On the choice of data transformation for modelling time series of significant wave height

1999 ◽  
Vol 26 (6) ◽  
pp. 489-506 ◽  
Author(s):  
C. Cunha ◽  
C. Guedes Soares
Keyword(s):  
Time Series ◽  
Wave Height ◽  
Significant Wave Height ◽  
Data Transformation ◽  
Significant Wave

“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.

scholarly journals Changes of Significant Wave Height, Energy Period and Wave Power in Italy in the Period 1979–2018

2020 ◽  
Vol 2 (1) ◽  
pp. 3
Author(s):  
Tommaso Caloiero ◽  
Francesco Aristodemo ◽  
Danilo Algieri Ferraro
Keyword(s):  
Time Series ◽  
Wave Height ◽  
Significant Wave Height ◽  
Kendall Test ◽  
Wave Power ◽  
Mean Values ◽  
Significant Wave ◽  
Wave Energy Converters ◽  
Long Wave ◽  
Significance Levels

An analysis of a 40-year long wave time series was performed, along the coasts of Italy, in order to identify ongoing trends of two synthetic parameters, significant wave height (Hs) and energy period (Te), and of the wave power (P). First, wave data were deduced from the global atmospheric reanalysis ERA-INTERIM by the ECMWF and checked to verify their consistency. Then, a trend analysis was performed on mean values evaluated at annual and seasonal scales through the non-parametric Mann–Kendall test for three different significance levels equal to 90%, 95% and 99%. The obtained results could be useful for analyses linked to beach morphodynamics and on the identification of field installations of Wave Energy Converters (WECs).

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.

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