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Acronyms

Introduction

European winter wind storms are a major cause of losses to the insurance sector. To help the sector better understand this risk the Operational Wind Storm Service for the Insurance Sector has been developed. The development of the service follows from a Proof of Concept for a Sectorial Information Service to provide information about European wind storms, which is the Windstorm Information Service (WISC), part of the Copernicus Climate Change Service (C3S). This document describes the storm tracking part of the development performed by the University of Reading.

Executive Summary

The operational storm tracks, available from the Climate Data Store (CDS), are derived from ERA5 reanalysis data for the period 1979 to 2020 using the following methodology. Extratropical cyclones are identified and tracked in the ERA5 reanalysis data for the October to March periods of 1979-2020 using an automated cyclone tracking algorithm based on the method described by Hodges (1995, 1999) and previously used for WISC and XWS (Roberts et al, 2014). Major European wind storms are identified (118) based on region and simple wind intensity criteria which are subsequently downscaled in the storm footprinting part of the system. The 118 storms for this period was the result of the application of a 25m/s threshold with minor adjustments to include additional well known storms just below this value.

Scope of Documentation

This document describes the C3S Storm Track dataset using the standard C3S format for product descriptions, i.e., in terms of product target requirements, product overview, input data and methodology as well as use of the dataset. It is the primary document for users.

Version History

Version 1.0. The underlying methodology used to derive the storm tracks has been consistent within this project and the previous proof of concept contract, WISC.

Product Description

Product Target Requirements

The cyclone track dataset consists of the most intense European wind storms in the period 1979-2020 of which there are 118. The track database provides information on the locations and intensities of the cyclones of use to the Insurance industry in combination with the storm footprinting and wind storm economic loss calculations which constitute the complete C3S windstorm operational system.

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Figure 1: Tracks selected for downscaling. Coloured dots indicate the 10m wind speeds within the 60 search radius.

Product Overview

The Operational Storm Tracks consist of 118 cyclone tracks that impact Europe with wind speeds above 25m/s together with additional cyclones of importance to the insurance industry.

DataDescription

VariableDescription

The tracks, which are provided in asci format, include the following fields:

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Input Data

The input data to the cyclone track production is summarised in Table 3 and described below.

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Input Data 1

ERA5 Reanalysis
Data from the ECMWF ERA5 reanalysis (Hersbach et al, 2020) extracted directly from the mars archive for the period 1979-2020 at 3 hourly intervals for the seasonal period of October to March when the majority of European wind storms occur. The 850hPa relative vorticity is spectrally filtered prior to applying the tracking algorithm. Full resolution MSLP, 925hPa winds and 10m winds are also added to the cyclone tracks.

Method

Background

The same tracking methodology has been applied to the ECMWF ERA5 data set as was used with ERA- Interim (Dee et al, 2011) in the C3S Proof of Concept contract Wind Storm Information Service (WISC¹) and eXtreme Wind Storms' (XWS²) catalogue (Roberts et al, 2014) projects. However, since ERA5 has 3-hourly analyses there is no need to use the forecast splicing to get 3-hourly data as was used for WISC and XWS. The ERA5 3 hourly analysis fields are used to be consistent with WISC and XWS.

Model/Algorithm

The 850hPa relative vorticity is first spectrally smoothed to T42 and the large-scale background removed in Hoskins and Hodges (2002), this reduces the inherent noisiness of the vorticity and makes the tracking more reliable. The cyclones are identified by determining the vorticity maxima by steepest ascent maximization on a polar stereographic projection in the filtered data as described in Hodges (1995). These feature points are linked together, initially using a nearest-neighbour search, and then refined by minimising a cost function for track smoothness (Hodges,1995) subject to adaptive constraints on the displacement distance and track smoothness (Hodges,1999). These constraints have been modified from those used for 6-hourly data to be suitable for the 3-hourly data. Storms that last longer than 1 day are retained for further analysis.

The MSLP minima and 925 hPa and 10m wind speed maxima associated with the vorticity maxima are added to the tracks. This is done by searching for a minimum/maximum within a certain radius of the vorticity maximum. A radius of 5° is used for the MSLP. For the 925 hPa and 10m wind speed, radii of 3° and 6° as these were found to be the best indicators of storm severity (Roberts et al 2014). For MSLP the minima are determined using a steepest descent minimization method, while for winds a direct search of the grid point values is used. For the MSLP the location of the minimum is only given if it is a true minimum. If not, the MSLP value given is that at the vorticity centre. For the winds the search is made for both the full fields and the land only fields.

Validation

Unlike for Tropical Cyclones where observational tracks and intensities are produced by the different operational centre's called Best Track data, there is no such data set for extra-tropical cyclones and effectively no "truth" to contrast with for the quality of the cyclone tracks. Therefore, the assessment of the cyclone tracks is made by comparing track locations and intensities with those obtained previously from ERA-Interim for WISC for the region 15W to 25E and 35N to 70N to highlight the uncertainties. The first comparison is for the total number of cyclones found in the region that have lifetimes more than 1 day and travel more than 1000km.

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Figure 3: Maximum intensity distributions for 925hPa and 10m winds (land) for ERA5 and ERA- Interim.

Concluding Remarks

The operational storm tracks are derived from ERA5 reanalysis data for the period 1979 to 2021. Extratropical cyclones are identified and tracked in the ERA5 reanalysis data for the October to March periods of 1979-2021 using an automated cyclone tracking algorithm based on the method described by Hodges (1995, 1999) and previously used for WISC and XWS (Roberts et al, 2014). 118 European wind storms are identified based on region and simple wind intensity criteria, which are subsequently downscaled in the storm footprinting part of the system. The 118 storms for this period was the result of the application of a 25m/s threshold with minor adjustments to include additional well-known storms just below this value. The storm tracks were used as the basis for the statistical downscaling of storm footprints.

The track data should not be considered as the same as Best Track data, as produced for Tropical Cyclones, as it is dependent on the reanalysis used to produce the tracks. Comparisons between cyclone tracks identified in different reanalyses have shown a good correspondence between the tracks but varying degrees of uncertainty in the intensities depending on what intensity variable is considered (Hodges et al, 2011). It should also be noted that different cyclone tracking schemes can produce different tracks depending on whether vorticity or MSLP is used as the tracking variables, although the correspondence for the most intense storms is better than for the weak storms (Neu et al, 2013).

References

Dee, D.P., Uppala, S.M., Simmons, A.J., Berrisford, P., Poli, P., Kobayashi, S., Andrae, U., Balmaseda, M.A., Balsamo, G., Bauer, P., Bechtold, P., Beljaars, A.C.M., van de Berg, L., Bidlot, J., Bormann, N., Delsol, C., Dragani, R., Fuentes, M., Geer, A.J., Haimberger, L., Healy, S.B., Hersbach, H., Hólm, E.V., Isaksen, L., Kållberg, P., Köhler, M., Matricardi, M., McNally, A.P., Monge-Sanz, B.M., Morcrette, J.-J., Park, B.-K., Peubey, C., de Rosnay, P., Tavolato, C., Thépaut, J.-N. and Vitart, F. (2011), The ERA-Interim reanalysis: configuration and performance of the data assimilation system. Q.J.R. Meteorol. Soc., 137: 553-597.

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