Tag Archives: Perils

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.

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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.

One Year Later: What We Learned from the Moore Tornadoes

This week marks the one-year anniversary of the severe weather outbreak that brought high winds, hail, and tornadoes to half of all U.S. states. The most damaging event in the outbreak was the Moore, Oklahoma tornado of May 20, 2013. Rated at the maximum intensity of EF5, it had maximum sustained wind speeds of up to 210 mph and was the most deadly and damaging tornado of the year for both Oklahoma and the U.S., causing roughly $2 billion in insured losses.

As we reflect upon the events that have taken place in Moore, the following can be discerned:

  • Understanding severe weather risks is key: According to the RMS U.S. Severe Convective Storm Model in RiskLink 13.1, the annual likelihood of a severe weather event causing at least $1 billion in insured losses in the U.S. is 92 percent, meaning it is almost certain to occur each year. For reference, from a loss perspective, the $2 billion 2013 Moore tornado loss represented a 1-in-50-year event in Oklahoma, or an event with a 2 percent chance of occurring in a given year. Similarly, a 1-in-100-year event, or an event with a 1 percent chance of occurring in a given year, would cause $4 billion or more in insured losses for Oklahoma. Events in excess of the 1-in-100-year return period would be driven by large, destructive tornadoes hitting more concentrated urban environments, such as a direct hit on Oklahoma City. Probabilistic severe storm models provide more perspective on these types of risks, and can better prepare the industry for the “big ones.”
  • What grabs the headlines doesn’t cause the most damage: Although tornadoes get all the news coverage and are often catastrophic, hail drives roughly 60 percent of the average annual loss in convective storms. This is mainly driven by the much higher frequency of hailstorms compared to tornadoes. Hailstorms also have a much larger average footprint size.
  • Tornado Alley isn’t the only risky place: Tornado Alley drives roughly 32 percent of the average annual loss for severe convective storms in the U.S., while the Upper Midwest drives 24 percent, Texas drives 16 percent, and the Southeast drives 12 percent. Buildings in affected areas need continued upgrades: For example, the Moore city council approved 12 changes to the residential building code after the Moore tornado, including mandates for continuous plywood bracing and wind-resistant garages (often the first point of failure during weak to moderate winds).

While we can never predict exactly when severe weather will occur, it’s imperative for communities, businesses, and individuals to understand its potential impact. Doing so will help people and industries exposed to severe weather be better prepared for the next big event.

Are you located in one of the regions affected by last May’s outbreak, or in another risk-prone area? Have you been affected by any recent severe weather events? If so, what did you learn, and what changes were made in your region to safeguard the community, businesses, and homes? Please share your experience in the comment section.

Jeff Waters also contributed to this post.

Uncertainty and Unknown Unknowns

At today’s inaugural ‘Catastrophe Risk Management & Modelling Australasia 2013’ in Sydney, the focus is on model uncertainty, unknown unknowns and best practice model usage in the context of these uncertainties.

As I have observed many times, every catastrophe is the “perfect storm” and the one common factor of all catastrophes is they are all unique. Best practice is looking beyond the models and having a strong sense of “plausible impossibilities”.

We must also make sure we do not forget lessons that are learned in the past, for example the importance of completeness and accuracy of data, and making sure that you understand the policy terms, for example sum insured or paying out for replacement costs. In the case of New Zealand, replacement must be to the latest building codes.

One key question today has been whether the Christchurch earthquake could occur under a big Australian city. An earthquake of the same magnitude of the Lyttleton earthquake is certainly possible, but the soil types are quite different.

As described in Robert Muir-Wood’s previous blog on ultra-liquefaction, one of the key characteristics of ultra-liquefiable soils is that they are glacially deposited; fortunately something that Australia, and even other cities in New Zealand such as Wellington do not have to the same extent as Christchurch. However, other potential surprises may occur, such as landslides in Wellington.

The earthquakes of 2011 are clearly an opportunity to learn and improve our models, but we all need to embrace the fact that there will continue to be sources of surprise – ‘unknown unknowns’ are called that for a reason.

Science and knowledge is always evolving. Best practice today will change tomorrow, just like in sports as diverse as rugby union or the Americas cup, where technology, training practices and even clothing was unimaginable 10 years ago. Our best understanding today will certainly change in the future.

However this does not make models irrelevant. My favorite quote is a play on General Eisenhower’s statement that “In preparing for battle, I have always found that plans are useless but planning is indispensable.” I would say, “all models are wrong, but modeling is indispensable”.

Modeling allows users to develop understanding of the models’ strengths and weaknesses, validate with whatever information is available, assess the methodologies and assumptions used, and decide what they are more comfortable with. In addition, users should consider stress tests and scenarios to further increase their intuition and knowledge of the risk potential.

In 2011, I was part of a working group in London, which produced the very useful report “Industry good practice for catastrophe modeling: A guide to managing catastrophe models as part of an internal model under Solvency II“.

Whilst written in Europe, the principles in this paper are applicable globally in all regions subject to all perils. As Australasia’s risks increase, together with regulatory interest in catastrophe modeling, this paper will continue to provide guidance and advice to all those involved in using catastrophe models to understand and manage their risk.