scholarly journals Extreme Value Analysis of Dynamical Wave Climate Projections in the Mediterranean Sea

2015 ◽  
Vol 66 ◽  
pp. 210-219 ◽  
Author(s):  
Zacharias G. Kapelonis ◽  
Panagiotis N. Gavriliadis ◽  
Gerassimos A. Athanassoulis
Keyword(s):  
Mediterranean Sea ◽  
Wave Climate ◽  
Extreme Value ◽  
Extreme Value Analysis ◽  
Climate Projections ◽  
Value Analysis ◽  
The Mediterranean

Uncertainties in Extreme Value Analysis of Wave Climate Data and Wave Climate Projections

2015 ◽  
Author(s):  
Erik Vanem
Keyword(s):  
Wave Height ◽  
Significant Wave Height ◽  
Wave Climate ◽  
Extreme Value ◽  
Extreme Value Analysis ◽  
Climate Projections ◽  
Data Sets ◽  
Climate Data ◽  
Value Analysis ◽  
Significant Wave

The extreme values of climate data are of interest in design of marine structures and the return values of certain met-ocean parameters such as significant wave height is of particular importance. However, there are various ways of analyzing the extremes and estimating the required return values, which introduce additional uncertainties. These are investigated in this paper by applying different methods to particular data sets of significant wave height, corresponding to the historic climate and two future projections of the climate assuming different forcing scenarios. In this way, the uncertainty due to the extreme value analysis can also be compared to the uncertainty due to a changing climate. The different approaches that will be considered is the initial distribution approach, the block maxima approach, the peak over threshold (POT) approach and the average conditional exceedance rate method (ACER). Furthermore, the effect of different modelling choices within each of the approaches will be explored. Thus, a range of different return value estimates for the different data sets is obtained. This exercise reveals that the uncertainty due to the extreme value analysis method is notable and, as expected, the variability of the estimates increases for higher return periods. Moreover, even though the variability due to the extreme value analysis is greater than the climate variability, a shift towards higher extremes in a future wave climate can clearly be discerned in the particular datasets that have been analysed.

Covariate Extreme Value Analysis Using Wave Spectral Partitioning

2020 ◽  
Vol 37 (5) ◽  
pp. 873-888 ◽  
Author(s):  
Jesús Portilla-Yandún ◽  
Edwin Jácome
Keyword(s):  
Extreme Values ◽  
Wind Waves ◽  
Spectral Energy Distribution ◽  
Wave Spectrum ◽  
Wave Climate ◽  
Extreme Value ◽  
Extreme Value Analysis ◽  
Spectral Energy ◽  
Value Analysis ◽  
Spectral Partitioning

AbstractAn important requirement in extreme value analysis (EVA) is for the working variable to be identically distributed. However, this is typically not the case in wind waves, because energy components with different origins belong to separate data populations, with different statistical properties. Although this information is available in the wave spectrum, the working variable in EVA is typically the total significant wave height Hs, a parameter that does not contain information of the spectral energy distribution, and therefore does not fulfill this requirement. To gain insight in this aspect, we develop here a covariate EVA application based on spectral partitioning. We observe that in general the total Hs is inappropriate for EVA, leading to potential over- or underestimation of the projected extremes. This is illustrated with three representative cases under significantly different wave climate conditions. It is shown that the covariate analysis provides a meaningful understanding of the individual behavior of the wave components, in regard to the consequences for projecting extreme values.

scholarly journals Extreme value analysis of wave climate around Farasan Islands, southern Red Sea

2020 ◽  
Vol 207 ◽  
pp. 107395 ◽  
Author(s):  
V.R. Shamji ◽  
V.M. Aboobacker ◽  
T.C. Vineesh
Keyword(s):  
Red Sea ◽  
Wave Climate ◽  
Extreme Value ◽  
Extreme Value Analysis ◽  
Value Analysis ◽  
Farasan Islands

Extreme value analysis of wave climate in Chesapeake Bay

2018 ◽  
Vol 159 ◽  
pp. 22-36 ◽  
Author(s):  
Arash Niroomandi ◽  
Gangfeng Ma ◽  
Xinyu Ye ◽  
Sha Lou ◽  
Pengfei Xue
Keyword(s):  
Chesapeake Bay ◽  
Wave Climate ◽  
Extreme Value ◽  
Extreme Value Analysis ◽  
Value Analysis

scholarly journals Extreme wave analysis based on atmospheric pattern classification: an application along the Italian coast

2020 ◽  
Vol 20 (5) ◽  
pp. 1233-1246
Author(s):  
Francesco De Leo ◽  
Sebastián Solari ◽  
Giovanni Besio
Keyword(s):  
Extreme Value Distribution ◽  
Extreme Value ◽  
Value Distribution ◽  
Extreme Value Analysis ◽  
Extreme Wave ◽  
Wind Fields ◽  
Value Analysis ◽  
Atmospheric Circulation Patterns ◽  
Circulation Patterns ◽  
The Mediterranean

Abstract. This paper provides a methodology for classifying samples of significant wave-height peaks in homogeneous subsets in terms of the atmospheric circulation patterns behind the observed extreme wave conditions. Then, a methodology is given for the computation of the overall extreme value distribution by starting from the distributions fitted to each single subset. To this end, the k-means clustering technique is used to classify the shape of the wind fields that occurred simultaneously to and prior to the occurrences of the extreme wave events. This results in a small number of characteristic circulation patterns related to as many subsets of extreme wave values. After fitting an extreme value distribution to each subset, bootstrapping is used to reconstruct the omni-circulation pattern's extreme value distribution. The methodology is applied to several locations along the Italian buoy network, and it is concluded from the obtained results that it yields a two-fold advantage: first, it is capable of identifying clearly differentiated subsets driven by homogeneous circulation patterns; second, it allows one to estimate high-return-period quantiles consistent with those resulting from the usual extreme value analysis. In particular, the circulation patterns highlighted are analyzed in the context of the Mediterranean Sea's atmospheric climatology and are shown to be due to well-known cyclonic systems typically crossing the Mediterranean basin.

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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