A discussion of statistical methods used to estimate extreme wind speeds

Perrin, Olivier; Rootzén, Holger; Taesler, Roger

doi:10.1007/s00704-005-0187-3

Cited by 54 publications

(38 citation statements)

References 10 publications

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“…It is sufficient that dependence decreases suitable fast with increasing time separation (Perrin et al, 2006) Given a monitoring station and once a sample of daily maxima of hourly measurements is calculated in the period of time under consideration, the first step in our study is to estimate the distribution function of that sample of extreme values. Following the lines indicated in Section 2, this can be done by means of parametric or nonparametric methods.…”

Section: Resultsmentioning

confidence: 99%

“…This methodology has been usually used in many fields of environmental studies such as climatology (Elsner et al, 2006;Perrin et al, 2006;Rajabi and Modarres, 2008), hydrology (Katz et al, 2002), agricultural management (Gomes et al, 2003) and many others. There are also interesting papers in the analysis of ground-level ozone using extreme value theory (see, e.g.…”

Section: Introductionmentioning

confidence: 99%

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Analysis of high level ozone concentrations using nonparametric methods

Quintela-del-Rı́o

Francisco‐Fernández

2011

Science of The Total Environment

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Controlling emissions of air pollutants and establishing air quality objectives to improve and protect ambient air quality are very important tasks of Governments. Ozone (O 3 ), one of those pollutants of concern, is not emitted directly into the atmosphere, but is a secondary pollutant produced by reaction between nitrogen dioxide (NO 2 ), hydrocarbons and sunlight. High levels of ozone can produce harmful effects on human health and the environment in general. Therefore, the study of extreme values of ozone represents an important topic of research in environmental problems. Classical extreme value theory has been usually used in air-pollution studies. It consists in fitting a parametric generalized extreme value (GEV) distribution to a data set of extreme values and using the estimated distribution to compute quantities like the probability of exceedance, the quantiles, the return levels or the mean return periods. In this paper, we propose nonparametric methods to esti-

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Analysis of high level ozone concentrations using nonparametric methods

Quintela-del-Rı́o

Francisco‐Fernández

2011

Science of The Total Environment

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“…A detailed simulation study looking at the effects of discretization for meteorological data was carried out by Cooley et al (2007). Perrin et al (2006) found that discretized wind speed values can result in erroneously low standard errors for parameter estimates. However, we do not look at standard errors of our parameter estimates here, as we are interested primarily in prediction, and not in parameter estimation for its own sake.…”

Section: Discussionmentioning

confidence: 99%

Probabilistic Wind Speed Forecasting using Ensembles and Bayesian Model Averaging

Sloughter¹,

Gneiting²,

Raftery³

2008

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Probabilistic forecasts of wind speed are becoming critical as interest grows in wind as a clean and renewable source of energy, in addition to a wide range of other uses, from aviation to recreational boating. Statistical approaches to wind forecasting offer two particular challenges: the distribution of wind speeds is highly skewed, and wind observations are reported to the nearest whole knot, a much coarser discretization than is seen in other weather quantities. The prevailing paradigm in weather forecasting is to issue deterministic forecasts based on numerical weather prediction models. Uncertainty can then be assessed through ensemble forecasts, where multiple estimates of the current state of the atmosphere are used to generate a collection of deterministic predictions. Ensemble forecasts are often uncalibrated, however, and Bayesian model averaging (BMA) is a statistical way of postprocessing these forecast ensembles to create calibrated predictive probability density functions (PDFs). It represents the predictive PDF as a weighted average of PDFs centered on the individual bias-corrected forecasts, where the weights reflect the forecasts' relative contributions to predictive skill over a training period. In this paper we extend BMA to provide probabilistic forecasts of wind speed, taking account of the skewness of the predictive distributions and the discreteness of the observations. The BMA method is applied to 48-hour ahead forecasts of maximum wind speed over the North American Pacific Northwest in 2003 using the University of Washington mesoscale ensemble, and is shown to provide calibrated and sharp probabilistic forecasts. Comparisons are made between a number of formulations that account for the discretization of the observations.

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“…Extreme wind speed prediction is an important issue for design of structures exposed to the weather variations. Significant efforts have been devoted to the problem of predicting extreme wind speeds on the basis of measured data by various authors over several decades, see, for example, [45][46][47][48] for extensive references to previous work. Hourly maximum gust wind was recorded during the 13 years 1999-2012 at Nordøyan and the 14 years 1998-2012 at Hekkingen.…”

Section: Measured Wind Speed Datamentioning

confidence: 99%

Estimation of Extreme Values by the Average Conditional Exceedance Rate Method

Næss

Gaidai

Karpa

2013

Journal of Probability and Statistics

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This paper details a method for extreme value prediction on the basis of a sampled time series. The method is specifically designed to account for statistical dependence between the sampled data points in a precise manner. In fact, if properly used, the new method will provide statistical estimates of the exact extreme value distribution provided by the data in most cases of practical interest. It avoids the problem of having to decluster the data to ensure independence, which is a requisite component in the application of, for example, the standard peaks-over-threshold method. The proposed method also targets the use of subasymptotic data to improve prediction accuracy. The method will be demonstrated by application to both synthetic and real data. From a practical point of view, it seems to perform better than the POT and block extremes methods, and, with an appropriate modification, it is directly applicable to nonstationary time series.

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A discussion of statistical methods used to estimate extreme wind speeds

Cited by 54 publications

References 10 publications

Analysis of high level ozone concentrations using nonparametric methods

Analysis of high level ozone concentrations using nonparametric methods

Probabilistic Wind Speed Forecasting using Ensembles and Bayesian Model Averaging

Estimation of Extreme Values by the Average Conditional Exceedance Rate Method

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