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

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.

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

Wind Energy ◽

10.1002/we.1630 ◽

2013 ◽

Vol 17 (8) ◽

pp. 1231-1245 ◽

Cited By ~ 9

Author(s):

G. Anastasiades ◽

P. E. McSharry

Keyword(s):

Reanalysis Data ◽

Extreme Value ◽

Extreme Value Analysis ◽

Value Analysis ◽

Wind Speeds

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Extreme-value analysis of canadian wind speeds

Canadian Journal of Statistics ◽

10.2307/3315249 ◽

1987 ◽

Vol 15 (4) ◽

pp. 312-316 ◽

Cited By ~ 3

Author(s):

G. A. Whitmore ◽

Jane F. Gentleman

Keyword(s):

Extreme Value ◽

Extreme Value Analysis ◽

Value Analysis ◽

Wind Speeds

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Spatial and Seasonal Variability of Metocean Design Criteria in the Southern South China Sea From Covariate Extreme Value Analysis

Volume 7B: Ocean Engineering ◽

10.1115/omae2019-95913 ◽

2019 ◽

Cited By ~ 1

Author(s):

Vadim Anokhin ◽

Emma Ross ◽

David Randell ◽

Philip Jonathan

Keyword(s):

Wind Speed ◽

South China Sea ◽

South China ◽

Upper Bound ◽

Shape Parameter ◽

Extreme Value ◽

Extreme Value Analysis ◽

Design Criteria ◽

Southern South China Sea ◽

Value Analysis

Abstract This paper describes spatial and seasonal variability of metocean design criteria in the southern South China Sea. Non-stationary extreme value analysis was performed using the CEVA approach (Covariate Extreme Value Analysis,[1]) for a 59-year long SEAFINE hindcast of winds and waves, estimating metocean design criteria up to 10,000-year return period. Wind design criteria are mostly driven by large-scale monsoonal events; at higher return periods infrequent cyclonic events have strong influence on the tail of the extreme value distribution but confined to a limited geographical area. The CEVA analysis of waves showed much less dependence on the tropical cyclone events; the spatial metocean design criteria were smoother, mostly influenced by the monsoonal wind strength, fetch and local bathymetry. Return value estimates illustrate the strong seasonality of metocean design criteria, with boreal winter (December-February, Northeasterly monsoon) contributing most to the extremes, while April and May are the mildest months. Estimates for the ratio of 10,000/100-year return values are also presented, both for winds and waves. There is empirical evidence that the range of “typical” values of generalised Pareto shape parameter observed for Hs is different to that observed for wind speed. For this reason, an upper bound of +0.2 for generalised Pareto shape was specified for wind speed analysis, compared to 0.0 for Hs. In some cases, increase of upper bound for waves to 0.1 is justified, leading to slightly more conservative Hs values. We confirmed that the upper end point constraint was not too influential on the distributions of generalised Pareto shape parameter estimated. Nevertheless, it is apparent that specification of bounds for generalised Pareto shape is a critical, but problematic choice in metocean applications.

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Robustness of precipitation and river discharge extremes in the surrogate world of seasonal forecasts

10.5194/egusphere-egu21-7126 ◽

2021 ◽

Author(s):

Katharina Klehmet ◽

Peter Berg ◽

Denica Bozhinova ◽

Louise Crochemore ◽

Ilias Pechlivanidis ◽

...

Keyword(s):

Extreme Precipitation ◽

River Discharge ◽

Value Theory ◽

Extreme Value ◽

Generalized Extreme Value Distribution ◽

Extreme Value Analysis ◽

Seasonal Forecasts ◽

Adaptation Measures ◽

Value Analysis ◽

Mitigation And Adaptation

Robust information of hydrometeorological extremes is important for effective risk management, mitigation and adaptation measures by public authorities, civil and engineers dealing for example with water management. Typically, return values of certain variables, such as extreme precipitation and river discharge, are of particular interest and are modelled statistically using Extreme Value Theory (EVT). However, the estimation of these rare events based on extreme value analysis are affected by short observational data records leading to large uncertainties.In order to overcome this limitation, we propose to use the latest seasonal meteorological prediction system of the European Centre for Medium-Range Weather Forecasts (ECMWF SEAS5) and seasonal hydrological forecasts generated with the pan-European E-HYPE model of the original period 1993-2015 and to extend the dataset to longer synthetic time series by pooling single forecast months to surrogate years. To ensure an independent dataset, the seasonal forecast skill is assessed in advance and months (and lead months) with positive skill are excluded. In this study, we simplify the method and work with samples of 6- and 4-month forecasts (instead of the full 7-month forecasts) depending on the statistical independency of the variables. It enables the record to be extended from the original 23 years to 3450 and 2300 surrogate years for the 6- and 4-month forecasts respectively.Furthermore, we investigate the robustness of estimated 50- and 100-year return values for extreme precipitation and river discharge using 1-year block maxima that are fitted to the Generalized Extreme Value distribution. Surrogate sets of pooled years are randomly constructed using the Monte-Carlo approach and different sample sizes are chosen. This analysis reveals a considerable reduction in the uncertainty of all return period estimations for both variables for selected locations across Europe using a sample size of 500 years. This highlights the potential in using the ensembles of meteorological and hydrological seasonal forecasts to obtain timeseries of sufficient length and minimize the uncertainty in the extreme value analysis.

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Extreme Value Analysis and Joint Densities of Wind Directionality, Wind Speed and Wave Height for Malaysian Waters

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.935.159 ◽

2014 ◽

Vol 935 ◽

pp. 159-162

Author(s):

Wan Sharil Ahmad Termizi ◽

Mohd Shahir Liew

Keyword(s):

Fluid Dynamics ◽

Wind Speed ◽

Computer Science ◽

Robust Design ◽

Joint Effect ◽

Extreme Value ◽

Extreme Value Analysis ◽

Offshore Platforms ◽

Value Analysis ◽

Maximum Value

Prediction of extreme environmental loadings is crucial in the design of offshore platforms. Combinations of independent 100-year loadings are usually used assuming that it will bring the maximum value that is suitable for the design. It is rather impossible for such independent combinations to occur at the same time. Addressing this issue requires approaches in two methods. The first would be to find the probabilities of joint effect of these parameters, while the second would be to forecast the extreme value of each parameter. Joint densities and extreme value analysis have become available due to significant advancements in fluid dynamics and computer science. By using the combination of these two techniques, the actual loading of wind and wave can be obtained, leading to optimum and robust design.

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Extreme-value analysis of Canadian wind speeds

Case Studies in Data Analysis - Lecture Notes in Statistics ◽

10.1007/978-1-4612-2688-8_5 ◽

1994 ◽

pp. 119-144 ◽

Cited By ~ 1

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

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A simulation method for the macro-meteorological wind speed and the implications for extreme value analysis

Journal of Wind Engineering and Industrial Aerodynamics ◽

10.1016/j.jweia.2013.12.003 ◽

2014 ◽

Vol 125 ◽

pp. 146-155 ◽

Cited By ~ 6

Author(s):

R. Ian Harris

Keyword(s):

Wind Speed ◽

Simulation Method ◽

Extreme Value ◽

Extreme Value Analysis ◽

Value Analysis

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An approach to model extreme wind speed distributions using the Weather Research and Forecasting model

Wind Engineering ◽

10.1177/0309524x19849852 ◽

2019 ◽

Vol 44 (4) ◽

pp. 341-360

Author(s):

Ignacio Franco ◽

Alejandro Gutierrez ◽

José Cataldo

Keyword(s):

Wind Speed ◽

Reanalysis Data ◽

Cumulative Distribution ◽

Extreme Value Analysis ◽

Weather Research And Forecasting ◽

Good Representation ◽

Value Analysis ◽

Extreme Wind ◽

Extreme Wind Speed ◽

Weather Research

The aim of this study was to generate hourly mean monthly maximum wind speed return period curves for heights 60 m above the ground using Weather Research and Forecasting modeled data. The methodology introduced produces long-term wind speed data in places where the available measured series for such heights are not long enough for a correct extreme value analysis. Climate Forecast System Reanalysis data are used as input for the Weather Research and Forecasting simulations, providing information for the period 1979–2015. The modeled results are compared with wind speed series measured in anemometric towers, available for the period 2008–2015. Weather Research and Forecasting output is then adjusted to model properly the wind speed at the measuring sites. A good representation of the cumulative distribution of monthly maxima was reached after applying a double linear adjustment.

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Wind and Wave Extremes from Atmosphere and Wave Model Ensembles

Journal of Climate ◽

10.1175/jcli-d-18-0217.1 ◽

2018 ◽

Vol 31 (21) ◽

pp. 8819-8842 ◽

Cited By ~ 16

Author(s):

Alberto Meucci ◽

Ian R. Young ◽

Øyvind Breivik

Keyword(s):

Time Series ◽

Wind Speed ◽

Wave Height ◽

Significant Wave Height ◽

Wave Model ◽

Extreme Value ◽

Extreme Value Analysis ◽

Ensemble Forecasts ◽

Value Analysis ◽

Model Ensembles

The present work develops an innovative approach to wind speed and significant wave height extreme value analysis. The approach is based on global atmosphere–wave model ensembles, the members of which are propagated in time from the best estimate of the initial state, with slight perturbations to the initial conditions, to estimate the uncertainties connected to model representations of reality. The low correlation of individual ensemble member forecasts at advanced lead times guarantees their independence and allows us to perform inference statistics. The advantage of ensemble probabilistic forecasts is that it is possible to synthesize an equivalent dataset of duration far longer than the simulation period. This allows the use of direct inference statistics to obtain extreme value estimates. A short time series of six years (from 2010 to 2016) of ensemble forecasts is selected to avoid major changes to the model physics and resolution and thus ensure stationarity. This time series is used to undertake extreme value analysis. The study estimates global wind speed and wave height return periods by selecting peaks from ensemble forecasts from +216- to +240-h lead time from the operational ensemble forecast dataset of the European Centre for Medium-Range Weather Forecasts (ECMWF). The results are compared with extreme value analyses performed on a commonly used reanalysis dataset, ERA-Interim, and buoy data. The comparison with traditional methods demonstrates the potential of this novel approach for statistical analysis of significant wave height and wind speed ocean extremes at the global scale.

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