Author Archives: Laurent Marescot

About Laurent Marescot

Director, Model Product Strategy, RMS
Based in Zurich, Laurent initially joined RMS in 2008 as part of the Zurich account management team, servicing the European (re)insurance and ILS market. He then moved to the model product management group, leading the technical product management team for European climatic perils, such as windstorm, severe convective storm and flood. Since 2014, he has joined the model product strategy group for Europe model product line. Prior to RMS, Laurent worked 3 years at the Swiss Federal Institute of Technology Zurich (ETHZ) as a Research Associate and Lecturer, managing multidisciplinary natural hazard research projects. Laurent still lectures regularly on geophysics and catastrophe modeling at universities, and gives seminars and invited talks in international meetings. He is a Lecturer and Scientific Collaborator at the University of Fribourg (Switzerland). Laurent co-authored numerous industry publications, reviewed scientific articles and proceeding papers. He holds an MSc in Geology from the University of Lausanne and a PhD in Geophysics from the University of Lausanne and the University of Nantes.

Are (Re)insurers Really Able To Plan For That Rainy Day?

Many (re)insurers may be taken aback by the level of claims arising from floods in the French Riviera on October 3, 2015. The reason? A large proportion of the affected homes and businesses they insure in the area are nowhere near a river or floodplain, so many models failed to identify the possibility of their inundation by rainfall and flash floods.

Effective flood modeling must begin with precipitation (rain/snowfall), since river-gauge-based modeling of inland flood risk lacks the ability to cope with extreme peaks of precipitation intensity. Further, a credible flood model must incorporate risk factors as well as the hazard: the nature of the ground, such as its saturation level due to antecedent conditions, and the extent of flood defenses. Failing to provide such critical factor can cause risk to be dramatically miscalculated.

A not so sunny Côte d’Azur

This was clearly apparent to the RMS event reconnaissance team who visited the affected areas of southern France immediately after the floods.

“High-water marks for fluvial flooding from the rivers Brague and Riou de l’Argentiere were at levels over two meters, but flash floodwaters reached heights in excess of one meter in areas well away from the rivers and their floodplains,” reported the team.

This caused significant damage to many more ground-floor properties than would have been expected, including structural damage to foundations and scouring caused by fast-floating debris. Damage to vehicles parked in underground carparks was extensive, as many filled with rainwater. Vehicles struck by more than 0.5 meters of water were written off, all as a result of an event that was not modeled by many insurers.

The Nice floods show clearly how European flood modeling must be taken to a new level. It is essential that modelers capture the entire temporal precipitation process that leads to floods. Antecedent conditions—primarily the capacity of the soil to absorb water must be considered, since a little additional rainfall may trigger saturation, causing “saturation excess overland flow” (or runoff). This in turn can lead to losses such as those assessed by our event reconnaissance team in Nice.

Our modeling team believes that to achieve this new level of understanding, models must be based on continuous hydrological simulations, with a fine time-step discretization; the models must simulate the intensity of rainfall over time and place, at a high level of granularity. We’ve been able to see that models that are not based on continuous precipitation modeling could miss up to 50% of losses that would occur off flood plains, leading to serious underestimation of technical pricing for primary and reinsurance contracts.

What’s in a model?

When building a flood model, starting from precipitation is fundamental to the reproduction, and therefore the modeling, of realistic spatial correlation patterns between river basins, cities, and other areas of concentrated risks, which are driven by positive relationships between precipitation fields. Such modeling of rainfall may also identify the potential for damage from fluvial events.

But credible defenses must also be included in the model. The small, poorly defended river Brague burst its banks due to rainfall, demolishing small structures in the town of Biot. Only a rainfall-based model that considers established defenses can capture this type of damage.

Simulated precipitation forms the foundation of RMS inland flood models, which enables representation of both fluvial and pluvial flood risk. Since flood losses are often driven by events outside major river flood plains, such an approach, coupled with an advanced defense model, is the only way to garner a satisfactory view of risk. Visits by our event reconnaissance teams further allow RMS to integrate the latest flood data into models, for example as point validation for hazard and vulnerability.

Sluggish growth in European insurance markets presents a challenge for many (re)insurers. Broad underwriting of flood risk presents an opportunity, but demands appropriate modeling solutions. RMS flood products provide just that, by ensuring that the potential for significant loss is well understood, and managed appropriately.

European Windstorm: Such A Peculiarly Uncertain Risk for Solvency II

Europe’s windstorm season is upon us. As always, the risk is particularly uncertain, and with Solvency II due smack in the middle of the season, there is greater imperative to really understand the uncertainty surrounding the peril—and manage windstorm risk actively. Business can benefit, too: new modeling tools to explore uncertainty could help (re)insurers to better assess how much risk they can assume, without loading their solvency capital.

Spikes and Lulls

The variability of European windstorm seasons can be seen in the record of the past few years. 2014-15 was quiet until storms Mike and Niklas hit Germany in March 2015, right at the end of the season. Though insured losses were moderate[1], had their tracks been different, losses could have been so much more severe.

In contrast, 2013-14 was busy. The intense rainfall brought by some storms resulted in significant inland flooding, though wind losses overall were moderate, since most storms matured before hitting the UK. The exceptions were Christian (known as St Jude in Britain) and Xaver, both of which dealt large wind losses in the UK. These two storms were outliers during a general lull of European windstorm activity that has lasted about 20 years.

During this quieter period of activity, the average annual European windstorm loss has fallen by roughly 35% in Western Europe, but it is not safe to presume a “new normal” is upon us. Spiky losses like Niklas could occur any year, and maybe in clusters, so it is no time for complacency.

Under Pressure

The unpredictable nature of European windstorm activity clashes with the demands of Solvency II, putting increased pressure on (re)insurance companies to get to grips with model uncertainties. Under the new regime, they must validate modeled losses using historical loss data. Unfortunately, however, companies’ claims records rarely reach back more than twenty years. That is simply too little loss information to validate a European windstorm model, especially given the recent lull, which has left the industry with scant recent claims data. That exacerbates the challenge for companies building their own view based only upon their own claims.

In March we released an updated RMS Europe Windstorm model that reflects both recent and historic wind history. The model includes the most up-to-date long-term historical wind record, going back 50 years, and incorporates improved spatial correlation of hazard across countries together with a enhanced vulnerability regionalization, which is crucial for risk carriers with regional or pan-European portfolios. For Solvency II validation, it also includes an additional view based on storm activity in the past 25 years. Pleasingly, we’re hearing from our clients that the updated model is proving successful for Solvency II validation as well as risk selection and pricing, allowing informed growth in an uncertain market.

Making Sense of Clustering

Windstorm clustering—the tendency for cyclones to arrive one after another, like taxis—is another complication when dealing with Solvency II. It adds to the uncertainties surrounding capital allocations for catastrophic events, especially due to the current lack of detailed understanding of the phenomena and the limited amount of available data. To chip away at the uncertainty, we have been leading industry discussion on European windstorm clustering risk, collecting new observational datasets, and developing new modeling methods. We plan to present a new view on clustering, backed by scientific publications, in 2016. These new insights will inform a forthcoming RMS clustered view, but will be still offered at this stage as an additional view in the model, rather than becoming our reference view of risk. We will continue to research clustering uncertainty, which may lead us to revise our position, should a solid validation of a particular view of risk be achieved.

Ongoing Learning

The scientific community is still learning what drives an active European storm season. Some patterns and correlations are now better understood, but even with powerful analytics and the most complete datasets possible, we still cannot yet forecast season activity. However, our recent model update allows (re)insurers to maintain an up-to-date view, and to gain a deeper comprehension of the variability and uncertainty of managing this challenging peril. That knowledge is key not only to meeting the requirements of Solvency II, but also to increasing risk portfolios without attracting the need for additional capital.

[1] Currently estimated by PERILS at 895m Euro, which aligns with the RMS loss estimate in April 2015

High Tides a Predictor for Storm Surge Risk

On February 21, 2015, locations along the Bristol Channel experienced their highest tides of the first quarter of the 21st century, which were predicted to reach as high as 14.6 m in Avonmouth. When high tides are coupled with stormy weather, the risk of devastating storm surge is at its peak.

Storm surge is an abnormal rise of water above the predicted astronomical tide generated by a storm, and the U.K. is subject to some of the largest tides in the world, which makes its coastlines very prone to storm surge.


A breach at Erith, U.K. after the 1953 North Sea Flood

The sensitivity of storm surge to extreme tides is an important consideration for managing coastal flood risk. While it’s not possible to reliably predict the occurrence or track of windstorms—even a few days before they strike land—it is at least possible to predict years with a higher probability of storm surge well in advance—which can help in risk mitigation operation planning, insurance risk management, and pricing.

Perfect timing is the key to a devastating storm surge. The point at which a storm strikes a coast relative to the time and magnitude of the highest tide will dictate the size of the surge. A strong storm on a neap tide can produce a very large storm surge without producing dangerously high water levels. Conversely, a medium storm on a spring tide may produce a smaller storm surge, but the highest water level can lead to extensive flooding. The configuration of the coastal geometry, topography, bathymetry, and sea defenses can all have a significant impact on the damage caused and the extent of any coastal flooding.

This weekend’s high tides in the U.K. remind us of the prevailing conditions of the catastrophic 1607 Flood, which also occurred in winter. The tides reached an estimated 14.3 m in Avonmouth which, combined with stormy conditions at the time, produced a storm surge that caused the largest loss of life in the U.K. from a sudden onset natural catastrophe. Records estimate between 500 and 2,000 people drowned in villages and isolated farms on low-lying coastlines around the Bristol Channel and Severn Estuary. The return period of such an event is probably over 500 years and potentially longer.

The catastrophic 1953 Flood is another example of a U.K. storm surge event. These floods caused unprecedented property damage along the North Sea coast in the U.K. and claimed more than 2,000 lives along northern European coastlines. This flood occurred close to a Spring tide, but not on an exceptional tide. Water level return periods along the east coast are varied, peaking at just over 200 years in Essex and just less than 100 years in the Thames. So, while the 1953 event is rightfully a benchmark event for the insurance industry, it was not as “extreme” as the 1607 Flood, which coincided with an exceptionally high astronomical tide.

Thankfully, there were no strong storms that struck the west coast of the U.K. this weekend. So, while the high tides may have caused some coastal flooding, they were not catastrophic.

The challenges around modeling European windstorm clustering for the (re)insurance industry

In December I wrote about Lothar and Daria, a cluster of windstorms that emphasized the significance of ‘location’ when assessing windstorm risk. This month we have the 25th anniversary of the most damaging cluster of European windstorms on record—Daria, Herta, Wiebke, and Vivan.

This cluster of storms highlighted the need for better understanding the potential impact of clustering for insurance industry.

At the time of the events the industry was poorly prepared to deal with the cluster of four extreme windstorms that struck in rapid succession over a very short timeframe. However, since then we have not seen such a clustering again of such significance, so how important is this phenomena really over the long term?

There has been plenty of discourse over what makes a cluster of storms significant, the definition of clustering and how clustering should be modeled in recent years.

Today the industry accepts the need to consider the impact of clustering on the risk, and assess its importance when making decisions on underwriting and capital management. However, identifying and modeling a simple process to describe cyclone clustering is still proving to be a challenge for the modeling community due to the complexity and variety of mechanisms that govern fronts and cyclones.

What is a cluster of storms?

Broadly, a cluster can be defined as a group of cyclones that occur close in time.

But the insurance industry is mostly concerned with severity of the storms. Thus, how do we define a severe cluster? Are we talking about severe storms, such as those in 1990 and 1999, which had very extended and strong wind footprints. Or is it storms like those in the winter 2013/2014 season, that were not extremely windy but instead very wet and generated flooding in the U.K.? There are actually multiple descriptions of storm clustering, in terms of storm severity or spatial hazard variability.

Without a clearly identified precedence of these features, defining a unique modeled view for clustering has been complicated and brings uncertainty in the modelled results. This issue also exists in other aspects of wind catastrophe modeling, but in the case of clustering, the limited amount of calibration data available makes the problem particularly challenging.

Moreover, the frequency of storms is impacted by climate variability and as a result there are different valid assumptions that could be applied for modeling, depending on the activity time frame replicated in the model. For example, the 1980s and 1990s were more active than the most recent decade. A model that is calibrated against an active period will produce higher losses than one calibrated against a period of lower activity.

Due to the underlying uncertainty in the model impact, the industry should be cautious of only assessing either a clustered or non-clustered view of risk until future research has demonstrated that one view of clustering is superior to others.

How does RMS help?

RMS offers clustering as an optional view that reflects well-defined and transparent assumptions. By having different views of risk model available to them, users can better deepen their understanding of how clustering will impact a particular book of business, and explore the impact of the uncertainty around this topic, helping them make more informed decisions.

This transparent approach to modeling is very important in the context of Solvency II and helping (re)insurers better understand their tail risk.

Right now there are still many unknowns surrounding clustering but ongoing investigation, both in academia and industry, will help modelers to better understand the clustering mechanisms and dynamics, and the impacts on model components to further reduce the prevalent uncertainty that surrounds windstorm hazard in Europe.

 

Location, location, location: what makes a windstorm memorable?

While wind speed can indicate a storm’s damageability, two storms with similar peak wind speeds can cause vastly different levels of damage if they pass over locations with different concentrations of exposure.

This month marks the 15th anniversary of Lothar and Martin. Two powerful storms that tracked violently across Europe on December 26-28, 1999.

The combined European loss of both storms is in excess of $11 billion (2013 values). Since the storms occurred within days of each other it’s difficult to calculate the exact split of damage, however a 70:30 ratio is commonly accepted, ranking Lothar as the second largest Europe windstorm loss on record after Daria (1990).

France was hit hardest by the stormsparticularly Paris, which was right in the bullseye of Lothar’s most extreme physical characteristics. The recorded wind speeds in the low-lying regions of Paris were above 160 km/h and as high as 200 km/h at the top of the Eiffel Tower.

An exceptional storm

While Lothar’s wind speeds are comparable to other historical Europe windstorms, it’s considered an exceptional event for the insurance industry because of its track and the timing of its maximum intensification over Paris. Today, Lothar is a key benchmark used by the industry to understand the potential magnitude of Europe windstorm losses.

Lothar – a one-off for France?

Many industry experts believe Lothar to be higher than a 100-year return period loss event for France; however this should be interpreted as a long-term average and France could potentially experience a similarly extreme storm this winter.

Using current industry exposures, RMS calculated the potential French losses that would result from a Lothar-like storm striking different locations in France. By relocating Lothar’s peak gusts along points up to 500 km in each direction from their original location, our modelers concluded that Lothar was the fourth worst-case storm that could have happened out of a total of 437 scenarios.

The worst-case scenario for France is a Lothar-like storm relocated approximately 100 km west of the original event but which would still significantly impact Paris. The losses from this scenario are not much higher than Lothar’s. At only 15 percent higher the small increase in loss reinforces Lothar as an exceptional benchmark for the insurance industry.

We found that the majority of scenarios in the study produced notably lower losses. This is because the displacement of the storm, by even small distances, meant that the most extreme wind speeds impacted much lower concentrations of insured exposures. The study reinforces our understanding of the sensitivity of windstorm loss to a storm’s path. It also highlights the importance of using a stochastic model containing tens of thousands of events to be able to comprehensively evaluate potential windstorm losses.

London at risk

No European city is immune from damaging windstorms. RMS also re-located Lothar over Londononly a 350 km shift to the northto see what the impacts would be. We calculated the insured loss for Europe could be as much as 25 percent higher than Lothar’s losses and potentially bigger than the $8.6 billion loss caused by Daria.

The uncertainty inherent to the climatic phenomena that drive windstorms makes it impossible to forecast exactly when and where the next strong storm will hit France or Europe. However, catastrophe models can at least help to evaluate the potential financial impact of extreme storms like Lothar.

Matching Modeled Loss Against Historic Loss in European Windstorm Data

To be Solvency II compliant, re/insurers must validate the models they use, which can include comparisons to historical loss experience. In working towards model validation, companies may find their experience of European windstorm hazard does not match the modeled loss. However, this seeming discrepancy does not necessarily mean something is wrong with the model or with the company’s loss data. The underlying timelines for each dataset may simply differ, which can have a significant influence for a variable peril like European windstorm.

Most re/insurers’ claims records only date back 10 to 20 years, whereas European windstorm models use much longer datasets – generally up to 50 years of the hazard. Looking over the short term, the last 15 years represented a relative lull in windstorm activity, particularly when compared to the more extreme events that occurred in the very active 1980s and 1990s.

Netherlands windstorm variability

 

 

 

 

 

 

RMS has updated its European windstorm model specifically to support Solvency II model validation. The enhanced RMS model includes the RMS reference view, which is based on the most up-to-date, long-term historical record, as well as a new shorter historical dataset that is based on the activity of the last 25 years.

By using the shorter-term view, re/insurers gain a deeper understanding of how historical variability can impact modeled losses. Re/insurers can also perform a like-for-like validation of the model against their loss experience, and develop confidence in the model’s core methodology and data. Alternate views of risk also support a deeper understanding of risk uncertainty, which enhances model validation and provides greater confidence in the models that are used for risk selection and portfolio management.

Beyond Solvency II validation, the model also empowers companies to explore the hazard variability, which is vitally important for a variable peril like European windstorm. If a catastrophe model and a company rely on different but equally valid assumptions, the model can present a different perspective to provide a more complete view of the risk.

Is Europe Due for Severe Hailstorms this Summer?

Summer has just started, but weather has already been warm over Europe. Many countries have experienced very high temperatures over the first weeks of June, and there is a chance the 2014 summer will be warmer than normal. A warm atmosphere can bring very high convection potential and potentially lead to a busy severe convective storm season. While seasonal forecasts are uncertain, severe hail events already experienced in June already point to a potential increase in hail risk this year.

The first noticeable hailstorm of the season hit Germany, France, and Belgium between June 7 and 9. Over that period, southern air masses were very warm and clashed with much cooler air from the north. This frontal system brought heavy local wind, rain, and hail, especially over the north of France, Belgium, and northwest region Germany, where large cities like Essen, Düsseldorf, or Köln experienced property damages and six casualties.

RMS scientists Dr. Navin Peiris and Panagiotis Rentzos led a reconnaissance survey in the region a few days after the event and noted that even if there was some evidence of direct hail damage to roofing, most of the substantial damages and transport disruption around Düsseldorf came from tree falls due to very strong wind gusts.

Tree Fallen in Hailstorm

July 12, 2014 will be the 30th anniversary of the most expensive hailstorm in the history of Germany, which generated losses around US$2 billion 1984—half of which was insured. The hailstorm developed amid a streak of late afternoon thunderstorms after a day of intense solar heating. A mass of moist sea air flowed into southern Germany overnight and the combination of moisture and rising air triggered a rapidly intensifying thunderstorm system over the Swiss Mittelland that propagated eastward. Hail fell within a 250-kilometer (150-mile) long and 5–15 kilometer (3–9 mile) wide swath from Lake Constance to eastern Bavaria near the Austrian border. At around 8 p.m. local time, the hailstorm passed over Munich, damaging approximately 70,000 houses, 200,000 cars, 150 aircraft, and most agricultural crops within the storm’s path. More than 400 people were injured. Over half of the insured losses were attributed to damaged cars.

July also marks the first anniversary of the 2013 German hailstorm, which caused insured losses of US$3.4 billion, the second highest from a single natural catastrophe in 2013. Like the June 2014 events, the storm hit after a prolonged period of above-average temperatures in central Europe. The first hail event hit northern Germany on July 27, and the second dropped hailstones with a diameter of up to 8 cm (3.1 in) over south Germany the next day.

Interestingly, all these major events occurred in regions with very high potential of hail damage, which can be described in catastrophe models such as the RMS HailCalc model in terms of kinetic energy to help better manage hail risk. In June, RMS presented the first results of a reconstruction of this hailstorm on at the 1st European Hail Workshop. The paper illustrates how a fast estimation of insured hail losses could be obtained following an event in the future. Developing methods of estimating insured loss totals and return periods immediately after an event are an ongoing area of research in the insurance industry, as illustrated in the RMS paper and others at the workshop.

Hailstorm Image Map

 

Lessons Learned from Winter Windstorm Season in Europe

The 2013–2014 winter windstorm season in Europe will be remembered for being particularly active, bringing persistent unsettled weather to the region, and with it some exceptional meteorological features. The insurance industry will have much to learn from this winter.

Past extreme windstorms, such as Daria, Herta, Vivian, and Wiebke in 1990, each caused significant losses in Europe. In contrast, the individual storms of 2013–2014 caused relatively low levels of loss. While not extreme on a single-event basis, the accumulated activity and loss across the season was notable, primarily due to the specific characteristics of the jet stream.

A stronger-than-usual jet stream off the U.S. Eastern Seaboard was caused by very cold polar air over Canada and warmer-than-normal sea-surface temperatures in the sub-tropical West Atlantic and Caribbean Sea. Subsequently, this jet stream significantly weakened over the East Atlantic.

Therefore, the majority of systems were mature and wet when they reached Europe. These storms, while associated with steep pressure gradients, brought only moderate peak gust wind speeds onshore, mainly to the U.K. and Ireland. In contrast, the storms that hit Europe in 1990 were mostly still in their development phase under a strong jet stream as they passed over the continent.

The 2013––2014 storms were also very wet, and many parts of the U.K. experienced record-breaking rainfall resulting in significant inland flooding. Again, individual storms were not uniquely severe, but the impact was cumulative, especially as the soil progressively saturated.

Not all events this winter season weakened before impact. Windstorms Christian and Xaver were exceptions, only becoming mature storms after crossing the British Isles into the North Sea and were more damaging.

Christian impacted Germany, Denmark, and Sweden with strong winds. RMS engineers visited the region and observed that the majority of building damage was dominated by the usual tile uplift along the edges of the buildings. Fallen trees were observed, but in most cases, there was sufficient clearance to prevent them from causing building damage.

Xaver brought a significant storm surge to northern Europe, although coastal defenses mostly withstood the storm. Xaver, as well as some of this year’s other events, demonstrated the importance of understanding tides when assessing surge hazard as many events coincided with some of the highest tides of the year. The size of a storm-induced surge is much smaller than the local tidal range; consequently, if these events had occurred a few days earlier or later, the astronomical tide would have been reduced, significantly reducing the high water level.

Windstorm3

Wind, flood, and coastal surge are three components of this variable peril that can make the difference between unsettled and extreme weather. This highlights the importance of modeling the complete life cycle of windstorms, the background climate, and antecedent conditions to fully understand the potential hazard.

This season has also raised questions about the variability of windstorm activity in Europe, how much we understand this variability, and what we can do to better understand it in the future. While this winter season was active, we have been in a lull of storm activity for about 20 years.

Given the uncertainty that surrounds our ability to predict the future of this damaging peril, perhaps for now we are best positioned to learn lessons from the past. This past winter provided a unique opportunity, compared to the more extreme events that have dominated the recent historical record.

RMS has prepared a detailed report on the 2013–2014 Europe windstorm season, which analyzes the events that occurred and their insurance and modeling considerations. To access the full report, visit RMS publications.

How Does Southern Europe Weather the Storm?

The 2013-14 European winter storm season has been pretty active so far. Early in the season, Windstorm Christian raced across northern Europe, followed by Xaver in early December, and then storms Dirk, Erich, Felix and Anne hit the U.K., Ireland, and northwest France over the Christmas and New Year period.

To date the season has been a great demonstration of how northern Europe is a common target for winter storms. However, this week sees the 5th anniversary of Windstorm Klaus, reminding us that storms can also impact southern Europe, affecting regions not acclimatized to extreme winds and causing severe damage.

What happened when Klaus hit and what have we learned from it?

Can such a storm occur again in the near future and more importantly, can we predict it, or at least estimate how bad it could be?

Windstorm Klaus sprung to life on January 23, 2009 in the central Atlantic, directly in line with southern France. The climate backdrop to this storm was pretty uncharacteristic. The large-scale Icelandic low-pressure system and the Azores high-pressure system were farther south than usual. Also, the North Atlantic Oscillation (NAO) was entering a negative phase.

A positive phase of the NAO creates favorable conditions for strong storms to pass over northern Europe, as Lothar and Anatol did in 1999. But a neutral or negative phase of the NAO can lead to storms that affect southern Europe and this is exactly what happened with Windstorm Klaus.

By midnight on January 24, as Klaus approached land, it had a central pressure of 963 hPa, comparable to Windstorm Lothar. Winds reached severe gale force in the southwest of France, peaking with gusts above 140 km/h at coastal locations near Bordeaux, accompanied by violent seas with wave heights of several meters. Local infrastructure was severely disrupted by fallen trees and electricity pylons.

Over 1.7 million households were without power immediately after the storm and over 60% of maritime pines in the Forêt des Landes were destroyed. Once the damage had been appraised, Klaus was estimated to have caused insured losses of €2.5billion (US$3.4 billion).

Shortly after the event, RMS scientists Dr. Navin Peiris and Dr. Christos Mitas conducted a reconnaissance survey, which helped to enhance our understanding of building vulnerability in this region. They observed frequent non-structural wind damage, such as the uplifting of roof tiles and collapsed chimneys, but also direct wind damages from tree fall, due to the high density of trees in close proximity to properties.

Source: RMS 2009 reconnaissance

Closer examination of the roof damage revealed little evidence of proper fixation, particularly along roof edges, leaving them more vulnerable to wind damage. Another observation was the use of canal-type tiles, which are prone to uplift from the build up of air pressure, caused by strong winds. Also, damage was more frequent in residential properties, compared to commercial or industrial buildings that are generally engineered in line with building codes.

This survey, combined with an assessment of claims data, provided us with an enhanced understanding of regional vulnerability differences. For example, we observed a significantly lower fragility of buildings in the Perpignan area compared to the southwest of France.

Ratio of the modeled and observed losses by postcode using non-regionalized vulnerability functions. Variation supports need for distinct vulnerability regions.

Ratio of the modeled and observed losses by postcode using non-regionalized vulnerability functions. Variation supports need for distinct vulnerability regions.

This information is vital for us to continually develop and inform our models, in order to represent the risk accurately. Due to the inherent uncertainty in the climatic phenomena driving windstorms, it is not possible to forecast exactly when the next strong storm will hit southern Europe. Catastrophe models provide a range of possible events, which can help the insurance industry prepare for the next big event.

The RMS Europe Windstorm Model contains storms comparable to Klaus, including some that impart larger wind intensities and damages. The below image compares two examples of stochastic storms with the actual Klaus wind footprint to illustrate storms that could potentially cause insured losses similar to or higher than Klaus.

Klaus and Stochastics

Currently we are in a close to neutral phase of the NAO, so does that mean a Klaus type storm could occur this winter? No one can answer that question for certain, but a model at least enables us to explore the possible worst-case scenarios and be prepared.