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WISC Windstorm Information Service

SIS Workshop, Warbrook House 17-19 October, 2016

CGI IT UK Ltd

paceWISC17-19/10/2015, SIS Workshop, Warbrook House

WISC Project Team


What are windstorms, why do they matter to the Insurance Industry?

Windstorms = “Extra tropical cyclones”

Infrequent, large storms, capable of causing widespread damage to property and large insurance losses. E.g. Storm Daria $8.2bn (indexed to 2012).


Insurance Risk (and Catastrophe Modelling)

Re-insurers aggregate risk from many insurers and have large exposure to windstorm risk.

(Re-)Insurance industry needs to understand the risk from windstorms (insuring for 1 in 200 year events).

Currently only 35 years of reliable historical data is available, including around 70 major storms, making risk difficult to assess.

Insurance industry is supported by specialist “cat modelling” companies. As historical data is limited Cat Models use “synthetic event sets” – large

numbers of storms. Methods for generating event sets are proprietary.

WISC aims to provide transparent, authoritative data to support understanding of windstorm risk.

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Overview of the supply process

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Tier 1 Tier 2/3

Weather observations

Analysis Modelfields

Previous Modelfield

ECMWFERA-INT / 20C

Re-analyses

Storm tracks Storm footprints

Stochastic Event set

Storage and presentation

Windstorm Hazard

Exposure / Vulnerability

Adaptation strategies

Cat Modellers

Insurers / re-insurers

WISC

Users

Met Services


Existing services

Extra-Tropical Cyclones less well covered than Tropical Cyclones Existing hazard services

Re-analysis limited production due to complexity / processing

Storm analysis Covered by XWS using ERA-INTERIM (1979 – 2014) Cat modellers develop stochastic event sets Insurers also analyse to an extent dependent on organisation scale

Hazard indicators Most services present information at the summary level

Existing exposure / vulnerability / loss Insurers and re-insurers undertake own analysis General services also provided – some shared eg PERILS

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Gaps

Need for a longer, more comprehensive time series Transparency of methods applied is very important Additional downscaling

Recognised that some prioritisation of sub-regions and storms will still be necessary to use the resources as efficiently as possible.

Concentrate on highest levels of financial exposure which will be defined by both the countries covered and the availability of windstorm cover in each.

User customisation important - eg ability to rank and flag storms according to a range of criteria

‘Standard’ approaches to storm characterisation, such as those applied in XWS are considered valuable, though alternative methods are also considered useful for comparison.

Support for in-house simulation platforms as well as cat modelling companies Improved support to stochastic modelling process so that the output event

sets are clearly dynamically realistic. Interest in the possibility of a WISC event set Validation support to Cat modelling companies

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User Interviews

User Sector Method TeamOasis Insurance Face-to-Face CGIXL Catlin Insurance Face-to-Face CGI, UoR, UKMOMunich Re Insurance Face-to-Face UKMOAspen Re Insurance Webex / Teleconf CGI, UoR, UKMOAtkins Civil Engineering Face-to-Face CGI, UoR, UKMOWillis Insurance Face-to-Face CGI, UoR, UKMOAON Benfield Insurance Webex / Teleconf CGI, UoR, UKMOChaucer Insurance Webex / Teleconf CGI, UoR, UKMOApplied Impact Research (AIR)

Catastrophe Modellers

Face-to-Face CGI, UoR, UKMO


User Requirements

Authoritative, comprehensive, free and open Historical Records to support Understanding of major storms Understanding of present risk Development of cat models, Validation of cat modelling results

Experienced users will typically download all data and use off-line show limited interest in “indicators” show limited interest in visualisation

Insurance Users showed limited interest in future climate change projections (also rather uncertain).

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WISC data products

Historical Storm Tracks Database From ERA-INT and ERA-20C Longer time series 1940 – present 1900 – 1939 if reanalysis data quality is sufficient

Historical Storm Footprints From ERA-INT and ERA-20C Coverage of at most major storms Bias corrections to be applied Validated against scatterometer and local observations

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WISC data products (II)

Tier 1 Indicators - Historical indicators will provide a quick comparison of ERA-20C to ERA-Interim for re-insurers: Number of cyclones in a given decade Typical intensities, and intensities of extremes, of cyclones in a

given decade For ERA-20C only: Decadal variability

Tier 3 Indicators Total Insurance losses due to windstorms

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WISC Products (III)

WISC Event set – will be based on ensemble runs of UPSCALE climate model giving 130 model years of “current climate conditions”

WISC will attempt to build a windstorm risk model based on the industry-supported, open source OASIS platform.

Challenge is to develop a robust vulnerability model that matches industry historical loss data.

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paceWISC17-19/10/2015, SIS Workshop, Warbrook House 13


WISC Additionality

Use of latest reanalysis datasets (ERA-20C as well as INT) Longer, more comprehensive time series (1900 or 1940 fwd) Lower thresholds so more storms available for analysis Additional downscaling

4km as well as 25km resolution footprints Some prioritisation of sub-regions and storms still necessary

Transparency of methods applied Improved validation and re-calibration of data Reference products to support in-house simulation platforms Event set will allow for cross checks with commercial cat models Support for insurance users with less in-house capabilities


WISC – V0 Prototype: Portal (1)

C3S portalWISC Sub-site



Explore for data display



Display track data - under construction

Implemented by

Storm Tracks Adrian Champion

University of Reading


Historical Catalogue

Re-insurers require a dataset of historical windstorms to understand risk of future windstorms:

Validate against data from catastrophe models Intensity distributions Numbers of cyclones Cyclone intensity

Compare to historical records Investigate specific ‘well known’ events Improve their understanding of ‘extreme’ events



ERA-Interim (1979 to present) High resolution Frequently used by re-insurers and catastrophe modelling

companies As used in the European windstorms catalogue

ERA-20C (1900 to 2010) Much longer period -> larger sample size Relatively high resolution New dataset, requires analysis

Catalogue of around 150 events in a given year



Hodges (1994,1995,1999) tracking algorithm Based on 850hPa relative vorticity at T42 resolution Vorticity centres used to calculate trajectory of individual extra-

tropical cyclones (cyclones that occur north of 30N at any point)

Extra fields referenced back to vorticity fields at full resolution at each timestep Minimum MSLP within 6 degrees of vorticity centre Maximum wind within 6 degrees of vorticity centre Maximum land-wind within 3 degrees of vorticity centre As used in the European windstorms catalogue

Filters require cyclones to last 2 days and travel 1000km


Historical Indicators

ERA-20C relatively new re-analysis product Unknown to re-insurers Still be analysed by scientific community Re-insurers want to know how reliable the data is Part of the work done by UoR will be to assess ERA-20C

Historical indicators will provide a quick comparison of ERA-20C to ERA-Interim for re-insurers: Number of cyclones in a given decade Typical intensities, and intensities of extremes, of cyclones in a

given decade For ERA-20C only: Decadal variability


Climate Change Projections

Re-insurers not interested in climate change projections: Policies renewed yearly/3 years Adjust pricing every year

Re-insurers are interested in understanding the general trend of the intensity distributions, to protect long-term business: Publication looking at the current understanding of the impact of

climate change on North Atlantic storm track

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