A nonstationary stochastic model for long-term time series of significant wave height

1995 ◽  
Vol 100 (C8) ◽  
pp. 16149 ◽  
Author(s):  
G. A. Athanassoulis ◽  
Ch. N. Stefanakos
Keyword(s):  
Time Series ◽  
Stochastic Model ◽  
Wave Height ◽  
Significant Wave Height ◽  
Significant Wave

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.

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.

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.

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.

“Three Sisters” Measured As a Triple Rogue Wave Group

2019 ◽  
Author(s):  
Anne Karin Magnusson ◽  
Karsten Trulsen ◽  
Ole Johan Aarnes ◽  
Elzbieta M. Bitner-Gregersen ◽  
Mika P. Malila
Keyword(s):  
Time Series ◽  
Wave Height ◽  
Significant Wave Height ◽  
Weather Conditions ◽  
Rogue Waves ◽  
Significant Wave ◽  
Wind Sea ◽  
Propagation Methods ◽  
Non Linear Propagation ◽  
Three Sisters

Abstract On November 30, 2018, our attention was caught when analyzing wave profile time series measured by a platform mounted wave sensor (a SAAB REX radar) at Ekofisk, central North Sea. The 20-minute time series had not only one, but three consecutive waves with individual heights that all were more than twice the significant wave height, the two last of them being almost equally high with a factor 2.35 to the significant wave height of 4m (from 4σ(η), over 20 minutes). Counting three rogue waves in one sequence seems to be very rare. In this study we analyze how the shape is evolving in space and time using linear and non-linear propagation methods developed by Mark Donelan [1,2] and Karsten Trulsen [3,4]. Weather conditions and characteristics of the sea state with the ‘Three Sisters’ (named the “Justine Three Sisters”) are presented. It is found that the Three Sisters occurred in a crossing sea condition, with wind sea and swell coming from directions 60 degrees apart, with about same frequency, but very different energy.

The Reconstruction of Significant Wave Height Time Series by Using a Neural Network Approach

2004 ◽  
Vol 126 (3) ◽  
pp. 213-219 ◽  
Author(s):  
Felice Arena ◽  
Silvia Puca
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Wave Height ◽  
Significant Wave Height ◽  
Error Function ◽  
Neural Network Approach ◽  
Significant Wave ◽  
Time Periods ◽  
Weighted Error ◽  
Storm Model

A Multivariate Neural Network (MNN) algorithm is proposed for the reconstruction of significant wave height time series, without any increase of the error of the MNN output with the number of modelled data. The algorithm uses a weighted error function during the learning phase, to improve the modelling of the higher significant wave height. The ability of the MNN to reconstruct sea storms is tested by applying the equivalent triangular storm model. Finally an application to the NOAA buoys moored off California shows a good performance of the MNN algorithm, both during sea storms and calm time periods.

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