Extreme Waves of Sea Storms

2010 ◽  
Author(s):  
Francesco Fedele ◽  
Felice Arena ◽  
M. Aziz Tayfun
Keyword(s):  
Stochastic Model ◽  
Power Law ◽  
Analytical Solutions ◽  
Return Periods ◽  
Extreme Waves ◽  
Extreme Wave ◽  
Relative Validity ◽  
Wave Measurements ◽  
Storm Model

We present a stochastic model of sea storms for describing long-term statistics of extreme wave events. The formulation generalizes Boccotti’s equivalent triangular storm model (Boccotti 2000) by describing an actual storm history in the form of a generic power law. The latter permits the derivation of analytical solutions for the return periods of extreme wave events and associated statistical properties. Finally, we assess the relative validity of the new model and its predictions by analyzing wave measurements retrieved from two NOAA-NODC buoys in the Atlantic and Pacific Oceans.

scholarly journals Long-Term Statistics and Extreme Waves of Sea Storms

2010 ◽  
Vol 40 (5) ◽  
pp. 1106-1117 ◽  
Author(s):  
Francesco Fedele ◽  
Felice Arena
Keyword(s):  
Stochastic Model ◽  
Analytical Solutions ◽  
Data Center ◽  
Return Periods ◽  
Extreme Waves ◽  
Extreme Wave ◽  
Wave Measurements ◽  
Oceanographic Data ◽  
Storm Model

Abstract A stochastic model of sea storms for describing long-term statistics of extreme wave events is presented. The formulation generalizes Boccotti’s equivalent triangular storm model by describing an actual storm history in the form of a generic power law. The latter permits the derivation of analytical solutions for the return periods of extreme wave events and associated statistical properties. Lastly, the relative validity of the new model and its predictions is assessed by analyzing wave measurements retrieved from two NOAA National Oceanographic Data Center (NODC) buoys in the Atlantic and Pacific Oceans.

The Equivalent Power Storm Model for Long-Term Predictions of Extreme Wave Events

2009 ◽  
Author(s):  
Francesco Fedele ◽  
Felice Arena
Keyword(s):  
Wave Height ◽  
Return Period ◽  
Shape Parameter ◽  
Extreme Wave ◽  
Maximum Wave Height ◽  
Wave Measurements ◽  
Sea Storm ◽  
Storm Model ◽  
Good Agreement

We present the Equivalent Power Storm (EPS) model as a generalization of the Equivalent Triangular Storm (ETS) model of Boccotti for the long-term statistics of extreme wave events. In the EPS model, each actual storm is modeled in time t by a power law ∼|t−t0|λ, where λ is a shape parameter and t0 is the time when the storm peak occurs. We then derive the general expression of the return period R(Hs > h) of a sea storm in which the maximum significant wave height Hs exceeds a fixed threshold h as function of λ. Further, given the largest wave height Hmax, we identify the most probable storm in which the largest wave occurs and derive an explicit expression for the return period R(Hmax >H) of a storm in which the maximum wave height exceeds a given threshold H. Finally, we analyze wave measurements retrieved from two of the NOAA-NODC buoys in the Atlantic and Pacific oceans and find that the EPS predictions are in good agreement with those from the ETS model.

Space-Time Extremes in Sea Storms

2011 ◽  
Author(s):  
Francesco Fedele ◽  
Felice Arena ◽  
M. Aziz Tayfun
Keyword(s):  
Analytical Solution ◽  
Stochastic Model ◽  
Maximum Height ◽  
Wave Surface ◽  
Extreme Wave ◽  
Euler Characteristics ◽  
Excursion Sets ◽  
Wave Measurements ◽  
Storm Model ◽  
Do So

We present a stochastic model of sea storms to predict the maximum height of the wave surface over a given area during storms. To do so, we exploit the theory of Euler Characteristics of random excursion sets combined with a generalization of Boccotti’s equivalent triangular storm model (Boccotti, 2000) that describes an actual storm history in the form of a generic power law (Fedele and Arena, 2010). An analytical solution for the return period of extreme wave events over a given area and the associated statistical properties are given. We then assess the relative validity of the new model and its predictions by analyzing wave measurements retrieved from NOAA-NODC buoys moored offshore of the Atlantic and Pacific coasts.

scholarly journals Evaluation of extreme wave probability on the basis of long-term data analysis

Ocean Science ◽  
2018 ◽  
Vol 14 (5) ◽  
pp. 1321-1327 ◽  
Author(s):  
Kirill Bulgakov ◽  
Vadim Kuzmin ◽  
Dmitry Shilov
Keyword(s):  
Wave Height ◽  
Wind Wave ◽  
Cumulative Probability ◽  
Extreme Waves ◽  
Model Data ◽  
Extreme Wave ◽  
Method Of Calculation ◽  
Wave Heights ◽  
Term Data

Abstract. A method of calculation of wind wave height probability based on the significant wave height probability is described (Chalikov and Bulgakov, 2017). The method can also be used for estimation of the height of extreme waves of any given cumulative probability. The application of the method on the basis of long-term model data is presented. Examples of averaged annual and seasonal fields of extreme wave heights obtained using the above method are given. Areas where extreme waves can appear are shown.

Stochastic Models for Long-Term Prediction of Extreme Waves: A Literature Survey

2010 ◽  
Author(s):  
Erik Vanem
Keyword(s):  
Statistical Modelling ◽  
Wave Climate ◽  
Literature Survey ◽  
Time Dependent ◽  
Extreme Waves ◽  
Extreme Wave ◽  
General Sense ◽  
Dependent Variables ◽  
Long Term Prediction

This paper presents a literature survey on time-dependent statistical modelling of extreme waves. The focus is twofold: on statistical modelling of extreme waves and time-dependent statistical modelling. The first part will consist of a thorough literature review of statistical modelling of extreme waves and wave parameters. The second part will focus on statistical modelling of time- and space-dependent variables in a more general sense, and will focus on the methodology and models used also in other relevant application areas. It was found that limited effort has been put on developing statistical models for waves incorporating spatial and long-term temporal variability and it is suggested that model improvements could be achieved by adopting approaches from other application areas. Finally, a review of projections of future extreme wave climate is presented.

Using regional scaling for temperature forecasts with the Stochastic Seasonal to Interannual Prediction System (StocSIPS).

2021 ◽  
Author(s):  
Lenin Del Rio Amador ◽  
Shaun Lovejoy
Keyword(s):  
Stochastic Model ◽  
Power Law ◽  
Grid Point ◽  
Relaxation Processes ◽  
Prediction System ◽  
Relaxation Equation ◽  
Deterministic Models ◽  
Global Circulation Models ◽  
Skill Scores

Abstract Over time scales between 10 days and 10–20 years – the macroweather regime – atmospheric fields, including the temperature, respect statistical scale symmetries, such as power-law correlations, that imply the existence of a huge memory in the system that can be exploited for long-term forecasts. The Stochastic Seasonal to Interannual Prediction System (StocSIPS) is a stochastic model that exploits these symmetries to perform long-term forecasts. It models the temperature as the high-frequency limit of the (fractional) energy balance equation (fractional Gaussian noise) which governs radiative equilibrium processes when the relevant equilibrium relaxation processes are power law, rather than exponential. They are obtained when the order of the relaxation equation is fractional rather than integer and they are solved as past value problems rather than initial value problems. StocSIPS was first developed for monthly and seasonal forecast of globally averaged temperature. In this paper, we extend it to the prediction of the spatially resolved temperature field by treating each grid point as an independent time series. Compared to traditional global circulation models (GCMs), StocSIPS has the advantage of forcing predictions to converge to the real-world climate. It extracts the internal variability (weather noise) directly from past data and does not suffer from model drift. Here we apply StocSIPS to obtain monthly and seasonal predictions of the surface temperature and show some preliminary comparison with multi-model ensemble (MME) GCM results. For one month lead time, our simple stochastic model shows similar values of the skill scores than the much more complex deterministic models.

Estimating Extreme Waves in the Gulf of Mexico Using a Simple Spatial Extremes Model

2019 ◽  
Author(s):  
Ryota Wada ◽  
Philip Jonathan ◽  
Takuji Waseda ◽  
Shejun Fan
Keyword(s):  
Gulf Of Mexico ◽  
Tropical Cyclones ◽  
Return Periods ◽  
Extreme Waves ◽  
Extreme Wave ◽  
Cyclone Track ◽  
Wave Hindcast ◽  
Spatial Pooling ◽  
Single Location ◽  
Using Data

Abstract We seek to characterize the behavior of extreme waves in the Gulf of Mexico, using a 109 year-long wave hindcast (GOMOS). The largest waves in this region are driven by strong winds from hurricanes. Design of offshore production systems requires the estimation of extreme metocean conditions corresponding to return periods from 1 year to 10,000 years and beyond. For extrapolation to long return periods, estimation using data for around 100 years from a single location will incur large uncertainties. Approaches such as spatial pooling, cyclone track-shifting and explicit track modeling have been proposed to alleviate this problem. The underlying problem in spatial pooling is the aggregation of dependent data and hence underestimation of uncertainty using naïve analysis; techniques such as block-bootstrapping can be used to inflate uncertainties to more realistic levels. The usefulness of cyclone track-shifting or explicit track modeling is dependent on the appropriateness of the physical assumptions underpinning such a model. In this paper, we utilize a simple spatial statistical model for extreme value estimation of significant wave height under tropical cyclones, known as STM-E, proposed in Wada et al. (2018). The STM-E model was developed to characterize extreme waves offshore Japan, also dominated by tropical cyclones. The method relies on the estimation of two distributions from a sample of data, namely the distribution of spatio-temporal maximum (STM) and the exposure (E). In the current work, we apply STM-E to extreme wave analysis in Gulf of Mexico. The STM-E estimate provides a parsimonious spatially-smooth distribution of extreme waves, with smaller uncertainties per location compared to estimates using data from a single location. We also discuss the estimated characteristics of extreme wave environments in this region.

Transient convective diffusion to a uniformly accessible surface under aparent slip conditions

1991 ◽  
Vol 56 (2) ◽  
pp. 334-343
Author(s):  
Ondřej Wein
Keyword(s):  
Power Law ◽  
Analytical Solutions ◽  
Convective Diffusion ◽  
Active Surface ◽  
Velocity Profiles ◽  
One Dimensional ◽  
Slip Conditions ◽  
Accessible Surface ◽  
Diffusion Problems

Analytical solutions are given to a class of unsteady one-dimensional convective-diffusion problems assuming power-law velocity profiles close to the transport-active surface.

scholarly journals Future behavior of wind wave extremes due to climate change

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Hector Lobeto ◽  
Melisa Menendez ◽  
Iñigo J. Losada
Keyword(s):  
Climate Change ◽  
Wave Climate ◽  
Climate Change Scenarios ◽  
Return Periods ◽  
Extreme Wave ◽  
Future Behavior ◽  
Climate Simulations ◽  
Wave Conditions ◽  
Ne Pacific ◽  
Change In Mean

AbstractExtreme waves will undergo changes in the future when exposed to different climate change scenarios. These changes are evaluated through the analysis of significant wave height (Hs) return values and are also compared with annual mean Hs projections. Hourly time series are analyzed through a seven-member ensemble of wave climate simulations and changes are estimated in Hs for return periods from 5 to 100 years by the end of the century under RCP4.5 and RCP8.5 scenarios. Despite the underlying uncertainty that characterizes extremes, we obtain robust changes in extreme Hs over more than approximately 25% of the ocean surface. The results obtained conclude that increases cover wider areas and are larger in magnitude than decreases for higher return periods. The Southern Ocean is the region where the most robust increase in extreme Hs is projected, showing local increases of over 2 m regardless the analyzed return period under RCP8.5 scenario. On the contrary, the tropical north Pacific shows the most robust decrease in extreme Hs, with local decreases of over 1.5 m. Relevant divergences are found in several ocean regions between the projected behavior of mean and extreme wave conditions. For example, an increase in Hs return values and a decrease in annual mean Hs is found in the SE Indian, NW Atlantic and NE Pacific. Therefore, an extrapolation of the expected change in mean wave conditions to extremes in regions presenting such divergences should be adopted with caution, since it may lead to misinterpretation when used for the design of marine structures or in the evaluation of coastal flooding and erosion.

scholarly journals EXTREME WAVE PRESSURES AND LOADS ON A PILE-SUPPORTED WHARF DECK - INFLUENCES OF AIR GAP AND WAVE DIRECTION

2018 ◽  
pp. 5
Author(s):  
Andrew Cornett
Keyword(s):  
Offshore Structures ◽  
Model Tests ◽  
Scale Model ◽  
Extreme Waves ◽  
Wave Direction ◽  
Coastal Structures ◽  
Extreme Wave ◽  
Wave Impact ◽  
The Past ◽  
Water Depths

Many deck-on-pile structures are located in shallow water depths at elevations low enough to be inundated by large waves during intense storms or tsunami. Many researchers have studied wave-in-deck loads over the past decade using a variety of theoretical, experimental, and numerical methods. Wave-in-deck loads on various pile supported coastal structures such as jetties, piers, wharves and bridges have been studied by Tirindelli et al. (2003), Cuomo et al. (2007, 2009), Murali et al. (2009), and Meng et al. (2010). All these authors analyzed data from scale model tests to investigate the pressures and loads on beam and deck elements subject to wave impact under various conditions. Wavein- deck loads on fixed offshore structures have been studied by Murray et al. (1997), Finnigan et al. (1997), Bea et al. (1999, 2001), Baarholm et al. (2004, 2009), and Raaij et al. (2007). These authors have studied both simplified and realistic deck structures using a mixture of theoretical analysis and model tests. Other researchers, including Kendon et al. (2010), Schellin et al. (2009), Lande et al. (2011) and Wemmenhove et al. (2011) have demonstrated that various CFD methods can be used to simulate the interaction of extreme waves with both simple and more realistic deck structures, and predict wave-in-deck pressures and loads.

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