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Acronyms
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Introduction
Executive Summary
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Preceding the operational stage of the Windstorm Service for the Insurance sector, the pre- operational stage WISC1 successfully demonstrated the estimation of economic losses for winter storm events over Europe, based on state-of-the-art numerical weather prediction models and economic loss models. The service applies a chain of models, from models to generate Tier 1 windstorm footprints to an economic model for estimating Tier 3 economic losses, where the latter uses the windstorm footprints as input. In the pre-operational stage the Tier 1 footprints were dynamically downscaled by the UK Met Office (UKMO) Unified mesoscale model based on the ERA- Interim and ERA-20C reanalysis datasets. As this model is not freely available therefore a different approach to develop Tier 1 windstorms was developed by KNMI. This new approach uses the new ERA5 wind fields, instead of the older ERA-interim wind fields, and applies a statistical downscaling using multiple linear regression (STATDOWN) approach described in van den Brink & Whan (2018). In the remainder of the user guide we will refer to the new footprints as STATDOWN footprints, and to the footprints from the pre-operational stage as WISC footprints.
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Product Description
Product Target Requirements
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Table 1: Overview of key characteristics of the Tier 3 windstorm loss indicatorsAnchor table1 table1
Data Description | |
Dataset title | Tier 3 windstorm loss indicators |
Data type | Loss indicators |
Topic category | Natural risk zones |
Sector | Insurance |
Keyword | Windstorm losses |
Dataset language | English |
Domain | Europe, for 21 countries (Austria, Belgium, Czech Republic, Denmark, Estonia, Spain, Finland, France, Great Britain, Germany, Ireland, Italy, Lithuania, Luxembourg, Latvia, Netherlands, Norway, Poland, Portugal, Sweden, Switzerland)
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Horizontal resolution | Each file covers a single NUTS3 region (Nomenclature of Territorial Units for Statistics). Within each file individual rows cover the building footprint, |
Temporal coverage | 1979-01-01/to/2020-01-01 |
Temporal resolution | Loss values represent the loss recorded for a particular storm event |
Vertical coverage | Single level |
Update frequency | None (static dataset) |
Version | n/a |
Model | high-resolution wind damage model for Europe |
Experiment | n/a |
Terms of Use | OpenStreetMaps Data made available through the Open Data Commons Open Database License (ODbL) was used in development of the Tier 3 Loss and Risk indicators. Therefore, works produced from it (OpenStreetMap), need to use the Open Database License (ODbL) https://cds.climate.copernicus.eu/api/v2/terms/static/licence-to-use-copernicus-products.pdf |
Variable Description
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Variables | |||
Long Name | Short Name | Unit | Description |
Building | Building | String | Type of building according to OSM. Varies from only "yes" it is a building, to the actual |
IDENTIFIER | ID_ | integer | Country / NUTS3 region / building based reference number (applied to risk and to loss). Buildings are numbered sequentially within each country / NUTS3 region |
COUNTRY | COUNTRY | String | The country in which the building is located |
LATITUDE | LAT | Float16 | Latitude of the centroid of the building |
LONGITUDE | LONG | Float16 | Longitude of the centroid of the building |
LANDUSE CLASS | CLC_2012 | integer | Land-use code corresponding to the Corine Land Cover classification |
AREA | AREA_m2 | Float16 | Area of the building footprint |
Date | Date | MM/DD/YYYY | Loss estimate per storm per building |
Loss estimates | Loss | USD ($) | Financial loss at each building location due to a particular storm event. |
Loss estimates aggregated to country (NUTS 1) level for all | NUTS1 loss | USD ($) | Summary file in .csv format on country level (NUTS1). |
Loss estimates aggregated to NUTS 3 level for all storms | NUTS3 loss | USD ($) | Summary file in .csv format on NUTS3 level |
Loss estimates aggregated to SECTOR level for | SECTOR loss | USD ($) | Summary file in .csv format for different sectors; agriculture, industry, residential, transport, and other. |
Input Data
Table 3: Overview of climate model data for input to Tier 3 windstorm loss indicators, summarizing the model properties and available scenario simulations.Anchor table3 table3
Input Data | ||||
Model name | Model centre | Scenario | Period | Resolution |
OpenStreetMaps | © OpenStreetMap- | n/a | n/a | Building level |
CORINE | Copernicus Land | n/a | ClC 2012 | 30" |
PAGER | U.S. geological service | n/a | n/a | n/a |
Storm footprints | Climate Data Store | n/a | 1979-2020 | 0.04° |
OpenStreetMaps (OSM)
All building footprint data are extracted from OSM, which has proven to be the most extensive dataset of publicly available building footprints for Europe. OpenStreetMap is a free, editable map of the whole world that is being built by volunteers largely from scratch and released with an open- content license. The OpenStreetMap License allows free (or almost free) access to our map images and all of our underlying map data. As OSM is user driven, it continuously evolves and improves, improving the building footprint coverage across Europe.
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Waisman, F. (2015) European windstorm vendor model comparison. in Slides of a presentation at IUA catastrophe risk management conference, London 30
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This document has been produced in the context of the Copernicus Climate Change Service (C3S). The activities leading to these results have been contracted by the European Centre for Medium-Range Weather Forecasts, operator of C3S on behalf of the European Union (Delegation agreementAgreement signed on 11/11/2014 and Contribution Agreement signed on 22/07/2021). All information in this document is provided "as is" and no guarantee or warranty is given that the information is fit for any particular purpose. The users thereof use the information at their sole risk and liability. For the avoidance of all doubt , the European Commission and the European Centre for Medium - Range Weather Forecasts have no liability in respect of this document, which is merely representing the author's view. |
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