0
3,128,895
3,128,895
0
3,128,895
3,128,895
0
8,651,110
8,651,110
7,522,215
8,586,156
8,651,110
0
8,620,789
8,651,110
0
8,649,908
8,651,110
0
8,651,110
8,651,110
0
9,244,868
9,244,868
2,593,758
9,200,655
9,244,868
0
9,161,336
9,244,868
0
9,154,154
9,244,868
0
9,244,868
9,244,868
0
9,244,868
9,244,868
0
9,244,868
9,244,868
0
9,244,868
9,244,868
0
9,244,868
9,244,868
0
9,244,868
9,244,868
0
9,244,868
9,244,868
0
Event Loss to
Per Occ Layer
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
Event Loss to
Agg Layer
0
0
0
0
0
0
0
5,522,215
0
0
0
0
593,758
0
0
0
0
0
0
0
0
0
0
Aggregation of daily losses
via local optimization rule
23
Agenda Tracker
Section 1
IF’s New Historical Severe Thunderstorm Model: STS RePlay
Section 2
Experience Analysis
Section 3
Enhanced Tornado Viewing Guide
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24
TVG Concentrations Are Tornado-Track Based
 TVG uses scenario or
“What if” analysis
 Example: what if the
Moore, OK tornado from
2013 hit other exposure
concentrations…
St. Joseph
Columbia
Kansas City
Sedalia
St. Louis
2013 Moore, OK EF-5 Track
Cape Girardeau
Springfield
Poplar Bluff
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25
Accumulations By Census Statistical Area
Kansas City, MO‐KS
TIV
No. Events
39,466,872
1
17,585,432
119
5,412,883
1,191
3,771,730
2,381
1,475,490
5,954
2,466,163
11,908
St. Joseph, MO‐KS
TIV
No. Events
37,366,568
1
29,453,144
32
7,266,844
317
3,195,987
633
832,917
1,584
2,836,071
3,168
Cape Girardeau‐Jackson, MO‐IL
TIV
No. Events
24,297,988
1
20,470,170
25
6,639,354
247
2,609,244
495
512,595
1,237
2,137,388
2,474
Springfield, MO
TIV
No. Events
23,878,528
1
17,553,980
81
7,988,126
809
4,723,276
1,617
1,415,083
4,043
2,872,752
8,086
Fayetteville‐Springdale‐Rogers, AR‐MO
TIV
No. Events
23,048,812
1
13,348,868
58
4,483,250
577
2,267,785
1,155
553,076
2,886
1,582,815
5,772
Micropolitan
Conditional
Percentile
Max
99.0%
90.0%
80.0%
50.0%
MEAN
Total Events
Poplar Bluff, MO
TIV
No. Events
63,045,104
1
50,838,456
20
12,989,744
199
5,914,346
397
1,541,905
994
4,923,481
1,988
Sedalia, MO
TIV
No. Events
49,566,616
1
41,498,012
18
9,920,330
180
3,409,520
361
1,268,927
902
3,920,615
1,804
Sikeston, MO
TIV
No. Events
34,808,164
1
30,377,070
12
9,320,646
119
3,991,476
238
1,499,919
596
3,515,049
1,192
Paragould, AR
TIV
No. Events
34,479,808
1
27,025,388
12
6,414,103
123
3,442,260
245
573,286
613
2,743,152
1,226
Hannibal, MO
TIV
No. Events
27,629,492
1
21,423,680
18
3,066,074
177
1,360,789
354
219,738
884
1,351,080
1,768
Conditional
Percentile
Max
99.0%
90.0%
80.0%
50.0%
MEAN
Total Events
Macon, MO
TIV
No. Events
42,836,672
1
42,046,328
3
23,580,500
35
9,327,144
70
2,887,726
174
6,871,468
348
Anabel, MO
TIV
No. Events
38,361,144
1
38,361,144
1
12,649,454
11
6,150,253
23
1,319,952
56
4,355,448
112
Bertrand, MO
TIV
No. Events
33,734,388
1
33,734,388
1
26,275,186
13
17,446,704
26
1,635,025
64
7,894,294
128
Matthews, MO
TIV
No. Events
32,353,402
1
31,614,578
3
6,482,090
30
1,986,192
60
436,107
150
2,514,532
300
Bevier, MO
TIV
No. Events
31,226,224
1
31,226,224
1
16,643,744
14
8,326,898
27
2,186,144
68
5,466,836
136
Metropolitan
Metropolitan
Conditional
Percentile
Max
99.0%
90.0%
80.0%
50.0%
MEAN
Total Events
Rural
Joplin, MO ‐ 2011 Historical Tornado Track
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Conditional
Kansas City, MO‐KS
Percentile
TIV
No. Events
MaxJoplin, MO ‐ 2011 Historical Tornado Track
39,466,872
1
99.0%
17,585,432
119
90.0%
5,412,883
1,191
80.0%
3,771,730
2,381
50.0%Joplin, MO ‐ 2011 Historical Tornado Track
1,475,490
5,954
MEAN
2,466,163
Total Events
11,908
26
Tornado Viewing Guide – Top Accumulations Loss Scenarios
Mayflower, AR ‐ 2014 Historical Tornado Track
Conditional
Percentile
Max
99.0%
90.0%
80.0%
50.0%
MEAN
Total Events
Sikeston, MO
TIV
No. Events
70,566,784
1
59,718,096
12
28,595,964
119
19,031,244
239
8,337,714
598
12,255,941
1,196
Poplar Bluff, MO
TIV
No. Events
66,802,704
1
56,327,168
22
23,891,564
216
16,505,950
433
6,080,895
1,082
10,235,657
2,164
Sedalia, MO
TIV
No. Events
51,827,928
1
43,565,912
18
25,989,804
181
9,558,846
362
3,506,265
905
8,144,893
1,810
Lebanon, MO
TIV
No. Events
41,964,000
1
39,725,136
20
29,740,616
203
23,858,452
406
12,674,278
1,015
15,490,019
2,030
US $ in Ones
Mayflower, AR ‐ 2014 Historical Tornado Track
Conditional
Percentile
Max
99.0%
90.0%
80.0%
50.0%
MEAN
Total Events
TIV
70,566,784
59,718,096
28,595,964
19,031,244
8,337,714
12,255,941
1,196
Sikeston, MO
Loss Potential Inside Track
Low Intensity (20%) High Intensity (50%)
14,113,357
35,283,392
11,943,619
29,859,048
5,719,193
14,297,982
3,806,249
9,515,622
1,667,543
4,168,857
2,451,188
6,127,971
US $ in Ones
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27
Kennett, MO
TIV
No. Events
40,750,440
1
36,633,496
14
16,024,542
142
10,517,483
284
3,058,194
711
6,411,309
1,422
Major (EF4 to EF5) Tornado Days Per Century
A “Tornado Day” is
any day in which you
see at least one EF4+
tornado within 80km
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28
TVG Loss Ranges Using Historical EF4 & EF5 Event Frequency
Exceedance Probabilities
Modeled Other Wind
Loss Potential Inside Track
Return Period
1,000
500
250
100
50
RMS
TIV
100%
58,500
47,008
36,813
25,456
18,656
Low Intensity
20%
11,700
9,402
7,363
5,091
3,731
High Intensity
50%
29,250
23,504
18,406
12,728
9,328
12,830
2,566
6,415
25
675
1,687
Annual avg
Std dev
25
Annualized Loss from EF4s and EF5s in TVG
US $ in Thousands
Assumptions:
Tornado frequency uses NOAA Storm Prediction Center data 1950-2012
TVG event frequency by smoothed historical average of EF4+ tornadoes is SPC data using 80km Gaussian filter
Excess of 1,000 Year Return Period
RiskLink
v13.1
46,842
44,035
29,864
17,335
10,786
Return Period
1,000
500
250
100
50
7,237
12,338
21,658
5,316
22,996
7,543
US $ in Thousands
Excess of 100 Year Return Period
Memphis
Memphis
Denver
Other
Denver
Monroe
Tulsa
Monroe
Other
Clovis
Clovis
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Tulsa
29
AIR
Touchstone
v1.5
45,405
39,339
30,260
20,955
16,684
TVG Loss Ranges Using Historical EF4 & EF5 Event Frequency
Exceedance Probabilities
2013 Other Wind
2011
Return Period
1,000
500
250
100
50
25
AAL from EF4s and EF5s in TVG
2013
Change
RMS
TIV
100%
17,191
14,590
11,360
7,825
5,888
Loss
50%
8,596
7,295
5,680
3,912
2,944
TIV
100%
15,423
13,045
10,042
6,792
4,996
Loss
50%
7,712
6,523
5,021
3,396
2,498
2011
to 2013
-10%
-11%
-12%
-13%
-15%
4,306
2,153
3,664
1,832
-15%
711
-9%
781
US $ in Thousands
Assumptions:
Tornado frequency uses NOAA Storm Prediction Center data 1950-2012
TVG event frequency by smoothed historical average of EF4+ tornadoes is SPC data using 80km Gaussian filter
Other
Louisville
Cincinnati
Cleveland
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3,821
3,417
Annual avg
Std dev
6,585
1,435
3,351
1,667
Return Period
1,000
500
250
100
50
Key Metropolitan Risks 2013
Excess of 1,000 Year RP
Cincinnati
Cleveland
Touchstone
v1.5
13,234
10,419
8,767
6,714
4,982
US $ in Thousands
Key Metropolitan Risks 2011
Excess of 100 Year RP
AIR
RiskLink
v13.1
12,575
10,318
8,379
6,274
4,941
Excess of 100 Year RP
Other
Excess of 1,000 Year RP
Louisville
Cincinnati
Cleveland
Louisville
Cincinnati
Cleveland
Louisville
30
TVG Historical Tornado Shapes
Moore, OK Tornado
Joplin, MO Tornado
Date:
May 22, 2011
Rating:
EF‐5
Date:
Rating:
Mayflower, AR Tornado
May 20, 2013
Date:
EF‐5
Rating:
April 27, 2014
EF‐4
Est. Max. Wind:
200+ mph
Est. Max. Wind:
210 mph
Est. Max. Wind:
Path Length:
22.1 Miles
Path Length:
17 Miles
Path Length:
41.3 Miles
Path Width:
0.75 Mile
Path Width:
1.3 Miles
Path Width:
0.75 Miles
Fatalities
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Fatalities
24
Fatalities
31
190 mph
16
What if you could run every EF4 and EF5 tornado?
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Stop Light Exposure Management
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Paul’s
Everyone’s Favorite Quotable Statistician
Essentially, all models are
wrong, but some are useful
George E. P. Box
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Biographies
Steve Drews, Associate Director & Lead Meteorologist
Steve Drews is Associate Director and Lead Meteorologist for Impact Forecasting. He currently heads the
development of model customization for Impact Forecasting’s catastrophe modeling suite, ELEMENTS.
Over his 12 years with Aon, Steve has provided expert analysis on hurricane and tornado damage and has
been a featured speaker at client, meteorological and government presentations about coastal urbanization,
global climate change, tropical cyclone frequency, and severe convective weather frequency as well as
provided scientific testimony on behalf of insurance and reinsurance companies in legal matters.
Paul Eaton, FCAS, Director
Paul Eaton has worked in the Aon Benfield Analytics Chicago office for seven years. Paul’s current role
focuses on catastrophe management including risk adjusted pricing and capacity allocation. Paul dabbled in
telephony consulting, home improvement sales, and even driving an ambulance prior to joining Aon
Benfield.
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Contacts
Steve Drews
Associate Director & Lead Meteorologist
Impact Forecasting
+1.312.381.5888
[email protected]
Paul Eaton, FCAS
Director
Aon Benfield Analytics
+1.312.381.5553
[email protected]
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Legal disclaimer: Impact Forecasting LLC
The results listed in this report are based on engineering / scientific analysis and data, information
provided by the client, and mathematical and empirical models. The accuracy of the results depends on
the uncertainty associated with each of these areas. In particular, as with any model, actual losses may
differ from the results of simulations It is only possible to provide plausible results based on complete
and accurate information provided by the client and other reputable data sources. Furthermore, this
information may only be used for the business application specified by Impact Forecasting, LLC and for
no other purpose. It may not be used to support development of or calibration of a product or service
offering that competes with Impact Forecasting, LLC. The information in this report may not be used as
a part of or as a source for any insurance rate filing documentation.
THIS INFORMATION IS PROVIDED "AS IS" AND IMPACT FORECASTING, LLC HAS NOT MADE AND
DOES NOT MAKE ANY WARRANTY OF ANY KIND WHATSOEVER, EXPRESSED OR IMPLIED,
WITH RESPECT TO THIS REPORT; AND ALL WARRANTIES INCLUDING WARRANTIES OF
MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE HEREBY DISCLAIMED BY
IMPACT FORECASTING, LLC. IMPACT FORECASTING, LLC WILL NOT BE LIABLE TO ANYONE
WITH RESPECT TO ANY DAMAGES, LOSS OR CLAIM WHATSOEVER, NO MATTER HOW
OCCASIONED, IN CONNECTION WITH THE PREPARATION OR USE OF THIS REPORT.
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