Linear models of the time series of significant wave height on the Southwest Coast of Portugal

Soares, C. Guedes; Ferreira, Ana; Cunha, Cleyzer Adrian da

doi:10.1016/s0378-3839(96)00022-1

Cited by 47 publications

(31 citation statements)

References 7 publications

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“…The simplest approach has been to focus on the most important season [24] or to piecewise model seasons or months [26,12,4]. Other studies have used a superposition of linear or cyclic functions of time [17,19,21,9,7,27] and climate indices as co-variates [5,6,28,13] to represent trends or seasonal cycles on semiannual to decadal time scales. Climate indices under consideration were the North Atlantic Oscillation (NAO), the Southern Oscillation Index (SOI), the Pacific-North America (PNA) and the El Niño-Southern Oscillation (ENSO) index.…”

Section: Introductionmentioning

confidence: 99%

A statistical simulation method for joint time series of non-stationary hourly wave parameters

Jäger

Nagler

Czado

et al. 2019

Coastal Engineering

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Statistically simulated time series of wave parameters are required for many coastal and offshore engineering applications, often at the resolution of approximately one hour. Various studies have relied on autoregressive moving-average (ARMA) processes to simulate synthetic series of wave parameters in a Monte Carlo sense. However, accurately representing inter-series dependencies has remained a challenge. In particular, the relationship between wave height and period statistics is complex, due to the limiting steepness condition. Here, we present a new simulation method for joint time series of significant wave height, mean zero-crossing periods and a directional regime variable. The latter distinguishes between northern and southwestern waves. The method rests on several model components which include renewal processes, Fourier series with random coefficients, ARMA processes, copulas and regime-switching. A particular feature is a data-driven estimate for a wave height-dependent limiting wave steepness condition which is used to facilitate copulabased dependence modeling. The method was developed for and applied to a data set in the Southern North Sea. For this site, the method could simulate time series with realistic annual cycles and inter-annual variability. In the time series data, the bivariate distribution of significant wave height and mean zero-crossing period was well represented. An influence of the directional regime on the bivariate distribution could also be modeled. However, the influence was not as strong in simulated data as in observed data. Finally, simulated series captured duration and inter-arrival time of storm events well. Potential applications for output of the simulation method range from the assessment of coastal risks or design of coastal structures to the planning and budgeting of offshore operations.

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Section: Introductionmentioning

confidence: 99%

A statistical simulation method for joint time series of non-stationary hourly wave parameters

Jäger

Nagler

Czado

et al. 2019

Coastal Engineering

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“…Of particular interest is a comparison of the panel in Fig. 9 with the RMSE plots for the same site (Figs 5,7,8). In several of the RMSE plots, the forecasters outperform persistence.…”

Section: A Model Testingmentioning

confidence: 99%

“…Auto-Regressive approaches to describe time series data were originally developed in [6], and have since been applied to a diverse range of applications. Of particular relevance to this work, AR models have been used to describe significant wave height [7], mean wind speeds for wind turbine power generation [8] and wind turbine maintenance [9]. The AR model, normalized to the mean of the data is described in Eq.…”

Section: Datamentioning

confidence: 99%

Statistical forecasting for offshore wind helicopter operations

Domínguez-Navarro

Dinwoodie

McMillan

2014

2014 International Conference on Probabilistic Methods Applied to Power Systems (PMAPS)

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Abstract-The influence of the wind and wave climate on offshore wind operations and maintenance is well known. These environmental factors dictate to a large extent whether turbine crew transfer (carried out by small vessels) or major lifting actions (carried out by large vessels) can be executed at sea. However the role of helicopter operations has received much less attention. In this paper the authors explore the helicopter access problem via statistical forecasting and implement a model innovation, by including cloud base as a key access metric. By understanding the practical limits of helicopter operation, offshore wind access calculations will be much improved and reflect more closely the reality of operations at sea.

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“…Therefore, detailed wave climate information of the site becomes critical to the further development and optimization of the wave energy converting technology. For wave climate analysis, different probabilistic methods have been proposed to describe the long-term wave distribution [8][9][10][11][12][13][14][15]. The Lognormal, Rayleigh and Weibull distribution are the most commonly used models for long-term wave distribution modelling [16,17].…”

Section: It Was Initiatedmentioning

confidence: 99%

Statistical Analysis of Wave Climate Data Using Mixed Distributions and Extreme Wave Prediction

Isberg

Waters

et al. 2016

Energies

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Abstract:The investigation of various aspects of the wave climate at a wave energy test site is essential for the development of reliable and efficient wave energy conversion technology. This paper presents studies of the wave climate based on nine years of wave observations from the 2005-2013 period measured with a wave measurement buoy at the Lysekil wave energy test site located off the west coast of Sweden. A detailed analysis of the wave statistics is investigated to reveal the characteristics of the wave climate at this specific test site. The long-term extreme waves are estimated from applying the Peak over Threshold (POT) method on the measured wave data. The significant wave height and the maximum wave height at the test site for different return periods are also compared. In this study, a new approach using a mixed-distribution model is proposed to describe the long-term behavior of the significant wave height and it shows an impressive goodness of fit to wave data from the test site. The mixed-distribution model is also applied to measured wave data from four other sites and it provides an illustration of the general applicability of the proposed model. The methodologies used in this paper can be applied to general wave climate analysis of wave energy test sites to estimate extreme waves for the survivability assessment of wave energy converters and characterize the long wave climate to forecast the wave energy resource of the test sites and the energy production of the wave energy converters.

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Linear models of the time series of significant wave height on the Southwest Coast of Portugal

Cited by 47 publications

References 7 publications

A statistical simulation method for joint time series of non-stationary hourly wave parameters

A statistical simulation method for joint time series of non-stationary hourly wave parameters

Statistical forecasting for offshore wind helicopter operations

Statistical Analysis of Wave Climate Data Using Mixed Distributions and Extreme Wave Prediction

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