D ATOS G ENERALES DE ACTIVIDAD 14

In order to select only the most intense storms, it is necessary to select a measure for intensity. A meteorological index is chosen called the Storm Severity Index (SSI). The format used here is that introduced by Leckebusch et al. (2008b), who built on the work of Klawa and Ulbrich (2003). The reason for choosing a meteorological measure for storm intensity, compared to a socio-economic measure, such as insured damage caused by the storm, is that it has fewer complex factors at play. The amount of economic damage inflicted depends upon the population density it passes over, the preparedness of the population, how well insured that population is, whether there have been any other windstorms passing through recently that have primed the area for further damage, and what industries are working in the area (for example, forestry can be particularly adversely affected by windstorms).

Alternative methods of measuring storm intensity have been used in the past, including meteo- rological quantities, such as minimum core pressure or maximum vorticity. However, these are not linked to the damage a storm inflicts, which is important to this work not only because it is related to the societal impacts that these storms have, but also because storm damage is a particular interest of the funder of this work the AXA Research Fund, affiliated with the insurance company.

Chapter 3. Data and Methods 53

A meteorological proxy for damage is a straightforward way to evaluate storm severity and is an approach that previous studies have taken to quantify storm intensity (e.g. Haas and Pinto, 2012; Hanley and Caballero, 2012).

SSI is calculated using readily-available meteorological variables, that only produce one value for each time step; a simple measure that does not depend on whether the storm passes over high or low population densities. The relationship to the wind speed is cubic, so is proportional to the power of the wind, which is related to the potential work that the wind can do in inflicting damage. It is summed over an area (Figure 3.1), and so it is related to the damage a storm could inflict over that area. SSI then considers by how much the wind at a grid point at a given time exceeds a threshold (i.e. the 98th percentile of wind speeds at that grid point for winter), and so calculates how exceptional the wind speed is at a point. Therefore, as a measure, SSI is a hybrid of measuring severity and extremity (Beniston et al., 2007), because it considers both the damage wreaked by the storm and the high value of the wind speeds. SSI is then summed up over an area (Figure 3.1), so is also affected by area.

F 3.1: Map illustrating the area over which SSI was summed [40oN to 60o_{N, 10}o_{W to}

20o_{E ]. Overplotted are the SSI values for 1200h on 2009-01-24, the day with the highest SSI in}

the time period, which was related to storm Klaus.

Alternative metrics for storm severity are available, for example that used by Lamb (1991, Chapter 2), which considers the greatest surface wind speed (cubed), the area affected by the storm, and the duration of the damaging winds. Firstly, the main strength of the SSI over Lamb’s index is that SSI implicitly considers how prepared the population is for damaging winds, by comparing the wind speed to climatology. In regions that regularly experience strong winds, it is likely that the population will have adapted to stronger winds and mitigating action will have been taken against

damage; for example, European building codes state that before many buildings are constructed, the wind loading must be modelled, so that it can be allowed for in the design. However, winds that are exceptional to a region could still cause damage. Secondly, the Lamb index considers storm duration, whereas the SSI does not, but it is valid to disregard storm duration. Recent research explores the idea that the duration of a storm makes a lesser contribution to the overall damage that a storm inflicts; instead, it is speculated that the short-lived but very high wind speeds caused by smaller-scale features such as a sting jet (Browning, 2004) inflict the greater part of the damage. Furthermore, it is difficult to objectively and accurately determine storm duration. Finally, (Klawa and Ulbrich, 2003) show that SSI is related to the monetary damage a storm inflicts. For these three reasons, SSI is used in the current study.

The formula for SSI is:

SSI = n X i =1        vi v_i98 − 1        3 ·          0 if Z 6 1000m 1 otherwise (3.1)

where i refers to the n grid points that are summed over, vi is the total wind speed at each grid point, v_i98is the 98th percentile of the wind speed, and Z is the height of the orography.

The SSI values are calculated using ERA-Interim data, between October and March, in the years 1979 to 2011. The wind variables used are the u and v components of wind speeds at 10m (vari- able numbers 165 and 166), which are then added together. These wind speeds are not those where additional post-processing has been applied, to make the model more comparable to SYNOP ob- servations by making allowances for roughness length. The wind climatology is constructed for each grid point, in order to find the 98th percentile of winter wind speed for the entire period. Then the wind climatology could be compared to the wind speed at each grid point, for every time step, meaning the SSI is calculated at each grid point before being summed over the specified area (Figure 3.1). This was only done where the altitude was less than 1000 metres, mainly be- cause winds at ten metres are used and the values can become unrealistic in mountainous areas, so could produce very high values of SSI and skew the selected cyclones towards those that passed over mountains. A secondary reason is because these areas have low populations, meaning any incurred damage is small. Only time steps after 1st January 1990 were considered, because before then, the forecasts’ resolutions were so low that storms would be difficult to track during the next stages of forecast analysis (described in Section 3.6). The dates and times are then listed by SSI

Chapter 3. Data and Methods 55

value, and the highest values taken forward to the next stage of analysis. The midlatitude cyclones in the resulting list are the selected storms.