Annual maximum wind speeds from parent distribution functions

1987 ◽  
Vol 25 (2) ◽  
pp. 163-178 ◽  
Author(s):  
R.V. Milford
Keyword(s):  
Distribution Functions ◽  
Annual Maximum ◽  
Maximum Wind ◽  
Wind Speeds ◽  
Parent Distribution

scholarly journals Extreme Wind and Waves in U.S. East Coast Offshore Wind Energy Lease Areas

Energies ◽  
2021 ◽  
Vol 14 (4) ◽  
pp. 1053
Author(s):  
Rebecca J. Barthelmie ◽  
Kaitlyn E. Dantuono ◽  
Emma J. Renner ◽  
Frederick L. Letson ◽  
Sara C. Pryor
Keyword(s):  
Return Period ◽  
East Coast ◽  
Offshore Wind ◽  
Time Interval ◽  
Annual Maximum ◽  
Outer Continental Shelf ◽  
Reanalysis Dataset ◽  
Maximum Wind ◽  
Wind Speeds ◽  
Extreme Wind

The Outer Continental Shelf along the U.S. east coast exhibits abundant wind resources and is now a geographic focus for offshore wind deployments. This analysis derives and presents expected extreme wind and wave conditions for the sixteen lease areas that are currently being developed. Using the homogeneous ERA5 reanalysis dataset it is shown that the fifty-year return period wind speed (U50) at 100 m a.s.l. in the lease areas ranges from 29.2 to 39.7 ms−1. After applying corrections to account for spectral smoothing and averaging period, the associated pseudo-point U50 estimates are 34 to 46 ms−1. The derived uncertainty in U50 estimates due to different distributional fitting is smaller than the uncertainty associated with under-sampling of the interannual variability in annual maximum wind speeds. It is shown that, in the northern lease areas, annual maximum wind speeds are generally associated with intense extratropical cyclones rather than cyclones of tropical origin. Extreme wave statistics are also presented and indicate that the 50-year return period maximum wave height may substantially exceed 15 m. From this analysis, there is evidence that annual maximum wind speeds and waves frequently derive from the same cyclone source and often occur within a 6 h time interval.

scholarly journals Comparison of Weibull Estimation Methods for Diverse Winds

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Ferhat Bingöl
Keyword(s):  
Wind Farm ◽  
Study Data ◽  
Distribution Functions ◽  
Likelihood Method ◽  
Estimation Methods ◽  
Weibull Function ◽  
Wind Speeds ◽  
Low Wind Speed ◽  
Low Wind Speeds ◽  
Wind Distribution

Wind farm siting relies on in situ measurements and statistical analysis of the wind distribution. The current statistical methods include distribution functions. The one that is known to provide the best fit to the nature of the wind is the Weibull distribution function. It is relatively straightforward to parameterize wind resources with the Weibull function if the distribution fits what the function represents but the estimation process gets complicated if the distribution of the wind is diverse in terms of speed and direction. In this study, data from a 101 m meteorological mast were used to test several estimation methods. The available data display seasonal variations, with low wind speeds in different seasons and effects of a moderately complex surrounding. The results show that the maximum likelihood method is much more successful than industry standard WAsP method when the diverse winds with high percentile of low wind speed occur.

scholarly journals Maximum wind speeds and US hurricane losses

2012 ◽  
Vol 39 (16) ◽  
pp. n/a-n/a ◽  
Author(s):  
R. J. Murnane ◽  
J. B. Elsner
Keyword(s):  
Maximum Wind ◽  
Wind Speeds

Comparison of Annual Maximum and Peaks Over Threshold Methods with Automated Threshold Selection in Flood Frequency Analysis: A Case Study for Australia

2021 ◽  
Author(s):  
Xiao Pan ◽  
Ataur Rahman
Keyword(s):  
Frequency Analysis ◽  
Flood Frequency ◽  
Distribution Functions ◽  
Flood Frequency Analysis ◽  
Annual Maximum ◽  
Threshold Selection ◽  
Peaks Over Threshold ◽  
Design Flood ◽  
Threshold Detection ◽  
Using Data

Abstract Flood frequency analysis (FFA) enables fitting of distribution functions to observed flow data for estimation of flood quantiles. Two main approaches, Annual Maximum (AM) and peaks-over-threshold (POT) are adopted for FFA. POT approach is under-employed due to its complexity and uncertainty associated with the threshold selection and independence criteria for selecting peak flows. This study evaluates the POT and AM approaches using data from 188 gauged stations in south-east Australia. POT approach adopted in this study applies a different average numbers of events per year fitted with Generalised Pareto (GP) distribution with an automated threshold detection method. The POT model extends its parametric approach to Maximum Likelihood Estimator (MLE) and Point Moment Weighted Unbiased (PMWU) method. Generalised Extreme Value (GEV) distribution using L-moment estimator is used for AM approach. It has been found that there is a large difference in design flood estimates between the AM and POT approaches for smaller average recurrence intervals (ARI), with a median difference of 25% for 1.01 year ARI and 5% for 50 and 100 years ARIs.

scholarly journals Case Study of a Barrier Wind Corner Jet off the Coast of the Prince Olav Mountains, Antarctica

2012 ◽  
Vol 140 (7) ◽  
pp. 2044-2063 ◽  
Author(s):  
Melissa A. Nigro ◽  
John J. Cassano ◽  
Matthew A. Lazzara ◽  
Linda M. Keller
Keyword(s):  
Strong Wind ◽  
Ice Shelf ◽  
Maximum Wind ◽  
Ross Ice Shelf ◽  
Wind Speeds ◽  
Wind Event ◽  
Transantarctic Mountains ◽  
Upper Level ◽  
Forcing Mechanisms

Abstract The Ross Ice Shelf airstream (RAS) is a barrier parallel flow along the base of the Transantarctic Mountains. Previous research has hypothesized that a combination of katabatic flow, barrier winds, and mesoscale and synoptic-scale cyclones drive the RAS. Within the RAS, an area of maximum wind speed is located to the northwest of the protruding Prince Olav Mountains. In this region, the Sabrina automatic weather station (AWS) observed a September 2009 high wind event with wind speeds in excess of 20 m s−1 for nearly 35 h. The following case study uses in situ AWS observations and output from the Antarctic Mesoscale Prediction System to demonstrate that the strong wind speeds during this event were caused by a combination of various forcing mechanisms, including katabatic winds, barrier winds, a surface mesocyclone over the Ross Ice Shelf, an upper-level ridge over the southern tip of the Ross Ice Shelf, and topographic influences from the Prince Olav Mountains. These forcing mechanisms induced a barrier wind corner jet to the northwest of the Prince Olav Mountains, explaining the maximum wind speeds observed in this region. The RAS wind speeds were strong enough to induce two additional barrier wind corner jets to the northwest of the Prince Olav Mountains, resulting in a triple barrier wind corner jet along the base of the Transantarctic Mountains.

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