Last modified on Nov 06, 2024 12:01

Contributors: CMCC 

Issued by: CMCC

Issued Date: October 2024

Ref: C3S3_413 – Enhanced Operational Windstorm Service

Official reference number service contract: 2023/C3S2_413_CMCC

Introduction

Executive summary 

The Enhanced Windstorm Service (EWS) objective is the development of a knowledge-based assessment of the nature and temporal evolution of windstorms associated with Extra-Tropical Cyclones (ETCs). Information produced is mainly targeted for rural insurance sector stakeholders (e.g., forestry and agriculture). 

EWS core is the detection and tracking of Pan-European potentially harmful windstorms, associated with ETCs. This represents a continuation and enhancement of the current C3S Windstorm Service. Main EWS novelties consist of a temporal and seasonal extension of the dataset characterising events over the whole ERA5 reanalysis period (1940-present) and the whole year, with periodic updates in a quasi-operational fashion. EWS also introduces a second tracking algorithm, TempestExtremes (TE, Ullrich et al., 2021), completing the one already used in the current service (TRACK, Hodges 1995, 1999, Hoskins and Hodges 2022).    

EWS is constituted of two main datasets:

(i) Windstorm tracks;

(ii) the associated footprints, mapping the maximum value of a 10m-height wind gust during a specific event. This latter is the maximum 10m wind gust at each grid point in the domain over a 72-hour time window (Roberts et al., 2014). A statistical downscaling, based on a multiple linear regression-based model (van den Brink, 2020; van den Brink and Whan, K. 2018), is applied to include orography-driven and wind shear effects on footprint wind gusts.

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.

Product Description

Product Target Requirements

Dataset development aims at promoting a knowledge-based assessment of the nature and temporal evolution of windstorms associated with Extra-Tropical Cyclones (ETCs). The information to be produced has to be tailored to the rural insurance sector stakeholders (e.g., forestry and agriculture). This contract presents a continuation, a temporal extension, and an enhancement of the current C3S Windstorm Service. The current 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 the ECMWF ERA5 reanalysis dataset (1940 - present) and to a second tracking algorithm, TempestExtremes (TE, Ullrich et al., 2021), in addition to the previously used TRACK algorithm (Hodges 1995, 1999, Hoskins and Hodges 2022). 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.

Product Overview

Data description

Windstorm tracks. These are the Pan-European potentially harmful windstorm tracks, associated with extratropical cyclones, for the whole available period provided by the ECMWF ERA5 reanalysis dataset (Hersbach et al., 2020) spanning from 1940 to present. Two tracking algorithms are used: (i) TRACK algorithm (Hodges 1995, 1999, Hoskins and Hodges 2022), already in use in the previous Windstorm service and (ii) TempestExtremes (TE, Ullrich et al., 2021). 

Windstorm footprints. For each of the selected tracks, during the period 1940-2023, an associated footprint is derived according to the approach proposed in (Roberts et al., 2014). Accordingly, we define footprint as the maximum 10m wind gust at each grid point in the domain over a 72-hour time window. To include orography-driven and wind shear dynamics potentially modulating footprint wind gusts, we derived a “downscaled” version of the same set of footprints derived from statistically downscaled ERA5 variables using a multiple linear regression-based model (van den Brink, 2020; van den Brink and Whan, K. 2018). Moreover, both original resolution and downscaled footprints are subjected to a “decontamination” process to void the possibility that storm footprints can cluster/overlap in time (maximum gust footprints deriving from two or more events) giving rise to a “contamination” which can obscure features belonging to an individual event.

Table 1: Overview of the windstorms tracks dataset.

 Table 2: Overview of the windstrom footprints dataset.

Variables description 

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

• Time
• longitude of the minimum mean sea level pressure (MSLP)
• latitude of the minimum mean sea level pressure (MSLP)
• minimum MSLP
• 10m height windgust
• Land-sea mask

Table 3: Overview and description of variables included in the windstorm tracks dataset.

Table 4: Overview and description of variables included in the windstorm footprints dataset.

Input data

Table 5: Overview of the windstorm tracks and footprints datasets input variables.

Methods 

Tracking algorithms

TempestExtremes Algorithm

TempestExtremes algorithm (TE, Ullrich et al., 2021) is run considering ERA5reanalysis (Hersbach et al., 2020) 6-hourly mean sea level pressure. In the present configuration, TE algorithm requires:

• storms persist for at least 60 hours;
• with a maximum gap of at most 18 hours;
• Latitude range [25 °N - 66 °N] and longitude [-80 °E - 30 °W];
• Finally, TE algorithm imposes ETCs moving at least 12° from the start to the end of the trajectory, to eliminate stationary features and spurious shallow lows generated over regions with complex orography.

The track candidates produced by the TE algorithm are further filtered according to spatial and temporal filtering criteria, filtering out tracks characterized by at least one of the following features:

• Tracks with less than 5 points;
• Tracks with more than 3 points north of 65 °N;
• Tracks starting eastern of 5°W;
• Tracks ending west of 5°W.

Catalogue entry with the list of tracks passing filtering criteria consists of text-like [.csv] files, as in the example shown in Table 1.

Each track is identified by a:

• 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.

TRACK algorithm

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

The input necessary to run the tracking algorithm is a 3-hourly frequency 850 hPa relative vorticity dataset, also from the ERA5 reanalysis (Hersbach et al., 2020). The input is on the native IFS gaussian grid (N320).

The following steps are performed starting from the input data:

• The 850 hPa relative vorticity fields is spectrally filtered to theT42 resolution (corresponding to about 480 km) in order 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 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.

Figure 1: Comparison between the output produced by the two tracking algorithms for the exemplary period 2000-2005. Upper panels: annual windstorm tracks and corresponding MSLP. 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. It is, in fact, likely that maximum gust footprints could be derived from two or more events. To minimize 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.

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. This approach is in use in the current windstorm service (see documentation).

\[ \text{wgSLh} = u_{10} + \alpha \cdot \frac{u_{100} - u_{10}}{\log\left( \frac{100}{10} \right)} \]

The destination resolution is imposed by the orography term (ELEV), at 1 km resolution. Three predictors are used to downscale wind gusts at 10m height (WindGust) is derived using three predictors:

• ERA5 denoting predictor 1 (wind gust at 10m height from ERA5);
• wgSLh denoting predictor 2 (wind gust estimated from wind-shear between two height levels, (van den Brink, 2020; van den Brink & Whan, 2018);
• ELEV denoting predictor 3 (observed elevation derived from the 1 km resolution elevation file).

In CDS, footprints are provided through different NetCDF files identifying:

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

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 Tempest Extremes and TRACK 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.  

Concluding Remarks

EWS represents an enhancement and temporal extension of the current C3S windstorm service. It presents a "quasi-operational" feature automatically updating windstorm datasets based on ERA5 period new releases.

EWS leverages two tracking algorithms and derives windstorm footprints at the original resolution and statistically downscaled. Taking advantage of the whole ERA5 time series EWS datasets presents an unprecedented temporal extension, of paramount importance for:

-a better characterization of large year-to-year fluctuations in terms of frequency and magnitude thus representing a valuable tool for the insurance and reinsurance sectors.

-Providing the basis for statistical inferences about trends involving crucial ETCs-related dynamics, their multi-decadal climate variability and associated climate risk.

-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 parametrization of gust profiles during severe wind conditions. Environmental Research Communications. Institute of Physics. https://doi.org/10.1088/2515-7620/ab5777

van den Brink, H. W., & Whan, K. (2018). Copernicus Climate Change Service. In Storm footprint generation through statistical downscaling (Vol. 509). https://doi.org/10.1088/1755-1315/509/1/012005

Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horányi, A., Muñoz-Sabater, J., et al. (2020). The ERA5 global reanalysis. Quarterly Journal of the Royal Meteorological Society, 146(730), 1999–2049. https://doi.org/10.1002/qj.3803

Hodges, K. I. (1995). Feature tracking on the unit sphere. Monthly Weather Review, 123(12), 3458-3465.

Hodges, K. I. (1999). Adaptive constraints for feature tracking. Monthly Weather Review, 127(6), 1362-1373.

Hoskins, B. J., & Hodges, K. I. (2002). New perspectives on the Northern Hemisphere winter storm tracks. Journal of the Atmospheric Sciences, 59(6), 1041-1061.

Roberts, J. F., Champion, A. J., Dawkins, L. C., Hodges, K. I., Shaffrey, L. C., Stephenson, D. B., et al. (2014). The XWS open access catalogue of extreme European windstorms from 1979 to 2012. Natural Hazards and Earth System Sciences, 14(9), 2487–2501. https://doi.org/10.5194/nhess-14-2487-2014

Ullrich, P. A., Zarzycki, C. M., McClenny, E. E., Pinheiro, M. C., Stansfield, A. M., & Reed, K. A. (2021). TempestExtremes v2.1: A community framework for feature detection, tracking, and analysis in large datasets. Geoscientific Model Development, 14(8), 5023–5048. https://doi.org/10.5194/gmd-14-5023-2021

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