Author Archives: Kevin Van Leer

About Kevin Van Leer

Senior Product Manager, Model Product Management
As a senior product manager in the Model Product Management group at RMS, Kevin is responsible for RMS climate-peril products for the Americas, including wildfire and custom vulnerability analytics. Kevin has been actively involved in model releases for both severe convective storm and hurricane models over the last four years at RMS. Kevin holds a master’s degree in atmospheric science from the University of Illinois at Urbana-Champaign, where he authored a thesis on tornado-genesis and severe convective storms, and a bachelor’s degree in atmospheric science from Purdue University. He also holds the Certified Catastrophe Risk Analyst (CCRA) designation from RMS. Kevin is a member of the American Meteorological Society (AMS), a mentor for the AMS Board of Private Sector Meteorologists, and a voting member of the ASCE Standards Committee on Wind Speed Estimation in Tornadoes.

California Wildfires: Latest Loss Estimates

Kevin Van Leer, senior product manager – Model Product Management, RMS

20:00 UTC Tuesday, October 17

Nearly ten days have passed since the first four wildfires spread rapidly in Northern California. As of Monday, October 16, over 10,000 firefighters battled 14 fires, principally in the wine-growing valleys north of San Francisco. Fires in Napa, Solano, Sonoma, Mendocino, Lake, Yuba, Butte, Fresno, Calveras, Orange and Nevada counties have burned about 213,000 acres (86,000 hectares), destroying about 5,700 structures and forcing the evacuation of over 100,000 people, according to CAL FIRE and local officials. Aerial photographs show whole neighborhoods in northern Santa Rosa destroyed, and a neighborhood of about 70 houses has been destroyed in east Santa Rosa. Reports on Monday, October 16 state that there are 41 recorded fatalities, and hundreds of people are missing.

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Wine Country Wildfire Progression and Loss Estimate

Kevin Van Leer, senior product manager – Model Product Management, RMS

22:00 UTC Thursday, October 12

Image Credit: AP Photo/Rich Pedroncelli

After four days of active burning throughout the wine growing regions of Northern California, there are still eight fires threatening both lives and significant amounts of exposure. Conditions conducive to fire ignition and spread persist, and have resulted in extreme fire behavior, especially during the nights of Sunday, October 8, and Wednesday, October 11, in which strong, dry Diablo winds were observed. In total, over 100,000 acres have been burned in Napa, Sonoma, and Solano counties alone, with several additional fires ongoing throughout the state of California.

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Rapidly Spreading Wildfires Impact Northern California Wine Country

Kevin Van Leer, senior product manager – Model Product Management, RMS

17:00 UTC Tuesday, October 10

Figure 1: Neighborhood near Coffey Park in Santa Rosa, California. Image credit: Golden Gate California Highway Patrol

Driven by Diablo wind gusts of up to 70 miles per hour (112 kilometers per hour) and with very low relative humidity, 14 fires burning across swaths of eight Northern California counties have resulted in significant property damage and loss of life.

These strong winds caused the fires to spread quickly. The Tubbs Fire, located just north of Santa Rosa, grew from 200 acres on Sunday night (October 8) to over 20,000 acres by Monday morning (October 9) and is now over 27,000 acres. As of 4 p.m. Pacific Time (PT) on Monday, October 9, the Tubbs Fire together with the Atlas Peak Fire, located just north of Napa, combined have destroyed over 50,000 acres of land, and impacted several wineries along with high value residential and commercial structures. So far, 1,500 structures are reportedly destroyed, making this at least the fifth most destructive fire in California history as shown in the table below.

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Taking Advantage of Open Vulnerability Modeling

Competing in the insurance market through differentiation, and demonstrating knowledge and expertise to a client, are central to so many business strategies in this industry. The client values the insight an insurance business delivers on their exposure which is reflected in their premium. Sometimes, taking the regular model output view of risk is exactly what’s called for. But to demonstrate this differentiated offer, what about a view of risk for a specific class of buildings, or even just one building?

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“Super” El Niño – Fact vs. Fiction

The idea of a “super” El Niño has become a hot topic, with many weighing in. What’s drawing all of this attention is the forecast of an unusually warm phase of the El Niño Southern Oscillation (ENSO). Scientists believe that this forecasted El Niño phase could be the strongest since 1997, bringing intense weather this winter and into 2016.

Anomalies represent deviations from normal temperature values, with unusually warm temperatures shown in red and unusually cold anomalies shown in blue. Source: NOAA

It’s important to remember the disclaimer “could.” With all of the information out there I thought it was a good time to cull through the news and try to separate fact from fiction regarding a “super” El Niño. Here are some of the things that we know—and a few others that don’t pass muster.

Fact: El Niño patterns are strong this year

Forecasts and models show that El Niño is strengthening. Meteorologist Scott Sutherland wrote on The Weather Network that there is a 90 percent chance that El Niño conditions will persist through winter and an over 80 percent chance that it will still be active next April. Forecasts say El Niño will be significant, “with sea surface temperatures likely reaching at least 1.5oC (2.7oF) above normal in the Central Pacific – the same intensity as the 1986/87 El Niño (which, coincidentally also matches the overall pattern of this year’s El Niño development).”

A “strong” El Niño is identified when the Oceanic Niño Index (ONI), an index tracking the average sea surface temperature anomaly in the Niño 3.4 region of the Pacific Ocean over a three-month period, is above 1.5oC. A “super” El Niño, like the one seen in 1997/98, is associated with an ONI above 2.0oC. The ONI for the latest May-June-July period was recorded as 1.0oC, identifying El Niño conditions present as of “moderate” strength with the peak anomaly model forecast consensus around 2.0oC.

Fiction: A “super” El Niño is a cure-all for drought plaguing Western states

Not necessarily. The conventional wisdom is that a “super” El Niño means more rain for drought-ravaged California, and a potential end to water woes that have hurt the state’s economy and even made some consider relocation. But, we don’t know exactly how this El Niño will play out this winter.

Will it be the strongest on record? Will it be a drought buster?

Some reports suggest that a large pool of warm water on the northeast Pacific Ocean and a persistent high-pressure ridge over the West Coast of the U.S., driven by dry, hot conditions, could hamper drought-busting rain.

The Washington Post has a good story detailing why significant rain from a “super” El Niño might not pan out for the Golden State.

And if the rain does come, could it have devastating negative impacts? RMS’ own Matthew Nielsen recently wrote an article in Risk and Insurance regarding the potential flood and mudslide consequences of heavy rains during an El Niño.

Another important consideration is El Niño’s impact on the Sierra snow pack, a vital source for California’s water reserves. Significant uncertainty exists around when and where snow would fall, or even if the warm temperatures associated with El Niño would allow for measureable snow pack accumulation. Without the snow pack, the rainwater falling during an El Niño would only be a short-term fix for a long-term problem.

Fact: It’s too early to predict doomsday weather

There are a vast number of variables needed to produce intense rain, storms, flooding, and other severe weather patterns. El Niño is just one piece of the puzzle. As writer John Erdman notes on, “El Niño is not the sole driver of the atmosphere at any time. Day-to-day variability in the weather pattern, including blocking patterns, forcing from climate change and other factors all work together with El Niño to determine the overall weather experienced over the timeframe of a few months.”

Fiction: A “super” El Niño will cause a mini ice age

This theory has appeared around the Internet, on blogs and peppered in social media. While reported some similarities between ice age and El Niño weather patterns to an ice age more than a decade ago you can’t assume we’re closing in on another big chill. The El Niño cycle repeats every three to 10 years; shifts to an ice age occur over millennia.

What other Super El Niño predictions have you heard this year? Share and discuss in the comments section.

What is Catastrophe Modeling?

Anyone who works in a field as esoteric as catastrophe risk management knows the feeling of being at a cocktail party and having to explain what you do.

So what is catastrophe modeling anyway?

Catastrophe modeling allows insurers and reinsurers, financial institutions, corporations, and public agencies to evaluate and manage catastrophe risk from perils ranging from earthquakes and hurricanes to terrorism and pandemics.

Just because an event hasn’t occurred in that past doesn’t mean it can’t or won’t. A combination of science, technology, engineering knowledge, and statistical data is used to simulate the impacts of natural and manmade perils in terms of damage and loss. Through catastrophe modeling, RMS uses computing power to fill the gaps left in historical experience.

Models operate in two ways: probabilistically, to estimate the range of potential catastrophes and their corresponding losses, and deterministically, to estimate the losses from a single hypothetical or historical catastrophe.

Catastrophe Modeling: Four Modules

The basic framework for a catastrophe model consists of four components:

  • The Event Module incorporates data to generate thousands of stochastic, or representative, catastrophic events. Each kind of catastrophe has a method for calculating potential damages taking into account history, geography, geology, and, in cases such as terrorism, psychology.
  • The Hazard Module determines the level of physical hazard the simulated events would cause to a specific geographical area-at-risk, which affects the strength of the damage.
  • The Vulnerability Module assesses the degree to which structures, their contents, and other insured properties are likely to be damaged by the hazard. Because of the inherent uncertainty in how buildings respond to hazards, damage is described as an average. The vulnerability module offers unique damage curves for different areas, accounting for local architectural styles and building codes.
  • The Financial Module translates the expected physical damage into monetary loss; it takes the damage to a building and its contents and estimates who is responsible for paying. The results of that determination are then interpreted by the model user and applied to business decisions.

Analyzing the Data

Loss data, the output of the models, can then be queried to arrive at a wide variety of metrics, including:

  • Exceedance Probability (EP): EP is the probability that a loss will exceed a certain amount in a year. It is displayed as a curve, to illustrate the probability of exceeding a range of losses, with the losses (often in millions) running along the X-axis, and the exceedance probability running along the Y-axis.
  • Return Period Loss: Return periods provide another way to express exceedance probability. Rather than describing the probability of exceeding a given amount in a single year, return periods describe how many years might pass between times when such an amount might be exceeded. For example, a .4% probability of exceeding a loss amount in a year corresponds to a probability of exceeding that loss once every 250 years, or “a 250-year return period loss.”
  • Annual Average Loss (AAL): AAL is the average loss of all modeled events, weighted by their probability of annual occurrence. In an EP curve, AAL corresponds to the area underneath the curve, or the average expected losses that do not exceed the norm. Because of this, the AAL of two EP curves can be compared visually. AAL is additive, so it can be calculated based on a single damage curve, a group of damage curves, or the entire event set for a sub-peril or peril. It also provides a useful, normalized metric for comparing the risks of two or more perils, despite the fact that peril hazards are quantified using different metrics.
  • Coefficient of Variation (CV): The CV measures the size, or degree of variation, of each set of damage outcomes estimated in the vulnerability module. This is important because damage estimates with high variation, and therefore a high CV, will be more volatile than an estimate with a low CV. More often than not, a property will “behave” unexpectedly in the face of a given peril, if the property’s characteristics were modeled with high volatility data versus a data set with more predictable variation. Mathematically, the CV is the ratio of the standard deviation of the losses (or the “breadth” of variation in a set of possible damage outcomes) over the mean (or average) of the possible losses.

Catastrophe modeling is just one important component of a risk management strategy. Analysts use a blend of information to get the most complete picture possible so that insurance companies can determine how much loss they could sustain over a period of time, how to price products to balance market needs and potential costs, and how much risk they should transfer to reinsurance companies.

Catastrophe modeling allows the world to predict and mitigate damage resulting from the events. As models improve, so hopefully will our ability to face these catastrophes and minimize the negative effects in an efficient and less costly way.

Serial Clustering Activity around the Baja Peninsula during September 2014

In the past two weeks, two major hurricanes have impacted the Baja Peninsula in Mexico. Hurricane Norbert bypassed a large portion of the west coast of the peninsula from September 5 to 7, and Hurricane Odile made landfall near Cabo San Lucas on September 14th as a Category 3 hurricane on the Saffir-Simpson Wind Scale. A third system, Hurricane Polo, formed Tuesday, September 16 and is forecasted to follow a similar track to Norbert and Odile, making it the third such tropical cyclone to develop in the region since the beginning of the month.

This serial cluster of storms has been driven primarily by steady, favorable conditions for tropical cyclone development and consistent atmospheric patterns present over the Eastern Pacific. A serial cluster is defined as a set of storms that form in the same part of a basin, and subsequently follow one another in an unbroken sequence over a relatively short period of time. To qualify as a cluster, there needs to be measurable consistency between the tracks. This is typically a result of steady, predominant atmospheric steering currents, which play a major role in influencing the speed and direction of tropical cyclones. One example of a serial cluster is the four major hurricanes (Charley, Francis, Ivan, and Jeanne) that impacted Florida during a six-week period in 2004.

During this recent two-week period, the area off the west coast of Mexico has maintained high sea-surface temperatures near 85.1 degree Fahrenheit and limited vertical wind shear, leading to an active tropical development region. A mid-level atmospheric ridge over northern Mexico has provided a consistent steering pattern towards the north-northwest, producing similar observed tracks for Norbert and Odile and forecasted track for Polo. Devastating amounts of rainfall have occurred with these storms. Hurricane Odile dropped nearly 18 inches of rain in areas around Cabo San Lucas, representing nearly 21 months-worth of typical rainfall. This cluster, while generating significant wind and flood damage along the Baja Peninsula, has also caused torrential rainfall in the southwestern U.S., including Arizona, southern Nevada, and southern California. Last week, Phoenix, AZ, one of the hardest hit areas, experienced over 3 inches of rain in a 7 hour span due to the remnants of Hurricane Norbert. This was the most rainfall to occur in a 24-hour period in the city since 1911, an estimated 1-in-200 year event by the National Oceanic and Atmospheric Administration. Significant rainfall and inland flooding is forecast to continue as the remnants of Odile and Polo move inland, which may lead to widespread flood losses and the potential for compound post-event loss amplification.