Monthly Archives: October 2015

Harnessing Your Personal Seismometer to Measure the Size of An Earthquake

It’s not difficult to turn yourself into a personal seismometer to calculate the approximate magnitude of an earthquake that you experience. I have employed this technique myself when feeling the all too common earthquakes in Tokyo for example.

In fact, by this means scientists have been able to deduce the size of some earthquakes long before the earliest earthquake recordings. One key measure of the size of the November 1, 1755 Great Lisbon earthquake, for example, is based on what was reported by the “personal seismometers” of Lisbon.

Lisbon seen from the east during the earthquake. Exaggerated fires and damage effects. People fleeing in the foreground. (Copper engraving, Netherlands, 1756) – Image and caption from the National Information Service for Earthquake Engineering image library via UC Berkeley Seismology Laboratory

So How Do You Become a Seismometer?

As soon as you feel that unsettling earthquake vibration, your most important action to become a seismometer is immediately to note the time. When the vibrations have finally calmed down, check how much time has elapsed. Did the vibrations last for ten seconds, or maybe two minutes?

Now to calculate the size of the earthquake

The duration of the vibrations helps to estimate the fault length. Fault ruptures that generate earthquake vibrations typically break at a speed of about two kilometers per second. So, a 100km long fault that starts to break at one end will take 50 seconds to rupture. If the rupture spreads symmetrically from the middle of the fault, it could all be over in half that time.

The fastest body wave (push-pull) vibrations radiate away from the fault at about 5km/sec, while the slowest up and down and side to side surface waves travel at around 2km/second. We call the procession of vibrations radiating away from the fault the “wave-train.” The wave train comprises vibrations traveling at different speeds, like a crowd of people some of whom start off running while others are dawdling. As a result the wave-train of vibrations takes longer to pass the further you are from the fault—by around 30 seconds per 100km.

If you are very close to the fault, the direction of fault rupture can also be important for how long the vibrations last. Yet these subtleties are not so significant because there are such big differences in how the length of fault rupture varies with magnitude.

Magnitude

Fault Length Shaking duration

Mw 5

5km

2-3 seconds

Mw 6

15km

6-10 seconds

Mw 7

60km

20-40 seconds

Mw 8

200km

1-2 minutes

Mw 9 500km

3-5 minutes

Shaking intensity tells you the distance from the fault rupture

As you note the duration of the vibrations, also pay attention to the strength of the shaking.  For earthquakes above magnitude 6, this will tell you approximately how far you are away from the fault. If the most poorly constructed buildings are starting to disintegrate, then you are probably within 20-50km of the fault rupture; if the shaking feels like a long slow motion, you are at least 200km away.

Tsunami height confirms the magnitude of the earthquake

Tsunami height is also a good measure of the size of the earthquake. The tsunami is generated by the sudden change in the elevation of the sea floor that accompanies the fault rupture. And the overall volume of the displaced water will typically be a function of the area of the fault that ruptures and the displacement. There is even a “tsunami magnitude” based on the amplitude of the tsunami relative to distance from the fault source.

Estimating The Magnitude Of Lisbon 

We know from the level of damage in Lisbon caused by the 1755 earthquake that the city was probably less than 100km from the fault rupture. We also have consistent reports that the shaking in the city lasted six minutes, which means the actual duration of fault rupture was probably about four minutes long. This puts the earthquake into the “close to Mw9” range—the largest earthquake in Europe for the last 500 years.

The earthquake’s accompanying tsunami reached heights of 20 meters in the western Algarve, confirming the earthquake was in the Mw9 range.

Safety Comes First

Next time you feel an earthquake remember self-preservation should always come first. “Drop” (beneath a table or bed), “cover and hold” is good advice if you are in a well-constructed building.  If you are at the coast and feel an earthquake lasting more than a minute, you should immediately move to higher ground. Also, tsunamis can travel beyond where the earthquake is felt. If you ever see the sea slowly recede, then a tsunami is coming.

Let us know your experiences of earthquakes.

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).

What Is In Store For Europe Windstorm Activity This Winter

From tropical volcanoes to Arctic sea-ice, recent research has discovered a variety of sources of predictability for European winter wind climate. Based on this research, what are the indicators for winter storm damage this season?

The most notable forcings of winds this winter – the solar cycle and the Arctic sea-ice extents – are forcing in opposite directions. We are unsure which forcing will dominate, and the varying amplitude of these drivers over time confuses the situation further: the current solar cycle is much weaker than the past few, and big reductions in sea-ice extent have occurred over the past 20 or so years, as shown in the graph below.

Figure: Standardized anomalies of Arctic sea-ice extent over the past 50 years. (Source: NSIDC)

There are two additional sources of uncertainty, which further undermine predictive skill. First, researchers examine strength of time-mean westerly winds over 3-4 months, whereas storm damage is usually caused by a few, rare days of very strong wind. Second, storms are a chaotic weather process – a chance clash of very cold and warm air – which may happen even when climate drivers of storm activity suggest otherwise.

RMS has performed some preliminary research using storm damages, rather than time-mean westerlies, and we obtain a different picture for East Pacific El Niños. Most of them have elevated storm damage in the earlier half of the storm season (before mid-January) and less later on. Of special note are the two storms Lower Saxony in November 1972 and 87J in October 1987: the biggest autumn storms in the past few decades happened during East Pacific El Niños. The possibility that East Pacific El Niños alter the seasonality of storms, and perhaps raise the chances of very severe autumn storms, highlights potential gaps in our knowledge that compromise predictions.

We have progressed to the stage that reliable, informative forecasts could be issued on some occasions. For instance, large parts of Europe would be advised to prepare for more storm claims in the second winter after an explosive, sulphur-rich, tropical volcano. Especially if a Central Pacific La Niña is occurring [vi] and we are near the solar cycle peak.

However, the storm drivers this coming winter have mixed signals and we dare not issue a forecast. It will be interesting to see if there is more damage before rather than after mid-January, and whatever the outcome, we will have one more data point to improve forecasts of winter storm damage in the future.

Given the uncertainty in windstorm activity levels, any sophisticated catastrophe model should give the user the possibility of exploring different views around storm variability, such as the updated RMS Europe Windstorm Model, released in April this year.

[i] Fischer, E. et al. “European Climate Response to Tropical Volcanic Eruptions over the Last Half Millennium.” Geophys. Res. Lett. Geophysical Research Letters, 2007, .
[ii] Brugnara, Y., et al. “Influence of the Sunspot Cycle on the Northern Hemisphere Wintertime Circulation from Long Upper-air Data Sets.” Atmospheric Chemistry and Physics Atmos. Chem. Phys., 2013.
[iii] Graf, Hans-F., and Davide Zanchettin. “Central Pacific El Niño, the “subtropical Bridge,” and Eurasian Climate.” J. Geophys. Res. Journal of Geophysical Research, 2013.
[iv] Baldwin, M. P., et al. “The Quasi-Biennial Oscillation.” Reviews of Geophysics, 2001.
[v] Budikova, Dagmar. “Role of Arctic Sea Ice in Global Atmospheric Circulation: A Review.” Global and Planetary Change, 2009.
[vi] Zhang, Wenjun, et al. “Impacts of Two Types of La Niña on the NAO during Boreal Winter.” Climate Dynamics, 2014.

South Carolina Floods: The Science Behind the Event and What It Means for the Industry

South Carolina recently experienced one of the most widespread and intense multi-day rain events in the history of the Southeast, leaving the industry with plenty to ponder.

Parts of the state received upwards of 27 inches (686 mm) of rain in just a four day period, breaking many all-time records, particularly near Charleston and Columbia (Figure 1). According to the National Oceanic and Atmospheric Administration, rainfall totals surpassed those for a 1000-year return period event (15-20 inches (381-508 cm)) for parts of the region. As a reminder, a 1000-year return period means there is a 1 in 1000 chance (0.1%) of this type of event occurring in any year, as opposed to once every thousand years.


Figure 1: Preliminary radar-derived rainfall totals (inches), September 29-October 4. Source: National Weather Service Capital Hill Weather Gang.

The meteorology behind the event

As Hurricane Joaquin tracked north through the Atlantic, remaining well offshore, a separate non-tropical low pressure system positioned itself over the Southeast U.S. and essentially remained there for several days. A ridge of high pressure to the north acted to initiate strong onshore windflow and helped keep the low-pressure system in place. During this time, it drew in a continuous plume of tropical moisture from the tropical Atlantic Ocean, causing a conveyor belt of torrential rains and flooding throughout the state, from the coast to the southern Appalachians.

Given the fact that Joaquin was in the area, the system funneled moist outflow from it as well, enhancing the onshore moisture profile and compounding its effects. It also didn’t help that the region had experienced significant rainfall just a few days prior, creating near-saturated soil conditions, and thus, minimal absorption options for the impending rains.

It’s important to note that this rain event would have taken place regardless of Hurricane Joaquin. The storm simply amplified the amount of moisture being pushed onshore, as well as the corresponding impacts. For a more detailed breakdown of the event, please check out this Washington Post article.

Notable impacts and what it means for the industry

Given the scope and magnitude of the impacts thus far, it will likely be one of the most damaging U.S. natural catastrophes of 2015. Ultimately, this could be one of the most significant inland flooding events in recent U.S. history.

This event will undoubtedly trigger residential and commercial flood policies throughout the state. However, South Carolina has just 200,000 National Flood Insurance Program (NFIP) policies in place, most of which are concentrated along the coast, meaning that much of the residential losses are unlikely to be covered by insurance.


Figure 2: Aerial footage of damage from South Carolina floods. Source: NPR, SCETV.

Where do we go from here?

Similar to how Tropical Storm Bill reiterated the importance of capturing risk from tropical cyclone-induced rainfall, there is a lot to take away from the South Carolina floods.

First, this event underscores the need to capture interactions between non-tropical and tropical systems when determining the frequency, severity, and correlation of extreme precipitation events. This  combined with high resolution terrain data, high resolution rainfall runoff models, and sufficient model runtimes will optimize the accuracy and quality of both coastal and inland flood solutions.

Next, nearly 20 dams have been breached or failed thus far, stressing the importance of developing both defended and undefended views of inland flood risk. Understanding where and to what extent a flood-retention system, such as a dam or levee, might fail is just as imperative as knowing the likelihood of it remaining intact. It also highlights the need to monitor antecedent conditions in order to properly assess the full risk profile of a potential flood event.

The high economic-to-insured loss ratio that is likely to result from this event only serves to stress the need for more involvement by private (re)insurers in the flood insurance market. NFIP reform combined with the availability of more advanced flood analytics may help bridge that gap, but only time will tell.

Lastly, although individual events cannot be directly attributed to climate change, these floods will certainly fuel discussions about the role it has in shaping similar catastrophic occurrences. Did climate change amplify the effects of the flooding? If so, to what extent? Will tail flood events become more frequent and/or more intense in the future due to a rising sea levels, warming sea surface temperatures, and a more rapid hydrologic cycle? How will flood risk evolve with coastal population growth and the development of more water impermeable surfaces?

This event may leave the industry with more questions than answers, but one stands out above the rest: Are you asking the right questions to keep your head above water?