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United States Department of Commerce

Technology Administration Nisr National Institute of Standards and Technology

NIST Technical Note 1416

Estimates of Hurricane Wind

Speeds by thie 'Peal<s Over

Ttirestioid' Mettiod

E. Simiu, N. A. Heckert, and T. Whalen

100

i5753

I0.U16

996
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NIST Technical Note 1416

Estimates of Hurricane Wind

Speeds by ttie 'Realms Over

Thireshioid' l\/lethiod

E. Simiu^ ^ N. A. Heckerf, and T. Whalen^

^Building and Fire Research Laboratory

National Institute of Standards and Technology

Gaithersburg, MD 20899-0001

^Computational and Applied Mathematics Laboratory

National Institute of Standards and Technology

Gaithersburg, MD 20899-0001

^Department of Civil Engineering

The Johns Hopl<ins University

Baltimore, MD 21218

Sponsored by

National Science Foundation

Arlington, VA 22230

NATO Scientific Affairs Division

NATO, B-110 Brussels, Belgium

Building and Fire Research Laboratory

National Institute of Standards and Technology

Gaithersburg, MD 20899-0001

Issued February 1996

^^ATES o*

U.S. Department of Commerce

Ronald H. Brown, Secretary

Technology Administration

Mary L. Good, Under Secretary for Technology

National Institute of Standards and Technology

Arati Prabhakar, Director
National Institute of Standards and Technology • Technical Note 1416

Natl. Inst. Stand. Technol. • Tech. Note 1416, 49 pages (February 1996)

CODEN: NTNOEF

U.S. GOVERNMENT PRINTING OFFICE

WASHINGTON: 1996

For sale by the Superintendent of Documents, U.S. Government Printing Office, Washington, DC 20402-9325
ABSTRACT

We report results that lend support to the hypothesis that extreme hurricane wind speeds are described predominantly by reverse Weibull distributions, which have limited upper tails. The

results are based on the analysis of hurricane wind speed data obtained in an earlier project and

used for the development of the ASCE 7-83 and ASCE 7-93 Standard wind speed map.

According to our results, wind load factors should be larger in hurricane-prone regions than the

load factor specified in current standard provisions. However, the requisite increases are smaller

than would be the case if the distributions were assumed to have infinite upper tails, as has been

done so far in all principal studies of hurricane winds in the United States.

Key words: Building technology; building (codes); climatology; extreme value theory;

hurricanes; load factors; structural engineering; structural reliability; threshold methods; wind

(meteorology).

ACKNOWLEDGEMENTS

We acknowledge with thanks partial support by the National Science Foundation (Grant No.

CMS-9411642 to Department of Civil Engineering, The Johns Hopkins University, subsequently

transferred to the University of Colorado at Boulder), and partial support by the NATO

Scientific Affairs Division for collaborative work with a team headed by Dr. S. Coles of the

Department of Mathematics and Statistics, University of Lancaster, U.K. Useful interactions

with Dr. Coles and with Prof. R.B. Corotis of the University of Colorado are also acknowledged

with thanks.

ni

TABLE OF CONTENTS

Page

ABSTRACT iii ACKNOWLEDGMENTS iii

LIST OF FIGURES vi

LIST OF TABLES vi

L INTRODUCTION.

2. HURRICANE WIND SPEED DATA 2

3. ANALYSES AND RESULTS 4

4. DISCUSSION OF RESULTS 7

5. CONCLUSIONS 9

REFERENCES. 10

APPENDIX I -- Estimated tail length parameter c and 95 % confidence

bounds, versus threshold and number of threshold

exceedances (top of each page). Estimated hourly

mean speeds at 10 m above ground over open terrain

for 25-yr, 50-yr, 100-yr, 500-yr, 1000-yr and 2000-

yr mean recurrence intervals (bottom of each page).... 11

APPENDIX II Instructions for accessing data sets and

computer programs .40
LIST OF FIGURES'

Figure 1 . Locator map with coastal distance intervals marked

(nautical miles) (Ho et al. , 1987)

LIST OF TABLES

Table 1 . Estimated hourly mean hurricane wind speeds with 50-yr,

100-yr and 2000-yr mean recurrence intervals (MRI) at 10 m above ground over open terrain near the coastline,

in m/s

^Plots of Appendix I are not included in this list

VI
1. INTRODUCTION

A fundamental theorem in extreme value theory states that sufficiently large values of

independent and identically distributed variates are described by one of three extreme value

distributions: the Frechet distribution (with infinite upper tail), the Gumbel distribution (whose

upper tail is also infinite, but shorter than the Frechet distribution's), and the reverse (negative)

Weibull distribution, whose upper tail is finite (Castillo, 1988).

The consideration of the reverse Weibull distribution in wind engineering is the result of recent

developments in extreme value theory, notably the use of the "peaks over threshold" approach.

All principal studies of extreme hurricane winds in the United States published so far (Batts et

al., 1980, Georgiou et al., 1983, and Vickery and Twisdale, 1995) have been based on the

assumption that extreme wind speed distributions have infinite upper tail. The purpose of this

report is to investigate the applicability of the reverse Weibull distribution to the modeling of

hurricane wind speeds and the effect of using that distribution on the estimation of extreme wind

speeds and wind load factors.

Simulations of hurricane wind speeds are based on censored probabilistic models of the various

climatological parameters that determine the wind speeds (i.e., the pressure difference between

center and periphery of the storm, the radius of maximum wind speeds, and the speed of

translation of hurricanes) — see Batts et al. (1980), or Simiu and Scanlan (1986). The censoring

is based on physical considerations. It is in principle consistent with a tail-limited, rather than an infinitely tailed, probabilistic model of the extreme wind speeds.

The data used in this report were generated by simulation (Batts et al. , 1980) to obtain estimates

of hurricane wind speeds that were used to develop the wind speed map included in the ASCE

7-83 and ASCE 7-93 Standards. They are available on tape ( Hurricane Wind Loads , Computer

Program, Accession No. PB821 32259, National Technical Information Service, Springfield,

VA, 1982), and are also available in anonymous files accessible to the reader as indicated in

Appendix II of this report. The data are briefly discussed in Section 2.

Section 3 briefly discusses the "peaks over threshold" method of analysis used in this work and

includes results obtained by that method. The results are discussed in Section 4, where a comparison is presented with other sets of available results. Section 5 presents our conclusions.
2. HURRICANE WIND SPEED DATA

As indicated earlier, the wind speed data analyzed in this report were obtained by simulation

(Batts et al., 1980) and are directly accessible to the reader on disk (see Section 1) or in

electronic form (see Appendix II). For each of 55 equidistant locations between mileposts 150

and 2850 (Fig. 1) and for each of 999 simulated hurricane events, data are available as maximum wind speeds within each of the sixteen half octants.^ In addition, the estimated mean

annual rates of occurrence of hurricanes events are available for each location.

In this study we analyze data sets in which each of the data points is the maximum wind speed

in a hurricane event, regardless of direction. The data represent fastest one-minute hurricane

speeds at 10 m above ground over open terrain at the coastline, in knots. To obtain the

corresponding speeds in miles per hour (mph), the values in knots must be multiplied by 1.151

mph/knot. To obtain corresponding nominal hourly mean speeds in m/s (henceforth referred to simply as hourly mean speeds), the fastest one-minute hurricane wind speeds in mph must be

multiplied by the factor 0.447 (m/s)/(mph), and by a conversion factor from fastest one-minute

speeds to hourly mean speeds. For hurricane data it was found by Krayer and Marshall (1992)

that the conversion factor from 10-min average speed to peak gust speed is about 10 percent

higher than the factor proposed by Durst (1960) on the basis of extratropical storm data (see,

for example, Simiu and Scanlan, 1986, p. 65). However, in our opinion, owing to the limited

amount of data analyzed by Krayer and Marshall, this finding, and its implications for

conversion factors pertaining to wind speeds averaged over other time intervals, are still somewhat tentative. For this reason we used in this report a conversion factor from fastest

minute to hourly mean of 1.24, as proposed by Durst. A different value could be used if warranted by analyses of additional hurricane data. This, however, would not affect the main

conclusions of this report, which pertain to the effect of the upper tail of the distribution on the

estimation of extreme wind speeds and on ratios between wind speeds corresponding to different

mean recurrence intervals.

^For mileposts 450 and 500 the differences between the parameters used in

the simulations were small, and the same set of simulations was assumed to be

applicable to both mileposts.
FIGURE 1. Locator map with coastal distance intervals marked (nautical miles; 1 nautical mile=1.9 km) (Ho et al . , 1987).
3. ANALYSES AND RESULTS

3.1 Estimation of Tail Length Parameter of Generalized Pareto Distribution. The

Generalized Pareto Distribution (GPD) is an asymptotic distribution whose use in extreme value

theory rests on the fact that exceedances of a sufficiently high threshold are rare events to which

the Poisson distribution applies. The expression for the GPD is

G(y) = Prob[Y < y] = l-{[l+(cy/a)]-i'^} a>0, (l+(cy/a))>0 (1)

where a and c are the location and the tail length parameter, respectively. Equation (1) can be

used to represent the conditional cumulative distribution of the excess Y = X - u of the variate

X over the threshold u, given X > u for u sufficiently large (Pickands, 1975). The cases c>0,

c=0 and c<0 correspond respectively to Frechet (Type II Extreme Value), Gumbel (Type I Extreme Value), and reverse Weibull (Type III Extreme Largest Values) domains of attraction.

For c=0 the expression between braces is understood in a limiting sense as the exponential

exp(-y/a) (Castillo, 1988, p. 215).

For mileposts 150 through 2850, Appendix I shows estimated values of the tail length parameter

c and 95 percent confidence bounds^ (top of each page), and mean hourly speeds Xr at 10 m

elevation over open terrain at the coastline for mean recurrence intervals R= 25, 50, 100, 1000

and 2000 years. The estimates are based on analyses of sets of data exceeding various thresholds

u. They were obtained by using the de Haan procedure (de Haan, 1994). The procedure is reviewed and the reasons for its choice are discussed in Simiu and Heckert (1995). The smaller

the threshold, (a) the larger the sample size (for example, for milepost 150, for a 38 m/s

threshold the sample size is 26; for a 37 m/s threshold the sample size is 36 — see Appendix I), and (b) the smaller the sampling errors (i.e., the narrower the confidence bands). However, as the threshold decreases, there tends to be an increase in bias due to the stronger violation of the

assumption — inherent in the modeling by any extreme value distribution — that the data are asymptotically large. Given the dependence of the estimates upon threshold, the estimation is performed subjectively on the basis of the plots of Appendix I, as discussed, for example, in

Simiu and Heckert (1995).

Since, as the threshold becomes lower, the bias in the estimation of the variates increases while

the sampling error decreases, an optimum threshold in principle exists. Over intervals where the

bias error is small the graph is nearly horizontal; a downward slope of the graph is indicative

These confidence bounds are associated with the sampling errors due to the

limited number of wind speed data being analyzed. In addition to these sampling

errors, the estimates are affected by climatological sampling errors, that is,

sampling errors due to the limited number of climatological parameter data

(pressure defect, radius of maximum wind speeds, traslation velocity) on the

basis of which the simulation of the wind speed data was carried out. For the

data of Batts et al . (1980) used in this report, the estimated standard deviation

of the climatological sampling errors is about 10 percent (Batts, Cordes and

Simiu, 1980)

.
of increasing bias. When choosing a reasonable value for the estimated value of c on the basis

of our inspection of the graphs, it should be recalled that a larger estimate implies a longer tail and is therefore conservative from a structural engineering viewpoint.

Consider, for example, the graph for the estimate of the tail length parameter c, for milepost 850

(Appendix I). It is reasonable to infer from this graph that the estimated value of c is about -0.2.

As a second example consider the graph for milepost 950. In this case a conservative choice for

the estimated value of c is -0.25, say. From the results of Appendix I it is clear that the

estimated values of the tail length parameter c are predominantly negative. This is an indication

that the reverse Weibull distribution is a better model of the hurricane wind speeds than the

Weibull (as opposed to reverse Weibull) or Gumbel distribution.

3.2 Estimation of Wind Speeds With Various Mean Recurrence Intervals. The mean

recurrence interval R of a given wind speed, in years, is defined as the inverse of the probability

that the wind speed will be exceeded in any one year. In this section we give expressions that

allow the estimation from the GPD of the value of the variate corresponding to any percentage

point 1 -l/[X(u)R], where X(u) is the mean crossing rate of the threshold u per year (i.e., the

average number per year of data points larger than u). Note that, for any given location,

\(u) = fjL„n(u)/999, where [x^ is the annual rate of occurrence of hurricane events at that location,

n(u) is the number of wind speed data in excess of the threshold u, and 999 is the number of

wind speed data in the set with the lowest possible threshold (i.e., the number of data obtained

by simulation for each location). Set

Prob[Y(u) < y] = 1 - l/[X(u)R] (2)

Using Equation (1)

1 - [1 + c(u)y(u)/a(u)]-'"='"^ = 1 - l/[X(u)R] (3)

Therefore

y(u) = -a(u)[l - [X(u)R]^("Vc(u) (4)

(Davison and Smith, 1990). The value being sought is

Xr(u) = y(u) + u. (5)

Consider, for example, the graph showing estimated speeds for milepost 850 in Appendix I. Note a similarity between the dependence on sample size of the estimate of parameter c on the one hand and estimates of speeds with large mean recurrence intervals (say, R = 2000 years and

R=1000 years) on the other. The similarity is less pronounced for speeds with smaller mean

recurrence intervals, say R = 50 years. With relatively small error, it may be inferred from the

graph that the mean hourly wind speeds are X25 ~ 29 m/s, X50 ~ 32 m/s, Xk^, = 34 m/s, X=,qq = 38
m/s, Xiooo~40 m/s, X2ooo~ 41 m/s. For milepost 950 the choices for the lOO-yr and 2000-yr

mean hourly speeds are about 32 m/s and 38 m/s, respectively.

Table 1 shows the estimated hourly mean hurricane wind speeds with 50-yr, 100-yr and 2000-yr

mean recurrence intervals at 10 m above ground over open terrain near the coastline. Also

shown in Table 1 are hourly mean speeds based on values estimated by Batts et al. (1980),

Georgiou et al. (1983), and Vickery and Twisdale (1995). For consistency all conversions to hourly mean speeds in Table 1 were effected as indicated in Section 2. As indicated by Vickery

and Twisdale's (1995) comparisons between fastest mile speeds estimated by these authors on

the one hand and by Batts et al. (1980) on the other, our choice of conversion factor does not

affect the comparability between the estimates of this report and the other estimates shown in

Table 1 . We note that the estimates of this report were not smoothed in Table 1 by averaging over three adjacent milepost locations, as was done in Batts et al. (1980).

Table 1. Estimated hourly mean hurricane wind speeds with 50-yr, 100-yr and 2000-yr mean recurrence intervals (MRI) at 10 m above ground over open terrain near the coastline, in m/s

Coastal distance* 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 2122 23 24 25 26 27 28

Batts etal. (1980) 34 33 32 3132 32 32 3129 30 33 36 36 35 34 32 30 33 34 34 34 30 27 30 33 33 29

50-yr This report 35 34 35 313133 32 32 29 30 32 34 36 37 35 3128 33 33 33 33 3129 30 34 33 31

MRI Georgiou etal. (1983) 33 3132 33 33 33 35 34 3134 35 36 39 38 35 313133 32 33 33 30 29 30 32 3128

Vickery & Twisdale (1995) 34 31 33 32 36 38 37 35 34 33 33 34 38 38 34 34 3134 36 36 35 30 32 3132 34 32

Batts et al. (1980) 37 36 35 34 35 35 35 34 3132 36 38 39 38 37 34 33 36 36 37 36 33 32 34 36 36 33

100-yr This report 38 36 37 34 35 35 35 34 313135 37 39 39 38 32 3135 36 34 37 34 32 33 37 36 35

MRI Georgiou etal. (1983) 36 35 36 36 37 37 39 37 34 36 39 40 43 43 40 33 36 35 35 36 36 33 31 32 35 35 31

Vickery & Twisdale (1995) 38 34 36 36 36 39 41 38 37 36 36 38 43 43 37 37 35 39 40 40 38 34 35 35 35 36 37

Batts etal. (1980) 47 46 45 45 44 44 45 44 40 43 46 46 47 47 45 40 4147 46 46 45 44 43 45 47 47 45

2,000-yr This report 46 42 43 44 44 41 44 41 35 35 46 43 46 47 44 39 38 44 43 42 47 43 41 43 46 46 46

MRI Georgiouet al. (1983) 5147 48 49 49 50 51 50 46 48 51 55 57 56 54 45 46 49 48 49 48 45 43 47 50 48 45

Vickery & Twisdale (1995) 49 46 46 46 50 56 52 49 48 46 49 5157 56 49 49 48 53 53 53 54 49 50 48 48 49 49

* In hundreds of nautical miles (see Fig. 1).

Note. Bold numbers indicate estimated speeds based on (Vickery and Twisdale, 1995) which exceed speeds estimated in this report by more than

10 percent. (The estimates of this report are based on the reverse WeibuU distribution, which has limited upper tail. All other estimates are based on distributions with infinite upper tail.)
4. DISCUSSION OF RESULTS

For wind speeds over the coastline the most significant difference between physical models used

by the various authors listed in Table 1 involves the representation of the hurricane boundary

layer. Unlike the other sets of estimates of Table 1, which used identical or similar empirical

boundary layer models, the estimates by Vickery and Twisdale (1995) were based on the Shapiro

boundary layer model. As indicated by Shapiro (1983, pp. 1995 and 1996), this model is not

able to describe the detailed structure of the boundary layer, and its use entails, in addition to modeling errors, a truncation error of about 25 percent in the estimation of wind speeds for any

simulated hurricane.

Modeling and computational errors notwithstanding, the various sets of estimates of 50-yr winds

listed in Table 1 are by and large comparable. This is understandable in view of the informal

calibration of the models effected in most investigations with a view to obtaining results that

"make sense. " We note, however, that there are differences of about +10 percent or -10 percent

between the estimates of this report and those of Georgiou et al. for mileposts 200, 300, 400,

800, 1100, 1200, 1400 and 1800. The estimates based on Vickery and Twisdale (1995) are in

most cases larger than the estimates of this report, although differences in excess of 10 percent occur only for a few locations (shown in bold figures in Table 1). Given the many uncertainties

that affect each set of estimates, it is difficult in our opinion to argue that one set of 50-yr wind

speed estimates is much better than another. However, the estimated 2000-yr speeds differ

significantly in many cases between the various sets of Table 1 . They are in most cases lowest

for the set of estimates of this report, followed in increasing order by the sets of estimates based on Batts et al. (1980), Georgiou et al. (1983), and Vickery and Twisdale (1995). This is ascribed

in part to the fact that these last three sets of estimates are based on distributions with infinite

upper tails.

The highest estimated 2000-yr speed at 10 m over water near the coastline based on the resuhs

of this report is about 47 m/s x 1.24 x 1.2 = 70 m/s (157 mph). This is a point estimate, that

is, it does not make allowance for sampling errors. Nor does this estimate make allowance for

modeling errors.

It can be verified from the plots of Appendix I that, for the estimates of this report, the wind

load factor (/)^ = 1 . 3 specified in the ASCE 7-95 Standard would in most cases correspond for

wind-sensitive structures to nominal ultimate wind loads with mean recurrence intervals of,

roughly, 500 years or less. This follows from the fact that the speeds associated with those

ultimate wind loads would be equal to the 50-yr speeds times (1.3)''^. For the other sets of

estimates of Table 1, the load factor 0^ = 1.3 would correspond to nominal ultimate wind loads

with even shorter mean recurrence intervals. The results of Table 1 therefore suggest that, for

wind-sensitive structures, the wind load factor for hurricane wind speeds should be larger than

1.3. This would be the case even if (1) hurricane design wind speeds were multiplied by a factor

of 1.05, as is done in the ASCE Standard 7-95 (see Commentary appended to the Standard), or

(2) if conversion factors from fastest-minute speeds to hourly speeds different from the factor

assumed in this report were used.
It follows from the results of Table 1 that, on average, load factors based on speeds modeled

by the reverse Weibull distribution differ least from the value (/)^ = 1.3 currently specified in the

ASCE Standard A7-95. To see this, note that the average estimated ratios of 2000-yr speeds to 50-yr speeds are about 1.3, 1.4, 1.45 and 1.5 for the sets based on this report, Batts et al. (1980), Vickery and Twisdale (1995), and Georgiou et al. (1983), respectively, so that the

squares of these values are about 1.7, 1.95, 2.1, and 2.25, respectively. A similar ordering

would be obtained if speeds with other large mean recurrence intervals were considered instead

of the 2,000-yr speeds. Increases of the load factor based on the tail-limited distributional model

supported by our results would therefore be smaller than the increases that would be called for

if distributions with infinite upper tails were assumed to be appropriate, as has been the case for

all other principal studies of hurricane winds in the United States.
5. CONCLUSIONS

The main conclusions of this work are:

1. The results of our analyses are consistent with the assumption that reverse Weibull

distributions describe the probabilistic behavior of extreme hurricane speeds at most if not all locations along the Gulf Coast and the Atlantic Coast. Note that a similar conclusion was

reached by Simiu and Heckert (1995) with regard to wind speeds in regions not affected by

hurricanes.

2. For any specified reasonably long mean recurrence interval, say, 2000 years, hurricane wind

speeds described by reverse Weibull distributions tend to be lower than speeds estimated by

earlier procedures that use infinitely tailed distributions.

3. The nominal ultimate wind loads obtained through multiplication of the 50-year loads by the

load factor <?!)^ = 1.3 specified in the ASCE 7-95 Standard appear to have relatively short mean

recurrence intervals, that is, to result in unsafe designs of wind-sensitive structures in hurricaneprone regions. Increases of the load factor based on the tail-limited distributional model

supported by our results appear therefore to be warranted. These would be smaller than the

increases that would be called for if distributions with infinite upper tails were assumed to be

appropriate, as has been the case for all other principal studies of hurricane winds in the United

States.

Our conclusions are subject to limitations inherent in the quality of the data and physical models

used in the analyses. An effort aimed at carrying out improved simulations that would reduce

those limitations is currently being envisaged.
REFERENCES

ASCE Standard A7-93 (1993), and ASCE Standard A7-95 (in press), American Society of Civil

Engineers, New York.

M.E. Batts, M.R. Cordes, L.R. Russell, J.R. Shaver, and E. Simiu (1980), Hurricane Wind

Speeds in the United States , NBS Building Science Series 124, Nat. Bur. Stand., Washington,

DC; see also J. Str. Div ., ASCE, 100 (1980), 2001-2015.

M.E. Batts, M.R. Cordes, and E. Simiu (1980), "Sampling Errors in Estimation of Extreme

Hurricane Winds," J. Str. Div ., ASCE, 106, 2109-2115.

Castillo, E. (1988), Extreme Value Theorv in Engineering , Acad. Press, New York.

Davison, A.C., and Smith, R.L. (1990), "Models of Exceedances Over High Thresholds," L

Roval Stat. Soc , B-52, 339-442.

L. de Haan (1994), "Extreme Value Statistics," in Extreme Value Theorv and Applications . Vol.

1 (J.Galambos, J. Lechner and E. Simiu, eds.), Kluwer Academic Publishers, Dordrecht and

Boston, 1994.

D. Delaunay (1988), Vents extremes dus aux cvclones tropicaux dans les DOM-TOM, Cahier

2078, Centre Scientifique et Technique du Batiment, 4 av. rect. Poincare, Paris 16.

C. Durst (1960), "Wind Speeds Over Short Periods of Time," Met. Mag . 89, 181-186.

P.N. Georgiou, A.G. Davenport, and B.J. Vickery (1983), "Design Wind Loads in Regions

Dominated by Tropical Cyclones, J. Wind Eng. Ind. Aerod . 13, 139-152.

F. Ho, J. Su, K. Hanevich, R. Smith, and F. Richards (1987), Hurricane Climatologv for the

Atlantic and Gulf Coasts of the United States , NWS 38, National Oceanic and Atmospheric

Administration, Silver Spring, MD.

W.R. Krayer and R.D. Marshall (1992), "Gust Factors Applied to Hurricane Winds," Bull. Am.

Met. Soc. 73, 613-617.

Pickands, J. (1975), "Statistical Inference Using Order Statistics," Annals of Statistics . 3, 119-

131.

L.J. Shapiro (1983), "The Asymmetric Boundary Layer Flow Under a Translating Hurricane,"

J. Atm. Sci . 40, 1984-1998.

E. Simiu and R.H. Scanlan (1986), Wind Effects on Structures , Wiley-Interscience, New York.

E. Simiu and N. A. Heckert (1995), Extreme Wind Distribution Tails: A Teaks Over Threshold'

Approach, NIST Building Science Series 174, Gaithersburg, MD (also Journal of Structural

Engineering , May 1996).

P.J. Vickery and L.A. Twisdale (1995), "Prediction of Hurricane Windspeeds in the U.S.," L

Struct. Eng . 121, 691-1699.

10
APPENDIX I

Estimated tail length parameter c and 95 %

confidence bounds, versus threshold and number

of threshold exceedances (top of each page).

Estimated hourly mean speeds at 10 m above

ground over open terrain for 25-yr, 50-yr,

100-yr, 500-yr, 1000-yr and 2000-yr mean

recurrence intervals (bottom of each page)

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APPENDIX II

Instructions for accessing data sets and computer programs

ftp enh.nist.gov (or ftp 129.6.16.1)

>user anonymous

enter password > guest

> cd emil/hurricane/datasets (to access data)

> prompt off

> dir (this lists available files)

> mget * (this copies all the data files)

>cd../../..

>cd emil/hurricane/programs (to access programs)

> dir (this lists all available files)

> mget * (this copies all the files)

get <enh.name> < local name> (this copies a specific file; example:get

milepost.350 milepost.350)

>quit

Note. The directory emil/hurricane contains a README file with details on the programs and

datasets.

40


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