Weathering the Stormevents.aon.com/SiteCollectionDocuments/201407_analytics... · 2017-09-15 · Based on recent historical data obtain ed from the Storm Prediction Center – Contains

Weathering the StormSteve Drews, Impact Forecasting & Paul Eaton, Aon Benfield Analytics

2014 Analytics Insights ConferenceJuly 22 - 24

Aon Benfield | AnalyticsProprietary & Confidential | July 2014 2

Agenda

Section 1 IF’s New Historical Severe Thunderstorm Model: STS RePlay

Section 2 Experience Analysis

Section 3 Enhanced Tornado Viewing Guide


Agenda Tracker





…are the current severe thunderstorm stochastic

catastrophe models so far off with their AAL values????

Not enough emphasis on or a lack of recent historical hazard data

Why…


Annual Severe Weather Frequency By Different Periods

Average Annual SPC Reports: All STS Perils

11,271

26,93128,642 29,638

0

5,000

10,000

15,000

20,000

25,000

30,000

35,000

Rep

orts

/ Ye

ar

1950-2013 1996-2013 2000-2013 2004-2013

Average Annual SPC Reports: All Tornadoes

924

1,284 1,283 1,336

0

500

1,000

1,500

Rep

orts

/ Ye

ar

Average Annual SPC Reports: All Hail

Average Annual SPC Reports: All Convective Wind

5,206

12,588 13,461 13,858

0

5,000

10,000

15,000

Rep

orts

/ Ye

ar

5,941

13,059 13,898 14,444

0

10,000

20,000

Rep

orts

/ Ye

ar


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


Annual Severe Weather Frequency: Hail

The vast majority of hail frequency increase stems from smaller hail occurrences


Annual Severe Weather Frequency: Wind

Both weaker AND stronger thunderstorm-produced winds continue to show frequency increases


…there was a severe thunderstorm catastrophe model that would more

accurately predict my AAL?

Well, now there is…

What if…


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


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)


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)


STS RePlay Event Set GenerationHail 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


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 in-

place Impact Forecasting research on characterization of hail and wind swaths

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

Multiple Hail Reports


STS RePlay Event Set GenerationTornadoes

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)


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


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


STS RePlay Results Verification

ClientClient Size*

IF STS RePlay

to Actual

Stochastic Model A

to Actual

Stochastic Model B

to Actual

Stochastic Model C

to ActualA 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

Weighted Average 0.93 0.54 0.58 0.41

Comparison of Severe Convective Storm Models To Actual Client Historical AALs

* Large client actual loss approx. 10x small client actual losses


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)


Agenda Tracker





What if I…

Need a realistic assessment of my average annual loss from

severe thunderstorms?

Want a quantifiable basis for comparing stochastic model

output to experience?

Don’t have credible loss experience because I’ve grown

into new regions?

Don’t have experience that represents my current

underwriting paradigm?

Want to see Derrick Rose fast break through the entire

Mavericks defense again?

InstantHave noisy data that makes trend

selections and on-leveling very difficult?


1

10

100

1,000

10,000

100,000

Jan-98 Jan-00 Jan-02 Jan-04 Jan-06 Jan-08 Jan-10 Jan-12

Loss

Thou

sand

s

Date

Daily Incurred LossesLog Scale

Severe Weather Experience TemplatesSuperior Trend Diagnostics

1

10

100

1,000

10,000

100,000


Clai

ms

Date

Daily Claim CountsLog Scale

100

1,000

10,000


Seve

rity

Date

Daily Average SeverityLog Scale

Trend: 8.2%

Trend: 8.1%

Trend: 9.1% Trend: 0.0%

Trend: 0.2%


Severe Weather Experience TemplatesAnalysis Of Daily Loss Data

Losses by day trackingInitial step to aggregate into 3 day step cumulative totals before and after an aggregate deductible

Aggregation of daily losses via local optimization rule

Automated Aggregator of EventsPer Occurrence XOL & Aggregate Structure Analysis

Optimization of ProgramCo-Part 5% Per Event Deductible 2,000,000

Limit 210,000,000 Deductible Type StandardTrended Retention 20,000,000 Per Event Limit 18,000,000

Selected Incurred AccidentDate DOY 96 Hour Total Regular XOL

Occ Ceded Loss Regular XOL Franchise XOL Ceded Loss Total Ceded

Max ceded through day n assuming an event ends on

day n Max ceded

through day n Events, Gross of Reinsurance

Event Loss to Per Occ Layer

Event Loss to Agg Layer

31,438 07-Jun-92 158 152,342 - 0 - - 0 0 3,128,895 3,128,895 0 0 0 18,838 08-Jun-92 159 164,301 - 0 - - 0 0 3,128,895 3,128,895 0 0 0 86,403 09-Jun-92 160 207,116 - 0 - - 0 0 3,128,895 3,128,895 0 0 0 588,810 10-Jun-92 161 725,489 - 0 - - 0 0 3,128,895 3,128,895 0 0 0 169,952 11-Jun-92 162 864,003 - 0 - - 0 0 3,128,895 3,128,895 0 0 0 32,875 12-Jun-92 163 878,039 - 0 - - 0 0 3,128,895 3,128,895 0 0 0 33,038 13-Jun-92 164 824,675 - 0 - - 0 0 3,128,895 3,128,895 0 0 0 7,286,350 14-Jun-92 165 7,522,215 - 0 5,522,215 7,522,215 5,522,215 5,522,215 8,651,110 8,651,110 7,522,215 0 5,522,215 104,998 15-Jun-92 166 7,457,260 - 0 5,457,260 7,457,260 5,457,260 5,457,260 8,586,156 8,651,110 0 0 0 67,508 16-Jun-92 167 7,491,894 - 0 5,491,894 7,491,894 5,491,894 5,491,894 8,620,789 8,651,110 0 0 0 62,157 17-Jun-92 168 7,521,013 - 0 5,521,013 7,521,013 5,521,013 5,521,013 8,649,908 8,651,110 0 0 0 61,605 18-Jun-92 169 296,268 - 0 - - 0 0 8,651,110 8,651,110 0 0 0 2,402,488 19-Jun-92 170 2,593,758 - 0 593,758 2,593,758 593,758 593,758 9,244,868 9,244,868 2,593,758 0 593,758 23,295 20-Jun-92 171 2,549,545 - 0 549,545 2,549,545 549,545 549,545 9,200,655 9,244,868 0 0 0 22,837 21-Jun-92 172 2,510,226 - 0 510,226 2,510,226 510,226 510,226 9,161,336 9,244,868 0 0 0 54,423 22-Jun-92 173 2,503,044 - 0 503,044 2,503,044 503,044 503,044 9,154,154 9,244,868 0 0 0 40,066 23-Jun-92 174 140,622 - 0 - - 0 0 9,244,868 9,244,868 0 0 0 52,521 24-Jun-92 175 169,848 - 0 - - 0 0 9,244,868 9,244,868 0 0 0 524,827 25-Jun-92 176 671,837 - 0 - - 0 0 9,244,868 9,244,868 0 0 0 37,133 26-Jun-92 177 654,547 - 0 - - 0 0 9,244,868 9,244,868 0 0 0 29,800 27-Jun-92 178 644,281 - 0 - - 0 0 9,244,868 9,244,868 0 0 0 15,283 28-Jun-92 179 607,043 - 0 - - 0 0 9,244,868 9,244,868 0 0 0 60,824 29-Jun-92 180 143,040 - 0 - - 0 0 9,244,868 9,244,868 0 0 0

Cat XOL Aggregate

IF R

ePla

y


Agenda Tracker





St. Joseph

Springfield

Poplar Bluff

Cape Girardeau

Kansas City

St. Louis

Columbia

Sedalia

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…

2013 Moore, OK EF-5 Track


Accumulations By Census Statistical Area M

etro

polit

anM

icro

polit

an

ConditionalPercentile TIV No. Events TIV No. Events TIV No. Events TIV No. Events TIV No. Events

Max 39,466,872 1 37,366,568 1 24,297,988 1 23,878,528 1 23,048,812 199.0% 17,585,432 119 29,453,144 32 20,470,170 25 17,553,980 81 13,348,868 5890.0% 5,412,883 1,191 7,266,844 317 6,639,354 247 7,988,126 809 4,483,250 57780.0% 3,771,730 2,381 3,195,987 633 2,609,244 495 4,723,276 1,617 2,267,785 1,15550.0% 1,475,490 5,954 832,917 1,584 512,595 1,237 1,415,083 4,043 553,076 2,886MEAN 2,466,163 2,836,071 2,137,388 2,872,752 1,582,815

Total Events 11,908 3,168 2,474 8,086 5,772

Joplin, MO ‐ 2011 Historical Tornado Track

Kansas City, MO‐KS St. Joseph, MO‐KS Cape Girardeau‐Jackson, MO‐IL Springfield, MOFayetteville‐Springdale‐Rogers,

AR‐MO


Max 63,045,104 1 49,566,616 1 34,808,164 1 34,479,808 1 27,629,492 199.0% 50,838,456 20 41,498,012 18 30,377,070 12 27,025,388 12 21,423,680 1890.0% 12,989,744 199 9,920,330 180 9,320,646 119 6,414,103 123 3,066,074 17780.0% 5,914,346 397 3,409,520 361 3,991,476 238 3,442,260 245 1,360,789 35450.0% 1,541,905 994 1,268,927 902 1,499,919 596 573,286 613 219,738 884MEAN 4,923,481 3,920,615 3,515,049 2,743,152 1,351,080

Total Events 1,988 1,804 1,192 1,226 1,768

Poplar Bluff, MO Sedalia, MO Sikeston, MO Paragould, AR Hannibal, MO



Max 42,836,672 1 38,361,144 1 33,734,388 1 32,353,402 1 31,226,224 199.0% 42,046,328 3 38,361,144 1 33,734,388 1 31,614,578 3 31,226,224 190.0% 23,580,500 35 12,649,454 11 26,275,186 13 6,482,090 30 16,643,744 1480.0% 9,327,144 70 6,150,253 23 17,446,704 26 1,986,192 60 8,326,898 2750.0% 2,887,726 174 1,319,952 56 1,635,025 64 436,107 150 2,186,144 68MEAN 6,871,468 4,355,448 7,894,294 2,514,532 5,466,836

Total Events 348 112 128 300 136

Macon, MO Anabel, MO Bertrand, MO Matthews, MO Bevier, MO


Rur

al Met

ropo

litan

ConditionalPercentile TIV No. Events

Max 39,466,872 199.0% 17,585,432 11990.0% 5,412,883 1,19180.0% 3,771,730 2,38150.0% 1,475,490 5,954MEAN 2,466,163

Total Events 11,908

Kansas City, MO‐KS


Sikeston, MOConditional Loss Potential Inside TrackPercentile TIV Low Intensity (20%) High Intensity (50%)

Max 70,566,784 14,113,357 35,283,39299.0% 59,718,096 11,943,619 29,859,04890.0% 28,595,964 5,719,193 14,297,98280.0% 19,031,244 3,806,249 9,515,62250.0% 8,337,714 1,667,543 4,168,857MEAN 12,255,941 2,451,188 6,127,971

Total Events 1,196

US $ in Ones

Mayflower, AR ‐ 2014 Historical Tornado Track

Tornado Viewing Guide – Top Accumulations Loss Scenarios


Max 70,566,784 1 66,802,704 1 51,827,928 1 41,964,000 1 40,750,440 199.0% 59,718,096 12 56,327,168 22 43,565,912 18 39,725,136 20 36,633,496 1490.0% 28,595,964 119 23,891,564 216 25,989,804 181 29,740,616 203 16,024,542 14280.0% 19,031,244 239 16,505,950 433 9,558,846 362 23,858,452 406 10,517,483 28450.0% 8,337,714 598 6,080,895 1,082 3,506,265 905 12,674,278 1,015 3,058,194 711MEAN 12,255,941 10,235,657 8,144,893 15,490,019 6,411,309

Total Events 1,196 2,164 1,810 2,030 1,422

US $ in Ones

Kennett, MO

Mayflower, AR ‐ 2014 Historical Tornado Track

Sikeston, MO Poplar Bluff, MO Sedalia, MO Lebanon, MO


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


TVG Loss Ranges Using Historical EF4 & EF5 Event Frequency

Exceedance Probabilities Modeled Other WindLoss Potential Inside Track RMS AIR

TIV Low Intensity High Intensity RiskLink TouchstoneReturn Period 100% 20% 50% Return Period v13.1 v1.51,000 58,500 11,700 29,250 1,000 46,842 45,405 500 47,008 9,402 23,504 500 44,035 39,339 250 36,813 7,363 18,406 250 29,864 30,260 100 25,456 5,091 12,728 100 17,335 20,955 50 18,656 3,731 9,328 50 10,786 16,684 25 12,830 2,566 6,415 25 7,237 12,338 Annualized Loss from EF4s and EF5s in TVG 675 1,687 Annual avg 21,658 22,996

Std dev 5,316 7,543 US $ in Thousands

US $ in ThousandsAssumptions:Tornado frequency uses NOAA Storm Prediction Center data 1950-2012TVG event frequency by smoothed historical average of EF4+ tornadoes is SPC data using 80km Gaussian filter

Memphis

Denver

Monroe

TulsaClovis

Other

Excess of 1,000 Year Return Period

Memphis

Denver

Monroe

Tulsa

ClovisOther

Excess of 100 Year Return Period


TVG Loss Ranges Using Historical EF4 & EF5 Event Frequency

Exceedance Probabilities 2013 Other Wind2011 2013 Change RMS AIR

TIV Loss TIV Loss 2011 RiskLink TouchstoneReturn Period 100% 50% 100% 50% to 2013 Return Period v13.1 v1.51,000 17,191 8,596 15,423 7,712 -10% 1,000 12,575 13,234 500 14,590 7,295 13,045 6,523 -11% 500 10,318 10,419 250 11,360 5,680 10,042 5,021 -12% 250 8,379 8,767 100 7,825 3,912 6,792 3,396 -13% 100 6,274 6,714 50 5,888 2,944 4,996 2,498 -15% 50 4,941 4,982 25 4,306 2,153 3,664 1,832 -15% 25 3,821 3,417 AAL from EF4s and EF5s in TVG 781 711 -9% Annual avg 6,585 3,351

Std dev 1,435 1,667 US $ in Thousands

US $ in ThousandsAssumptions:Tornado frequency uses NOAA Storm Prediction Center data 1950-2012TVG event frequency by smoothed historical average of EF4+ tornadoes is SPC data using 80km Gaussian f ilter

Louisville

ClevelandCincinnati

Other

Excess of 100 Year RP

LouisvilleCleveland

Cincinnati

Excess of 1,000 Year RP

Louisville

ClevelandCincinnati

Other

Excess of 100 Year RP

Louisville

Cleveland

Cincinnati

Excess of 1,000 Year RP

Key Metropolitan Risks 2011 Key Metropolitan Risks 2013


TVG Historical Tornado Shapes

Joplin, MO TornadoDate: May 22, 2011

Rating: EF‐5

Est. Max. Wind: 200+ mph

Path Length: 22.1 Miles

Path Width: 0.75 Mile

Fatalities 162

Mayflower, AR TornadoDate: April 27, 2014

Rating: EF‐4

Est. Max. Wind: 190 mph

Path Length: 41.3 Miles

Path Width: 0.75 Miles

Fatalities 16

Moore, OK TornadoDate: May 20, 2013

Rating: EF‐5

Est. Max. Wind: 210 mph

Path Length: 17 Miles

Path Width: 1.3 Miles

Fatalities 24


What if you could run every EF4 and EF5 tornado?


Stop Light Exposure Management


Everyone’s Favorite Quotable Statistician

Essentially, all models are wrong, but some are useful

George E. P. Box

Paul’s


Biographies

Steve Drews, Associate Director & Lead MeteorologistSteve 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, DirectorPaul 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.


Contacts

Steve DrewsAssociate Director & Lead Meteorologist Impact [email protected]

Paul Eaton, FCASDirectorAon Benfield [email protected]


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.