Tag Archives: North Atlantic Hurricane Models

New Florida Flood Modeling Standards Will Help Develop the Insurance Market

Hurricane Irma has placed flooding firmly back on the agenda for Florida. Irma did affect the entire state and flooding was widespread, affecting areas from Brickell in Miami’s financial district, to the northern counties. With dire storm surge forecasts predicted for the Gulf Coast, a new storm surge record was set in Jacksonville, but places such as Tampa, St. Petersburg, Naples, and Fort Myers experienced shallower flood depths than the predictions.

Florida homeowners are more aware of flood risk than most — and are well versed in buying flood insurance. For those who live in high-risk flood areas and have a mortgage from a federally regulated or insured lender, it is a mandatory requirement to purchase flood insurance from either the National Flood Insurance Program (NFIP) or alternatively through a private flood insurer.

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Hurricane Irma: The Exposure Variable

11:00 UTC  Thursday, September 7

Rhett Austell, director – Client Solutions, RMS

In the days leading up to landfall for a major hurricane such as Irma, you will find RMS employees and clients glued to their devices. We are all reading weather blogs, studying RMS HWind snapshots, monitoring Twitter, and sharing each other’s projections and observations on LinkedIn. This is all to get the latest view on a dynamic system – what is the maximum sustained wind? What is the Rmax? Central pressure? What is the integrated kinetic energy?

In such a dynamic situation, it is important to also consider what is static: the concentration of exposure within the hurricane uncertainty cone. In the most general sense, the industry insured loss for such an event is a function of the physical characteristics of the storm and the scale of exposure that is impacted. As has been stated elsewhere on the RMS blog, loss scenarios will vary significantly depending on the concentrations of exposure underlying the event footprint. For hurricanes, a few miles can be the difference between a footnote on a quarterly earnings statement or front page headlines. This was the story last year with Hurricane Matthew after it “wobbled” to the east and spared much of southeast Florida.

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Gearing Up for Irma: Using RMS Modeling to Generate Potential Loss Estimates

23:30 UTC  Wednesday, September 6

Tom Sabbatelli, hurricane risk expert, RMS

Hot on the heels of Hurricane Harvey, Irma looks like it could be the second major landfall in the U.S. this season, as it currently moves towards the Caribbean as a category 5 hurricane, with sustained winds around 185 miles per hour (297 kilometers per hour).

As always, the RMS Event Response starts early in the life of tropical storms, to provide the latest commentary, following up with RMS HWind footprints as data becomes available and providing initial sets of stochastic event selections around 48 hours before landfall. RMS Event Response practices have been designed to best serve our clients and the industry as a whole, and speculation of industry losses whilst such uncertainty remains can be counterproductive. Clients can see full information on the RMS Event Response processes by reading the following document available on RMS Owl.

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We’re Still All Wondering – Where Have All The Hurricanes Gone?

The last major hurricane to make landfall in the U.S. was Hurricane Wilma, which moved onshore at Cape Romano, Florida, as a Category 3 storm on October 24, 2005. Since then, a decade has passed without a single major U.S. hurricane landfall—eclipsing the old record of eight years (1860-1869) and sparking vigorous discussions amongst the scientific community on the state of the Atlantic Basin as a whole.

Research published in Geophysical Research Letters calls the past decade a “hurricane drought,” while RMS modelers point out that this most recent “quiet” period of hurricane activity exhibits different characteristics to past periods of low landfall frequency.

Unlike the last quiet period—between the late 1960s and early 1990s—the number of hurricanes forming during the last decade was above average, despite a below average landfall rate.

According to RMS Lead Modeler Jara Imbers, these two periods could be driven by different physical mechanisms, meaning the current period is not a drought in the strictest sense. Jara also contends that developing a solid understanding of the nature of the last ten years’ “drought” may require many more years of observations. This additional point of view from the scientific community highlights the ongoing uncertainty around governing Atlantic hurricane activity and tracks.

To provide our clients with a rolling five-year, forward-looking outlook of annual hurricane landfall frequency based on the current climate state, RMS issues the Medium-Term Rate (MTR), our reference view of hurricane landfall frequency. The MTR is a product of 13 individual forecast models, weighted according to the skill each demonstrates in predicting the historical time series of hurricane frequency.

Accounting for Cyclical Hurricane Behavior With Shift Models

Among the models contributing to the MTR forecast are “shift” models, which support the theory of cyclical hurricane frequency in the basin. This was recently highlighted by commentary published in the October 2015 edition of Nature Geosciences and in an associated blog post from the Capital Weather Gang, speculating whether or not the active period of Atlantic hurricane frequency, generally accepted as beginning in 1995, has drawn to a close. This work suggests that the Atlantic Multidecadal Oscillation (AMO), an index widely accepted as the driver of historically observed periods of higher and lower hurricane frequency, is entering a phase detrimental to Atlantic cyclogenesis.

Our latest model update for the RMS North Atlantic Hurricane Models advances the MTR methodology by considering that a shift in activity may have already occurred in the last few years, but was missed in the data. This possibility is driven by the uncertainty in identifying a recent shift point: the more time that passes after a shift and the more data that is added to the historical record, the more certain you become that it occurred.

The AMO has its principle expression in the North Atlantic sea surface temperatures (SST) on multidecadal scales. Generally, cool and warm phases last for up to 20-40 years at a time, with a difference of about 1°F between extremes. Sea level pressure and wind shear typically are reduced during positive phases of the AMO, the predominant phase experienced since the mid-1990s, supporting active periods of Atlantic tropical cyclone activity; conversely, pressure and shear typically increase during negative phases and suppress activity.

Monthly AMO index values, 1860-present. Positive (red) values correspond with active periods of Atlantic tropical cyclone activity, while negative (blue) values correspond with inactive periods. Source: NOAA ESRL

The various MTR “shift” models consider Atlantic multidecadal oscillations using two different approaches:

  • Firstly, North Atlantic Category 3-5 hurricane counts determine phases of high and low activity.
  • Secondly, the use of Atlantic Main Development Region (MDR) and Indo-Pacific SSTs (Figure 2) captures the impact of observed SST oscillations on hurricane activity.

As such, low Category 3-5 counts over many consecutive years and recent changes in the internal variability within the SST time series may point to a potential shift in the Atlantic Basin activity cycle.

The boundaries considered by RMS to define the Atlantic MDR (red box) and Indo-Pacific regions (white box) in medium-term rate modeling.

The “shift” models also consider the time since the last shift in activity. As the elapsed time since the last shift increases, the likelihood of a shift over the next few years also increases, which means it is more likely 20 years after a shift than two years after a shift.

Any uncertainty in tropical cyclone processes is considered through the “shift” models and the other RMS component models, based on competing theories related to historical and future states of hurricane frequency.

Given the interest of the market and the continuous influx of new science and seasonal data, RMS reviews its medium-term rates regularly to investigate whether this new information would contribute to a significant change in the forecast.

If we continue to observe below average tropical cyclone formation and landfall frequency, a shift in the multidecadal variability will become more evident, and the forecasts produced by the “shift” models will decrease. However, it is mandatory that the performance and contribution of these models relative to the other component models are considered before the final MTR forecast is determined.

This post was co-authored by Jeff Waters and Tom Sabbatelli. 

Tom Sabbatelli

Product Manager, Model Product Management, RMS
Tom is a Product Manager in the Model Product Management team, focusing on the North Atlantic Hurricane Model suite of products. He joined RMS in 2009 and spent several years in the Client Support Services organization, primarily providing specialist peril model support. Tom joined RMS upon completion of his B.S. and M.S. degrees in meteorology from The Pennsylvania State University, where he studied the statistical influence of climate state variables on tropical cyclone frequency. He is a member of the American Meteorological Society (AMS).

New Storms, New Insights: Two Years After Hurricane Sandy

When people think about the power of hurricanes, they imagine strong winds and flying debris. Wind damage will always result from hurricanes, but Hurricane Sandy highlighted the growing threat of storm surge as sea levels rise.

While Sandy’s hurricane-force winds were not unusual, the storm delivered an unprecedented storm surge to parts of the Mid-Atlantic and Northeast U.S. In total, Sandy caused insured losses of nearly $20 billion in the U.S., 65 percent of which resulted from surge-driven coastal flooding.

Considering the hazard and severity of the event, we used Sandy as the first real opportunity to validate our hydrodynamic storm surge model, which we released in 2011 and embedded in the RMS U.S. Hurricane Model. We verified the model against more than 300 independent wind and flood observations, the Federal Emergency Management Agency’s (FEMA) 100-year flood zones, and the FEMA best surge inundation footprint for New York City. The model captured the extent and severity of Sandy’s coastal flooding exceptionally well.

We also conducted extensive analysis of claims data from Sandy, which involved reviewing nearly $3 billion in location-level claims and exposure data across seven lines of business, provided by several companies. The purpose of the study was to deepen our understanding of the impacts of flooding on coastal exposures, particularly for commercial and industrial structures.

What struck us was how vulnerable buildings are to below-ground flooding. In many cases, damage to ground- and basement-level property and contents contributed a much higher proportion of the overall losses than expected, particularly for commercial structures in New York’s central business districts.

This insight has prompted us to improve the flexibility of how losses are modeled for contents and business interruption, specifically for basements. Early next year, we will release an update to our flagship North Atlantic Hurricane Models to provide the most-up-to-date view of hurricane risk with new vulnerability modeling capabilities based on insights gained from Sandy.

The model update includes new location-specific content triggers to enable users to make business interruption loss projections dependent on either contents or building damage, rather than on building damage alone. The model also allows users to assess the impact of multiple basement levels in a building, as well as the total value of contents stored within.

The claims data analysis also highlighted the importance of using high-resolution data to model high-gradient perils, such as coastal flooding. Flood losses are extremely sensitive to the locations of coastal exposures, as well as the surrounding topographical and bathymetrical features. Using high quality data with location-level specificity across a variety of building characteristics, as well as a high-resolution storm surge model that can accurately capture the flow of water around complex coastlines and local terrain, minimizes uncertainty.

At this time, RMS remains the only catastrophe modeling firm to integrate a hydrodynamic, time-stepping storm surge model into its hurricane models to represent the complex interactions of wind and water throughout a hurricane’s life-cycle, and we continue to implement lessons learned from new storms.