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Introduction

Executive summary 

European winter windstorms, or extra-tropical cyclones (ETC), are a major cause of losses to the insurance sector. To help the sector better understand this risk, the Enhanced Windstorm Service (EWS) for the insurance sector has been developed. The development of the service follows user feedback from previous phases (operational and proof of concept activities), implemented as part of the Copernicus Climate Change Service (C3S). 

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Datasets will be updated through an automatised procedure running codes underlying data production over the periodic ERA5 dataset new releases.  

Scope of the documentation

Windstorm data documentation describes the production, underlying methodological phases, and publication of the “enhanced operational windstorm service” (EWS) developed in the framework of the ECMWF-implemented C3S2_413 contract.

Dataset Description

Dataset Target Requirements

The present dataset aims to promote a knowledge-based assessment of the nature and temporal evolution of windstorms associated with ETCs. This contract presents a continuation, a temporal extension, and an enhancement of the original C3S Windstorm Service. The original service data content will be extended to the detection and tracking of Pan-European potentially harmful windstorms, associated with extratropical cyclones, for the whole available period provided by ECMWF's ERA5 reanalysis dataset (1940 - present) and to a second tracking algorithm, TempestExtremes (TE, Ullrich et al., 2021), in addition to the previously used TRACK / Hodges algorithm (Hodges 1995, 1999, Hoskins and Hodges 2022) hereafter called Hodges. This documentation traces all the steps made in the production of two main types of windstorm datasets: (i) Extratropical cyclones (ETC) tracks based on the use of two detecting/tracking algorithms and (ii) the associated footprints mapping the maximum value of 10m-height wind gust during a specific event.

Dataset Overview

Data description

Storm track. This product contains the Pan-European potentially harmful windstorm tracks, associated with extratropical cyclones, for the whole available period provided by ECMWF's ERA5 reanalysis dataset (Hersbach et al., 2020) spanning from 1940 to present. A storm track is defined as a sequence of longitude–latitude points which track an extra-tropical windstorm over time and is defined by the tracking algorithm. Two tracking algorithms are used: (i) Hodges algorithm (Hodges 1995, 1999, Hoskins and Hodges 2002), already in use in the previous Windstorm service and (ii) TempestExtremes (TE, Ullrich et al., 2021). The two tracking algorithms rely on an automated object identification leveraging two different variables, i.e., relative vorticity and mean sea level pressure, respectively (see "Methods" in section 3 for more details). Spatial and temporal filtering is applied to these variables to isolate key characteristics of low-pressure systems, identifying potential candidates suitable for track definition.

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Table 1: Overview of the dataset.

Product description 

Windstorm track

The windstorm tracks contain the 6-hourly evolution of a storm event; they are provided in text format (.csv) and include the fields listed in Table 2.

Table 2: Overview and definition of the fields included in the windstorm tracks product.

Windstorm footprint

The windstorm footprints, which are provided in NetCDF format (.nc), include for each storm event the maximum 3-second 10-m wind gust (m s-1) over the 72 hours centred around the footprint central time. The NetCDF file of a footprint contains a single variable which depends on three principal coordinates (latitude, longitude and track_id). The track_id coordinate is included to easily merge several footprints during an analysis. On top of the principal coordinates three secondary coordinates (track_start_time, footprint_central_time and track_end_time) characterise respectively the start, central and end time of the event corresponding to the footprint. All the coordinates of a footprint file are listed in Table 3. 

Table 3: Overview and definition of the coordinates of the windstorm footprints product.

Each footprint NeCDF contains a set of attributes characterising the footprint and summarised in Table 4.

Table 4: Overview and definition of the attributes characterising the windstorm footprints product.

Storm summary indicator

The storm summary indicators product is provided in text format (.csv) and include the fields listed in Table 5.

Table 5: Overview and description of the fields included in the storm summary indicators product.

Input data

Table 5: Overview of data for input to the Enhanced Windstorm Service. 

Methods 

Tracking algorithms

Hodges and TempestExtremes tracking algorithms use different atmospheric variables for detecting and tracking ETC tracks, 850 hPa relative vorticity and mean sea level pressure, respectively. This determines differences in synoptic-scale dynamics influencing tracks’ detection. In general terms, relative vorticity is better at capturing smaller spatial-scale processes compared to mean sea level pressure based tracking (Hodges et al., 2003). In the context of the windstorm dataset within EWS, this translates into a substantially larger number of events produced by Hodges compared to TempestExtremes. However, not all the events tracked by TempestExtremes are included within the Hodges dataset. This indicates that considering a different tracking variable determines not only a different number of detected events but also events having different dynamical features. In terms of historical events reproducibility, a comparison with a series of 50 windstorms reported in the Extreme Windstorm catalogue (XWS, Roberts et al., 2014) shows larger matching events from Hodges. Hodges reproduces 39 out of 50 XWS events against 17 reproduced by TempestExtremes. However, the comparison between the two algorithms and the reference XWS is suboptimal since this latter leverages the same Hodges algorithm (though a previous release), to detect and track historical events. In this regard, spatial and temporal parameters defining shared events (i.e., at least half of event points with a temporal and spatial discrepancy lower than one day and two degrees respectively), can modulate the number of matching events found in the two tracking algorithms and reference XWS.

TempestExtremes Algorithm

TempestExtremes algorithm (TE, Ullrich et al., 2021) is run considering ERA5 reanalysis (Hersbach et al., 2020) 6-hourly mean sea level pressure. In the present configuration, TempestExtremes algorithm requires:

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• ID;
• Longitude and latitude;
• Mean sea level pressure at each step;
• Date;
• Wind gust value at 10m height;
• Land-sea-mask reporting the percentage of land area of ETC track grid nodes.

Hodges algorithm

The tracking algorithm which has already been used to produce the original Windstorm Service is the Hodges algorithm (Hodges 1995, 1999, Hoskins and Hodges 2022). The application of this algorithm has been extended to the entire ERA5 reanalysis period, so the two algorithms are operationally available for the entire ERA5 period (1940-present).

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• The 850 hPa relative vorticity fields are spectrally filtered to the T42 resolution (corresponding to about 480 km) to discard features related to small-scale background noise;
• Relative vorticity maxima with a value larger than 1×10−5s−1 are retained;
• The adaptive algorithm described in Hodges 1999 is applied to merge vorticity features into a cyclone track;
• The tracks that last less than 1 day or travel less than 1000 km are discarded;
• The mean sea level field is added by searching for the nearest pressure minimum with a 5° radius of the vorticity centre.

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Figure 1: Comparison between the output of the two tracking algorithms produced for the exemplary period 2000-2005. Upper panels: annual windstorm tracks and corresponding mean sea level pressure. Central panels: mean sea level pressure of windstorm tracks crossing the domain divided into cells of 3ºx3º during the same period. Bottom panels: Number of unique windstorm track passages binned into each grid cell of a 3°x3° resolution grid.   

Footprints decontamination

European storms can cluster in time. In fact, maximum gust footprints could likely be derived from two or more events. To minimise this “contamination”, instead of taking the maximum gust over the whole domain, only gusts inside a 1000km radius of the track position at that time are assumed to be part of the event.

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Figure 2: Original and decontaminated footprints for one representative windstorm

Footprint statistical downscaling

Original footprints are statistically downscaled through a multiple linear regression-based model (van den Brink, H. W. (2020)).

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• Original resolution full (not decontaminated);
• Original resolution decontaminated;
• Downscaled full (not decontaminated);
• Downscaled decontaminated.

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Figure 3: Decontaminated footprints for two representative events (Jeanette and Gero). On the left panels, the original resolution and on the right panels statistically downscaled footprints. Windstorms tracked with the TempestExtremes and Hodges algorithms are displayed in the upper and bottom panels respectively. The same event corresponds to a different ID since the two algorithms identify a different number of tracks during the period considered.  

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Table 6: Overview of the windstorm tracks and footprints datasets input variables.

Windstorm summary indicators

The catalogue entry contains summary indicators which are aggregated annually over NUTS 0, 1, and 2 regions. All indicators are contained in a single csv file covering the whole period from 1940 to the present. The available indicators are defined in the sections below and are:

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All four indicators have been computed considering four wind gust thresholds 0, 15.6, 20 and 25 m/s.

Yearly storm count

The yearly storm count looks at the occurrence of storms exceeding a given threshold in a given region. A storm event is considered to affect a region if its decontaminated footprint intersects the region. For each storm event, the decontaminated footprint of wind gust exceeding a threshold is considered to count storm events exceeding a given threshold.

Mean wind gust

The mean wind gust indicator looks at the average wind gust speed for storms exceeding a given threshold in a given region. For a given year all the decontaminated footprints are filtered for each threshold to keep the footprint area exceeding the threshold and averaged over time. The resulting average mean wind gust speed is then aggregated spatially in the predefined NUTS regions. Areas of a region that are not affected by a storm are not considered as “no values” and are ignored when performing the spatial average.

Storm Severity Index (SSI)

The Storm Severity Index (SSI, Dawkins et al., 2016) aims at quantifying the severity of the storm affecting a region. To do so the indicator combines both the total area of a region affected by a storm in a year and the mean wind speed gust speed exceeding a threshold. The SSI can be summarised by the following formula:

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The complete time series of any region can be consulted by clicking on the regions even if it shows no values on the map.

Normalised Storm Severity Index (NSSI)

The Normalised Storm Severity Index (NSSI) aims to quantify the severity of the storm affecting a country/region with a dimensionless number. The NSSI is based on the SSI but uses two extra quantities in order to normalise the SSI in terms of spatial extent and wind gust climatology.

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• Mathinline
  \overline{P_{98}}

  is the 98th percentile of the wind gusts PDF considering all the decontaminated footprints over the period 1991 – 2020. The overline stands for the spatial average.

Concluding Remarks

EWS represents an enhancement and temporal extension of the original C3S windstorm service. It is operational meaning that it automatically updates windstorm datasets based on ERA5 period new releases on a monthly basis.

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-EWS datasets can serve as reference products to evaluate the capability of global and regional climate models to reproduce windstorm features and temporal evolution.

References

van den Brink, H. W. (2020). An effective parametrisation of gust profiles during severe wind conditions. Environmental Research Communications. Institute of Physics. https://doi.org/10.1088/2515-7620/ab5777

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