ClimateCast US Hurricane Risk Index - ALERT

Transcription

ClimateCast® U.S. Hurricane Risk Index Technical Description
June 2012
ClimateCast U.S. Hurricane Risk Index - Technical Description
Copyright
2011 AIR Worldwide. All rights reserved.
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Revision History
Revision Date
Description
September 2011
Initial release.
June 2012
2012 season update.
2
Contact Information
If you need assistance in understanding the information in this document, or in using the software, please contact
AIR Worldwide.
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3
Table of Contents
1
Overview ........................................................................................................................................................... 5
Customization of the AIR U.S. Hurricane Risk Index for Individual Portfolios or Properties ............................. 6
2
Ensemble Models ............................................................................................................................................. 7
3
Model and Forecast Reliability ....................................................................................................................... 9
4
Using Operational Models to Generate Real-Time Loss Scenarios .......................................................... 12
5
Assessing the Realism of Stochastically Simulated Tracks and Intensities ........................................... 15
6
Modeling the Risk ........................................................................................................................................... 18
7
Calculating the U.S. Hurricane Risk Index................................................................................................... 20
8
ClimateCast U.S. Hurricane Risk Index Graphical Display ........................................................................ 25
Index Summary (Upper Left Panel) .................................................................................................................. 26
U.S. Landfall Probability (Upper Right Panel) .................................................................................................. 27
Index Seasonal History (Lower Left Panel) ...................................................................................................... 27
Index History over the Most Recent Two Weeks (Lower Right Panel) ............................................................ 28
9
Summary ......................................................................................................................................................... 29
Contact the ClimateCast Team ........................................................................................................................ 30
About AIR Worldwide ............................................................................................................................................. 31
AIR Client Confidential
4
1
Overview
®
AIR’s ClimateCast service provides continuous real-time risk analyses of active tropical cyclone
activity in the Atlantic basin. ClimateCast can be accessed via the web from AIR’s ALERT homepage
at http://alert.air-worldwide.com/.
This web-based tool provides two analyses:

ClimateCast Atlantic Hurricane Conditions

ClimateCast U.S. Hurricane Risk Index
The ClimateCast Atlantic Hurricane Conditions analysis depicts the current environmental factors
that influence the development, movement, and potential landfall of active tropical cyclones in the
basin. The ClimateCast U.S. Hurricane Risk Index analysis provides real-time assessment of
property and casualty risk to the U.S. Potential risk is captured in the form of insured industry loss
that is indexed against the AIR U.S. Hurricane Model’s exceedance probability (EP) curve. The higher
the risk index, the higher the loss potential to onshore properties in the U.S. from active storms in the
Atlantic basin.
In order to generate the U.S. Hurricane Risk Index, AIR makes use of forecasts from “operational
forecast models.” These state-of-the-art models, some dynamical and others statistical, project the
movement and intensity of active tropical cyclones. To ensure that the index is robust, an “ensemble”
of operational model projections is used. These are then augmented through the application of
stochastic modeling techniques to achieve a final ensemble of 500 unique scenarios that describe a
more complete spectrum of damage scenarios and potential for loss. The ensemble forecast—and
the risk index—is updated once every 6 hours, thereby producing four risk assessments per day.
This document describes in detail the process by which forecast data are collected, processed, and
used to generate the ClimateCast U.S. Hurricane Risk Index.
Note
The ClimateCast U.S. Hurricane Risk Index is a new AIR product not meant to replace AIR’s “Loss
Estimates in Real-Time” (ALERT™) service. Post-landfall estimates issued through ALERT reflect
observed damage data, observed wind speeds, and anecdotal information from the field, all used to
improve and refine the modeled loss estimates once actual damage has occurred.
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Customization of the AIR U.S. Hurricane Risk Index for
Individual Portfolios or Properties
In the same way that the risk index is related to U.S. industry loss—by running the simulated
scenarios through the AIR U.S. Hurricane Model and against the U.S. Industry Exposure Database—
the system can be customized to evaluate real-time risk for individual portfolios, or even for a highvalue individual location such as an industrial facility. For current users of CATRADER and CLASIC/2
software, both of these steps are straightforward.
If you are interested in learning more about a customized version of ClimateCast, please contact your
AIR account executive directly, or send your request to [email protected].
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2
Ensemble Models
ClimateCast continuously processes the latest observational and forecast data, including up-to-date
forecasts for active tropical cyclones at all stages of their development, from tropical depression to
major hurricane. For each active tropical cyclone, up to 50 or more individual forecasts are collected
from a variety of modeling organizations.
Table 1 identifies some of the more widely known models and their attributes, such as the
organizations that run and maintain the models and the coverage/types of forecasts they provide. For
a more detailed information on the full suite of operational hurricane forecast models, visit:

http://www.srh.noaa.gov/ssd/nwpmodel/html/nhcmodel.htm

http://www.nhc.noaa.gov/modelsummary.shtml
Table 1
Commonly Known Atlantic Hurricane Forecast Models and Their Attributes
Model
Full Name
Organization
Type
GFDL
Geophysical Fluid Dynamics Lab
GFDL Laboratory, NOAA
Global Dynamical
GFS
Global Forecast System
National Centers for
Environmental Prediction
(NCEP)
Global Dynamical
HWRF
Hurricane Weather and
Research Forecast model
National Center for
Atmospheric Research
(NCAR)
Regional Dynamical
NOGAPS Navy Operational Global
Atmospheric Prediction System
U.S. Navy Operations
(U.S. Dept. of Defense)
Global Dynamical
UKMET
United Kingdom Meteorological
Office modeling system
The Met Office (UK)
Global Dynamical
SHIPS
Statistical Hurricane Intensity
Prediction System
National Hurricane Center
(NHC/NOAA)
Statistical/Dynamical
Intensity
BAMM
Beta and Advection Model
(Medium Layer)
National Hurricane Center
(NHC/NOAA)
Single Layer trajectory
(Track Only)
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Note that some models are dynamical (numerical), others are statistical, and still others are hybrids
(statistical-dynamical). Some models predict track only (trajectory) and others predict intensity, but
most models—especially the dynamical ones—predict both.
Typically, as storms intensify through the depression stage (winds <39mph) to the tropical storm
stage (winds <74mph) and then to the hurricane stage (winds >74 mph), more model forecasts
become available for evaluation. This is beneficial because as storms become more threatening—
and the risk of loss rises—the number of individual model forecasts contained within the ensemble
increases, thereby allowing for improved quantification of forecast uncertainty. The manner in which
the U.S. Hurricane Risk Index accounts for this uncertainty is discussed in detail in Chapter 7 .
Every six hours, the operational forecast models are re-initialized with the latest environmental
conditions and then run forward in time for five to ten days—or until the model forecasts that the
tropical cyclone will dissipate. Models are initialized and run each day at 0:00, 6:00, 12:00, and 18:00
Coordinated Universal Time (abbreviated UTC, Zulu or Z), or as meteorologists refer to them: 0Z, 6Z,
12Z, and 18Z. These times correspond to the deployment around the world of weather balloons and
the collection of other vital observation data, which are necessary for the initialization of the models
and their ability to produce accurate forecasts. Some of the models also incorporate data collected by
reconnaissance aircraft, popularly known as “Hurricane Hunters,” that circle and penetrate active
storms. (For more information, visit http://www.nhc.noaa.gov/hunters.shtml.)
Figure 1 is an example of one operational model ensemble forecast for Hurricane Ivan, the ninth
th
named storm of the 2004 season; it is based on a model initialization time of September 13 at 18:00
UTC (18Z). Yellow and red colors indicate forecast intensity at hurricane strength along the projected
track.
Figure 1
Operational Forecast Ensemble for Hurricane Ivan (2004)
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3
Model and Forecast Reliability
The dependability of operational forecast models is of central concern to decision makers who must
work with them. Are the models reliable? Are they becoming more accurate? And what are their
strengths and weaknesses?
The forecasts of two fundamental parameters are critical to estimating a hurricane’s impact: forecast
track and forecast intensity. In general, forecast movement has been quite accurate (skillful) because
storm track is highly correlated with large-scale atmospheric airflow several kilometers above the
surface. Such large-scale flow can be forecast accurately by “general circulation models” (GCMs);
consequently, the tracks of storms can also be forecast with good accuracy. The correlation,
however, is not 100%. There are situations in which even a large ensemble of track forecasts is not
able to anticipate the movement of a tropical cyclone.
An example is shown in Figure 2. In August 2006, Tropical Storm Ernesto was situated south of Haiti
and moving to the northwest. The track forecast models projected that the storm would enter the Gulf
of Mexico over the course of the next few days. The “cone of uncertainty” (COU) issued by the
National Hurricane Center (NHC) at the time is also shown. The COU takes into account the
envelope of forecasts and also historical error in track forecasts as a function of forecast time.
According to the NHC, the COU captures the 67% confidence interval around the consensus (or
expected) track. Thus, actual tracks would be expected to deviate from the COU only about 33% of
the time. An advantage of relying more heavily on the forecast ensemble than on the COU is that the
ensemble spread changes from one forecast to the next, while, because the COU depends on
historical experience, it remains the same width regardless of current environmental conditions.
Sometimes the ensemble will be completely contained within the COU; other times it will extend
outside of it.
Figure 2 presents the operational forecast ensemble for Tropical Storm Ernesto (2006). Colored lines
indicate track forecasts from a variety of operational models; the white dotted line indicates the
observed track. Overlaid is the National Hurricane Center’s “cone of uncertainty”. Note that the
operational ensemble of tracks and the NHC forecast correlate well, but neither accurately projected
Ernesto’s movement at the time. Both the ensemble and the COU projected that Ernesto would enter
the Gulf—while the storm actually took a more northerly route toward south Florida and the U.S. East
Coast. The cause was an abrupt change in the steering currents within the early part of the forecast.
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Figure 2
Operational Forecast Ensemble for Tropical Storm Ernesto (2006)
Because of improvements in dynamical and statistical modeling techniques and the introduction of
new and improved models to the ensemble, this type of storm track “model miss” is becoming
increasingly rare.
On the other hand, storm intensity—which is the single most important hazard characteristic for
estimating loss—is much more challenging to forecast. This is because intensity develops at the
“mesoscale,” meaning that it is related primarily to local eddies of warmth in the ocean and moisture
in the atmosphere and to shorter timescales (during which wind shear and atmospheric instability
change), while the steering currents that affect storm tracks are large-scale (or climate-level)
phenomena. Thus, intensity changes can be sudden and difficult to predict, which is one reason why
Hurricane Katrina’s rapid intensification over the Gulf prior to making its second landfall was largely
missed by the forecast models.
It is important to note, however, that the operational models for both track and intensity forecasts are
improving. In the last decade in particular, significant strides have been made in forecast research
and model validation that have led to improvements in both the dynamical and statistical approaches
to forecasting.
Figure 3 shows a quantitative assessment of skill (accuracy) in track forecasts during the period from
1970 to 2010 (the top graph), and a similar assessment of intensity forecasts (bottom graph) during
the period from 1990 to 2010. Note the substantial improvement in track forecasts. While in the 1970s
the average 2-day track error (green line) was about 300 miles, today it has been reduced to less
than 100 miles. Longer-range forecasts of 5 to 7 days also have seen substantial improvement. The
5-day forecast was officially introduced in 2001, and since then it has also seen reduced error. In fact,
the average error in 5-day forecasts today is about the same as the average error in 2-day forecasts
in the late 1980s.
The performance is different for intensity forecasts. Although there has been gradual improvement, it
is not as prominent as with track forecasts. Twenty-four-hour forecasts verify, on average, to within
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about 10 knots (about 12 miles per hour) of the actual wind speed, while 48- to 120-hour forecasts
verify to within 15 to 20 knots (18 to 23 miles per hour). Even so, and especially for longer-term
projections, intensity forecasts are improving. There is still a long way to go, and given the sensitivity
of losses to wind intensity, especially for major hurricane landfalls, even marginal improvement will
translate into significantly better risk management in real time.
The good news is that forecasts—even those for five or more days ahead—show real skill compared
to long-term climatology. Stated differently, there is an advantage to using model projections in real
time as opposed to relying solely on historical experience. Historical experience simply points to
where storms from certain parts of the Atlantic tend to track, on average, and to what degree, on
average, storms have historically intensified before landfall and dissipated after landfall.
Finally, by taking advantage of a large number of forecast models, skill can be gained by applying the
forecasts probabilistically. That is, by “diversifying the portfolio” of model forecasts, the risk that all of
them are wrong is greatly reduced.
Figure 3
Official Skill Levels of Atlantic Track (left) and Intensity (right) Forecasts from NHC
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4
Using Operational Models to
Generate Real-Time Loss Scenarios
In the course of each six-hour forecast cycle, once the operational model forecasts are collected and
checked for quality and consistency, ClimateCast supplements the operational track and intensity
estimates with a stochastically simulated catalog, or event set, of scenarios. This simulated event set
begins with the operational model forecasts and then, by employing a statistical sampling technique,
the catalog is augmented to generate a final event set comprising a total of 500 unique scenarios.
Thus, each real-time ClimateCast risk assessment is based on a 500-member ensemble that is made
up of a combination of operational scenarios and simulated scenarios.
The sampling technique involves two basic operations: one involving track, the other involving
intensity.
A single track is simulated as follows: First, two tracks are randomly drawn from the full sample of
operational forecasts. Next, a random number is drawn from a uniform distribution (min=0.0,
max=1.0), and is used to “blend” the two drawn tracks at each time-referenced point in the track. The
operational models supply 3-hourly, 6-hourly or 12-hourly points, from which hourly points are linearly
interpolated. This process is repeated until a total of 500 tracks are populated in the final event set.
For example, two tracks are randomly drawn from the Hurricane Ivan operational model ensemble
shown in Figure 1—one track from the GFS model and the other from the BAMM model. The tracks
are blended as just described—based (in this example) on a random blending draw of 0.500—to
produce the simulated track (in red) shown in Figure 4. Specifically, Figure 4 shows the two
operational forecast tracks (in black), which are then blended 50/50 to simulate a third potential track
(in red). The simulated track is not found in the original set of scenarios, but is a plausible path for the
storm since it will fall within the envelope of the operational ensemble.
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Figure 4
Example of a ClimateCast Simulated Track for Hurricane Ivan
This process is repeated until a 500-member scenario set is generated for complete coverage.
Because there were 46 members of the operational ensemble at this forecast cycle, an additional 454
simulated members would be generated to complete the set. The full 500-member simulated
ensemble that corresponds to the operational forecast ensemble for Ivan is shown in Figure 5.
Because the blending process draws solely from the operational set of tracks, all simulated tracks fall
within the envelope of the operational tracks. The simulated scenario set in Figure 5 is valid for the
same date and time as in Figure 1 and Figure 4; it is made up of 46 operational model forecasts and
454 stochastically simulated scenarios, as previously mentioned.
Figure 5
ClimateCast Simulated Ensemble for Hurricane Ivan (500 Tracks)
For each simulated track, intensity is estimated at one-hour intervals. The intensity at a single track
point is determined by following three steps. First, all operational wind speed forecasts (expressed as
10-meter, 1-minute sustained winds) at the desired forecast hour are collected. Second, using an
inverse distance-weighting algorithm, the wind intensity is estimated at the point on the simulated
track. For example, to make a wind speed forecast six hours ahead on the simulated track, the
intensity estimates from all the models at the six hour forecast time are gathered as a distribution,
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then weighted on the proximity of each intensity point forecast to the simulated track point at that
forecast time. In effect, this amounts to a two-dimensional spatial interpolation of operational
forecasts to the simulated track point.
The third and final step involves an “intensity perturbation draw.” A value is drawn from a normal
1
distribution (µ=0.0,σ=1.0) , and this value (also termed a “standard score” or “z-score”) is used to
determine how many standard deviations (from the operational intensity distribution) are to be used to
perturb the previously estimated intensity. For example, if (1) the initial (inverse distance-weighted)
intensity estimate is 75.0 mph, (2) the standard deviation of wind intensity derived from the
operational ensemble at that forecast time is 20.0 mph, and (3) the drawn perturbation value is 0.500, then the simulated intensity for that track point at that forecast time will be set to 75.0 + (-0.500
X 20.0) or 65.0 mph.
One intensity perturbation draw (z-score) is made per simulated scenario. This assures coherence in
the evolution of intensity along each track. In the example of the previous paragraph, the drawn zscore of “-0.500” is applied throughout the simulated track. This draw reduces the simulated intensity
relative to the spatially varying mean value based on the operational intensity distributions. When
repeated numerous times, the result is an implicit bootstrapping of the spatial intensity field and
variation levels that are consistent with those of the original ensemble.
Figure 5 shows the result of such track and intensity simulation for the full 500-member scenario set
based on the operational forecast ensemble shown in Figure 1.
1
In this version of the simulation algorithm, the residual distribution is assumed to be a normal distribution, but since this is often not the case,
future versions will allow for more flexibility.
14
5
Assessing the Realism of
Stochastically Simulated Tracks and
Intensities
Using the same historical example of Hurricane Ivan and the operational forecast cycle of September
13 at 18Z (the same time illustrated in Figure 1), Figure 6 compares the NHC‘s “cone of uncertainty”
to the operational forecast ensemble and the simulated-event set based on the track and intensity
generation procedure described previously. The left panel shows a range of expected tracks from the
National Hurricane Center; the middle panel shows the operational ensemble of individual model
forecasts; and the right panel shows the ClimateCast simulated ensemble.
Figure 6
Comparing Hurricane Tracks: NHC, Operational Ensemble, ClimateCast
15
The intensity perturbation draw described in Chapter 4 is validated against the National Hurricane
Center forecast intensity probability table. Figure 7 compares the NHC probability at different intensity
levels and forecast times with the same statistics developed from the ClimateCast ensemble. To
ensure consistency, wind speeds from the simulated ensemble were binned to the Saffir-Simpson
scale.
Figure 7
Comparing Intensity Forecast for Hurricane Ivan
Note the statistics derived from the stochastically expanded set of scenarios in ClimateCast are
consistent with the NHC official forecast intensity table. This assures that the perturbations applied to
the intensity of each simulated track are in line with the ensemble distribution of intensity as well as
projections made by NHC forecasters.
Importantly, the ClimateCast scenario set has several distinct advantages over both the NHC forecast
and the raw set of ensemble forecasts. Specifically, the ClimateCast forecast is:
1. Robust. Provides a more complete range of landfall locations and corresponding landfall
intensities.
2. Precise. Simulates storms on an hourly time interval (as opposed to 3-hourly or 6-hourly).
3. Complete. Captures track and intensity at times other than the standard NHC forecasted lead
times (12-hour, 24-hour, 36-hour, 72-hour, etc.).
16
4. Consistent. Simulates tracks and intensities that reflect forecasted environmental conditions,
thus allowing for asymmetric or non-normal distributions (e.g., higher intensities right of the
central track).
5. Objective. Avoids assumptions or interpretation of the official NHC forecast, which often
incorporates forecaster subjectivity and conservatism (i.e., erring on higher wind speeds due to
NHC’s public warning function)
6. Continuous. Is available around the clock and throughout the hurricane season for continuous
assessment of the risk, unlike other risk estimation services.
The large number of simulated tracks and intensities that make up the 500-member scenario set
provides a robust view of risk for landfalling storms. Such a large sample of potential events is
necessary to achieve reliable loss estimates due to the spotty nature of coastal exposure and the
corresponding sensitivity of loss to landfall location and intensity, especially at high wind speeds.
17
6
Modeling the Risk
The ClimateCast U.S. Hurricane Risk Index is an evaluation of the threat of active storms in the
Atlantic to produce significant levels of insured loss to onshore properties in the U.S. To relate the
ensemble of forecasted tracks and intensities to their potential financial “risk”—namely, damage and
ultimately loss—a robust vulnerability and loss model must be used. AIR has more than 20 years of
experience in simulating the potential of hurricanes to cause wind and storm surge damage to literally
hundreds of combinations of building construction and occupancy types.
The ClimateCast U.S. Hurricane Risk Index leverages the latest version of the AIR Hurricane Model
for the United States to produce the most accurate and robust projections of risk to the U.S.
insurance industry. The model incorporates differences in building vulnerability between—and even
within—states, as well as over time, and thus captures the effects of evolving building codes (or their
absence). The model also captures the latest scientific research into wind field variation, inland
decay, inland re-intensification, local surface roughness conditions, and the importance of wind
duration (which can exacerbate damage from slow moving storms).
The loss estimation process involves three steps for each property location:
1. Estimate wind speed and surge height for the duration of the storm.
2. Estimate wind speed and surge mean damage ration for the building and its contents. The
mean damage ratio is the ratio of the repair (or replacement) cost to the replacement value.
3. Estimate ground up loss, and then after applying policy conditions, insured loss.
Wind speed and surge height at any given location are estimated using a coupled physically based
module that computes wind speed based on a location’s distance to a storm’s center while also taking
into account storm intensity, size, and other storm characteristics, as well as local conditions (e.g.,
surface roughness). The model then uses the wind estimate together with local bathymetry and other
surge parameters to estimate surge height.
The locally estimated wind and surge intensities are then used to determine a mean damage ratio.
Unique “damage functions” for both wind and surge are based on property characteristics such as
construction, occupancy, height, building age, and similar factors.
Insured loss estimates for buildings, contents, and time element coverage are computed using the
AIR U.S. Industry Exposure Database, which reflects the total value of all insured residential,
commercial, industrial, and automotive lines of business. Once damage and loss are computed at the
18
local scale (ZIP Code level for industry estimates), it is rolled up to the county, state, and countrywide
level, and then reported back to ClimateCast for each active storm in the basin.
Note
The total loss as calculated by the ClimateCast U.S. Hurricane Risk Index reflects AIR's view that
insurers will ultimately pay 10% of modeled storm surge damage as wind losses. For commercial
lines, a 10% take-up rate for flood policies is assumed. Note, too, that in the current initial release,
the total loss does not include demand surge, which is the increase in costs of materials, services,
and labor due to increased demand following a catastrophic event.
19
7
Calculating the U.S. Hurricane Risk
Index
The loss estimation process described in Chapter 6 is carried out for each of the 500 unique
members of the ClimateCast ensemble. The outcome is a large probabilistic loss distribution that can
be used to assess expected loss (median and mean)—as well as the uncertainty associated with that
loss.
Note
The method used to evaluate the real-time loss distribution is consistent with—although not a
substitute for—AIR’s ALERT process, which also uses a 500-member event set to estimate a 90%
confidence interval.
Based on the distribution from the full 500-member scenario set, losses are rank-ordered to
th
th
th
determine the median value (50 percentile) and the 5 and 95 percentile values. The mean of the
distribution is also computed. These four statistics provide a useful view of the overall risk, with the
th
th
mean and median providing guidance on the range of expected risk, and the 5 and 95 percentile
values providing an estimate of uncertainty. Because loss distributions for landfalling hurricanes tend
to be highly skewed, the mean is typically higher than the median, and has much more relative weight
in the right (high loss) tail. Since high loss “tail events” can have a great deal of influence on the mean
loss but little influence on the median in a sample as large as 500 scenarios, we find that both metrics
are very useful.
Note
In any skewed distribution, the mean and the median values are not equal. Loss distributions for U.S.
hurricane landfalls often exhibit “negative skew” by intensity (long left tail, indicating weaker
landfalls), and a “positive skew” by loss (long right tail, indicating a greater weighting of the mean by
th
high loss events). Therefore, in the typical loss distribution, the 5 percentile value is often a minimal
th
or zero loss, and the median or 50 percentile loss is often well below the mean. The right tail of the
th
loss distribution, as indicated by the 95 percentile value, has a great deal of influence on the mean
(or average) value, but less on the median, which is a single member of the large distribution. As a
result, both the median (equal probability of seeing a higher loss as a lower one) and the mean (the
true expected value of the distribution, assuming each event is equally probable) provide useful
th
guidance in the ClimateCast risk index. The 5 percentile value is not displayed, as it is often zero;
th
and the 95 percentile value provides useful guidance both for taking into account uncertainty in the
forecast and when a “worst case scenario” influences decision making.
20
Continuing with the example of Hurricane Ivan (2004) on September 13 at 18Z, the loss distribution is
quite wide given that the storm is still well offshore and still several days from making landfall; the
th
dispersion of the forecast tracks and intensities tell the story. The mean index value is 3.1, with a 50
th
and 95 percentile value of 1.7 and 6.3, respectively. The final observed loss for Ivan (in 2011
dollars) is estimated by the AIR U.S. Hurricane Model to have an index value of 2.9. Thus, although
the distribution is wide, the portion of that distribution between the median and mean (1.7 to 2.9) still
provides useful guidance. More on the Ivan case will be discussed later.
th
th
In general, the confidence band (5 to 95 percentile loss) will tend to tighten as storms approach
land because the range of potential landfall locations shrinks and the predicted landfall intensity
becomes clearer. One advantage of tracking risk in real time using ClimateCast is that when
forecasts change dramatically from one forecast cycle to the next, the user is immediately aware of
the implications to the risk level along with concurrent changes to the uncertainty level. In fact, the
ClimateCast risk index provides a view of risk consistent with the very latest forecast data as its
released, which can reduce the potential for unexpected outcomes. This is readily apparent for a
hurricane like Katrina (2005), which strengthened rapidly from a tropical storm to a major hurricane
after exiting the west coast of Florida, or for a hurricane like Charley (2004) which saw large changes
in the forecast track and intensity within just hours of landfall.
The values reported by ClimateCast are related to the risk metrics referred to earlier (mean and
median loss, and confidence interval) by converting the estimate of U.S. insured losses to an index
logarithmic scale (base = 1.585). Using the AIR U.S. Hurricane Model’s exceedance probability (EP)
curve, each loss is translated first to an exceedance probability, then to a return period (the reciprocal
of the EP), and then to the log scale index value.
Figure 8 relates exceedance probability and return period to the final index value. Based on a logscale, the values are associated with no risk (index = 0.0), moderate risk (index = 5.0) and extreme
risk (index = 10.0). Stated differently, an index value of 5.0 corresponds to a U.S. industry loss level
which is expected to recur once every 10 years, or to a loss level with an annual exceedance
probability of 10.0%. Note, index levels 5 and 10 are conveniently tied to 10.0% and 1.0% annual
exceedance probabilities respectively, and are derived from the AIR U.S. Hurricane Model. Note also
that while the upper end of the index is capped at 10.0 for display purposes, losses with exceedance
probabilities of less than 1.0% and corresponding risk index values exceeding 10.0 are possible.
Figure 8
ClimateCast U.S. Hurricane Risk Index Scale
21
The log scale was selected so that the index values of 0, 5, and 10 are anchored to easy-toremember return periods. Specifically, an index value of 0 corresponds to zero risk (no significant loss
potential); an index value of 5 corresponds to a 10-year return period loss, and an index value of 10
2
corresponds to a 100-year return period loss.
On most days during the Atlantic hurricane season, the value of the risk index will be zero—a result of
three possibilities: (1) there are no active storms in the basin; (2) any active storms are not
threatening the U.S; or (3) those storms that are threatening to make landfall in the U.S. are forecast
to be too weak at landfall to produce significant loss.
To give a sense of the risk level associated with historical storms were they to recur today, Table 2
shows the ClimateCast U.S. Hurricane Risk Index value for several notable events. According to the
AIR U.S. Hurricane Model, the largest loss from an historical event since 1900, were it to recur today,
would come from the Great Miami Hurricane of 1926. Based on current industry exposure
information, this storm would produce a U.S. Hurricane Risk Index value of 11.6. Since this value
exceeds 10.0, it would be displayed as 10.0. Other recent and notable storms are shown in the table.
Note also that one would expect only one storm in the last 100 years with an index of 10.0 or greater,
given that it reflects an extreme loss level corresponding to a return period of 100 years. Similarly,
one would expect about 10 storms in the last 100 years to have an index of 5.0 or greater since this
index level corresponds to a return period of 10 years.
Table 2
ClimateCast U.S. Hurricane Risk Index for Selected Historical Storms
U.S. Landfalling
Hurricane
ClimateCast U.S.
Hurricane Risk Index
1900 Galveston
6.8
1926 Miami
11.6
1928 Okeechobee
7.7
1954 Hazel
4.7
1969 Camille
3.4
1992 Andrew
8.1
2004 Charley
2.9
2005 Katrina
6.9
2005 Wilma
4.0
2008 Ike
3.6
The index scale was designed for convenience. Because the index value is related to the return
period of a particular loss, it is a true reflection of the current risk level (in relative terms). Once
ClimateCast users become familiar with the scale, the index can be used to conveniently evaluate the
level of risk today and how that risk has been changing over time for a single storm and over the
course of the entire season. Additionally, by using the risk index to make comparisons to key
historical events, the user gets an immediate sense of the potential impact of a recent storm. In this
2
These loss levels do not include demand surge.
22
way, the ClimateCast U.S. Hurricane Risk Index scale can be seen as an analog to the SaffirSimpson Hurricane Wind Scale; but rather than scaling storms based on wind intensity, the estimated
financial impact is scaled on U.S. industry insured loss.
To provide additional perspective on the progression of ClimateCast U.S. Hurricane Risk Index
values during the course of multiple storms, Figure 9 displays a time series of the index for the entire
2004 Atlantic hurricane season. Several significant storms—specifically Hurricane Ivan—are
highlighted chronologically. Hurricane Ivan made landfall on September 16. Several references have
been made in this document to Ivan on September 13 at 18Z, and that time is highlighted in both
panels by the dotted green line.
Figure 9 ClimateCast U.S. Hurricane Risk Index for the 2004 Atlantic Hurricane Season and Zoom-in of September 6th through
September 20th
In the seasonal time series in Figure 9 (top panel), index values derived from the forecast ensemble
(represented by the red line) are compared to index values for selected storms based on the Property
3
Claims Service (PCS) final insured loss estimates (represented by the black dots). The final
observed loss at the time of landfall is captured well. Note, too, that for each of these storms,
ClimateCast provides several days of “advance warning” of the potential for significant loss.
The two-week “zoom” (bottom panel) shows the index progression during the two-week period when
Hurricane Ivan was active. It indicates that the observed risk index for Ivan at the time of landfall on
September 16 is approximately 2.9. Note that this value is contained within the uncertainty band (gray
3
The PCS loss is brought to 2011 dollars using a 5% per annum inflation factor, and then converted to the ClimateCast U.S. Hurricane Risk
Index scale.
23
shaded area) for more than seven days prior to landfall; thus the index has done a good job of
anticipating the eventual level of realized loss. In addition, the mean index (in red) and the median
“index” (the lower bound of the gray uncertainty band) provide reasonable risk guidance throughout
the period.
The risk level for other storms is not anticipated so far in advance. For example, consider the
evolution of the index prior to the landfall of Hurricane Frances, which occurred just weeks earlier
(upper panel). There was a period of time—approximately one week before Frances’ landfall—when
the risk index indicates a much higher risk than was actually realized (compare the red line peak
around September 1 to the black dot at Frances’ landfall). This indicates two things: the forecast
models brought Frances onshore at a much higher intensity than was later observed, and that the
models projected Frances’ strongest wind speeds over higher exposure than the storm actually
encountered. In fact, on September 1 the National Hurricane Center’s cone of uncertainty included all
of the very high-value exposure in Miami-Dade county and also showed a 15% probability that
Frances would intensify to Category 4/5 strength. As actually developed, Hurricane Frances made
landfall well north of Miami as a much weaker Category 2 hurricane.
Thus, while the mean index value will not necessarily match the observed value many days in
advance of landfall, the confidence interval will very likely capture the potential risk—and often well in
advance, whether the expected (mean) risk is ultimately realized or not. The risk index is thus a true
reflection of risk in that it combines forecasted storm track and intensity with the potential exposure at
risk—and it does so dynamically, even as the model forecasts are changing. To the extent that the
forecasts are skillful, the risk index will reflect the risk appropriately. In this way, the index can be
viewed as a “common currency of real-time risk” for active storms, both in terms of its numerical value
and the manner in which it changes from day to day.
24
8
ClimateCast U.S. Hurricane Risk
Index Graphical Display
The ClimateCast U.S. Hurricane Risk Index provides an assessment of financial risk to U.S. insured
exposure from active tropical cyclones in the Atlantic basin. Potential risk is captured in the form of
insured loss that is indexed against the AIR U.S. Hurricane Model’s exceedance probability (EP)
curve. The higher the risk index, the higher the loss potential. This chapter provides descriptions of
each of four panels on the U.S. Hurricane Risk Index page of AIR’s ALERT website here:
http://alert.air-worldwide.com/RiskIndex.aspx.
These panels, as illustrated in Figure 10 showing 2011’s Hurricane Irene, include:

Index Summary

U.S. Landfall Probability

Index Seasonal History

Index History over the Last Two Weeks
Figure 10 ClimateCast U.S. Hurricane Risk Website Layout
25
Index Summary (Upper Left Panel)
This panel provides an “executive
summary” of current financial risk as
quantified by the latest available
ClimateCast U.S. Hurricane Risk
Index. The index is displayed to one
decimal precision (2.4, for example,
in the case of Hurricane Irene on
August 25, 2011), and is shown in
red in the upper left of this panel
(which includes a map of the Atlantic
basin and the modeled hurricane
states of the United States and
Mexico).
Just to the right and below the risk
index are two 24-hour metrics. The
first (to the right of the risk index) is a
trend indicator. An upward pointing
arrow indicates that the index is trending upward, while a downward pointing arrow indicates a
decreasing index; a horizontal line indicates no trend. The second (below the risk index) is the 24hour coefficient of variation (COV), which reflects swings, or volatility, in the index value over the
previous four 6-hourly forecast cycles. These possible swings are represented by five vertical bars,
indicating very low, low, medium, high, or very high variation. The current COV is indicated by a red
bar.
The COV and 24-hour trend indicators in the ClimateCast U.S. Hurricane Risk Index reflect changes
in the operational model forecasts over time, the changing probability of landfall as storms move
about, and the different exposures at risk as the forecasts change.
An index value of “0.0” indicates either that there are no active tropical cyclones in the Atlantic or that
active systems have little to no chance of making landfall at sufficient strength within the next seven
days to cause loss, according to the latest available forecast data. A value of “5.0” indicates that the
mean, or expected, loss corresponds to an annual exceedance probability for the U.S. of 10% (10year return period loss), while a value of “10.0” indicates that the expected loss corresponds to an
annual exceedance probability for the U.S. of 1% (100-year return period loss). Note that the return
periods are based on output from the AIR U.S. Hurricane Model.
This panel also displays the simulated scenario set for active storms in the basin. Tracks for each
scenario are color-coded according to their intensity (as indicated by a legend in the upper right of the
panel). When multiple storms threaten the U.S., the storm with the highest index value is displayed in
the panel.
26
U.S. Landfall Probability (Upper Right Panel)
This panel displays a landfall
probability map, which shows the
likelihood of a landfall occurring
along one of the 62 segments (each
50 nautical miles long) that comprise
the U.S. Gulf and eastern seaboard
coastlines.
Segments that are unaffected by
damaging winds from any of the
simulated scenarios are shown in
gray. Non-zero levels of landfall
probability are depicted in color. Line
segments colored purple, for
example, indicate the highest
probability of landfall, which is
defined as having at least 10% of the
scenarios reaching that segment with damaging winds of at least 40 mph (one-minute sustained). A
legend of the colors that indicate the different levels of probability is displayed in the upper right part
of the panel. In addition, the historical part of the track of the storm is shown.
Index Seasonal History (Lower Left Panel)
This panel shows the evolution of the
st
risk index starting from May 1 . The
blue line reflects the actual risk
index, which represents the mean
value of the distribution. The median
th
(50 percentile) of the distribution
represents the lower bound of the
gray shading in the figure, while the
th
95 percentile value represents the
upper bound of the gray shading.
The current position in the plot (in
time and in risk index value) is
marked by a red dot. The trend and
the coefficient of variation are
computed based on the evolution of
the risk index (the blue line) during
the most recent 24 hours.
The lower half of the Seasonal History panel lists the storm names designated for the current season.
The “Forecasted Index Values” of currently active storms are listed next to the storms’ names. Once a
storm has fully dissipated, a “Realized Risk Index Value” is also displayed, reflecting the level of
expected total loss.
27
Index History over the Most Recent Two Weeks (Lower Right
Panel)
This panel displays the index’s
continuously assessed risk level
during only the most recent twoweek period.
In its lower half, this panel also
displays the risk index at a more
granular level—that is, it reflects the
risk to specifically these more
narrowly defined geographic
regions: Florida, Gulf (TX, LA, MS,
AL), Southeast (GA, SC, NC),
Northeast (CT, ME, MA, NH, NY,
RI, VT, VA, DE, MD, PA, NJ), and
Inland (OK, AK, TN, KY, WV, OH,
IL, IN, MO).
28
9
Summary
AIR’s ClimateCast service provides continuous up-to-date risk assessment in two forms: (1) a
climatological assessment of the current conditions that are used to forecast the movement and
intensity of active tropical cyclones (http://alert.air-worldwide.com/ClimateCast.aspx), and; (2) a risk
assessment based on those forecasts that is used to evaluate the potential for U.S. insured losses
(http://alert.air-worldwide.com/RiskIndex.aspx). Both assessments are updated four times daily and,
together, develop a hurricane risk profile of the Atlantic basin and the U.S. coastline for the entire
hurricane season from June 1 through November 30.
By distilling the risk of insured loss into a return-period based scale that is indexed to the AIR U.S.
Hurricane Model’s exceedance probability (EP) curve, an analyst can quickly evaluate the level of
current risk relative to several useful benchmarks, such as significant historical seasons and events,
an earlier period in the present season, or the most recent two-week period. Analysts can apply
information derived from the full loss distribution to evaluate how uncertainty levels are changing in
time. In many situations, changes in forecasts from the operational models—which have been
initialized with the latest environmental conditions and reconnaissance observations—require a view
of risk that is frequently updated, especially when intense storms are approaching land. The high
sensitivity of damage to small increases in wind speed makes critical the need for a large robust set
of plausible scenarios.
By customizing the index to the EP curve of a particular portfolio, the risk to that portfolio can be
continually assessed in real-time. Customization can be made at various scales (e.g., regional, state,
county, high-value location) and for various risk metrics (e.g., actual loss levels, probability
thresholds, trends, volatility). The ClimateCast support team can be contacted for a complete list of
available customization and delivery options. Contact information is provided below.
Successful risk management is not a matter of simply managing expected levels of risk. Rather, it
must be balanced with an assessment of the degree of confidence one can place in estimates of risk.
ClimateCast can help risk managers in this task by providing guidance concerning the expected
levels of risk (for example, the mean and median index values), as well as guidance concerning the
th
th
current confidence level based on the full distribution of potential losses—that is, the 5 and 95
percentile values, or the range of loss from the full distribution of 500 events.
When it comes to real-time hurricane risk, both expectation and uncertainty levels can change quickly
and in unexpected ways. By following the risk index closely and continuously, it is possible to gain a
competitive edge over others who may simply assume that all forecasts are wrong (uncertainty is
29
infinite all the time) or that forecasts are bound to become more accurate as storms make landfall
(uncertainty decreases monotonically as storms approach land). In the first case, expected risk is
disregarded entirely, and in the second, risk is considered carefully only when large losses are
imminent or have already occurred. In both cases, faulty assumptions can lead to poor risk
management. The ClimateCast U.S. Hurricane Risk Index provides an objective and quantitative
means by which changes in expected risk and the corresponding level of uncertainty can be
assessed as they are happening.
Contact the ClimateCast Team
AIR is actively engaged in climate research tied not only to real-time risk assessment, but also to
other often-discussed topics such as the impact of warming ocean temperatures, climate change
projections, and the implications of climate change for risk management. This research will ultimately
lead to further enhancements and refinements in the ClimateCast suite of risk assessment tools. In
the end, it is feedback from users that guides the improvements that will make ClimateCast more
directly applicable to the catastrophe risk management process. We therefore encourage you to
contact AIR at [email protected] with comments and suggestions, and with thoughts
about how the tools can be used to improve day-to-day decision making.
If you have technical or business-related questions pertaining to this or other ClimateCast products,
please contact your AIR representative. You can learn more about AIR and its modeling and risk
assessment products and services by visiting our website at http://www.air-worldwide.com.
30
About AIR Worldwide
AIR Worldwide (AIR) is the scientific leader and most respected provider of risk modeling software
and consulting services. AIR founded the catastrophe modeling industry in 1987 and today models
the risk from natural catastrophes and terrorism in more than 90 countries. More than 400 insurance,
reinsurance, financial, corporate, and government clients rely on AIR software and services for
catastrophe risk management, insurance-linked securities, detailed site-specific wind and seismic
engineering analyses, and agricultural risk management. AIR is a member of the Verisk Insurance
Solutions group at Verisk Analytics (Nasdaq:VRSK) and is headquartered in Boston with additional
offices in North America, Europe, and Asia.
31

ClimateCast US Hurricane Risk Index - ALERT

Transcription

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