Transcript Atlas of Probable Storm Effects in the Caribbean Sea Sponsored by the Caribbean Disaster Mitigation Project Models and data output by Watson Technical Consulting, Inc. Editing and presentation.

Atlas
of
Probable Storm Effects
in the
Caribbean Sea
Sponsored by the
Caribbean Disaster Mitigation Project
Models and data output by Watson Technical Consulting, Inc.
Editing and presentation by Ross Wagenseil, Ph.D.
March 2000
The Caribbean Disaster Mitigation Project (CDMP) was a joint effort of the Organization of American States (OAS) and the US
Agency for International Development (USAID) to promote the adoption of natural disaster preparedness and loss reduction
practices by both the public and private sectors
The CDMP was started in 1993, and was completed in 1999. During the course of the project, country agencies responsible for
coastal planning and regional agencies such as the Caribbean Institute for Meteorology and Hydrology (CIMH) expressed
concern with the lack of data on the impact of tropical storms on coastal areas. In response to these concerns, the CDMP
developed a regional storm hazard assessment capacity, now installed at CIMH, and undertook a comprehensive study to
estimate the probable storm effects throughout the Caribbean basin.
Link to the World Wide Web
Caribbean Disaster Mitigation Project (CDMP)
Organization of American States (OAS)
US Agency for International Development (USAID)
or
Continue
on this
Disk
To Navigate Through this Atlas,
there are hyperlinks on each page.
On most pages you will see a button labeled “Return to Directory” to take you directly to the
Directory and Table of Contents. That is a page that links to all sections.
Return to Directory
Previous
slide
Back-track to
last slide viewed
(within the region)
Next
slide
You may also see green buttons which allow you to go back or forward in the slide sequence or
to back-track to the last slide viewed. These buttons are restricted to a particular section.
You may click to the next slide right now to see the Directory (with links to support materials), or
you may click on a key map, below, to pick a region.
10yr
Wind
Wave
Surge
25yr
50yr
100yr
When you jump to a new region, you will see an orientation map with a few place names. You will
also see a key pad like the one at left. Use the key pad to jump to another map for your current
region. You can select by the probable return time and by the phenomenon. For instance, if you
want to view the maps of wave heights with probable return times of 10, 25, 50, and 100 years, just
click along the second row, from left to right. Once you have a map displayed, the corresponding
button on the keypad is orange. (The keypad at left is not connected; you will have to pick a region first.)
To leave the Atlas, press the ESC
Esc key on your computer. You may have to press it several times to close all the sections.
Directory and
Table of
Contents
Supporting Materials in this Atlas
Title Page
Includes a short note on the sponsoring project, CDMP.
To Navigate Through This Atlas
Hyperlinks and graphical keys.
Introduction
Brief, for the generalist.
Methodology of the Statistics
For technical background.
Validation of the Model
Examples of statistical and field validation.
Known Issues in the Input Data
Weaknesses in the input data show in several specific ways.
A Short Review of Storm Effects
The interaction of wind, waves, and surges.
Distortions of the Projection
There are cartographic distortions in the maps, but not the model.
Definitions
“Wind,” “wave,” and “surge” have specific meanings in this Atlas.
Measures of Wind Speed
Discussion of alternative wind durations and altitudes.
Supporting Materials on the World Wide Web
Links to the Maps
Caribbean Disaster Mitigation Project (CDMP)
TAOS Storm Hazard Modeling
TAOS Data Sources
Caribbean Institute of Meteorology and Hydrology (CIMH)
US Agency for International Development (USAID)
US National Hurricane Center
Organization of American States (OAS)
Watson Technical Consulting, Inc.
Introduction
page 1/3
This Atlas presents the Caribbean as it has never been seen before. The maps in this Atlas show potential storm phenomena
which are most likely to occur (Maximum Likelihood Estimates, or MLEs) during specific durations of time. There are three
phenomena: maximum winds, maximum wave heights, and maximum storm surges. Each of the three phenomena is shown for
four return periods: 10, 25, 50 and 100 years. There are twelve regional sections of maps: starting with views of the Caribbean as
a whole, windowing-in on three main basins of the sea, and windowing even further onto eight sub-regions with significant land
masses. Each of the regions includes a simple orientation map, but the essence of the Atlas is in the twelve maps which follow.
This could be a bewildering array of information, so every effort has been made to help the user explore without getting lost. The
maps are color-coded, and it takes no more than two hyperlinks to go from one map to any other in the Atlas.
20.25 N
59 W
The figure at right is an example. It shows the magnitude of storm surge most likely
to occur once in 50 years, on a long-term average, over the East Basin of the
Caribbean. In any one location, there is only a 2% chance of such a large surge
occurring in any single year, and there is a 64% chance† that the value could be
exceeded in any particular period of 50 years. Most important, it is impossible for all
these values to happen at the same time because the sea water must be “borrowed”
from one area to surge up in another.
72 W
The maps do not show what exists, but what might exist. Indeed, the concept is
even more restricted than that, since the phenomena shown on a single map could
not possibly exist at a single point in time.
Ross Wagenseil
for CDMP
January 2000
CDMP
10.25 N
Hurricane Historical Records
In that sense, the map is not a continuous field but an array of points. Each of these points got its value from mathematical
manipulation of the historical record kept by the US National Hurricane Center. The historical record includes 973 tropical
cyclones (tropical storms and hurricanes), over the 114 years from 1885 to 1998, inclusive.
What makes the maps coherent is that the historical record was processed by an advanced numerical model, TAOS (The Arbiter
of Storms), which applied basic equations of physics to a digital, three-dimensional topographic map. For the map above, TAOS
calculated the surge that each one of those 973 storms would have caused at each location. This required mapping the storms as
they passed, calculating the resultant winds and pressure, and calculating the fluid dynamics of the sea water as it flowed around
the coasts and over the depths of a three-dimensional model of the Caribbean until it reached the location in question.
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†Probability
that the 50 year return value will be exceeded at least once in a 50-year
period: P= 1-(1-1/T)^N. With T=50 and N=50, P = 0.63583
Introduction
page 2/3
Once all the storms had been modeled for a point, the maximum for each year was selected. That gave 114 maxima, to which a
smooth curve was fitted. That curve was taken as the probability density function of surge for the single point. The 2%
cumulative probability was taken as the Maximum Likelihood Estimate (MLE) for the surge with a 50-year return time at that
location, and the corresponding surge value was mapped for the location.
Each point on the map was calculated individually in this way.
Marilyn,
And yet the points do fit together. Anyone who has followed storm reports during the
1995
hurricane season in the Caribbean has developed an intuition for what is likely to
develop. There is a pattern.
Recognizing Patterns
Hurricane Marilyn and Hurricane Gilbert are examples. Although they were not
predictable, they were both, in some way, typical. The maps of this Atlas show that
they both moved through areas of high probability: a southern pathway over Jamaica
and a pathway curving north of Puerto Rico. The model calculated the pattern by
applying the laws of physics to the reefs and islands of the topographic map.
Gilbert, 1988
Click for enlargement
Both Marilyn and Gilbert started in the Western Atlantic and passed just north of Barbados. This pathway is sometimes referred to
as “hurricane alley.” The hurricane alley is far enough south for the sea water to have warmed to 27C, a critical temperature that
sustains convective clouds which move along with the trade winds. The alley is also far enough north for a strong Coriolis effect,
and it is far enough west for the Coriolis effect to have had time enough to twist convective clouds, moving with the trade winds,
into circular storm systems. These storm systems are tropical cyclones, and the strongest of them, in the Caribbean, are the
hurricanes. This part of the pattern is already well-known.
The Atlas shows other parts of the pattern, some of which are less-known or only guessed at up until now. For instance, there is
a distinct “shadow” to the west and north of the Greater Antilles. This is not a result of decreased activity or changes to the
steering patterns directing the tropical cyclones. A review of the track maps indicates that there are as many tropical cyclones
moving over the Greater Antilles as there are to the immediate north and south. What decreases is not the frequency of the
storms but their intensity. This is due to several related factors:
1. Air circulation at the low and middle levels is disrupted by the extensive mountains of Hispaniola and Cuba.
2. Surface humidities decrease slightly under the larger air mass.
3. Direct evaporation from the sea is cut off while the convective core of the storm is over land.
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Introduction
page 3/3
Most of the islands do not disrupt storms very much, because the islands are smaller than the core circulation of a tropical
cyclone. Jamaica and Puerto Rico are almost big enough to disrupt a storm. In fact, Jamaica does affect storms moving from
south to north, but such storms are much rarer than storms arriving from the east and this barely shows on the probability maps.
As storms move over Hispañola, however, they begin to break up and they cannot recover until they pass Cuba.
By far the prevailing direction of motion of storms in the Caribbean is from east-southeast to west-northwest, but some storms
such as Marilyn move on a track closer to southeast-northwest, depending on upper-level winds and frontal systems in the
Temperate Zone. Thus, the shadow of the Greater Antilles is a complex interaction of the increase in intensity from south to
north, the disruptive effect of the largest islands, and the interaction of tropical cyclones with the Easterlies above 20 degrees
north latitude. The effect is subtle and complex, but it is real. The Atlas shows the probable results of all these factors together.
Interpretation of Maps
These maps are not designed to be queried out of context, on a cell-by-cell basis. Doing so would create a false impression of
accuracy which cannot be delivered from the input data available at this time. The input topographic data has a nominal
resolution of 30 arc-seconds (slightly less that 1 kilometer), but in some areas the data are from coarser datasets. Site-specific
accuracy can only be obtained from an analysis at a much higher resolution (in the range of 3 arc-seconds), which requires a
significant investment in high-resolution bathymetry and elevation data. CDMP has done several high-resolution studies with good
success. Evaluation of these studies shows that the results are consistent with the results obtained in this Atlas. On the other
hand, some of the patterns in the Atlas are poorly understood, and site-specific studies may help to investigate these effects.
Hurricane Marilyn and Hurricane Gilbert caused substantial damage and they are fresh in memory, but it has been difficult to
know how soon such storms would come again. It has been difficult to gauge the risk, to plan for the next emergency. The
information contained in this Atlas enables emergency managers and physical planners to better understand the probability of
occurrence of winds, waves, and surges associated with tropical cyclones. Areas of higher risk from one or more of these
hazards may require specific development policies or building standards. Emergency management plans will need to pay special
attention to settled areas or critical infrastructure located in areas of high risk.
The definition of MLE used in this study is consistent with the definition commonly used in building codes such as the ASCE-7†.
MLE values can thus be used in the formulas suggested in the codes. Since the MLE values corresponding to a given return
period can easily be exceeded during that period (the 50-year return wind speed has a 64% probability of being exceeded), higher
estimates, corresponding to more stringent prediction limits (75%, 90% or 95%), may be called for when planning or designing
facilities that need to withstand even the most unlikely events. These estimates can be produced for given locations by the CIMH.
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†Wind
Loading Standards produced by the American Society of Civil Engineers
Statistical
Methodology
Page 1/3
Slight variations in storm track can make large differences in the effects a storm has on one area. For any
given location, a hurricane passing fifty miles away may cause the same winds as a moderate tropical storm
passing right overhead. For each grid cell in the study area, the TAOS model was used to calculate wind
effects for each storm in the tropical cyclone database (973 events in the Atlantic as of December 1998) on
that location. We then used the output to perform a maximum-likelihood-analysis to generate the optimal
estimates of parameters for a two-parameter Weibull model.
The two-parameter Weibull distribution has the cumulative distribution function (cdf)

x





x





F ( x)  1  e
And the probability density function (pdf)

f ( x) 

 1
e
x
  





where
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x>0 is the magnitude of the event,
 is the shape parameter,
 is the scale parameter.
This distribution is positive, right skewed, unimodal and flexible enough to accommodate
distribution shapes encountered in this project. If the shape parameter  is unity (1), then the curve
is a simple exponential, with the highest probability density at zero. That would imply that most
years have no wind or storm surge at all. If  is higher than one, then there is a mode at some value
above zero. Either way, there are more low values than high ones, but high values are possible.
The shape parameter and the scale parameter can both be estimated from data using the method of
maximum likelihood. The maximum likelihood estimators of the two parameters are approximately
bivariate normally distributed with mean vector (, ) and covariance provided by the observed
Fisher information matrix.
Statistical
Methodology
Page 2/3
The maximum likelihood estimator of the return period wind is obtained by inverting the distribution function
at the appropriate percentile:
X

1
 
 
  ln1  p 
Where
90th percentile implies 10 year return period wind speed,
96th percentile implies 25 year return period wind speed,
98th percentile implies 50 year return period wind speed,
99th percentile implies 100 year return period wind speed.
To obtain simulated confidence limits, we generate realizations of (, ) according to its asymptotic
distribution, compute the corresponding return period wind speed, and then sort the values to extract suitable
limits reflecting the uncertainty in estimation. General principles of maximum likelihood estimation can be
found in standard graduate mathematical statistics books. The simulation process is straightforward (Johnson,
Multivariate Statistical Simulation, Wiley, 1987). In brief:
1. The annual maxima are treated in the fitting process as independent and identically distributed
variates. Extensive consideration of lag correlations reveals little regularity in cycles relative to noise.
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2. The two-parameter Weibull distribution is used for annual maxima. Consideration of potential
competing lognormal and inverse Gaussian distributions revealed the relative superiority of the Weibull
distribution. Goodness-of-fit tests applied throughout the Atlantic Basin (over 600,000 locations)
demonstrated the adequacy of the Weibull distribution.
3. In terms of data “quality,” many sensitivity analyses have been conducted to support the use of the
full 1886-present data set. Supposed difficulties with the “older” events are not reflected in analyses
with various subsets of the data. Hence, there appears to be no gain for dropping pre-1950 data.
4. Our analyses are not dominated by the single most extreme event at a particular site. This is quite
comforting in that we wish to smooth the storm history to regions that have not experienced many
extreme events. The Weibull fitting methodology provides an indirect smoothing that appears
reasonable and is consistent with the historical record.
Statistical
Methodology
Page 3/3
Below is an example of the Weibull curve fitted to the HURDAT historic record for a project completed in 1998.
For each storm, the model TAOS calculated the winds produced over downtown Kingston, Jamaica. The winds
were grouped by years, and the peak wind for each year of the 112 years in the database selected. Then the 112
peak yearly winds were grouped for this histogram.
0.5
Kingston, Jamaica
Fraction of All Occurrences
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0.4
Histogram of Historic Occurrences
and
Two-Parameter Weibull fit
 = 1.194302
 = 28.483850
0.3
2 = 20.568573
K-S = 0.098214
K-S prob. = 0.630202
0.2
0.1
0.0
0
10
20
30
40
50
60
70
80
90
100
110
Peak Wind Speed for the Year: Knots
120
130
140
150
160
The TAOS Model and
Model Validation
Page 1/2
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TAOS/C 1min DTM Observed vs Computed Peak Surges
460 Observations, meters
(18 US Atlantic/Gulf Hurricanes)
9.00
8.00
7.00
6.00
Computed
The Arbiter of Storms (TAOS) is a computerbased numerical model that produces estimates
of maximum sustained wind vectors at the
surface and still water surge height and wave
height at the coastline for any coastal area in
the Caribbean basin. Model runs can be made
for any historical storm, for probable maximum
events, or using real-time tropical storm
forecasts from the US National Hurricane
Center (NHC). TAOS is integrated into a
geographic information system (GIS), which
eases entry of model data, enables the
presentation of model results in a format familiar
to meteorological officials in the Caribbean
region and allows the results to be combined
with locally available GIS and map information.
5.00
4.00
3.00
2.00
1.00
0.00
0.00
1.00
Best Fit Line:
2
y = 0.992x + 0.0149, R = 0.9664
2.00
3.00
4.00
5.00
6.00
7.00
8.00
Observed
The TAOS model has been tested extensively against hurricanes and typhoons around the world.
There are 460 observations on the US Gulf and Atlantic coasts, 36 observations in Hawaii, 42
observations in the Caribbean, and 28 observations in the remainder of the world (such as Japan,
Taiwan, India and Bangladesh), for a total of 566 peak surge observations from 27 storms
worldwide. Including comparisons with hourly tide-gauge readings, there are over 1200
observations in the TAOS verification database. From this, TAOS/C appears to generate results
within 0.3 meters (less than 1 foot) 80% of the time, and less than 0.6 meters (about 2 feet) 90% of
the time. The scatter plot above shows the results of US mainland storm surge comparisons.
The TAOS Model and
Model Validation
Page 2/2
Anse Mulatre
Because the TAOS model uses basic physical
relationships, it works across a wide range of
scales. For instance, a study was done of the west
coast of Dominica, using a resolution of 30 meters.
0
20
p
ee
sD
ter
Me
In 1995, as the study was finishing, Hurricane
Marilyn visited the island.
Colihaut
A field visit several weeks later found that the
model had accurately predicted damage areas as
small as two to three cells wide.
N
CDMP Storm Hazard Modeling Page
1000 Meters
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Gueule de Lion
(approx .)
Coulibistri
Undersea
Depression,
or “Notch”
Depth contours at 10-meter intervals
Grande Savanne
Known Issues
in the Input Data
The input data used to develop this atlas is based on USGS digital data for deep ocean
bathymetry, Digital Chart of the World data for land boundaries and rough topography,
satellite imagery for foreshore bathymetry and land cover. This base information was
then updated with point sounding and trackline information. The input data has a
nominal resolution of 30 arc-seconds (926 meters or less, depending on latitude and
orientation), but it is not reliable in such detail. Storm track information used for
modeling was derived from a database developed by the U.S. National Hurricane
Center. See the web link in the directory for more information on data sources.
The database is not designed to be queried out of context, on a cell-by-call basis. Doing
so would create a false impression of accuracy which can not be delivered from the
input data available at this time. It is a measure of the power of the model that it reveals
weaknesses in the input data at the geographic locations where they occur, without
spreading inconsistencies across wide areas. Data issues show up in several specific
ways:
1. Bathymetry in most areas only has a resolution of
5 arc-minutes. This creates a “blocky” sea floor which
shows on some of the wave maps.
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2. The lack of input detail in the open sea also
caused the model to produce horizontal “banding”
5 arc-minutes wide on some maps of winds and
waves.
3. Where the bathymetry was updated with marine
track line data from another source, the depths
appear inconsistent, and “speckles” appear in a few
areas on the model results.
4. Some of the track lines are out of place. This
had little effect on the overall results, but there are a
few stray cells of erroneous data on shore.
Example: Belize,
100-year Waves
Example: Jamaica
100-year Waves
Example: West Basin
100-year Waves
Example: Windward Islands,
North, 100-year Surges
A Short Review
of Storm Effects
Rain and wind
Page 1/5
In an ordinary thunderstorm, the rain falls out of the cloud leaving the air warmer and drier. The
warm air rises, drawing winds from outside the cloud to fill the space. In a hurricane, the
thunderstorm is so large that it is twisted by the spin of the Earth and the winds form a spiral,
directed inwards from all points of the compass.
Photo by permission of Michael Bath. http://australiansevereweather.simplenet.com/photography/cbincu11.htm
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A Short Review
of Storm Effects
Cyclonic structure
Page 2/5
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All of the Caribbean is north of the Equator, so hurricanes in the Caribbean spin counter-clockwise.
Photo by permission of Scott Dommin. http://members.aol.com/hotelq/index.html
A Short Review
of Storm Effects
Topographic effects
Page 3/5
Acceleration
LAND
OPEN
SEA
When winds reach an obstacle, they may accelerate in order to squeeze past or they may be
slowed by back pressure. In the lee of an obstacle, the winds are confused and turbulent.
Definitions
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A Short Review
of Storm Effects
Wind over water
As storm winds blow over the sea, they drag on the water,
forming waves and storm currents
Page 4/5
In this Atlas, wind speeds represent
sustained 1-minute winds at 10
meters above the surface.
Wind Stress
Wave build-up
Wind-induced Current
Deep counter-currents and upwelling
develop in order to compensate for
the drift near the surface.
These effects may penetrate down to
200 meters depth.
Definitions
Counter-current
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A Short Review
of Storm Effects
Page 5/5
Definitions
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In this Atlas,
1. Wave heights are the heights of wave crests
above the storm surge.
2. Storm surges include astronomical tide and
setups from pressure, wind and wave,
but not wave runup.
Marilyn,
1995
Hurricane Gilbert passed directly
over Jamaica without being disrupted.
If it had passed over the Dominican Republic, Haiti, or
Cuba, the large land masses would have changed and
weakened it.
Gilbert, 1988
Hurricane Marilyn
passed just north of
Puerto Rico and then
turned northeast as it
caught the effect of
weather systems in
the north temperate
region.
Storms
originating
east of
Barbados
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may
head
directly
westnorthwest
or veer to
the north.
Distortions of the Projection
The WGS84 datum models the Earth as an ellipsoid.
The formula of an ellipse is
b
x2
y2


1
a2
b2
a
Where a and b are the major
and minor semiaxes.
In re-projecting to the Plate Carrée, north-south
distances are undistorted, but east-west distances
are distorted in proportion to their latitude.
The distortion can be calculated by finding the ratio
of the local rotational radius (x) to the length of the
major semiaxis (a)
x
a
 b
1
b  a tan2 (latitude)
2
2
x
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y
Latitude
a
Calculations of distortions of units of Longitude
for using a square grid representation of
Latitudes & longitudes on a WGS84 datum
Major semiaxis =
Minor semiaxis =
Latitude
0
8
9
10.25
10.75
11.5
14
14.25
14.25
15.8
16.5
16.6667
17
17
17.5
17.5
18.6
18.75
19
19.5
20.166667
20.25
20.3333
20.75
23
35
45
75
a
tangent(lat)
0 b
0.140540835
0.15838444
0.180829457
0.189855932
0.203452299
0.249328003
0.253967646
0.253967646
0.282971477
0.296213495
0.299380981
0.305730681
0.305730681
0.315298789
0.315298789
0.336537181
0.339454259
0.344327613
0.354118573
0.367267976
0.368919477
0.370572096
0.378866109
0.424474816
0.700207538
1
3.732050808
6378137.00000 meters
6356752.31400
ratio
=
1.00000
=
0.99020
0.98761
0.98394
0.98234
0.97979
0.97010
0.96903
0.96903
0.96198
0.95856
0.95772
0.95603
0.95603
0.95343
0.95343
0.94744
0.94660
0.94518
0.94229
0.93832
0.93781
0.93731
0.93474
0.92003
0.81825
0.70592
0.25801
stretching
1
1.009893
1.012549
1.016327
1.017982
1.020623
1.030817
1.031957
1.031957
1.039525
1.043232
1.044142
1.045993
1.045993
1.048849
1.048849
1.055472
1.056412
1.057998
1.061247
1.065737
1.066311
1.066888
1.069816
1.086919
1.222127
1.416594
3.875832
comments
equator
Full frame, S
Carib Frame, S
East Basin, S
Mid Basin, S
Windwards, South, S
West Basin, S
Windwards North, S
Windards, South, N
Belize, S
Leewards, S
Windwards North, N
Jamaica, S
PRVI, S
Haiti, S
Dom Rep, S
Belize N
Leewards, N
Jamaica, N
PRVI, N
DomRep, N
East Basin, N
Haiti, N
Mid basin, N
Carib frame, N
North Carolina
Maine
Hudson Bay
Definitions
•WINDS: The winds displayed in this product are compatible with “one-minute sustained” winds, 10
meters above the surface, as reported by the U.S. National Hurricane center (NHC).
For a brief discussion of converting from one standard of wind measurement to
another, click HERE:
Measures of Wind Speed
•SURGES include astronomical tide and setups from pressure, wind and wave, but not wave runup. Surges over land are shown as elevation above sea level, not water depth.
•WAVES are the heights of wave crests above the storm surge level in open water. Shoreline
effects do not appear at this resolution.
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Measures of Wind Speed
The winds displayed in this Atlas are “one-minute sustained winds, 10 meters above the surface,” which are compatible with
the wind speed representation used by the U.S. National Hurricane center (NHC) in its forecasts and reports of tropical
cyclones. The NHC is designated by the World Meteorological Organization (WMO) as the Regional Specialized Forecast
Center for tropical cyclones in the Atlantic Basin.
Internally, TAOS computes instantaneous values for mean wind at the top of the boundary layer, which is effectively the same
as the 10-minute averaged wind used by the WMO. To conform to the slightly different “one-minute, sustained winds 10
meters above the surface” reported by the NHC, the wind values produced by the TAOS model are then brought down to the
surface with boundary-layer calculations and converted to “one-minute sustained averages at an elevation of 10 meters.”
Users requiring alternate wind representations may use the following conversion factors to obtain approximate values:
Desired Wind Measure
Gradient wind speed
Conversion Factor
1.25
(often taken to be at the flight level of
the reconnaissance plane)
3-second gust over water
5-second gust over water
1-minute “sustained” (NHC)
2-minute average (ASOS)
10-minute average (WMO)
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1.125
1.0625
1.00
0.95
0.8125
For example, to get 10-minute winds, multiply values from this Atlas by 0.8125.
Research is continuing into the relationships between these various measures. Turbulent flow
over land is particularly complex, and gust factors may need to be site-specific. Further
discussion is in Simiu and Scanlan, Wind Effects on Structures, 3rd edition, Wiley, 1996, and
in Sparks, P.R., and Huang, Z., "Wind speed characteristics in tropical cyclones", Proceedings
of the Tenth International Conference on Wind Engineering, Copenhagen Denmark, 21-24
June 1999.
In this Atlas, wind speed over land includes both surface friction (keyed to land cover) and
topography along the flow path at a resolution of 30 arc-seconds. If using wind damage
models or building codes which internally include surface friction or topographic
corrections, the nearest open-water wind speed should be used as input.