Improvement of theoretical storm characterization for different climate conditions

Soldevilla, Ma Jesús Martín; Martín-Hidalgo, Melva; Negro, Vicente; Gutiérrez, José Santos López; Aberturas, Paloma

doi:10.1016/j.coastaleng.2014.11.004

Cited by 20 publications

(21 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…1c). A theoretical basis for the proposal of these two wave-height-evolution models can be found in Martin-Soldevilla et al (2015), who conducted a shape analysis for one point at the NW Mediteranean Sea. This analysis is herein extended on a regional scale.…”

Section: Maxmentioning

confidence: 99%

“…The conventional approach is usually based on the probabilistic denition of a single parameter, typically the wave height. Other concurrent components as the duration of the storm, the storm total energy and the associated wave period inuence the nal response of a beach or the damage evolution of a structure (Martin-Soldevilla et al, 2015;Melby and Kobayashi, 2011). These variables are known to be semi-dependent (Salvadori et al, 2007; de Waal and van Gelder, 2005), but the classical methodology either a) assumes one variable to be stochastic and the other ones to be deterministic or, b) assumes all variables to be stochastic but completely independent.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A multivariate statistical model of extreme events: An application to the Catalan coast

Lin-Ye¹,

Garcı́a-León²,

Gràcia³

et al. 2016

Coastal Engineering

View full text Add to dashboard Cite

Wave extreme events can be understood as the combination of storm-intensity, directionality and intra-time distribution. However, the dependence structure among these factors is still unclear. A methodology has been developed to model wave-storms whose components are linked together. The model is composed by three parts: an intensity module, a wave directionality module, and a intra-time distribution module. In the Storm-intensity sub-model, generalized Pareto distributions and hierarchical Archimedean copulas have been used to characterize the storm energy, unitary energy, peak wave-period and duration. In the Directionality and the Intra-time sub-models, the wave direction (at the peak of the storm) and the storm growth-decay rates are linked to the variables from the intensity model, respectively. The model is applied to the Catalan coast (NW Mediterranean). The outcomes denote spatial patterns that coincide with the state of knowledge. The proposed methodology is able to provide boundary conditions for wave and near-shore studies, saving computational time and establishing the dependence of the proposed variables. Such synthetic storms reproduce the inter-variable co-dependence of the original data.

show abstract

Section: Maxmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A multivariate statistical model of extreme events: An application to the Catalan coast

Lin-Ye¹,

Garcı́a-León²,

Gràcia³

et al. 2016

Coastal Engineering

View full text Add to dashboard Cite

show abstract

“…However, the storm history evolution of the sea states in sea and swell waves is not adequately reproduced by these theoretical models and do not provide enough information for applications where the features and evolution of the metocean time series are relevant (Jäger et al, 2018;Martín-Soldevilla et al, 2015) and there is still need to account for the random temporal evolution of storm events.…”

Section: Introductionmentioning

confidence: 99%

Storm characterization and simulation for damage evolution models of maritime structures

Lira-Loarca

Cobos

Losada

et al. 2020

Coastal Engineering

View full text Add to dashboard Cite

This paper presents a new approach to statistically characterize and simulate the wave climate under storm conditions. The methodology includes the joint selection of the parameters that identify storm events (significant wave height threshold, minimum storm duration and minimum interarrival time between consecutive storms) by means of hypothesis testing on the distribution functions of the number of storm events and the elapsing time between storms, providing an improved characterization of the parameters that define storm events. The main wave variables and their temporal dependence are characterized by non-stationary mixture distribution functions and a vector autoregressive model. This allows to adequately reproduce the random temporal evolution of storm events, crucial for the study of damage progression in maritime structures without the use of predefined geometries. The long-term time series of storm events and calm periods is obtained using copula functions which analyze the joint dependence of storm duration and interarrival time for separate climate intervals. The model is applied to hindcast data at a location of the Mediterranean sea close to the Granada coast in Spain to show its ability to reproduce wave storm conditions accounting for the time variability of the storminess. An example of application, using a large number of simulations and a damage progression model in a maritime structure, is presented.

show abstract

“…One should note that the definition of a storm duration and the storm threshold of H s is always questionable. Soldevilla et al (2015) provides further details on storm characterizations for different climate conditions, which is not the main focus of this work. However, according to the autocorrelation analysis (Figure 1), subsampling the maximum significant wave height and the associated up-crossing mean period for a 2-day interval still shows a bit of short dependence.…”

Section: Case Study and Wave Datamentioning

confidence: 99%

Asymmetric copula–based distribution models for met-ocean data in offshore wind engineering applications

et al. 2018

View full text Add to dashboard Cite

Joint statistical models for long-term wave climate are a key aspect of offshore wind engineering design. However, to find a joint model for sea-state characteristics is often difficult due to the complex nature of the wave climate and the physical constraints of seastate phenomena. The available records of wave heights and periods are often very asymmetric in their nature. This paper presents a copula-based approach to obtain the joint cumulative distribution function of significant wave heights and the mean up-crossing periods. This study is based on 124 months hindcast data concerning Horns Rev 3 offshore wind farm. The extra-parametrization technique of symmetric copulas is implemented to account for the asymmetry present in the data. The analysis of the total sea, the wind-sea and primary swell components is performed separately. The results show that the extraparametrization technique with pairwise copulas consistently provided a better goodnessof-fit when compared to symmetric copulas. Moreover, it is demonstrated that the separation of the total sea into its components does not always improve the extraparametrized copula's performance. Furthermore, this paper also discusses copula application to offshore wind engineering.

show abstract

Improvement of theoretical storm characterization for different climate conditions

Cited by 20 publications

References 16 publications

A multivariate statistical model of extreme events: An application to the Catalan coast

A multivariate statistical model of extreme events: An application to the Catalan coast

Storm characterization and simulation for damage evolution models of maritime structures

Asymmetric copula–based distribution models for met-ocean data in offshore wind engineering applications

Contact Info

Product

Resources

About