# Comparison of Extreme Wind and Waves Using Different Statistical Methods in 40 Offshore Wind Energy Lease Areas Worldwide

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## Abstract

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## 1. Introduction

#### 1.1. Background and Motivation

#### 1.2. Literature Review

#### 1.3. Research Objectives and Novelties

#### 1.4. Novelties

## 2. Methodology

#### 2.1. Criteria for Choosing Offshore Wind Lease Areas

#### 2.2. Source of Raw Data

#### 2.3. Block-Maxima Approach

#### 2.4. POT Approach

#### 2.5. Verification

#### 2.6. Assumptions and Limitations

## 3. Results and Discussion

#### 3.1. Block-Maxima Approach

#### 3.2. POT Approach

#### 3.3. Comparison of Extreme Values from the Block-Maxima and POT Approaches

## 4. Conclusions

- LA30 (Huaneng Hainan Wenchang I, China) recorded the outright highest annual maximum ${U}_{W}$ of 36.2 m/s, while LA10 (Allan array, Canada) recorded the highest mean annual maximum ${U}_{W}$ of 25.2 m/s.
- LA25 (GoliatVIND, Norway) recorded the highest mean annual maximum ${H}_{S}$ of 9.85 m, as well as the outright highest annual maximum ${H}_{S}$ of 14.2 m.
- The GPD CDF and Gumbel CDF showed good agreement with the empirical CDF for both the ${U}_{W}$ and ${H}_{S}$ values for all of the sites.
- The results from the POT approach varied significantly based on the chosen threshold.
- For smaller thresholds, the results from the POT approach were sensitive to the time window chosen. However, the time window did not have an impact on the results for larger thresholds, which were generally obtained from the mean residual life method.
- The POT approach is only effective for a small range of thresholds. Smaller thresholds lead to a poor fit to the GPD, while larger thresholds may not provide sufficient data points.
- The 50-year ${U}_{W}$ at 10 m above the sea level ranged between 16.5 m/s and 36.5 m/s. The 500-year ${U}_{W}$ ranged from 18.1 m/s to 49.5 m/s.
- The 50-year ${H}_{S}$ lay between 2.8 m and 15.1 m. The 500-year ${H}_{S}$ varied from 3.2 m to 18.7 m.
- It is found that the block-maxima approach using the Gumbel LS and GEV distributions provides upper bound estimates for the 50- and 500-year extreme values for both the ${U}_{W}$ and ${H}_{S}$.
- The estimates from the POT approach were generally lower by around 3% on average, although there were some outliers.
- European sites are more prone to extreme ${H}_{S}$ values in general. LA25 (GoliatVIND, Norway) produced the highest 50- and 500-year ${H}_{S}$ values of 15.1 m (Gumbel LS) and 18.7 m (Gumbel LS), respectively.
- Sites along the east coast of China have high estimates of extreme ${U}_{W}$ values. LA32 (Minyang Jieyang Qianzhan III, China) was prone to the highest 50- and 500-year ${U}_{W}$ values of 36.5 m/s (Gumbel LS) and 49.5 m/s (GEVD), respectively.
- The distribution parameters were provided for all of the methods, which would be helpful for extrapolating the extreme values to longer return periods.
- The mean residual life method used for estimating the optimal threshold has yielded results that lie close to or within the bounds of the estimates from the block-maxima approach.

#### Future Work

## Author Contributions

## Funding

## Data Availability Statement

## Acknowledgments

## Conflicts of Interest

## References

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**Figure 1.**Offshore wind targets for different markets [1].

**Figure 3.**Sample annual maximum selection for the block-maxima approach: (

**a**) ${H}_{S}$. (

**b**) ${U}_{W}$.

**Figure 6.**Sample annual maximum selection for the peaks over threshold approach: (

**a**) ${H}_{S}$. (

**b**) ${U}_{W}$.

**Figure 7.**Annual maximum ${U}_{W}$ at 10 m above the sea level in different lease areas around the world: (

**a**) LA1–5. (

**b**) LA6–10. (

**c**) LA11–15. (

**d**) LA16–20. (

**e**) LA21–25. (

**f**) LA26–30. (

**g**) LA31–35. (

**h**) LA36–40.

**Figure 8.**Annual maximum ${H}_{S}$ in different lease areas around the world: (

**a**) LA1–5. (

**b**) LA6–10. (

**c**) LA11–15. (

**d**) LA16–20. (

**e**) LA21–25. (

**f**) LA26–30. (

**g**) LA31–35. (

**h**) LA36–40.

**Figure 11.**Threshold selection for LA1: (

**a**) ${H}_{S}$ threshold selection using mean residual life method. (

**b**) Sample data reduction using the selected threshold for ${U}_{W}$. (

**c**) Wind speed threshold selection using mean residual life method. (

**d**) Sample data reduction using the selected threshold for ${U}_{W}$.

**Figure 12.**Threshold selection for LA22: (

**a**) ${H}_{S}$ threshold selection using mean residual life method. (

**b**) Sample data reduction using the selected threshold for ${H}_{S}$. (

**c**) ${U}_{W}$ threshold selection using mean residual life method. (

**d**) Sample data reduction using the selected threshold for ${U}_{W}$.

**Figure 13.**Effect of threshold on the number of data points for (

**a**) ${U}_{W}$ LA1–20; (

**b**) ${U}_{W}$ for LA21–40; (

**c**) ${H}_{S}$ for LA1–20; and (

**d**) ${H}_{S}$ for LA21–40.

**Figure 14.**Sensitivity analysis: Effects of threshold and time window on (

**a**) 50-year ${H}_{S}$ for LA1; (

**b**) 50-year wind speed for LA1; (

**c**) 50-year ${H}_{S}$ for LA22; and (

**d**) 50-year wind speed for LA22.

**Figure 16.**Comparison between block-maxima and POT approaches for LA1: (

**a**) 50-year ${H}_{S}$. (

**b**) 500-year ${H}_{S}$. (

**c**) 50-year ${U}_{W}$. (

**d**) 500-year ${U}_{W}$.

**Figure 17.**Comparison between block-maxima and POT approaches for LA22: (

**a**) 50-year ${U}_{W}$. (

**b**) 500-year ${U}_{W}$. (

**c**) 50-year ${H}_{S}$. (

**d**) 500-year ${H}_{S}$.

**Figure 18.**Comparison of hazard curves: (

**a**) LA1 Extreme ${U}_{W}$. (

**b**) LA1 Extreme ${H}_{S}$. (

**c**) LA22 Extreme ${U}_{W}$. (

**d**) LA22 Extreme ${H}_{S}$.

**Figure 19.**Comparison of hazard curves: (

**a**) LA25 Extreme ${U}_{W}$. (

**b**) LA25 Extreme ${H}_{S}$. (

**c**) LA32 Extreme ${U}_{W}$. (

**d**) LA32 Extreme ${H}_{S}$.

**Figure 20.**Comparison of extreme ${U}_{W}$ values obtained from different statistical methods: (

**a**) 50-year ${U}_{W}$. (

**b**) 500-year ${U}_{W}$.

**Figure 21.**Comparison of extreme ${H}_{S}$ values obtained from different statistical methods: (

**a**) 50-year ${H}_{S}$. (

**b**) 500-year ${H}_{S}$.

Lease Area # | Name | Country | GPS Coordinates | Water Depth (m) |
---|---|---|---|---|

1 | Maine research array | USA | 43${}^{\circ}$23${}^{\prime}$ N, 69${}^{\circ}$21${}^{\prime}$ W | ≈175 |

2 | Revolution wind | USA | 41${}^{\circ}$8${}^{\prime}$ N,71${}^{\circ}$4${}^{\prime}$ W | ≈35 |

3 | Ocean wind | USA | 39${}^{\circ}$6${}^{\prime}$ N, 74${}^{\circ}$17${}^{\prime}$ W | ≈37.5 |

4 | Garden State offshore energy | USA | 38${}^{\circ}$40${}^{\prime}$ N, 74${}^{\circ}$42${}^{\prime}$ W | ≈20 |

5 | Empire wind | USA | 40${}^{\circ}$17${}^{\prime}$ N, 73${}^{\circ}$19${}^{\prime}$ W | 21.9–41.14 |

6 | OCS-A 0545 | USA | 33${}^{\circ}$27${}^{\prime}$ N, 77${}^{\circ}$58${}^{\prime}$ W | ≈26 |

7 | CVOW Commercial Project | USA | 36${}^{\circ}$54${}^{\prime}$ N, 75${}^{\circ}$20${}^{\prime}$ W | 21.9–38.1 |

8 | Cascadia wind | USA | 46${}^{\circ}$46${}^{\prime}$ N, 124${}^{\circ}$39${}^{\prime}$ W | ≈150 |

9 | Morro Bay E | USA | 35${}^{\circ}$31${}^{\prime}$ N, 121${}^{\circ}$41${}^{\prime}$ W | ≈150 |

10 | Allan array | Canada | 51${}^{\circ}$37${}^{\prime}$ N, 128${}^{\circ}$43${}^{\prime}$ W | ≈35 |

11 | Sea-Breeze Tech | Canada | 46${}^{\circ}$2${}^{\prime}$ N, 61${}^{\circ}$49${}^{\prime}$ W | ≈50 |

12 | UY01 | Uruguay | 34${}^{\circ}$14${}^{\prime}$ S, 51${}^{\circ}$40${}^{\prime}$ W | ≈50 |

13 | Projeto Acu | Brazil | 22${}^{\circ}$8${}^{\prime}$ S, 40${}^{\circ}$44${}^{\prime}$ W | ≈50 |

14 | Farol wind | Brazil | 28${}^{\circ}$51${}^{\prime}$ S, 48${}^{\circ}$41${}^{\prime}$ W | ≈50 |

15 | Sopros do RJ | Brazil | 21${}^{\circ}$37${}^{\prime}$ S, 40${}^{\circ}$25${}^{\prime}$ W | ≈27 |

16 | Projeto Ubu | Brazil | 20${}^{\circ}$51${}^{\prime}$ S, 40${}^{\circ}$23${}^{\prime}$ W | ≈27 |

17 | Voyage | Ireland | 51${}^{\circ}$21${}^{\prime}$ N, 7${}^{\circ}$21${}^{\prime}$ W | ≈85 |

18 | Inch Cape | United Kingdom | 56${}^{\circ}$29${}^{\prime}$ N, 2${}^{\circ}$11${}^{\prime}$ W | ≈25 |

19 | Nordlicht I | Germany | 54${}^{\circ}$17${}^{\prime}$ N, 6${}^{\circ}$13${}^{\prime}$ E | ≈35 |

20 | Baltic offshore alpha | Sweden | 58${}^{\circ}$17${}^{\prime}$ N, 18${}^{\circ}$21${}^{\prime}$ E | ≈36 |

21 | Bornholm bassin syd | Denmark | 54${}^{\circ}$50${}^{\prime}$ N, 15${}^{\circ}$34${}^{\prime}$ E | ≈57 |

22 | Vigso bay | Denmark | 57${}^{\circ}$10${}^{\prime}$ N, 8${}^{\circ}$39${}^{\prime}$ E | ≈14 |

23 | Calabria | Italy | 38${}^{\circ}$26${}^{\prime}$ N, 16${}^{\circ}$52${}^{\prime}$ E | ≈475 |

24 | Normandie | France | 49${}^{\circ}$52${}^{\prime}$ N, 0${}^{\circ}$49${}^{\prime}$ W | ≈45 |

25 | GoliatVIND | Norway | 71${}^{\circ}$49${}^{\prime}$ N, 22${}^{\circ}$34${}^{\prime}$ E | 300–400 |

26 | Nao Victoria | Spain | 36${}^{\circ}$17${}^{\prime}$ N, 4${}^{\circ}$43${}^{\prime}$ W | ≈300 |

27 | Genesis Hexicon | South Africa | 30${}^{\circ}$2${}^{\prime}$ S, 31${}^{\circ}$38${}^{\prime}$ E | ≈500 |

28 | E3 | India | 7${}^{\circ}$50${}^{\prime}$ N, 77${}^{\circ}$49${}^{\prime}$ E | ≈50 |

29 | Miaoli | Taiwan | 24${}^{\circ}$39${}^{\prime}$ N, 120${}^{\circ}$38${}^{\prime}$ E | ≈50 |

30 | Huaneng Hainan Wenchang 1 | China | 19${}^{\circ}$58${}^{\prime}$ N, 111${}^{\circ}$3${}^{\prime}$ E | ≈120 |

31 | Huaneng Daishan I | China | 30${}^{\circ}$18${}^{\prime}$ N, 121${}^{\circ}$42${}^{\prime}$ E | ≈10 |

32 | Minyang Jieyang Qianzhan III | China | 22${}^{\circ}$38${}^{\prime}$ N, 116${}^{\circ}$27${}^{\prime}$ E | ≈40 |

33 | Boryeong | South Korea | 36${}^{\circ}$14${}^{\prime}$ N, 126${}^{\circ}$4${}^{\prime}$ E | ≈6 |

34 | Satsuma | Japan | 31${}^{\circ}$49${}^{\prime}$ N, 130${}^{\circ}$8${}^{\prime}$ E | ≈40 |

35 | Southern Mindoro | Philippines | 11${}^{\circ}$52${}^{\prime}$ N, 121${}^{\circ}$28${}^{\prime}$ E | ≈26 |

36 | Leeuwin | Australia | 33${}^{\circ}$1${}^{\prime}$ S, 115${}^{\circ}$17${}^{\prime}$ E | ≈40 |

37 | Mid West | Australia | 29${}^{\circ}$32${}^{\prime}$ S, 114${}^{\circ}$35${}^{\prime}$ E | ≈50 |

38 | Southern winds | Australia | 38${}^{\circ}$9${}^{\prime}$ S, 140${}^{\circ}$47${}^{\prime}$ E | ≈35 |

39 | Barwon | Australia | 38${}^{\circ}$44${}^{\prime}$ S, 142${}^{\circ}$18${}^{\prime}$ E | ≈78 |

40 | South Taranaki | New Zealand | 39${}^{\circ}$32${}^{\prime}$ S, 173${}^{\circ}$40${}^{\prime}$ E | ≈36 |

Lease Area # | Gumbel LS | Gumbel ML | Gumbel MOM | GEVD | POT | ||||||
---|---|---|---|---|---|---|---|---|---|---|---|

$\mathit{\mu}$ | $\mathit{\beta}$ | $\mathit{\mu}$ | $\mathit{\beta}$ | $\mathit{\mu}$ | $\mathit{\beta}$ | k | $\mathit{\sigma}$ | $\mathit{\mu}$ | k | $\mathit{\sigma}$ | |

1 | 22.97 | 1.98 | 22.98 | 1.89 | 23.03 | 1.74 | −0.16 | 1.98 | 23.14 | 0.21 | 3.31 |

2 | 22.24 | 2.19 | 22.27 | 2.04 | 22.31 | 1.92 | −0.11 | 2.12 | 22.39 | 0.15 | 3.13 |

3 | 21.26 | 2.32 | 21.34 | 2.03 | 21.34 | 2.03 | −0.02 | 2.04 | 21.36 | 0.05 | 2.73 |

4 | 20.90 | 2.06 | 20.93 | 1.96 | 20.97 | 1.79 | −0.15 | 2.02 | 21.08 | 0.13 | 2.81 |

5 | 20.01 | 2.40 | 20.12 | 2.02 | 20.10 | 2.09 | 0.02 | 2.01 | 20.10 | 0.12 | 2.90 |

6 | 20.82 | 3.42 | 20.87 | 3.06 | 20.92 | 3.00 | −0.02 | 3.08 | 20.90 | 0.02 | 2.70 |

7 | 19.28 | 2.11 | 19.28 | 2.14 | 19.37 | 1.82 | −0.36 | 2.33 | 19.69 | 0.16 | 2.99 |

8 | 21.49 | 2.03 | 21.50 | 2.05 | 21.56 | 1.78 | −0.19 | 2.10 | 21.71 | 0.23 | 3.62 |

9 | 18.12 | 2.06 | 18.19 | 1.77 | 18.19 | 1.81 | 0.03 | 1.74 | 18.16 | 0.001 | 1.73 |

10 | 24.28 | 1.75 | 24.30 | 1.69 | 24.34 | 1.52 | −0.14 | 0.65 | 24.43 | 0.22 | 3.11 |

11 | 22.34 | 2.55 | 22.48 | 2.07 | 22.43 | 2.21 | 0.06 | 2.02 | 22.42 | 0.12 | 3.12 |

12 | 21.16 | 2.15 | 21.20 | 1.90 | 21.23 | 1.89 | 0.020 | 1.88 | 21.18 | 0.14 | 2.94 |

13 | 13.97 | 0.92 | 13.96 | 0.98 | 14.01 | 0.78 | −0.41 | 1.04 | 14.18 | 0.17 | 1.15 |

14 | 17.55 | 1.47 | 17.60 | 1.29 | 17.60 | 1.28 | −0.02 | 1.30 | 17.61 | 0.12 | 1.62 |

15 | 14.46 | 1.23 | 14.49 | 1.08 | 14.52 | 1.05 | −0.05 | 1.11 | 14.52 | 0.24 | 1.50 |

16 | 14.83 | 1.86 | 14.86 | 1.63 | 14.89 | 1.61 | 0.08 | 1.56 | 14.79 | 0.14 | 1.68 |

17 | 21.74 | 1.62 | 21.81 | 1.30 | 21.79 | 1.41 | 0.12 | 1.24 | 21.73 | 0.20 | 2.70 |

18 | 21.48 | 2.29 | 21.55 | 1.96 | 21.55 | 2.01 | 0.06 | 1.90 | 21.49 | 0.15 | 3.20 |

19 | 22.13 | 1.90 | 22.21 | 1.63 | 22.20 | 1.66 | −0.005 | 1.64 | 22.22 | 0.16 | 2.90 |

20 | 19.18 | 1.54 | 19.23 | 1.37 | 19.24 | 1.35 | −0.03 | 1.38 | 19.25 | 0.20 | 2.62 |

21 | 20.33 | 1.84 | 20.43 | 1.59 | 20.42 | 1.56 | −0.04 | 1.61 | 20.47 | 0.17 | 2.90 |

22 | 22.18 | 2.40 | 22.25 | 2.03 | 22.25 | 2.10 | 0.08 | 1.97 | 22.17 | 0.09 | 2.76 |

23 | 21.42 | 2.24 | 21.46 | 2.30 | 21.51 | 1.93 | −0.16 | 2.29 | 21.66 | 0.20 | 3.89 |

24 | 19.93 | 1.52 | 19.92 | 1.57 | 19.99 | 1.32 | −0.26 | 1.63 | 20.15 | 0.25 | 2.67 |

25 | 22.20 | 2.42 | 22.33 | 1.93 | 22.28 | 2.10 | 0.12 | 1.83 | 22.20 | 0.14 | 2.92 |

26 | 18.46 | 2.10 | 18.46 | 2.03 | 18.54 | 1.83 | −0.29 | 2.25 | 18.78 | 0.07 | 2.49 |

27 | 21.98 | 2.05 | 21.98 | 1.97 | 22.06 | 1.76 | −0.23 | 2.12 | 22.24 | 0.19 | 2.45 |

28 | 14.54 | 1.38 | 14.70 | 0.98 | 14.64 | 1.12 | 0.08 | 0.95 | 14.66 | −0.06 | 0.84 |

29 | 21.54 | 3.16 | 21.61 | 2.86 | 21.64 | 2.77 | −0.05 | 2.91 | 21.69 | 0.11 | 2.87 |

30 | 17.83 | 4.44 | 18.10 | 3.46 | 17.99 | 3.85 | 0.15 | 3.23 | 17.83 | 0.03 | 3.04 |

31 | 15.98 | 2.78 | 16.03 | 2.43 | 16.08 | 2.42 | 0.05 | 2.37 | 15.97 | −0.01 | 1.95 |

32 | 20.68 | 4.08 | 20.87 | 3.32 | 20.82 | 3.55 | 0.12 | 3.15 | 20.66 | 0.02 | 2.57 |

33 | 18.17 | 3.22 | 18.51 | 2.16 | 18.33 | 2.72 | 0.28 | 1.86 | 18.20 | 0.02 | 2.08 |

34 | 18.89 | 2.89 | 18.95 | 2.53 | 18.99 | 2.53 | 0.02 | 2.51 | 18.92 | 0.04 | 2.65 |

35 | 15.62 | 3.66 | 16.33 | 1.97 | 15.97 | 2.80 | 0.16 | 1.82 | 16.15 | 0.03 | 2.16 |

36 | 20.13 | 1.44 | 20.12 | 1.47 | 20.19 | 1.23 | −0.30 | 1.56 | 20.36 | 0.33 | 3.38 |

37 | 19.11 | 1.90 | 19.11 | 1.90 | 19.18 | 1.65 | −0.20 | 1.98 | 19.32 | 0.16 | 2.09 |

38 | 18.25 | 1.62 | 18.23 | 1.83 | 18.33 | 1.37 | −0.44 | 1.84 | 18.64 | 0.28 | 2.42 |

39 | 16.89 | 1.75 | 16.87 | 2.11 | 17.00 | 1.45 | −0.40 | 1.95 | 17.30 | 0.25 | 2.36 |

40 | 22.04 | 1.64 | 22.03 | 1.64 | 22.10 | 1.42 | −0.30 | 1.78 | 22.31 | 0.16 | 2.36 |

Lease Area # | Gumbel LS | Gumbel ML | Gumbel MOM | GEVD | POT | ||||||
---|---|---|---|---|---|---|---|---|---|---|---|

$\mathit{\mu}$ | $\mathit{\beta}$ | $\mathit{\mu}$ | $\mathit{\beta}$ | $\mathit{\mu}$ | $\mathit{\beta}$ | k | $\mathit{\sigma}$ | $\mathit{\mu}$ | k | $\mathit{\sigma}$ | |

1 | 5.09 | 0.67 | 5.11 | 0.57 | 5.11 | 0.58 | 0.05 | 0.56 | 5.10 | 0.13 | 1.22 |

2 | 5.76 | 1.22 | 5.84 | 0.94 | 5.81 | 1.05 | 0.11 | 0.90 | 5.79 | 0.09 | 1.19 |

3 | 3.68 | 0.58 | 3.68 | 0.54 | 3.70 | 0.51 | −0.12 | 0.56 | 3.72 | 0.08 | 0.74 |

4 | 5.43 | 1.01 | 5.44 | 1.01 | 5.47 | 0.87 | −0.22 | 1.05 | 5.56 | 0.07 | 1.13 |

5 | 3.39 | 0.79 | 3.45 | 0.58 | 3.43 | 0.68 | 0.16 | 0.54 | 3.41 | 0.05 | 0.71 |

6 | 4.96 | 1.10 | 4.99 | 0.91 | 5.00 | 0.94 | 0.20 | 0.82 | 4.90 | 0.02 | 0.89 |

7 | 3.98 | 0.79 | 4.04 | 0.63 | 4.02 | 0.67 | 0.09 | 0.63 | 4.04 | 0.07 | 0.80 |

8 | 7.18 | 0.78 | 7.17 | 0.83 | 7.20 | 0.67 | −0.24 | 0.83 | 7.28 | 0.22 | 1.48 |

9 | 4.74 | 0.92 | 4.75 | 0.85 | 4.79 | 0.78 | −0.36 | 1.00 | 4.93 | 0.03 | 0.83 |

10 | 6.35 | 0.69 | 6.37 | 0.64 | 6.38 | 0.60 | −0.08 | 0.65 | 6.40 | 0.18 | 1.21 |

11 | 4.45 | 0.73 | 4.46 | 0.73 | 4.48 | 0.64 | −0.17 | 0.75 | 4.52 | 0.10 | 0.88 |

12 | 6.80 | 0.71 | 6.83 | 0.57 | 6.82 | 0.62 | 0.08 | 0.56 | 6.81 | 0.15 | 1.21 |

13 | 3.68 | 0.40 | 3.68 | 0.38 | 3.69 | 0.35 | −0.14 | 0.40 | 3.71 | 0.11 | 0.47 |

14 | 5.10 | 0.59 | 5.12 | 0.51 | 5.12 | 0.52 | 0.02 | 0.51 | 5.11 | 0.13 | 0.79 |

15 | 2.84 | 0.26 | 2.85 | 0.24 | 2.85 | 0.23 | −0.05 | 0.24 | 2.85 | 0.13 | 0.30 |

16 | 2.16 | 0.22 | 2.17 | 0.18 | 2.17 | 0.19 | 0.09 | 0.17 | 2.16 | 0.15 | 0.27 |

17 | 7.56 | 1.06 | 7.60 | 0.93 | 7.60 | 0.91 | −0.04 | 0.94 | 7.62 | 0.17 | 1.71 |

18 | 5.43 | 1.03 | 5.42 | 1.04 | 5.47 | 0.88 | −0.36 | 1.13 | 5.63 | 0.06 | 1.13 |

19 | 6.40 | 0.71 | 6.41 | 0.64 | 6.42 | 0.62 | −0.07 | 0.66 | 6.43 | 0.23 | 1.33 |

20 | 4.49 | 0.53 | 4.50 | 0.44 | 4.50 | 0.46 | 0.07 | 0.43 | 4.49 | 0.15 | 0.88 |

21 | 4.40 | 0.67 | 4.43 | 0.55 | 4.42 | 0.58 | 0.05 | 0.54 | 4.42 | 0.12 | 0.87 |

22 | 6.15 | 0.87 | 6.15 | 0.88 | 6.20 | 0.73 | −0.59 | 1.04 | 6.43 | 0.13 | 1.31 |

23 | 4.69 | 0.80 | 4.69 | 0.82 | 4.73 | 0.68 | −0.32 | 0.87 | 4.84 | 0.09 | 1.00 |

24 | 4.32 | 0.56 | 4.33 | 0.53 | 4.34 | 0.48 | −0.15 | 0.55 | 4.38 | 0.20 | 0.90 |

25 | 9.02 | 1.56 | 9.03 | 1.51 | 9.07 | 1.37 | −0.16 | 1.58 | 9.16 | 0.10 | 1.82 |

26 | 4.00 | 0.67 | 4.00 | 0.64 | 4.03 | 0.58 | −0.23 | 0.69 | 4.09 | 0.10 | 0.90 |

27 | 7.49 | 0.93 | 7.51 | 0.84 | 7.52 | 0.80 | −0.07 | 0.86 | 7.54 | 0.18 | 1.41 |

28 | 2.60 | 0.47 | 2.66 | 0.29 | 2.63 | 0.38 | 0.14 | 0.27 | 2.64 | 0.06 | 0.36 |

29 | 4.01 | 0.84 | 4.05 | 0.71 | 4.05 | 0.72 | −0.003 | 0.72 | 4.05 | 0.09 | 0.72 |

30 | 4.49 | 1.01 | 4.52 | 0.87 | 4.53 | 0.89 | 0.11 | 0.83 | 4.47 | −0.001 | 0.70 |

31 | 2.08 | 0.45 | 2.09 | 0.41 | 2.10 | 0.39 | −0.09 | 0.42 | 2.11 | 0.01 | 0.32 |

32 | 6.14 | 1.77 | 6.25 | 1.44 | 6.21 | 1.51 | 0.01 | 1.44 | 6.24 | −0.04 | 0.86 |

33 | 3.19 | 0.59 | 3.24 | 0.42 | 3.21 | 0.51 | 0.24 | 0.37 | 3.19 | 0.07 | 0.55 |

34 | 4.00 | 0.82 | 4.02 | 0.75 | 4.03 | 0.71 | −0.09 | 0.77 | 4.06 | 0.06 | 0.79 |

35 | 2.40 | 0.58 | 2.46 | 0.42 | 2.43 | 0.49 | 0.15 | 0.39 | 2.42 | 0.07 | 0.55 |

36 | 4.70 | 0.61 | 4.69 | 0.67 | 4.73 | 0.52 | −0.35 | 0.68 | 4.82 | 0.22 | 1.05 |

37 | 6.73 | 0.69 | 6.74 | 0.65 | 6.75 | 0.60 | −0.12 | 0.67 | 6.78 | 0.14 | 1.12 |

38 | 7.52 | 0.80 | 7.51 | 0.84 | 7.56 | 0.68 | −0.31 | 0.88 | 7.66 | 0.20 | 1.34 |

39 | 5.28 | 0.54 | 5.28 | 0.52 | 5.30 | 0.47 | −0.17 | 0.54 | 5.33 | 0.19 | 0.86 |

40 | 6.54 | 0.72 | 6.55 | 0.69 | 6.57 | 0.63 | −0.14 | 0.71 | 6.60 | 0.17 | 1.19 |

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## Share and Cite

**MDPI and ACS Style**

Bhaskaran, S.; Verma, A.S.; Goupee, A.J.; Bhattacharya, S.; Nejad, A.R.; Shi, W.
Comparison of Extreme Wind and Waves Using Different Statistical Methods in 40 Offshore Wind Energy Lease Areas Worldwide. *Energies* **2023**, *16*, 6935.
https://doi.org/10.3390/en16196935

**AMA Style**

Bhaskaran S, Verma AS, Goupee AJ, Bhattacharya S, Nejad AR, Shi W.
Comparison of Extreme Wind and Waves Using Different Statistical Methods in 40 Offshore Wind Energy Lease Areas Worldwide. *Energies*. 2023; 16(19):6935.
https://doi.org/10.3390/en16196935

**Chicago/Turabian Style**

Bhaskaran, Saravanan, Amrit Shankar Verma, Andrew J. Goupee, Subhamoy Bhattacharya, Amir R. Nejad, and Wei Shi.
2023. "Comparison of Extreme Wind and Waves Using Different Statistical Methods in 40 Offshore Wind Energy Lease Areas Worldwide" *Energies* 16, no. 19: 6935.
https://doi.org/10.3390/en16196935