Monthly Archives: December 2016

Extreme Wind Speeds Over the Ocean – an International Workshop of Experts

It’s one thing being invited to speak at an industry event in front of dozens of the leading scientists in your field. It’s another to find, with a certain astonishment, that virtually all of them use RMS HWind to validate their scientific work.

Last month, at a U.K. Met Office-hosted workshop, I spoke about the RMS HWind hurricane modeling solutions to a group of high-wind remote-sensing scientists from academic and government agencies from around the world, including:

  • European Space Agency (ESA)
  • National Aeronautics and Space Administration (NASA)
  • French Research Institute for Exploitation of the Sea (IFREMER)
  • Royal Netherlands Meteorological Institute (KNMI)
  • Met Office (U.K.)
  • European Center for Medium-Range Weather Forecasts
  • National Space Science Center, Chinese Academy of Sciences
  • Institute of Applied Physics of the Russian Academy of Sciences
  • National Oceanic and Atmospheric Administration (U.S.)

All of the above agencies are researching how satellite-mounted remote wind sensors can be used, most effectively, to inform on hurricanes and typhoons developing over the ocean.

During the workshops I was delighted to learn that every major remote sensing agency had used the RMS HWind archive of historical storms to validate and calibrate their sensor programs for detecting high winds from space. RMS HWind also provides real-time analysis of hurricanes as they happen with observational data from instruments in the air, in the sea and on land – including aircraft reconnaissance, GPS dropsonde instruments, sea buoys and satellites.

By citing HWind products and research in their peer-reviewed publications, these agencies provide independent endorsements that enhance the scientific credibility of the HWind archives and services, while also giving us a chance to evaluate cutting-edge technology before it becomes operationally available.

There is tremendous value in scientific collaboration and, as such, RMS facilitates the science community’s understanding of hurricanes by providing our academic partners free access to HWind products for their scientific investigations.

Sensors in Space

None of the satellites we discussed at the Met Office workshop actually measure wind directly – rather, they measure a signal which is influenced by the wind. So, for example, the new NASA CYGNSS system measures the reflection of GPS signals off sea’s surface, which is like the reflection of the moon on the surface of a lake. Winds disturb the surface and this scatters the signal.

Another satellite, the Canadian RADARSAT-2, has already been up for a few years and can capture images of the fine scale roughness of the ocean surface. But to collect these images and convert them to a wind speed reading requires a lot of advance planning, followed by lengthy processing.

Which is where RMS HWind comes in. Our 1 km gridded HWind Snapshots make it easy for scientists to overlay their satellite instrument measurements (typically a microwave signal reflected from the sea surface) over our wind analyses. They can do this for several storms of various sizes and intensities to convert the measured signal to wind speed over a range of meteorological and oceanic conditions.

Due for release this winter, the HWind Enhanced Archive of wind hazard metrics will provide a high resolution library of tropical cyclone wind fields for the North Atlantic, the Caribbean, Gulf of Mexico, and the east and central Pacific. In the coming years we’d hope to see the expansion of tropical cyclone wind field monitoring globally.

This expansion could benefit areas of the world with insurance protection gaps hugely. Increased insurance penetration in the Asian and Australian markets, together with new risk transfer products using parametric triggers, could help improve financial resilience to catastrophic tropical cyclones in whole new regions of the world.

Understanding Risk Accumulations in Taiwan’s Science Parks

“The 6.4 magnitude Tainan earthquake in February 2016 resulted in a sizeable insured loss from the high-tech industrial risks and reminded the insurance industry of the potential threat from the risk accumulated in science parks.” (A.M. Best Special Report, Sept 2016)

Reading the sentence above you might be forgiven for wondering why science parks would give insurers and reinsurers any particular cause for concern. But consider this statistic: although Taiwan’s three major science and industrial parks occupy only 0.1% of the island’s total land mass, they represent 16% of Taiwan’s overall manufacturing – they are hugely significant, both economically and with regards to the insured exposure in Taiwan.

For example, the Hsinchu Science Park (HSP), known for semiconductor production, employs more than 150,000 people and contributes over $32 billion in revenues – approximately 6% of national GDP. By one estimate HSP represents over $319 billion in total insured values. In addition, some of the latest high tech areas within HSP, such as advanced “clean rooms,” present additional challenges due to their vulnerability to ground shaking or power interruption. The importance of this risk was observed in February’s Tainan earthquake where some significant losses to high-tech industrial risks were caused by damage to the equipment and the related business interruption due to power outage.

Improving data quality for advanced and detailed modeling is an important way to manage these risks, concludes the A.M. Best report quoted above. This is so as to accurately assess the potential loss impact on insurers’ books. RMS has already been analysing earthquake risk in Taiwan for 12 years – long before this year’s Mw 6.4 event – and in that time our view of seismic risk in Taiwan has not changed, since our model benefits from spectral response-based hazard and damage functions, that even include local liquefaction and landslide susceptibilities.

The 1999 Chi-Chi Earthquake (known in Taiwan as the 921 Earthquake) was the key event in building the RMS® Taiwan Earthquake Model in terms of the quake’s seismicity, ground motion, soil secondary effects and building response. Since then there have been no significant events to justify a re-calibration of the components of the model. In fact, the damages observed in this year’s event were broadly in line with RMS’ expectations and validated the robustness of the current model.

But although A.M. Best views the Taiwan insurance industry as prudently managed with relatively high catastrophe management capability, there are still lessons to be learnt from the 2016 event, and RMS has solutions which offer additional insight into understanding the risk posed by these business parks in Taiwan.

Concentration of Exposure into Science Parks

The RMS® Asia Industrial Clusters Catalogs were released in 2014 to identify hotspots of exposure, and profile their risk. The locations and geographic extent of the science parks within Taiwan are detailed to help understand risk accumulations for industrial lines and develop more robust risk management strategies.


Example of industrial cluster captured in the RMS Taiwan Industrial Clusters Catalog. The red outline illustrates the digitized boundaries of the Formosa Petrochemical Co. Plant in Yunlin Hsien.

High Fragility of the Semiconductor Industry

For coding of Industrial Plants, the RMS® Industrial Facilities Model (IFM) captures the unique nature of different industrial risks, as a high percentage of property value is often associated with machinery and equipment (M&E) and stock. This advanced vulnerability model supports the earthquake model to define the damageability of a comprehensive set of industrial facilities more accurately, and calculate the financial risk to these specific types of facilities, including building, contents, and business interruption (BI) loss estimates. The IFM differentiates the risks for different types of business within the science parks, and highlights the higher fragility of semiconductor plants compared to other industrial units, as shown below.


Lessons Learnt?

The huge damage from the 1999 Chi Chi earthquake has not halted the rapid development of Taiwan’s science parks in this seismically active area – indeed the island’s third biggest science park has since been built there. But this year’s comparatively small Mw 6.4 event further highlighted the substantial exposures concentrated within this sector, reminding the industry of the potential for significant losses without sound accumulation management practices, informed by the best modeling insights.

“Italy is Stronger than any Earthquake”

Those were the words of the then Italian Prime Minister, Matteo Renzi, in the aftermath of two earthquakes on the same day, October 26, 2016. As a statement of indomitable defiance at a scene of devastation it suited the political and public mood well. But the simple fact is there is work to do, because Italy is not as strong as it could be in its resilience to earthquakes.

There’s a long history of powerful seismic activity in the central Apennines: only recently we’ve seen L’Aquila (2009, Mw6.3), Amatrice (August 2016, Mw6.0), two earthquakes in the area near Visso (October 2016, Mw 5.4 and 5.9) and Norcia (October 2016, Mw6.5). These have resulted in hundreds of fatalities, mainly attributed to widespread collapse of old buildings, emphasizing that earthquakes don’t kill people – buildings do. Whilst Italy’s Civil Protection Department provides emergency management and support after earthquakes, there is too little insurance help for the financial resiliency of the communities most affected by all these events. While the oft-repeated call for earthquake insurance to be compulsory continues to be politically unobtainable, one way it could be spread more widely is through effective modeling. And RMS expertise can help with this, allowing the market to better understand the risk and so build resilience.

Examining High Building Fragility

The two most significant factors for earthquake risk in Italy are (i) construction materials and (ii) the age of the buildings. The majority of the damaged and destroyed buildings were made from unreinforced masonry, and built prior to the introduction of the most recent seismic design and building codes, making them particularly susceptible. With the RMS® Europe Earthquake model capturing both the variations in construction types and age, as well as other vulnerability factors, (re)insurers can accurately reflect the response of different structures to earthquakes.  This allows the models to be used to evaluate the cost benefits of retrofitting buildings.  RMS has worked with the Italian National Institute for Geophysics and Volcanology (INGV) to see how such analyses could be used to optimize the allocation of public funds for strengthening older buildings, thereby reducing future damage and costs.

Seismic Risk Assessment

The high-risk zone of the central Apennines is described well by probabilistic seismic hazard assessment (PSHA) maps, which show the highest risks in that region resulting from the movement of tectonic blocks that produce the extensional, ‘normal’ faulting observed. The maps also show earthquake risk throughout the rest of Italy. RMS worked with researchers from INGV to develop our view of risk in 2007, based on the latest available databases at that time, including active faults and earthquake catalogs. The resulting hazard model produces a countrywide view of seismic hazard that has not been outdated by newer studies, such as the 2009 INGV Seismic Hazard Map and the 2013 European Seismic Hazard Map published by the SHARE consortium, as shown below:


The Route to Increased Resiliency

Increasing earthquake resiliency in Italy should also involve further development of the private insurance market. The seismic risk in Italy is relatively high for western Europe, whilst the insurance penetration is low, even outside the central Apennines. For example, in 2012, there were two large earthquakes in the Emilia-Romagna region of the Po valley, where there are higher concentrations of industrial and commercial risks. Although the type of faults and risks vary by region, such as the potential impact of liquefaction, the RMS model captures such variations in risk and can be used for the development of risk-based pricing and products for the expansion of the insurance market throughout the country.

Whilst Italy’s seismic events in October caused casualties on a lesser scale than might have been, the extent of the damage highlights once again the prevalence of earthquake risk. It is only a matter of time before the next disaster strikes, either in the Central Apennines or elsewhere. When that happens, the same questions will be asked about how Italy could be made more resilient. But if, by then, the country’s building stock is being made less susceptible and the private insurance market is growing markedly, then Italy will be able to say, with justification, it is becoming stronger than any earthquake.

See How Quickly and Easily You Can Access the Exposure Metrics That Matter

Exposure Manager is a risk management solution that provides executives, underwriters, risk analysts, and other decision-makers with the exposure analytics needed to offer a comprehensive view of risk and understand loss potential.

As the first solution released on the RMS(one) platform, Exposure Manager was developed based on the understanding that organizations not only need quick and reliable assessments of exposure concentrations, but also the right tools to ensure they can access key metrics and insights.

The videos below illustrate two of the important capabilities that enhance users’ ability to build portfolio intuition faster and quickly access the metrics that are most important.

Build Portfolio Intuition Faster provides insights into how Exposure Manager enables customers to quickly and efficiently derive deeper portfolio insights using an intuitive and user-friendly interface.

With a customizable interface that conveys the information that’s most important to the user, Exposure Manager’s analytics, enabled by an intuitive best-in-class user experience, can be configured without knowledge of SQL or support from IT.

This enhances the ability for customers to create quick insights into their portfolio or perform a deep dive into their book to make quick assessments.

Access Metrics That Matter shows how Exposure Manager leverages the RMS financial model to provide an exposed limit metric. This offers a consistent view of loss potential to enable precise identification of loss drivers.

The flexible interface provides users with precise control to quickly make informed decisions about their book and help identify threats and opportunities in the portfolio.

All of these benefits allow customers to become more incisive about their portfolio.

Earthquake Hazard: What Has New Zealand’s Kaikoura Earthquake Taught Us So Far?

The northeastern end of the South Island is a tectonically complex region with the plate motion primarily accommodated through a series of crustal faults. On November 14, as the Kaikoura earthquake shaking began, multiple faults ruptured at the same time culminating in a Mw 7.8 event (as reported by GNS Science).

The last two weeks have been busy for earthquake modelers. The paradox of our trade is that while we exist to help avoid the damage this natural phenomenon causes, the only way we can fully understand this hazard is to see it in action so that we can refine our understanding and check that our science provides the best view of risk. Since November 14 we have been looking at what Kaikoura tells us about our latest, high-definition New Zealand Earthquake model, which was designed to handle such complex events.

Multiple-Segment Ruptures

With the Kaikoura earthquake’s epicenter at the southern end of the faults identified, the rupture process moved from south to north along this series of interlinked faults (see graphic below). Multi-fault rupture is not unique to this event as the same process occurred during the 2010 Mw 7.2 Darfield Earthquake. Such ruptures are important to consider in risk modeling as they produce events of larger magnitude, and therefore affect a larger area, than individual faults would on their own.

Map showing the faults identified by GNS Sciences as experiencing surface fault rupture in the Kaikoura Earthquake.
Source: +land%3A+observations+from+the+air

In keeping with the latest scientific thinking, the New Zealand Earthquake HD Model provides an expanded suite of events that represent complex ruptures along multiple faults. For now, these are included only for areas of high slip fault segments in regions with exposure concentrations, but their addition increases the robustness of the tail of the Exceedance Probability curve, meaning clients get a better view of the risk of the most damaging, but lower probability events.

Landsliding and Liquefaction

While most property damage has been caused directly by shaking, infrastructure has been heavily impacted by landsliding and, to a lesser extent, liquefaction. Landslides and slumps have occurred across the region, most notably over Highway 1, an arterial route. The infrastructure impacts of the Kaikoura earthquake are a likely dress rehearsal for the expected event on the Alpine Fault. This major fault runs 600 km along the western coast of the South Island and is expected to produce an Mw 8+ event with a probability of 30% in the next 50 years, according to GNS Science.

As many as 80 – 100,000 landslides have been reported in the upper South Island, with some creating temporary dams over rivers and in some cases temporary lakes (see below). These dams can fail catastrophically, sending a sudden increase of water flow down the river.



Examples of rivers blocked by landslides photographed by GNS Science researchers.

Source: Landslides+and+Landslide+dams+caused +by+the+Kaikoura+Earthquake









Liquefaction occurred in discrete areas across the region impacted by the Kaikoura earthquake. The Port of Wellington experienced both lateral and vertical deformation likely due to liquefaction processes in reclaimed land. There have been reports of liquefaction near the upper South Island towns (Blenheim, Seddon, Ward), but liquefaction will not be a driver of loss in the Kaikoura event to the extent it was in the Christchurch earthquake sequence.

RMS’ New Zealand Earthquake HD Model includes a new liquefaction component that was derived using the immense amount of new borehole data collected after the Canterbury Earthquake Sequence in 2010-2011. This new methodology considers additional parameters, such as depth to the groundwater table and soil-strength characteristics, that lead to better estimates of lateral and vertical displacement. The HD model is the first probabilistic model with a landslide susceptibility component for New Zealand.


The Kaikoura Earthquake generated tsunami waves that were observed in Kaikoura at 2.5m, Christchurch at 1m, and Wellington at 0.5m. The tsunami waves arrived in Kaikoura significantly earlier than in Christchurch and Wellington indicating that the tsunami was generated near Kaikoura. The waves were likely generated by offshore faulting, but also may be associated with submarine landsliding. Fortunately, the scale of the tsunami waves did not produce significant damage. RMS’ latest New Zealand Earthquake HD Model captures tsunami risk due to local ocean bottom deformation caused by fault rupture, and is the first model in the New Zealand market to do this, that is built from a fully hydrodynamic model.

Next Generation Earthquake Modeling at RMS

Thankfully the Kaikoura earthquake seems to have produced damage that is lower than we might have seen had it hit a more heavily populated area of New Zealand with greater exposures – for detail on damage please see my other blog on this event.

But what Kaikoura has told us is that our latest HD model offers an advanced view of risk. Released only in September 2016, it was designed to handle such a complex event as the Kaikoura earthquake, featuring multiple-segment ruptures, a new liquefaction model at very high resolution, and the first landslide susceptibility model for New Zealand.