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.

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.

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.

Time Series Analysis of Significant Wave Height Data for Identification of Trends in the Ocean Wave Climate

2013 ◽  
Author(s):  
Erik Vanem ◽  
Sam-Erik Walker
Keyword(s):  
Time Series ◽  
Wave Height ◽  
Significant Wave Height ◽  
Wave Climate ◽  
Ocean Wave ◽  
Significant Wave ◽  
Long Time ◽  
Long Term Trends ◽  
Ocean Wave Climate

Reliable return period estimates of sea state parameters such as the significant wave height is of great importance in marine structural design and ocean engineering. Hence, time series of significant wave height have been extensively studied in recent years. However, with the possibility of an ongoing change in the global climate, this might influence the ocean wave climate as well and it would be of great interest to analyze long time series to see if any long-term trends can be detected. In this paper, long time series of significant wave height stemming from the ERA-40 reanalysis project, containing 6-hourly data over a period of more than 44 years are investigated with the purpose of identifying long term trends. Different time series analysis methods are employed, i.e. seasonal ARIMA, multiple linear regression, the Theil-Sen estimator and generalized additive models, and the results are discussed. These results are then compared to previous studies; in particular results are compared to a recent study where a spatio-temporal stochastic model was applied to the same data. However, in the current analysis, the spatial dimension has been reduced and spatial minima, mean and maxima have been analysed for temporal trends. Overall, increasing trends in the wave climate have been identified by most of the modelling approaches explored in the paper, although some of the trends are not statistically significant at the 95% level. Based on the results presented in this paper, it may be argued that there is evidence of a roughening trend in the recent ocean wave climate, and more detailed analyses of individual months and seasons indicate that these trends might be mostly due to trends during the winter months.

Modelling Long-Term Trends in Significant Wave Height and its Potential Impacts on Ship Structural Loads

2013 ◽  
Author(s):  
Erik Vanem ◽  
Elzbieta M. Bitner-Gregersen
Keyword(s):  
Wave Height ◽  
Significant Wave Height ◽  
Wave Climate ◽  
Ocean Wave ◽  
Future Trends ◽  
Significant Wave ◽  
Long Term Trends ◽  
Ocean Wave Climate ◽  
The Impact

This paper presents the results from a statistical model for significant wave height in space and time. In particular, various model alternatives were applied to extract long-term temporal trends towards the year 2100. Future projections of the North Atlantic ocean wave climate based on two of these alternatives are presented, i.e. an extrapolated linear trend and trends based on regression on atmospheric levels of CO2 and assuming future emission scenarios proposed by IPCC. It is further explored how such future trends can be related to the structural load calculations of ships. It will be demonstrated how the estimated future trends can be incorporated in joint environmental models to yield updated environmental contour lines that take possible changes in the ocean wave climate into account. In this way, the impact of climate change on the wave climate can be accounted for in stress and loads calculations and hence in the structural dimensioning of ships and offshore installations. The proposed approach is illustrated by an example showing the potential impact of the estimated long-term trends in the wave climate on the wave-induced structural loads of an oil tanker. Results indicate that the impact may be far from negligible, and that this may need to be considered in the future when performing loads calculations.

Statistical Uncertainty in Long-Term Distributions of Significant Wave Height

1996 ◽  
Vol 118 (4) ◽  
pp. 284-291 ◽  
Author(s):  
C. Guedes Soares ◽  
A. C. Henriques
Keyword(s):  
Probability Distribution ◽  
Wave Height ◽  
Significant Wave Height ◽  
Data Sets ◽  
Monthly Basis ◽  
Significant Wave ◽  
Fitting Procedure ◽  
The One ◽  
Homogeneous Data

This work examines some aspects involved in the estimation of the parameters of the probability distribution of significant wave height, in particular the homogeneity of the data sets and the statistical methods of fitting a distribution to data. More homogeneous data sets are organized by collecting the data on a monthly basis and by separating the simple sea states from the combined ones. A three-parameter Weibull distribution is fitted to the data. The parameters of the fitted distribution are estimated by the methods of maximum likelihood, of regression, and of the moments. The uncertainty involved in estimating the probability distribution with the three methods is compared with the one that results from using more homogeneous data sets, and it is concluded that the uncertainty involved in the fitting procedure can be more significant unless the method of moments is not considered.

scholarly journals Long-Term and Seasonal Trends in Global Wave Height Extremes Derived from ERA-5 Reanalysis Data

2020 ◽  
Vol 8 (12) ◽  
pp. 1015
Author(s):  
Alicia Takbash ◽  
Ian R. Young
Keyword(s):  
Southern Hemisphere ◽  
Wave Height ◽  
Significant Wave Height ◽  
Reanalysis Data ◽  
Extreme Value Analysis ◽  
Winter Storms ◽  
Value Analysis ◽  
Weather Forecasts ◽  
Significant Wave

A non-stationary extreme value analysis of 41 years (1979–2019) of global ERA5 (European Centre for Medium-Range Weather Forecasts Reanalysis) significant wave height data is undertaken to investigate trends in the values of 100-year significant wave height, Hs100. The analysis shows that there has been a statistically significant increase in the value of Hs100 over large regions of the Southern Hemisphere. There have also been smaller decreases in Hs100 in the Northern Hemisphere, although the related trends are generally not statistically significant. The increases in the Southern Hemisphere are a result of an increase in either the frequency or intensity of winter storms, particularly in the Southern Ocean.

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.

scholarly journals ON LONG-TERM STATISTICS FOR OCEAN AND COASTAL WAVES

1978 ◽  
Vol 1 (16) ◽  
pp. 2 ◽  
Author(s):  
Michel K. Ochi
Keyword(s):  
Wave Height ◽  
Significant Wave Height ◽  
Contingency Table ◽  
Statistical Properties ◽  
Significant Wave ◽  
Coastal Waves ◽  
Normal Probability ◽  
Log Normal ◽  
Confidence Domain

This paper discusses the statistical properties of long-term ocean and coastal waves derived from analysis of available data. It was found from the results of the analysis that the statistical properties of wave height and period obey the bi-variate log-normal probability law. The method to determine the confidence domain for a specified confidence coefficient is presented so that reliable information in severe seas where data are always sparse can be obtained from a contingency table. Estimation of the extreme significant wave height expected in the long-term is also discussed.

Sign in / Sign up

Export Citation Format

Share Document