A Bayesian-Hierarchical Space-Time Model for Significant Wave Height Data

2011 ◽  
Author(s):  
Erik Vanem ◽  
Arne Bang Huseby ◽  
Bent Natvig
Keyword(s):  
Wave Height ◽  
Significant Wave Height ◽  
North Atlantic Ocean ◽  
Space Time ◽  
Maritime Transportation ◽  
Time Model ◽  
Bayesian Hierarchical ◽  
Significant Wave ◽  
The North ◽  
Future Return

Bad weather and rough seas contribute significantly to the risk to maritime transportation. This stresses the importance of taking severe sea state conditions adequately into account, with due treatment of the uncertainties involved, in ship design and operation. Hence, there is a need for appropriate stochastic models describing the variability of sea states. This paper presents a Bayesian hierarchical space-time stochastic model for significant wave height. The model has been fitted by data for an area in the North Atlantic ocean and aims at describing the temporal and spatial variability of significant wave height in this area. It could also serve as foundation for further extensions used for long-term prediction of significant wave height and future return periods of extreme significant wave heights. The model will be outlined in this paper, and the results will be discussed. Furthermore, a discussion of possible model extensions will be presented.

A Stochastic Model for Long-Term Trends in Significant Wave Height With a CO2 Regression Component

2012 ◽  
Author(s):  
Erik Vanem
Keyword(s):  
Stochastic Model ◽  
Wave Height ◽  
Significant Wave Height ◽  
North Atlantic Ocean ◽  
Maritime Transportation ◽  
Significant Wave ◽  
The North ◽  
Future Return ◽  
Long Term Trends

Bad weather and rough seas continue to be a major cause for ship losses and is thus a significant contributor to the risk to maritime transportation. This stresses the importance of taking severe sea state conditions adequately into account, with due treatment of the uncertainties involved, in ship design and operation. Hence, there is a need for appropriate stochastic models describing the variability of sea states. These should also incorporate realistic projections of future return levels of extreme sea states, taking into account long-term trends related to climate change and inherent uncertainties. The stochastic model presented in this paper allows for modelling of complex dependence structures in space and time and incorporation of physical features and prior knowledge, yet at the same time remains intuitive and easily interpreted. A regression component with CO2 as an explanatory variable has been introduced in order to extract and project long-term trends in the data. The model has been fitted by significant wave height data for an area in the North Atlantic ocean. The different components of the model will be outlined in the paper, and the results will be discussed.

Hybrid wavelet-GMDH model to forecast significant wave height

2015 ◽  
Vol 16 (2) ◽  
pp. 453-459 ◽  
Author(s):  
Sajad Shahabi ◽  
Mohammad-Javad Khanjani ◽  
Masoudreza Hessami Kermani
Keyword(s):  
Time Series ◽  
Wave Height ◽  
Significant Wave Height ◽  
North Atlantic Ocean ◽  
High Reliability ◽  
Group Method ◽  
Lead Times ◽  
Data Handling ◽  
Significant Wave ◽  
The North

In this study, the group method of data handling (GMDH)-based wavelet transform (WT) was developed to forecast significant wave height (SWH) in different lead times. The SWH dataset was collected from a buoy station located in the North Atlantic Ocean. For this purpose, the time series of SWH was decomposed into some subseries using WT and then decomposed time series were imported to the GMDH model to forecast the SWH. Performance of the wavelet group method of data handling (WGMDH) model was evaluated using an index of agreement (Ia), coefficient of efficiency and root mean square error. The analysis proved that the model accuracy is highly dependent on the decomposition levels. The results showed that the WGMDH model is able to forecast the SWH with a high reliability.

Comparison of Bivariate Models of the Distribution of Significant Wave Height and Peak Wave Period

2007 ◽  
Author(s):  
Catarina S. Soares ◽  
C. Guedes Soares
Keyword(s):  
Wave Height ◽  
Significant Wave Height ◽  
Bivariate Normal Distribution ◽  
Wave Period ◽  
Peak Period ◽  
Data Set ◽  
Significant Wave ◽  
The North ◽  
Bivariate Models ◽  
The North Sea

This paper presents the results of a comparison of the fit of three bivariate models to a set of 14 years of significant wave height and peak wave period data from the North Sea. One of the methods defines the joint distribution from a marginal distribution of significant wave height and a set of distributions of peak period conditional on significant wave height. Other method applies the Plackett model to the data and the third one applies the Box-Cox transformation to the data in order to make it approximately normal and then fits a bivariate normal distribution to the transformed data set. It is shown that all methods provide a good fit but each one have its own strengths and weaknesses, being the choice dependent on the data available and applications in mind.

Comparison Between a Fatigue Model for Voyage Planning and Measurements of a Container Vessel

2009 ◽  
Author(s):  
Wengang Mao ◽  
Jonas W. Ringsberg ◽  
Igor Rychlik ◽  
Gaute Storhaug
Keyword(s):  
Fatigue Damage ◽  
Wave Height ◽  
Significant Wave Height ◽  
Route Planning ◽  
Estimation Model ◽  
Ongoing Research ◽  
Significant Wave ◽  
The North ◽  
Fatigue Damage Accumulation ◽  
Fatigue Model

This paper presents results from an ongoing research project which aims at developing a numerical tool for route planning of container ships. The objective with the tool is to be able to schedule a route that causes minimum fatigue damage to a vessel before it leaves port. Therefore a new simple fatigue estimation model, only using encountered significant wave height, is proposed for predicting fatigue accumulation of a vessel during a voyage. The formulation of the model is developed based on narrow-band approximation. The significant response height hs, is shown to have a linear relationship with its encountered significant wave height Hs. The zero up-crossing response frequency fz, is represented as the corresponding encountered wave frequency and is expressed as a function of Hs. The capacity and accuracy of the model is illustrated by application on one container vessel’s fatigue damage accumulation, for different voyages, operating in the North Atlantic during 2008. For this vessel, all the necessary data needed in the fatigue model, and for verification of it, was obtained by measurements. The results from the proposed fatigue model are compared with the well-known and accurate rain-flow estimation. The conclusion is that the estimations made using the current fatigue model agree well with the rain-flow method for almost all of the voyages.

scholarly journals Variability of the Winter Wind Waves and Swell in the North Atlantic and North Pacific as Revealed by the Voluntary Observing Ship Data

2006 ◽  
Vol 19 (21) ◽  
pp. 5667-5685 ◽  
Author(s):  
Sergey K. Gulev ◽  
Vika Grigorieva
Keyword(s):  
Interannual Variability ◽  
Wave Height ◽  
North Atlantic ◽  
North Pacific ◽  
Significant Wave Height ◽  
Wind Wave ◽  
Significant Wave ◽  
The North ◽  
The North Atlantic ◽  
Wind Sea

Abstract This paper analyses secular changes and interannual variability in the wind wave, swell, and significant wave height (SWH) characteristics over the North Atlantic and North Pacific on the basis of wind wave climatology derived from the visual wave observations of voluntary observing ship (VOS) officers. These data are available from the International Comprehensive Ocean–Atmosphere Data Set (ICOADS) collection of surface meteorological observations for 1958–2002, but require much more complicated preprocessing than standard meteorological variables such as sea level pressure, temperature, and wind. Visual VOS data allow for separate analysis of changes in wind sea and swell, as well as in significant wave height, which has been derived from wind sea and swell estimates. In both North Atlantic and North Pacific midlatitudes winter significant wave height shows a secular increase from 10 to 40 cm decade−1 during the last 45 yr. However, in the North Atlantic the patterns of trend changes for wind sea and swell are quite different from each other, showing opposite signs of changes in the northeast Atlantic. Trend patterns of wind sea, swell, and SWH in the North Pacific are more consistent with each other. Qualitatively the same conclusions hold for the analysis of interannual variability whose leading modes demonstrate noticeable differences for wind sea and swell. Statistical analysis shows that variability in wind sea is closely associated with the local wind speed, while swell changes can be driven by the variations in the cyclone counts, implying the importance of forcing frequency for the resulting changes in significant wave height. This mechanism of differences in variability patterns of wind sea and swell is likely more realistic than the northeastward propagation of swells from the regions from which the wind sea signal originates.

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