Extreme value analysis for estimating 50 year return wind speeds from reanalysis data

Wind Energy ◽  
2013 ◽  
Vol 17 (8) ◽  
pp. 1231-1245 ◽  
Author(s):  
G. Anastasiades ◽  
P. E. McSharry
Keyword(s):  
Reanalysis Data ◽  
Extreme Value ◽  
Extreme Value Analysis ◽  
Value Analysis ◽  
Wind Speeds

scholarly journals A Nonstationary Extreme Value Analysis for the Assessment of Changes in Extreme Annual Wind Speed over the Gulf of St. Lawrence, Canada

2008 ◽  
Vol 47 (11) ◽  
pp. 2745-2759 ◽  
Author(s):  
Y. Hundecha ◽  
A. St-Hilaire ◽  
T. B. M. J. Ouarda ◽  
S. El Adlouni ◽  
P. Gachon
Keyword(s):  
Wind Speed ◽  
Gumbel Distribution ◽  
Reanalysis Data ◽  
Extreme Value ◽  
Significant Trend ◽  
Generalized Extreme Value Distribution ◽  
Extreme Value Analysis ◽  
Value Analysis ◽  
Significance Level ◽  
Wind Speeds

Abstract Changes in the extreme annual wind speed in and around the Gulf of St. Lawrence (Canada) were investigated through a nonstationary extreme value analysis of the annual maximum 10-m wind speed obtained from the North American Regional Reanalysis (NARR) dataset as well as observed data from selected stations of Environment Canada. A generalized extreme value distribution with time-dependent location and scale parameters was used to estimate quantiles of interest as functions of time at locations where significant trend was detected. A Bayesian method, the generalized maximum likelihood approach, is implemented to estimate the parameters. The analysis yielded shape parameters very close to 0, suggesting that the distribution can be modeled using the Gumbel distribution. A similar analysis using a nonstationary Gumbel model yielded similar quantiles with narrower credibility intervals. Overall, little change was detected over the period 1979–2004. Only 7% of the investigated grids exhibited trends at the 5% significant level, and the analysis performed on the reanalysis data at locations of significant trend indicated a rise in the median extreme annual wind speed by up to 2 m s−1 per decade in the southern coastal areas with a corresponding increase in the 90% and 99% quantiles of the extreme annual wind speeds by up to 5 m s−1 per decade. Also in the northern part of the gulf and some offshore areas in the south, the 50%, 90%, and 99% quantile values of the extreme annual wind speeds are noted to drop by up to 1.5, 3, and 5 m s−1, respectively. While the directions of the changes in the annual extremes at the selected stations are similar to those of the reanalysis data at nearby grid cells, the magnitudes and significance levels of the changes are generally inconsistent. Change at the same significance level over the same period of the NARR dataset was noted only at 2 stations out of 13.

Extreme-value analysis of canadian wind speeds

1987 ◽  
Vol 15 (4) ◽  
pp. 312-316 ◽  
Author(s):  
G. A. Whitmore ◽  
Jane F. Gentleman
Keyword(s):  
Extreme Value ◽  
Extreme Value Analysis ◽  
Value Analysis ◽  
Wind Speeds

Extreme-value analysis of Canadian wind speeds

1994 ◽  
pp. 119-144 ◽  
Author(s):  
Jane F. Gentleman ◽  
G. A. Whitmore ◽  
F. W. Zwiers ◽  
W. H. Ross
Keyword(s):  
Extreme Value ◽  
Extreme Value Analysis ◽  
Value Analysis ◽  
Wind Speeds

scholarly journals Extreme value analysis of Fiji's wind records

2005 ◽  
Vol 23 (1) ◽  
pp. 1 ◽  
Author(s):  
S P Rattan ◽  
R N Sharma
Keyword(s):  
Tropical Cyclones ◽  
Extreme Value ◽  
Wind Loading ◽  
Extreme Value Analysis ◽  
Value Analysis ◽  
Analysis Techniques ◽  
Wind Speeds ◽  
Extreme Wind ◽  
Modern Methods ◽  
Extreme Wind Speeds

A number of extreme value analysis techniques are utilised to predict basic design gust wind speeds for Fiji, which lies in a tropical cyclone prone region. The study shows that a number of modern methods tend to highly under-predict extreme wind speeds in regions of Fiji severely affected by tropical cyclones, although their skills improve in less severely affected regions. The reference for comparison was Dorman?s method, which has been previously used as a guidance for development of Region D wind speeds in the Australian wind loading code ? the AS1170.2-1989. In the case of Fiji, this study recommends the AS1170.2-1989 Region C provisions for Suva and the eastern coasts of the main island of Viti Levu only, and the AS1170.2-1989 Region D provisions elsewhere. This is significantly different to the provisions of the current National Building Code of Fiji (1990) which allow for the use of AS1170.2-1989 Region C provisions for all of Fiji. This difference is attributed to differences in the frequency and intensity of tropical cyclones visiting Fiji as compared with those for Australian Region C.

scholarly journals Statistics of Extreme Synoptic-Scale Wind Speeds in Ensemble Simulations of Current and Future Climate

2004 ◽  
Vol 17 (23) ◽  
pp. 4564-4574 ◽  
Author(s):  
H. W. van den Brink ◽  
G. P. Können ◽  
J. D. Opsteegh
Keyword(s):  
Climate Model ◽  
Extreme Value Distribution ◽  
Extreme Value ◽  
Extreme Value Analysis ◽  
Jet Stream ◽  
Return Periods ◽  
Value Analysis ◽  
Wind Speeds ◽  
Westerly Jet ◽  
Extreme Wind Speed

Abstract Statistical analysis of the wind speeds, generated by a climate model of intermediate complexity, indicates the existence of areas where the extreme value distribution of extratropical winds is double populated, the second population becoming dominant for return periods of order 103 yr. Meteorological analysis of the second population shows that it is caused when extratropical cyclones merge in an extremely strong westerly jet stream such that conditions are generated that are favorable for occurrence of strong diabatic feedbacks. Doubling of the greenhouse gas concentrations changes the areas of second population and increases its frequency. If these model results apply to the real world, then in the exit areas of the jet stream the extreme wind speed with centennial-to-millennial return periods is considerably larger than extreme value analysis of observational records implies.

Modeling European hot spells using extreme value analysis

2014 ◽  
Vol 58 (3) ◽  
pp. 193-207 ◽  
Author(s):  
C Photiadou ◽  
MR Jones ◽  
D Keellings ◽  
CF Dewes
Keyword(s):  
Extreme Value ◽  
Extreme Value Analysis ◽  
Value Analysis ◽  
Hot Spells

scholarly journals Threshold selection in univariate extreme value analysis

Extremes ◽  
2021 ◽  
Author(s):  
Laura Fee Schneider ◽  
Andrea Krajina ◽  
Tatyana Krivobokova
Keyword(s):  
Mean Squared Error ◽  
Extreme Value ◽  
Hill Estimator ◽  
Small Samples ◽  
Extreme Value Analysis ◽  
Threshold Selection ◽  
Value Analysis ◽  
Wide Range ◽  
Asymptotic Mean Squared Error ◽  
The Hill

AbstractThreshold selection plays a key role in various aspects of statistical inference of rare events. In this work, two new threshold selection methods are introduced. The first approach measures the fit of the exponential approximation above a threshold and achieves good performance in small samples. The second method smoothly estimates the asymptotic mean squared error of the Hill estimator and performs consistently well over a wide range of processes. Both methods are analyzed theoretically, compared to existing procedures in an extensive simulation study and applied to a dataset of financial losses, where the underlying extreme value index is assumed to vary over time.

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