Simulation of Tropical Cyclone Winds for Coastline Regions in Vietnam - Haiphong Case Study

Le Truong Giang PhD Candidate, WERC, Tokyo Polytechnic University, 1583 Iiyama, Atsugi, Kanagawa, Japan Yukio Tamura Professor/Director, ditto Masahiro Matsui Associate Professor, ditto 1. Introduction

The use of traditional methods of extreme applied for Tropical cyclone (TC) wind records which are often short or unreliable due to failures/limitations of anemometers, to define long-term TC-winds is usually resulted in very high wind speeds at high return period (e.g. 300 years or more). These predicted values are often judged to be unrealistic. To overcome this shortcoming, Monte-Carlo Simulation (MCS) technique has been used by researchers to characterize the long-term wind climate in TC-prone regions after initial works done by Russell since 1968 for offshore structures in US. No longer, MCS is adopted as a leading probabilistic method for evaluating design wind speeds in coastal areas subjected by TC in many major codes. For instance, in US, Hurricane simulations resulted in series of ASCE-7 [e.g. 1] (since first version published in 1988) after works of Batts et al. [2], Georgiou et al. [3, 4], Vickery and co-authors [5 to 8], in Australia, MCS results given in AS 1170-1989 [9], and AS/NZS 1170-02 [10] after works of Gomes and Vickery [11], Tryggavson [12], etc. In Japan, although numerous studies on typhoon simulation started soon since 1970’s but it is only recognized to be well applicable to predict TC-winds after works of Tamura’s group since early 1990s and their recent work [13] were addressed in AIJ-RLB-2004 [14]. Moreover, it is worthy to note that MCS probably is very useful for reviewing field measurements during TC-events and supporting input data for evaluation of TC-induced fatigue of structures. Incidentally, design wind speeds stipulated by Vietnamese loading code [15] for northern coastal areas are much higher than values given by Chinese loading code [16] for Hainan Island and even for southern coastal area of China where often suffered more severe tropical cyclones (see Fig. 1 and it will be explained in detailed in the next section). This unreality is probably due to TC-winds was predicted based on the short record length as noted above. This study conducted an extensive MCS for Phulien/Haiphong meteorological station located in one of most TC-prone region in the north of Vietnam. In addition, results obtained by MCS are compared to those obtained by conventional method of extreme using surface records.

Figure 1. Comparison of design wind speeds in adjacent region of Vietnam and China given by TCVN 2737-2006 and GB50009-2001 (wind speeds are in 10 min-mean).

102 104 106 108 110 112 114

E-Longtitude (deg.)

16

18

20

22

24

N-L

atitu

de (d

eg.)

102 104 106 108 110 112 114

16

18

20

22

24

CHINA

LAOS

SOUTH CHINA SEA

HainanGulf of Tonkin

Hanoi

Phulien Meteor. Station

36.1

38.3

Bachlongvi 43.0 39.2

Mean TC-direction

Macau

2. Characteristic of tropical cyclones made landfall of Vietnam and questions on the current design wind speeds stipulated by Vietnam loading code (VN Code)

Since 1951, there have been 224 TC-events hit to Vietnamese mainland (data obtained from Japan Meteorological Agency). The landing positions of 110 events associated with minimum central pressure of TC right before landing Pc≤ 990hPa are shown in Fig. 2 where two regions have high intensity of TC-landing, N-latitudes 17.5 21.5o and 13.0 16.5o o are observed. However, statistics have showed only 14 TC-events with Pc≤ 970hPa and the lowest value of Pc was 950 hPa documented during TC Betty in 1987 hit to Badon/Quangbinh (located in central region of the country). Clearly, magnitudes of Pc are not high. Recently, a severe TC Xangsane 2006 (T200615) hit to Danang city (Central region), ranking the 3rd lowest value in historical records, Pc≤ 965hPa, and maximum 10 min-mean wind speed of 31 m/s were reported. Statistically, there have been 47 TC-events are equivalent to Hurricane category 2 (Saffir-Simpson Hurricane scale, see [1]) but only 02 events were Hurricane category 3. Regarding to the approximate relation between Pc and wind speed as given in [1], roughly speaking, for the sites (in expose terrain country) located in the coastal areas ranged in N-latitudes of 11.0 21.5o o probably are expected the surface 3s-gust wind speeds of 52.0-62.6 m/s for sea-sites and 48.4-58.1 m/s for inland-sites. In VN Code, current version TCVN 2735-1995 and draft version TCVN 2737-2006 (VN06, see [15]), design wind speeds have been predicted by conventional Gumbel method. In general, predicted wind speeds were highest in the coastal regions in the northern areas (N-latitudes of 17.5 21.5o o ) and central areas (around N-14o ) of the country. Although the landing intensities are highest in N-latitudes 17.5 21.5o o and 13.0 16.5o o but TC-events associated with lower Pc evidently exhibited in N-latitudes of 13.0 16.5o o . This is questionable on the accuracy of design wind speeds drawn from short-record lengths. In addition, Figs. 1 and 3 delivered an interesting feature of TC hit to Vietnam; in which, almost TC after passing Hainan Island (China) then moving towards the northern coastline of Vietnam, the TC-central pressures have increased a little but it is not always the case in the central coastline regions of the country. The design wind speeds given in Chinese loading code GB50009-2001 [16] for southern coastline of China would be considered higher than those given for the sites located near Vietnam coastline (Noted that, In addition, Chinese wind data probably are longer and better than Vietnamese ones). In fact, wind map presented in VN06 [15] also have admitted this assumption. Consequently, the design wind speeds given in [15] seems to be not reasonable (see in Fig. 1). Moreover, in rating hurricane category, decision based on central pressure at landing time is more confident than using surface wind speeds. Examination of 9 hurricanes during last three decades in US shown that decision based on surface wind speed, sometimes lead to reduce hurricane category than in fact they were (Vickery et al. [7]). Nevertheless, checking Vietnamese wind data of several stations located along the coastline, there have been very curious wind records, in which, the wind speeds measured during 14 severe TC (as noted above) are not highest values in historical records.

Particularly, at Phulien/Haiphong station, TC winds reanalyzed by Giang et al [17, 18] showed very similar results given by VN06 (with return periods of R-50 years and R-100 years). However, an extrapolation of 3s-gust speed values of R-500 years (Also Bachlongvi Island station as well, the location see Fig. 1) was resulted in very high 3s-gust speeds, which is probably equivalent to 3s-gust speed for coastal region of Florida in the US (see [1]) where often suffers Hurricane category 4 or 5. Clearly, conventional extreme methods applied directly to TC-wind records in TC-prone region are unrealistic. 3. Outline of Monte Carlo Simulation technique applied for Tropical cyclones

In dealing with Monte-Carlo simulation, the starting point of model is the gradient-level wind field, where, the horizontal pressure distribution with respect to TC-center is usually assumed to be symmetric and expressed as following equation [e.g. 19].

( ) exp Bc P mP r P R r (1)

where ( )P r is atmospheric pressure at radial distance ( r ) from TC-center; Pc is minimum central pressure of TC; ∆P is central pressure difference; Rm is radius of maximum wind and B is scale parameter ranging from 1.0 to 2.5 as suggested by Holland [19] . In the case B=1.0, Eq. 1 is well known Schloemer’s equation.

In gradient balance, the tangential gradient wind speed Ug can be expressed by following equations [e.g. 12]

( , ) sin 2g rU r C fr

1 22sin 2rC fr r P r (2)

920

940

960

980

1000

1020

78910111213141516171819202122

Cen

tral p

ress

ure

Pc (h

Pa)

Latitude (deg.)

Values (Pc1) at the time TC is far from coastline of around 300 km

Values (Pc2) : right before TC crossed the coastline

Along coastline in North-South direction

and:

sin sinr (3)

Figure 2. Landing positions of 110 TC-events having Pc≤990hPa right before cross the coastline (Period of 1951-2007).

Figure 3. Variation of central pressures of 110 TC-events having 990cP hPa before landings (Period of 1951-2007).

where ρ is air density; C is translation velocity of TC-center; α is angle of interest for evaluating gradient wind; θ is approach angle of TC (α and θ are counterclockwise positive from East direction). Other parameters are as explained above. A typical procedure of MCS applied for TC-winds can be divided into 4 steps including:

Step1: Historical records of tropical cyclones are used to draw basic characteristics of storms affected to a given site/location. This information are represented by a set of statistical distributions of TC-parameters including central pressure difference ∆P, translation velocity C, size of the TC characterized by Radius of

61 TC-events with 980<Pc≤990hPa

49 TC-events with Pc≤980hPa

Hanoi

Ho Chi Minh

102 104 106 108 110 112 114E-Longtitude (deg.)

8

10

12

14

16

18

20

22

24

N-L

atitu

de (d

eg.)

102 104 106 108 110 112 114

8

10

12

14

16

18

20

22

24

Hainan Island

CHINA

LAOS

THAILAN

CAMBODIA

SOUTH CHINA SEA

Gulf of Tonkin

Gulf of Thailand

Macau

maximum wind Rm, approach angle θ of storms and occurrence rate λ. In order to define above statistical characteristics, a spatial domain is required, for instance, a circular sub-region centered on the target site with radius of 250 km are often used;

Step 2: All above statistical distributions of TC-parameters are inputs to generate randomly virtue TC by using Monte-Carlo simulation technique. Number of virtue TC-events to be generated corresponding to length of 2000 years or more;

Step 3: A set of gradient wind speeds and relevant directions can be obtained by using a pressure model to calculate gradient wind at given distance in any direction from TC-center.

Step 4: These gradient winds then are converted to surface winds by using appropriate relationship between gradient winds and surface winds. In this stage, good historical records of surface TC-winds are important for calibrating the TC-model. Finally, long term TC-winds at this site/location can be evaluated by using the method of extreme wind analysis.

Above procedure implies that many factors may play an important effect to the result of a simulation. Basically, they are the use of probabilistic models to represent TC-parameters, the technique applied for generating randomly virtue TC (sets of TC-parameter), wind field models used to determine gradient winds, the inquired surface observations to be used for calibrating the TC-model and so on. However, discussion on these aspects is out of scope of this paper (Such a discussion can be found in [e.g. 17]).

4. Monte Carlo simulation applied for Phulien/Haiphong meteorological station

Phulien/Haiphong meteorological station is one of the best meteorological stations in Vietnam. The station is located in the top of a hill of about 112 m height with is an almost symmetric shape. By employing AIJ-RLB 2004’s method, a speed up factor of 1.2 is derived to convert actual wind records at the station to the standard meteorological condition of 10 m height above ground. Figure 4 shows the location of Phulien and spatial domain which is used to select TC. There have been several TC after passing China-inland in range of N-latitudes of 21 22o o and moving towards Vietnam-inland and finally recurving southwest, and in last period almost TC already weaken into tropical depression. Hence, a virtue line as given in Fig. 4 was set to obtain all TC-events coming inside the circle and all TC-parameters were taken right before TC reached the virtue line.

4.1. Methodology of simulation

a) Statistical models of TC-parameters

Two parameters of ∆P and Rm were confirmed, which strongly influence the results of simulated gradient wind. ∆P may be obtained from peripheral pressure that is often assumedly about 1013 hPa [e.g. 6]. On the other hand, Rm may be more complicated to well define. Probably just the US has very good database of hurricanes with all parameters. Unfortunately, database of Rm are not available in Vietnam. However, Rm has correlation with latitude ψ and ∆P, hence, Rm may be obtained by empirically fitting observations of air pressure from different locations. Fujii [20] has divided Japan coastline into several segments and characterizing the typhoons hit Japan in which regardless of the latitude-factor for typhoons approaching to each certain coastline segments. Recently, Vickery et al. [7] investigated Hurricane database of US during period of 1886-1996, and proposed several equations to determine Rm. Among them, Vickery et al. [7] used Eq. 4 to evaluate Rm of hurricanes having ∆P >25 hPa.

2ln 2.636 0.000 0.039m PR (4)

During period of 1971-200, there have been 52 TC events approaching inside circle (see Fig. 5) with radius of 250 km centered at Phulien station. However, for better description of the general characteristics of TC-winds and based on experiences of previous studies on TC simulation, in this study, only 9 TC-events (see Table 1) having Pc≤ 980hPa are included for analysis. As parameters of λ, θ, C can obtain directly from TC-track data, two case studies were conducted as follows depending on methods applied to evaluate Rm and ∆P.

Case study 1: Measuring air-pressures of several stations (see Fig. 4), TC-central pressure Pc and distance d from TC-center to each meteorological station are inputs to empirically fit by Schloemer equation (B=1.0, see Eq. 1). Outputs will be values of Rm and ∆P.

Case study 2: Using Eq. 4 to evaluate mR and ∆P=1013- Pc.

Table 2 presents the distributions obtained by fitting each data set of TC-parameters. A mean value of all observed approach angles (Table 1) of 0174.79 was defined as reference-angle and the new data set

102 104 106 108 110 112 114

E-Longtitude (deg.)

16

18

20

22

24

N-L

atitu

de (d

eg.)

102 104 106 108 110 112 114

16

18

20

22

24CHINA

LAOS

of approach angles were obtained by subtracting from observations. For central pressure difference, with confident levels of 95%, both Weibull-and Lognormal distribution were well applied. Figures 6 and 7 illustrate the distributions used for Rm and ∆P in this study.

Figure 4. Location of Phulien Meteorological station and spatial domain used in simulation

Figure 5. TC-events having tracks inside circle 250 km-radius during period of 1971-2000

Table 1. Parameters of 9 TC-events having Pc≤980 hPa which are used in simulation

TC. No. cP (hPa) P (hPa) mR (km) C (m/s) (deg.) *p (hPa) *

mR (km) d(km)

T8008 976.4 26.8 59.1 7.4 187.1 37.6 29.5 12.7 T8015 979.6 28.5 22.3 5.4 189.3 34.4 29.2 67.2 T8106 978.9 25.8 23.5 3.5 164.7 35.1 28.4 142.3 T8614 967.5 35.7 20.0 9.1 183.1 46.5 28.3 20.9 T8711 978.1 31.8 64.9 5.1 169.0 35.9 27.4 234.6 T8905 980.0 26.6 84.8 3.6 144.1 33.1 29.8 15.9 T9111 973.2 30.3 64.1 3.5 225.2 40.7 27.5 217.8 T9607 979.0 22.4 75.2 4.0 154.0 35.0 29.0 81.5

102 104 106 108 110 112 114

E-Longtitude (deg.)

16

18

20

22

24

N-L

atitu

de (d

eg.)

102 104 106 108 110 112 114

16

18

20

22

24CHINA

LAOS

PhulienTienyen

BaichayBachlongviNamdinh

Vanly

SOUTH CHINA SEA

N-19.0 E-105.2

N-22.3 E-108.6

Circle with radius of 250 km centered at Phulien station

Macau

T9613 978.0 28.7 39.9 7.7 156.6 36.0 28.5 133.6

Notes: *p , and *

mR are values of case study 2; d : taken at the time what TC-parameters were selected.

Table 2. Statistical expression of TC-parameters (Case study 1)

Distribution parameters Param-eters

Distribution Probability density function, ( )xf x Case study 1 Case study 2

(deg.)

Normal 1 2 22 exp 2x m

for both cases 0m ; 24.53

C (m/s)

Lognormal 1 2 2ln ln ln2 exp ln 2x x xx x m

for both cases

ln xm 1.64; ln x 0.37

Weibull 1 exp kkk C x C x C

C 30.11; k 8.13

C 39.05; k 8.55

P (hPa)

Lognormal ln xm 3.34;

ln x 0.13 ln xm 3.61;

ln x 0.10

mR (km)

Lognormal ln xm 3.79;

ln x 0.57 ln xm 3.35;

ln x 0.03

Poisson !xe x for both cases 0.3

Figure 6. Distributions of radius of maximum winds in two case studies.

Figure 7. Central pressure difference expressed by Weibull- and Lognormal distribution

b) Generating tropical cyclones and their parameters randomly

In literature, two methods have been used to generate TC-parameters randomly. Of course, both methods rely on the chosen distributions of TC-parameters and correlations between TC-parameters. In the conventional method, correlations between TC-parameters were added in the chosen distributions of TC-parameters, then randomly generating virtue TC as stated in many works [e.g. 6, 7]. The other method recently has been

Radius of maximum winds 10 20 30 40 50 60 70 80 900

0.2

0.4

0.6

0.8

1

ObsLognormalconfidence bounds

Cum

ulat

ive

prob

abili

ty

95%

20 25 30 35 400

0.2

0.4

0.6

0.8

1

ObsLognormalconfidence bounds

Cum

ulat

ive

prob

abili

ty

Radius of maximum winds

95%

a) Case study 1 b) Case study 2

Central pressure difference P (hPa)

Cum

ulat

ive

prob

abili

ty

30 35 40 45 500

0.2

0.4

0.6

0.8

1

ObsWeibullLognormal

20 25 30 35 400

0.2

0.4

0.6

0.8

1

ObsWeibullLognormalC

umul

ativ

e pr

obab

ility

Central pressure difference P (hPa)

a) Case study 1 b) Case study 2

introduced by Matsui and Tamura [21], was the so called “POD based pressure model”. Here, Matsui and Tamura’s method is employed. Tables 3 and 4 show an example for Case study 1, in which the correlation matrix obtained from TC-records and 10000-year simulated values and corresponding to 2 types of distribution of P . Observed matrices of TC-parameters were obtained from following steps: i) TC-parameters (Table 1) and their chosen distributions (Table 2) are used to calculate probabilities in corresponding to each observation; ii) These probabilities are taken as inputs for standard normal distribution to calculate standard normal values; and, iii) Above standard normal values for each TC-parameter are used to determine the correlation matrices. Results shown in Tables 3 and 4 confirmed that correlations are well reproduced by using POD based pressure model.

Table 3. Case study 1-Observed and simulated matrix with Weibull distribution for P

Observed matrix Simulation matrix (10,000 years) P mR C P mR C

P 1.00 -0.42 0.55 0.39 1.00 -0.39 0.55 0.39

mR -0.42 1.00 -0.41 -0.17 -0.39 1.00 -0.40 -0.12 C 0.55 -0.41 1.00 0.05 0.55 -0.40 1.00 0.05 0.39 -0.17 0.05 1.00 0.39 -0.12 0.05 1.00

Table 4. Case study 1- Observed and simulated matrix with Lognormal distribution for P

Observed matrix Simulation matrix (10,000 years) P mR C P mR C

P 1.00 -0.40 0.53 0.44 1.00 -0.37 0.52 0.44

mR -0.40 1.00 -0.41 -0.17 -0.37 1.00 -0.40 -0.13 C 0.53 -0.41 1.00 0.05 0.52 -0.40 1.00 0.05 0.44 -0.17 0.05 1.00 0.44 -0.13 0.05 1.00

Figure 8. Correlation of simulated wind and observations (in 2 min-mean): a) 44 TC-events with 1000cP hPa; and b) 9 TC-events with 980cP hPa (Period of 1971-2000)

c) Establishing relationship between Gradient wind Ug and observed surface wind Us

Temporal gradient winds of a moving virtue TC can be obtained directly from Eqs. 1 to 3. If consecutive records of several intense storms are available and examinations of the surrounding topographical features at a site are conducted, the relationship of gradient- and surface winds may be well defined. Unfortunately, for most of the stations in Vietnam, such the data are not available (or missed) because of the limitation of wind anemographs and many other reasons. In this case, it seems to be hard to establish a reasonable relationship of observed surface winds and simulated gradient winds as a function of Pc≤ 1,000 hPa and 9 TC-events having Pc≤ 980 hPa. Here, an averaged value of Us/Ug of 0.99 (see Fig. 8b) is adopted for calculating surface winds from

0

10

20

30

40

50

0 10 20 30 40 50

Obs

erve

d w

ind

spee

d (m

/s)

Simulated gradient wind speed (m/s)

fitted line: Y=0.99 X

Y(max)=1.25X

Y(min)=0.82 X

0

10

20

30

40

50

0 10 20 30 40 50

Obs

erve

d w

ind

spee

d (m

/s)

Simulated gradient wind speed (m/s)

Fitted line: Y=0.89 X

a) 44 TC-events with 1000cP hPa b) 9 TC-events with 980cP hPa

simulation of 10000-year. It should be noted that, observations are in the averaged time of 2 minutes, so Us/Ug =0.99 seems to be higher than the field experiments, for instance, recent results given by Sparks and Huang [22] for inland stations.

4.2. Results of simulation and discussions

For each case study, numbers of simulated TC-events are equivalent to 10,000 years. For any virtue TC-events, all gradient winds were calculated; and for every year in which TC occurred, a largest value among simulated gradient winds are selected, taken in to account to form a set of annual maxima of simulated gradient TC-winds. Simulated gradient winds with return periods are shown in Fig. 9. It is observed that, there are no significant differences between simulated gradient winds for both case studies, in which Weibull- and Lognormal distribution were used to represent P . Case study 2 gave higher gradient winds. This is because, in general, evaluations of central pressure differences (∆P=1013- Pc) often give out higher values and

mR calculated by Eq. 3 lead to smaller values comparing to those obtained from fitting of Scholemer’s equation (Table 1).

However, the differences between two case studies are small, say less than 10%. Figure 10 shows the comparison of simulated surface wind speeds, in which the results associated with lognormal distribution used for representing P (Here, the Gumbel distribution is employed to fit the upper values only), and the TC-winds predicted by conventional extreme method applied directly to maxima TC-observations (called CM) as shown in Giang et al [17]. Differences between simulated values with R-50years of case studies 1 and 2 and CM’s results are about 27% and 17% respectively. The upper- and lower-bound of the simulated surface winds in regarding to ratio of Us/Ug 0.82 and 1.25 (Fig. 8b) also shown in Fig. 10. Nevertheless, the ratio Us/Ug of 1.25 (upper-bound) is not realistic for wind speed of 2min-mean. But, the surface simulated winds with this upper-bound are very close to CM’s results for return period 50R -year. Hence, physically, CM’s results are very conservative.

Using Dust’s model [23] which is still being widely used in many codes, e.g. ASCE 7-05 [1], conversion factors of 0.9 and 1.26 are derived to convert 2 min-mean wind speeds to 10 min-mean and 3-s gust values respectively for flat open terrain. Results of predicted TC-winds in 10 min-mean are shown in Tables 5. Simulation results lead to R-50 years design wind speeds (10 min-mean) are 28.3 m/s and 32.2 m/s in corresponding to Case studies 1 and 2. As seen in Fig. 1, deign wind speed of 36.1 m/s (10 min-mean, R-50 years) is used for coastlines of Hainan Island of China. Simulation results of Phulien station are smaller than Hainan’s design wind speed about 10-21% (Table 5), which are reasonably accepted. Moreover, looking to the observed TC-winds and the shortcoming of surface wind records in Vietnam, probably, the actual averaged time of measured winds is shorter than 2min as documented in the wind reports.

5. Conclusions

An extensive Monte-Carlo simulation of TC-winds has been carried out for Phulien/Haiphong meteorological station. It is found that, if only TC-events having 980cP hPa are taken into account for analysis, the use of whether Weibull- or Lognormal distribution to represent central pressure difference do not cause significant differences in the final results of simulation. An interesting point on the differences of the design wind speeds in adjacent coastal regions between North of Vietnam and South of China were subjected for examination. Results obtained by TC-simulation can explain well at this point.

Moreover, simulation results drive to conclude that, wind speeds given by Vietnamese loading code for northern coastal areas of the country, probably were overestimated about 17% to 27%. It is therefore, more works of TC-simulation should be done to better draw the fact of TC-winds in coastal regions of Vietnam. Finally, the consecutive wind measurements during every strong TC-events should be conducted to better calibrate the TC-models.

Acknowledgements

The authors gratefully acknowledge the full support of the Ministry of Education, Culture, Sports, Science and Technology of Japan through the 21st Century COE Program.

0

10

20

30

40

50

60

1 10 100 1000 10000

Win

d sp

eed

(m/s)

Return period, R-year

Solid line: Case1-Lognormal

Open circles: Case1-Weibull

Dashed line: Case2-Lognormal

Open squares: Case2-Weibull

Figure 9. Simulated gradient wind speeds versus return periods. (Lognormal- and Weibull denoted distribution types were used to fit central pressure difference.

Table 5. Comparison wind speeds given by Vietnamese code (VN06) and those predicted by simulations

and method applied Poisson process for TC-observations with fitted by Gumbel distribution (CM) as shown in Giang [17] and value given by Chinese code for coastal area of Hainan Island [16] .

Return Predicted surface wind speed (m/s) converted to 10 min-mean speeds period VN06[15] CM Simulated results-Case1 Simulated results-Case2 Hainan (years) Fit Max Min Fit Max Min Island 10 32.0 30.6 25.7 32.1 21.0 29.5 36.9 24.2 20 36.4 34.3 26.8 33.5 22.0 30.7 38.4 25.2 50 39.2 39.1 28.3 35.4 23.2 32.2 40.3 26.4 36.1 100 42.1 42.7 29.4 36.7 24.1 33.3 41.7 27.3 Note: Fit, Max and Min: are based on ratios Us/Ug as shown in Fig. 8b;

Figure 10. Comparison of surface winds predicted by simulations (Central pressure difference is represented by Lognormal distribution) and those obtained by applying Poisson process and Gumbel

distribution for all TC-observations (wind speeds are in averaging time of 2 minutes).

References 1. ASCE 7-05, Minimum design loads for buildings and other structures, New York , 2005.

2. Batts M.E., Cordes M.R., Russell L.R. and Simiu E., Hurricane wind speeds in the United States, NBS

Building Science Series-124, National Bureau of Standards, Washington, D.C, 1980.

1 10 100 1000 10000

Return period (R-year)

0

10

20

30

40

50

60

Win

d sp

eed

(m/s

)

Simulated surface winds with (Us/Ug)=0.82

Simulated surface winds with (Us/Ug)=1.25

Fitted by Type I (with upper tail)

Using Poisson process and fitted by Gumbel(Type I) for all TC-observations

a) Case study 1

Simulated surface winds with fitted line (Fig. 8b), Us/Ug=0.99

TC-surface observations: largest values of daily maxima during each tropical cyclone (Period of 1971-2000)

1 10 100 1000 10000

Return period (R-year)

0

10

20

30

40

50

60

Win

d sp

eed

(m/s

)

Simulated surface winds with (Us/Ug)=0.82

Simulated surface winds with (Us/Ug)=1.25

Fitted by Type I (with upper tail)

Using Poisson process and fitted by Gumbel(Type I) for all TC-observations

b) Case study 2