Omnidirectional Return Values for Storm Severity From Directional Extreme Value Models: The Effect of Physical Environment and Sample Size

Author(s):  
David Randell ◽  
Elena Zanini ◽  
Michael Vogel ◽  
Kevin Ewans ◽  
Philip Jonathan

Ewans and Jonathan [2008] shows that characteristics of extreme storm severity in the northern North Sea vary with storm direction. Jonathan et al. [2008] demonstrates, when directional effects are present, that omnidirectional return values should be estimated using a directional extreme value model. Omnidirectional return values so calculated are different in general to those estimated using a model which incorrectly assumes stationarity with respect to direction. The extent of directional variability of extreme storm severity depends on a number of physical factors, including fetch variability. Our ability to assess directional variability of extreme value parameters and return values also improves with increasing sample size in general. In this work, we estimate directional extreme value models for samples of hindcast storm peak significant wave height from locations in ocean basins worldwide, for a range of physical environments, sample sizes and periods of observation. At each location, we compare distributions of omnidirectional 100-year return values estimated using a directional model, to those (incorrectly) estimated assuming stationarity. The directional model for peaks over threshold of storm peak significant wave height is estimated using a non-homogeneous point process model as outlined in Randell et al. [2013]. Directional models for extreme value threshold (using quantile regression), rate of occurrence of threshold exceedances (using a Poisson model), and size of exceedances (using a generalised Pareto model) are estimated. Model parameters are described as smooth functions of direction using periodic B-splines. Parameter estimation is performed using maximum likelihood estimation penalised for parameter roughness. A bootstrap re-sampling procedure, encompassing all inference steps, quantifies uncertainties in, and dependence structure of, parameter estimates and omnidirectional return values.

Author(s):  
Philip Jonathan ◽  
Kevin Ewans

Statistics of storm peaks over threshold depend typically on a number of covariates including location, season and storm direction. Here, a non-homogeneous Poisson model is adopted to characterise storm peak events with respect to season for two Gulf of Mexico locations. The behaviour of storm peak significant wave height over threshold is characterised using a generalised Pareto model, the parameters of which vary smoothly with season using a Fourier form. The rate of occurrence of storm peaks is also modelled using a Poisson model with rate varying with season. A seasonally-varying extreme value threshold is estimated independently. The degree of smoothness of extreme value shape and scale, and the Poisson rate, with season, is regulated by roughness-penalised maximum likelihood; the optimal value of roughness selected by cross-validation. Despite the fact that only the peak significant wave height event for each storm is used for modelling, the influence of the whole period of a storm on design extremes for any seasonal interval is modelled using the concept of storm dissipation, providing a consistent means to estimate design criteria for arbitrary seasonal intervals. Characteristics of the 100-year storm peak significant wave height, estimated using the seasonal model, are examined and compared to those estimated ignoring seasonality.


Author(s):  
Philip Jonathan ◽  
Kevin Ewans

Statistics of storm peaks over threshold depend typically on a number of covariates including location, season, and storm direction. Here, a nonhomogeneous Poisson model is adopted to characterize storm peak events with respect to season for two Gulf of Mexico locations. The behavior of storm peak significant wave height over threshold is characterized using a generalized Pareto model, the parameters of which vary smoothly with season using a Fourier form. The rate of occurrence of storm peaks is also modeled using a Poisson model with rate varying with season. A seasonally varying extreme value threshold is estimated independently. The degree of smoothness of extreme value shape and scale and the Poisson rate with season are regulated by roughness-penalized maximum likelihood; the optimal value of roughness is selected by cross validation. Despite the fact that only the peak significant wave height event for each storm is used for modeling, the influence of the whole period of a storm on design extremes for any seasonal interval is modeled using the concept of storm dissipation, providing a consistent means to estimate design criteria for arbitrary seasonal intervals. The characteristics of the 100 year storm peak significant wave height, estimated using the seasonal model, are examined and compared with those estimated ignoring seasonality.


2019 ◽  
Vol 36 (3) ◽  
pp. 333-351 ◽  
Author(s):  
Xining Zhang ◽  
Hao Dai

AbstractIn recent years, deep learning technology has been gradually used for time series data prediction in various fields. In this paper, the restricted Boltzmann machine (RBM) in the classical deep belief network (DBN) is substituted with the conditional restricted Boltzmann machine (CRBM) containing temporal information, and the CRBM-DBN model is constructed. Key model parameters, which are determined by the particle swarm optimization (PSO) algorithm, are used to predict the significant wave height. Observed data in 2016, which are from nearshore and offshore buoys (i.e., 42020 and 42001) belonging to the National Data Buoy Center (NDBC), are taken to train the model, and the corresponding data in 2017 are used for testing with lead times of 1–24 h. In addition, we trained the data of 42040 in 2003 and tested the data in 2004 in order to investigate the prediction ability of the CRBM-DBN model for the extreme event. The prediction ability of the model is evaluated by the Nash–Sutcliffe coefficient of efficiency (CE) and root-mean-square error (RMSE). Experiments demonstrate that for the short-term (≤9 h) prediction, the RMSE and CE for the significant wave height prediction are <10 cm and >0.98, respectively. Moreover, the relative error of the short-term prediction for the maximum wave height is less than 26%. The excellent short-term and extreme events forecasting ability of the CRBM-DBN model is vital to ocean engineering applications, especially for designs of ocean structures and vessels.


2008 ◽  
Vol 35 (1) ◽  
pp. 131-138 ◽  
Author(s):  
Fernando J. Méndez ◽  
Melisa Menéndez ◽  
Alberto Luceño ◽  
Raúl Medina ◽  
Nicholas E. Graham

Author(s):  
Erik Vanem

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.


Author(s):  
Erik Vanem

The joint distribution of several met-ocean variables is required for risk assessment and load and response calculations in marine engineering. For example, a joint description is needed to construct environmental contours for probabilistic structural reliability analyses. Typically, the joint distribution of significant wave height and wave period is required as a minimum. This paper presents a study on various bivariate modelling techniques for the joint distribution of significant wave height and zero-crossing wave period, i.e. a conditional model, a bi-variate log-normal model and several meta-models based on parametric copulas. Each of the models is fitted to data generated from a numerical wave model for the current climate and for two future climates consistent with the RCP 4.5 and RCP 8.5 scenarios. Thus, the objective of this study is twofold. First, the joint models obtained by the various modelling techniques will be compared. Secondly, the potential effect of climate change on the simultaneous distribution of significant wave height and wave period will be explored. The results indicate that straightforward application of many of the most common families of copulas fails to capture the dependence structure in the data, and that the conditional model performs better than these naive approaches. However, if more advanced copula construction techniques are applied, significant improvements can be achieved. The results also suggest that significant wave height and zero-crossing wave period tend to be more correlated in a future climate, at least in the extremes.


Author(s):  
Jan Mathisen ◽  
Torfinn Hørte

Hindcast data for a specific location is utilised to develop a joint probability function for the metocean variables that are expected to have a significant effect on mooring line tensions for a floating platform moored at that location. The main random variables comprise: peak significant wave height, peak wind speed, peak surface current speed, peak wave direction, peak wind direction and peak current direction, where “peak” indicates the maximum intensity of the metocean effect during a random hurricane. The time lead of peak wind relative to peak waves and the time lag of peak current after peak wind are included as random variables. It is also necessary to describe the time variation around the peak events. Simple models are assumed based on inspection of the time variations during severe hurricanes. Only the part of the hurricane during which the significant wave height exceeds 80% of the peak value is taken into account. The duration of this interval is included. Linear variation is assumed for the directions, hence the rates of change of the 3 directions are included. A linear (triangular) plus parabolic model is assumed for the time variation of the intensities of the 3 metocean effects around their respective peaks. A single parameter is required to define the proportion of linear and parabolic models for each effect and the values of this parameter for each of the 3 metocean effects are also included as random variables. A random hurricane can be drawn from this metocean model, such that the time variation of the metocean actions is deterministic once the values of the random variables have been selected. The overall duration of the hurricane is split into short intervals, each of 15 minutes duration, such that stationary response may be assumed during each short interval. The extreme value distribution of line tension during each short interval is obtained. These distributions are combined to obtain the extreme distribution of line tension during the hurricane. Second order reliability methods are applied to integrate over the distribution of the metocean variables and obtain the distribution of extreme tension during a random hurricane. The annual frequency of hurricanes is used to derive the annual extreme value distribution of line tension. The model is intended for the reliability analysis of the ultimate limit state of mooring lines, but may also be applicable to other response variables. The present paper is primarily concerned with the metocean model, but it is intended to include sample results for the extreme line tension.


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