scholarly journals RELATION BETWEEN SHAPE OF EXTREME WAVE HEIGHT DISTRIBUTIONS AND WEATHER SYSTEM

2018 ◽  
pp. 73
Author(s):  
Maki Chiwata ◽  
Tomoya Shimura ◽  
Nobuhito Mori
Keyword(s):  
Wave Height ◽  
Extreme Value ◽  
Structure Design ◽  
Extreme Value Analysis ◽  
Observation Data ◽  
Height Distribution ◽  
Extreme Wave ◽  
Engineering Applications ◽  
Value Analysis

The extreme value analysis of wave height has been used to estimate design value of coastal structure design. The procedure of extreme value analysis is standardized but distributions change highly depending on target locations. A long-term atmospheric reanalysis is useful for engineering applications to complement observation data. Although the reanalysis dataset was insufficient for coastal engineering applications due to shorter length of period and sparse spatial resolution of modeling, recent reanalysis (e.g. CFSR) give reasonable performance for engineering applications as wave climate both accuracy and length of periods (e.g. Menendez and Losada, 2017). This study analyzes characteristics of extreme wave heights and understands relation between extreme wave height distribution and its dependence on weather systems based on long-term analysis and observed data.

Extreme Wave Loads

2008 ◽  
Author(s):  
Sofia Caires ◽  
Marcel R. A. van Gent
Keyword(s):  
Wave Height ◽  
Ad Hoc ◽  
Value Theory ◽  
Extreme Value ◽  
Wave Period ◽  
Extreme Value Analysis ◽  
Wave Loads ◽  
Extreme Wave ◽  
Value Analysis ◽  
Return Value

This paper compares three main methods for estimating extreme wave loads with a view towards determining the sensitivity of estimates to the particular approach chosen. The approaches considered include: a) The generally used ad-hoc procedure of performing an extreme value analysis of the Hs data, trying to find a relationship between wave height and period at the storm peaks and then, once the return values of extreme wave heights are estimated, estimating the associated return value of the wave period by means of the relationship found. b) The ‘structure variable method’ in which the pairs wave height and period observations are converted into univariate loads to which univariate extreme value theory is applied to estimate the return value of the structural load. c) The multivariate extreme value approach suggested by [1] in which a ‘multivariate return value’, namely the most probable value of the wave period conditional on a return value of the wave height, is estimated. Our study is based on a 44-yr long timeseries of wave conditions created using the shallow water wave model SWAN and calibrated ERA-40 data. The results suggest that the three approaches yield similar estimates. However, the ad-hoc procedure a gives the least conservative estimates. Approach c provides results that apply to any choice of load function and which to a certain extent are independent of the location in which the estimates are obtained, for which reason it may generally be the preferred one.

Likelihood-Weighted Method of General Pareto Distribution for Extreme Wave Height Estimation

2013 ◽  
Author(s):  
Ryota Wada ◽  
Takuji Waseda
Keyword(s):  
Probability Density Function ◽  
Confidence Interval ◽  
Bayesian Statistics ◽  
Probability Density ◽  
Density Function ◽  
Wave Height ◽  
Extreme Value ◽  
Structure Design ◽  
Observation Data ◽  
Extreme Wave

In designing ocean structures, estimating the largest wave height it may encounter over its lifetime is a critical issue, but wave observation data is often sparse in space and time. Because of the limited data available, estimation errors are inevitably large. For an economical and robust structure design, the probability density function of the extreme wave height and its confidence interval must be theoretically quantified from limited information available. Extreme values estimations have been made by finding the best fitting distribution from limited observations, and extrapolating it for the desired long period. Estimations based on frequentist method lack of generality in confidence interval estimations, especially when the data size is small. Another technique recently developed is based on Bayesian Statistics, which provides the inference of uncertainty. Previous studies use informative and non-informative priors and Markov Chain Monte Carlo (MCMC) simulation for estimation. We have developed a “Likelihood-Weighted Method (LWM)” to objectively evaluate probability density function of the extreme value. The method is based on Extreme Theory and Bayesian Statistics. Our attempt is to use the ignorant prior to relate each parameter set’s likelihood to its probability. This method is pragmatic, because the numerical implementation does not require the use of MCMC. The theoretical background and practical advantages of LWM are described. Examples from randomly produced data show the performance of this method, and application to real wave data reveals the poor estimations of previous methods that do not use the Bayesian theorem. The quantification of probability for each extreme value distribution enables the probability-weighted evaluation for inference such as maximum wave height probability density function. The new inference derived from this method is useful to change structure design methodologies of ocean structures.

scholarly journals Long-Term and Seasonal Trends in Global Wave Height Extremes Derived from ERA-5 Reanalysis Data

2020 ◽  
Vol 8 (12) ◽  
pp. 1015
Author(s):  
Alicia Takbash ◽  
Ian R. Young
Keyword(s):  
Southern Hemisphere ◽  
Wave Height ◽  
Significant Wave Height ◽  
Reanalysis Data ◽  
Extreme Value Analysis ◽  
Winter Storms ◽  
Value Analysis ◽  
Weather Forecasts ◽  
Significant Wave

A non-stationary extreme value analysis of 41 years (1979–2019) of global ERA5 (European Centre for Medium-Range Weather Forecasts Reanalysis) significant wave height data is undertaken to investigate trends in the values of 100-year significant wave height, Hs100. The analysis shows that there has been a statistically significant increase in the value of Hs100 over large regions of the Southern Hemisphere. There have also been smaller decreases in Hs100 in the Northern Hemisphere, although the related trends are generally not statistically significant. The increases in the Southern Hemisphere are a result of an increase in either the frequency or intensity of winter storms, particularly in the Southern Ocean.

scholarly journals Projected 21st Century changes in extreme wind-wave events

2020 ◽  
Author(s):  
Alberto Meucci
Keyword(s):  
Wave Height ◽  
21St Century ◽  
Wind Wave ◽  
Ocean Waves ◽  
Extreme Value Analysis ◽  
High Latitudes ◽  
Extreme Wave ◽  
Value Analysis ◽  
The North ◽  
Uncertainty Estimates

<p>Extreme ocean waves shape world coastlines and significantly impact offshore operations. Climate change may further exacerbate these effects increasing losses in human lives and economic activities. Studies generally agree on the trends in the mean values, yet there is no consensus on the extreme events, and whether their magnitude and/or frequency are changing. The present work applies an innovative extreme value analysis approach to a multi-model ensemble wind-wave climate dataset, derived from seven global climate models, to evaluate projected extreme wave height changes towards the end of the 21st century. Under two greenhouse gas emission scenarios, we find that at the end of the 21st century, the one in 100-year wave height event increases across the scenarios by 5 to 15 % over the Southern Ocean. The North Atlantic shows a decrease at low to mid-latitudes (5 to 15 %) and an increase at the high latitudes (10 %). The extreme wave heights in the North Pacific increase at the high latitudes by 5 to 10 %. The present work suggests that pooling an ensemble of future projected ocean storms from different GCMs might significantly improve uncertainty estimates connected to future coastal and offshore wave extremes, thereby improving climate adaptation strategies.</p>

Bias and Uncertainty in the Estimation of Extreme Wave Heights and Crests

2009 ◽  
Author(s):  
Richard Gibson ◽  
Colin Grant ◽  
George Z. Forristall ◽  
Rory Smyth ◽  
Peter Owrid ◽  
...  
Keyword(s):  
North Sea ◽  
Extreme Value ◽  
Accurate Prediction ◽  
Design Parameters ◽  
Extreme Value Analysis ◽  
Extreme Wave ◽  
Data Set ◽  
Value Analysis ◽  
Design Values ◽  
Wave Heights

The accurate prediction of extreme wave heights and crests is important to the design of offshore structures. For example, knowledge of the extreme crest elevation is required to set the deck elevation of the topside of a jacket structure. However, methods of extreme value analysis have an inherent bias, and the manner in which they are applied affects this bias. Furthermore, there is uncertainty in the design parameters at the time of design and the possibility that the predictions will change during the life of the structure. This paper is concerned with the accurate prediction of design values that incorporate uncertainty. In the first part of this paper the details of commonly applied extreme value analysis techniques are examined. This is achieved through analysis of simulated data of known distribution. In particular it is the application of least squares minimisation routines that is investigated; however, comparisons are made with maximum likelihood estimation. From this, preferred approaches to the analysis are recommended and their advantages and disadvantages discussed. The methods are applied to the analysis of a North Sea data set and the implications for the design values ascertained. In the second part of the paper Bayesian inference is used to consider the effect of uncertainty in the predicted wave heights and crest elevations. The practical implications are determined by the analysis of a measured North Sea data set.

scholarly journals Wind and Wave Extremes from Atmosphere and Wave Model Ensembles

2018 ◽  
Vol 31 (21) ◽  
pp. 8819-8842 ◽  
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.

A Model for Long-Term Distribution of Wave Induced Loads in Steep and Breaking Shallow Water Waves

2012 ◽  
Author(s):  
Hans Fabricius Hansen ◽  
Iris Pernille Lohmann ◽  
Jacob Tornfeldt Sørensen ◽  
Flemming Schlütter
Keyword(s):  
Transfer Function ◽  
Shallow Water ◽  
Wave Height ◽  
Wave Breaking ◽  
Extreme Value Analysis ◽  
Offshore Wind Turbine ◽  
Height Distribution ◽  
Wave Load ◽  
Wave Induced

A new approach to determine the design wave load on bottom-fixed structures in shallow water breaking waves is presented here. The method takes into account the effects that wave breaking has on both the wave height distribution and the wave induced loads on the structure. The loads on offshore wind turbine foundations in irregular seas with a significant amount of wave breaking are modeled in a physical wave tank. The loads are related to wave characteristics as steepness and Ursell number, and a non-linear transfer function between wave height/period and wave load is established. Characteristic historical load events are now established by combining the transfer function with a record of the wave climate at the site. The latter is taken from a hindcast database, but could also come from site measurements. The long-term distribution of the load is estimated by adopting traditional extreme value analysis techniques to the historical characteristic loads.

Discussion on “Plotting positions for fitting distributions and extreme value analysis”

2013 ◽  
Vol 40 (9) ◽  
pp. 927-929 ◽  
Author(s):  
Lasse Makkonen ◽  
Matti Pajari ◽  
Maria Tikanmäki
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Simulations ◽  
Extreme Value ◽  
Cumulative Distribution ◽  
Extreme Value Analysis ◽  
Engineering Applications ◽  
Distribution Free ◽  
Value Analysis

Plotting positions are used in the extreme value analysis for many engineering applications. The authors of the discussed paper concluded based on their simulations that distribution dependent plotting position formulae provide a better fit to the underlying cumulative distribution than the distribution free Weibull formula. We show here by Monte Carlo simulations following the theory of probability that the opposite is true, and outline that the criteria used in the comparisons made by the authors of discussed paper are inappropriate. Accordingly, the Weibull formula should be used as the unique plotting position.

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