...

Acronyms

Introduction 

Executive Summary

A storm footprint is defined as the maximum 3-second 10-m wind gust speed (in m s^-1) over a 72-hour period at each model grid point for a significant winter storm. As such, a storm footprint shows the spatial distribution of maximum wind gust speed for a storm crossing the area of interest. For European storms, the area of interest spans the area defined by 15W to 25E, 35N to 70N though in fact the storms have been tracked over their full course from formation over the Atlantic.

...

The storm footprints serve as input for the calculation of economic loss caused by the storm by means of so-called Tier 3 indicators, which indicate the effect of windstorms on the infrastructure and financial system of a sector.

Scope of Documentation

This document describes the C3S Storm Footprint dataset using the standard C3S format for product descriptions, i.e., in terms of product target requirements, product overview, input data and method. It is based on statistical downscaling, in contrast to dynamical downscaling, which was used in the 'Proof of Concept' contract called WISC (Windstorm Climate Service). In dynamical downscaling, a high-resolution regional model is applied using reanalysis model fields as boundary conditions. The statistical downscaling approach is outlined in the Method section, section 2.4.

Version History

The operational footprints are based on statistical downscaling of the ERA5 dataset, using the main high resolution ERA5 field in each case. The operational footprints are derived from the new ERA5 storm tracks produced for the operational service. Since these yielded a slightly different set of storms from the earlier C3S Proof of Concept (WISC) tracks, WISC tracks were also used to provide statistically downscaled storms from ERA5 to match those provided by WISC. This will allow for comparisons with the equivalent statistically downscaled WISC footprints.

Product Description

Product Target Requirements

Wind gust (m s^-1) is the prime input parameter for footprint generation. It was found that wind gust from reanalysis (ERA-Interim and ERA5) underestimates measured wind gust on average. Dynamical and statistical downscaling are methods to improve on this. For the operational service, statistical downscaling was selected as statistical downscaling proved a suitable alternative as is computationally efficient in comparison.

Product Overview

DataDescription

The C3S storm footprint dataset consists of footprints from all identified winter storms, by the Storm Tracking module, over the period 1979-2021. For those years excluded from the dataset download form, no storms exceeded the selection criteria threshold of 25m/s 10m winds over land using a 3-degree sampling region. Storm footprint are available in NetCDF format. Figure 1 shows an example of the storm footprint for storm Christian which crossed Europe around 28 November 2013, obtained from the statistical downscaling method using the Multiple Linear Regression (MLR) technique, starting from ERA5 model fields.

...

VariableDescription

Input Data

The input data for the statistical downscaling method includes ERA5 10-meter wind gust, wind gusts estimated from the wind-shear between 100 m and 10 m and station elevation height.

...

InputData1

ERA5 10-meter wind gust (m s^-1)

InputData2

ERA5 wind field(m s^-1) at 10 m and 100 m above the earth surface

InputData3

Station elevation height (m). Land stations, more in particular their height, have been used to estimate the parameters of the MLR scheme.

Method

Background

Accurate estimates of wind gusts are important for many sectors, including the insurance industry, e.g., for damage calculations and potential changes therein for instance due to climate change. It is difficult to obtain the complete picture from high-resolution observed wind gusts, due to the sparse observational network. Many locations remain unobserved, even in places with a dense observational network, such as many countries in western Europe. The relatively sparse observational network can be particularly problematic for variables that are spatially heterogeneous, such as wind gusts.

Dynamical, statistical, or combined dynamical-statistical (where a dynamical model is downscaled, bias-corrected and combined with other predictive information using statistical methods) approaches can be taken to develop high-resolution gridded estimates of wind gusts. There are positive and negative aspects of each approach. For example, a dynamical (numerical weather prediction meso- scale) model is computationally expensive to run and contains errors due to errors in the initial conditions and the physical parameterisation schemes. On the other hand, a statistical model (e.g. multiple linear regression) is limited by the strength of relationships in the training data set, the number of extreme observations in the training data set and cannot easily handle changes in the relationships over time. Despite some limitations, multiple linear regression (MLR) is one of the most popular ways of making predictions. MLR models the linear relationship between a response variable (e.g., observed wind gusts) and two or more predictor variables. This relationship (i.e. the statistical equation) is developed on a training data set (a set of observations and potential predictor variables) and then applied to new independent data.

Model/Algorithm

The MLR technique estimates wind gusts (Y) at unobserved locations in Europe. Various sets of potential predictors were tested. The preferred statistical model is one that uses three predictors (X1, X2 and X3);

...

Validation

The validation of the MLR technique has been performed over land using wind gust data from land stations. A total of 17 storm footprints and corresponding land station data were used for validation. Figure 2 shows the locations of wind gust data over land and the mean monthly maximum wind gust between 2000-2018, the period capturing the 17 storm footprints.

...

Figure 3: QQ plot of observed (see Figure 2) and predicted monthly maxima wind gusts during the 17 storm events from the raw ERA5 forecasts (black) and the MLR model, equation in section 2.4.2 (blue).

Known issues

1. During publication an issue was identified with a small subset of the Windstorm footprints NetCDF files. The issue only affects the NetCDF metadata labels FX:long_name "Wind Gust estimates: MLR monthly maximum wind gust". These are incorrectly labelled and should instead state max_wind_gust:long_name = "maximum 10-m wind gust". All files contain the correct variable data and the wind gusts were calculated from a 72-hour maximum as stated in Table 2.
2. The filename for data from Storm Xynthia is incorrectly labelled 2009 instead of 2010 (correct). Users should continue to use the data for Storm Xynthia understanding that it is from the year 2010. Users should be aware that the mis-labelled filename may affect their workflows.

Concluding Remarks

Storm footprints serve as input for economic loss calculations as part of the Windstorm Service. The validation results presented in section 2.4.3 give good confidence that statistical downscaling through the Multiple Linear Regression (MLR) model, described in section 2.4.2, provides good estimates of the strongest wind gusts which are of most relevance for economic loss and risk calculations. The MLR model has proven robust for new (recent) storms entering the Windstorm Service.
The use of storm footprints based on the statistical downscaling approach is limited to land areas. This does not limit the purpose of the Windstorm Service since damage and economic loss from windstorms is mainly over land areas. Hence, high quality storm footprints over land suffices for economic loss estimate calculations.

References

An effective parametrization of gust profiles during severe wind conditions, Henk W van den Brink, Published 28 November 2019, Environmental Research Communications, Volume 2, Number 1.

Related articles