Neural Networks Based Simulation of Significant Wave Height

Author(s):  
H. Bazargan ◽  
H. Bahai ◽  
A. Aminzadeh-Gohari ◽  
A. Bazargan

A large number of ocean activities call for real time or on-line forecasting of wind wave characteristics including significant wave height (Hs). The work reported in this paper uses statistics, and artificial neural networks trained with an optimization technique called simulated annealing to estimate the parameters of a probability distribution called hepta-parameter spline for the conditional probability density functions (pdf’s) of significant wave heights given their eight immediate preceding 3-hourly observed Hs’s. These pdf’s are used in the simulation of significant wave heights related to a location in the Pacific. The paper also deals with short and long term forecasting of Hs for the region through generating random variates from the spline distribution.

RBRH ◽  
2017 ◽  
Vol 22 (0) ◽  
Author(s):  
Natália Lemke ◽  
◽  
Lauro Julio Calliari ◽  
José Antônio Scotti Fontoura ◽  
Déborah Fonseca Aguiar

ABSTRACT The wave climate characterization in coastal environments is essentially important to oceanography and coastal engineering professionals regarding coastal protection works. Thus, this study aims to determine the most frequent wave parameters (significant wave height, peak period and peak direction) in Patos Lagoon during the period of operation of a directional waverider buoy (from 01/27/2015 to 06/30/2015). The equipment was moored at approximately 14 km from the São Lourenço do Sul coast at the geographic coordinates of 31º29’06” S and 51º55’07” W, with local depth of six meters, registering significant wave height, peak period and peak direction time series. During the analyzed period, the greatest wave frequencies corresponded to short periods (between 2 and 3.5 seconds) and small values of significant wave heights (up to 0.6 meters), with east peak wave directions. The largest wave occurrences corresponded to east peak wave directions (33.3%); peak wave periods between 2.5 and 3 seconds (25.6%) and between 3 and 3.5 seconds (22.1%); and to significant wave heights of up to 0.3 meters (41.2%) and from 0.3 to 0.6 meters (38%). This research yielded unprecedented findings to Patos Lagoon by describing in detail the most occurring wave parameters during the analyzed period, establishing a consistent basis for several other studies that might still be conducted by the scientific community.


2019 ◽  
Author(s):  
Zhuxiao Shao ◽  
Bingchen Liang ◽  
Huajun Li ◽  
Ping Li ◽  
Dongyoung Lee

Abstract. An assessment of extreme significant wave heights is performed in the South China Sea (SCS), which is crucial for the coastal and offshore engineering in this area. Two significant factors influencing the assessment are the initial database and the assessing method. The initial database is a basic for assessment, and the assessing method is used to extrapolate appropriate return significant wave heights based on this database during a period. In this study, a 40-year (1975–2014) hindcasted significant wave height of tropical cyclone waves is adopted as the initial database. Based on this database, the peak significant wave height of every tropical cyclone wave is directly extracted as the initial sample; the independent and identically distributed assumption is satisfied; and the interference for the selection of the sample is avoided. The peak over threshold (POT) method with the generalized Pareto distribution (GPD) model is employed to extract the sufficiently large and high sample for model estimation. The peak excesses over a sufficiently high value (i.e., threshold) are fitted; thus, the return significant wave heights are highly dependent on the threshold. To determine the unique threshold for the POT method, characteristics of tropical cyclone waves are researched. The research results reveal that the separation value shown in the distribution of the initial sample is suitable for sampling in the SCS. Because the separation value is within the stable threshold range and the asymptotic tail approximation and estimation uncertainty are reasonable, the selected threshold is suitable and the corresponding return significant wave height is reliable.


2020 ◽  
Vol 8 (4) ◽  
pp. 236 ◽  
Author(s):  
Huijun Gao ◽  
Zhuxiao Shao ◽  
Guoxiang Wu ◽  
Ping Li

The study of extreme waves is important for the protection of coastal and ocean structures. In this work, a 22-year (1990–2011) wave hindcast in the Yellow Sea is employed to perform the assessment of extreme significant wave heights in this area. To extract the independent sample from this database, the fixed window method is used, which takes the peak significant wave height within five d. With the selected samples, directional declustering is studied to extract the homogenous sample. The results show that most of the independent samples (especially large samples) are observed in the North. In this direction, the peak over threshold (POT) method is used to extract the extreme sample from the homogenous sample, and then the generalized Pareto distribution model is used to extrapolate the extreme significant wave height. In addition to this combination, the annual maxima method with the Gumbel model is also used for estimating extreme values. The comparisons show that the return significant wave heights of the first combination are reliable, resulting from a flexible sampling window in the POT method. With this conclusion, the extreme significant wave height is extrapolated from the Yellow Sea, which can be used to protect the structure in the main directional bin.


2016 ◽  
Vol 31 (6) ◽  
pp. 2035-2045 ◽  
Author(s):  
Charles R. Sampson ◽  
James A. Hansen ◽  
Paul A. Wittmann ◽  
John A. Knaff ◽  
Andrea Schumacher

Abstract Development of a 12-ft-seas significant wave height ensemble consistent with the official tropical cyclone intensity, track, and wind structure forecasts and their errors from the operational U.S. tropical cyclone forecast centers is described. To generate the significant wave height ensemble, a Monte Carlo wind speed probability algorithm that produces forecast ensemble members is used. These forecast ensemble members, each created from the official forecast and randomly sampled errors from historical official forecast errors, are then created immediately after the official forecast is completed. Of 1000 forecast ensemble members produced by the wind speed algorithm, 128 of them are selected and processed to produce wind input for an ocean surface wave model. The wave model is then run once per realization to produce 128 possible forecasts of significant wave height. Probabilities of significant wave height at critical thresholds can then be computed from the ocean surface wave model–generated significant wave heights. Evaluations of the ensemble are provided in terms of maximum significant wave height and radius of 12-ft significant wave height—two parameters of interest to both U.S. Navy meteorologists and U.S. Navy operators. Ensemble mean errors and biases of maximum significant wave height and radius of 12-ft significant wave height are found to be similar to those of a deterministic version of the same algorithm. Ensemble spreads capture most verifying maximum and radii of 12-ft significant wave heights.


2019 ◽  
Vol 19 (10) ◽  
pp. 2067-2077 ◽  
Author(s):  
Zhuxiao Shao ◽  
Bingchen Liang ◽  
Huajun Li ◽  
Ping Li ◽  
Dongyoung Lee

Abstract. Extreme significant wave heights are assessed in the South China Sea (SCS), as assessments of wave heights are crucial for coastal and offshore engineering. Two significant factors include the initial database and assessment method. The initial database is a basis for assessment, and the assessment method is used to extrapolate appropriate return-significant wave heights during a given period. In this study, a 40-year (1975–2014) hindcast of tropical cyclone waves is used to analyse the extreme significant wave height, employing the peak over threshold (POT) method with the generalized Pareto distribution (GPD) model. The peak exceedances over a sufficiently large value (i.e. threshold) are fitted; thus, the return-significant wave heights are highly dependent on the threshold. To determine a suitable threshold, the sensitivity of return-significant wave heights and the characteristics of tropical cyclone waves are studied. The sample distribution presents a separation that distinguishes the high sample from the low sample, and this separation is within the stable threshold range. Because the variation in return-significant wave heights in this range is generally small and the separation is objectively determined by the track and intensity of the tropical cyclone, the separation is selected as a suitable threshold for extracting the extreme sample in the tropical cyclone wave. The asymptotic tail approximation and estimation uncertainty show that the selection is reasonable.


2021 ◽  
Vol 9 (12) ◽  
pp. 1426
Author(s):  
Valentina Laface ◽  
Felice Arena

The paper is focused on the formulation of an adequate criterion for associating wave storm events to the generating wind storm ones, and on the study of correlation between their characteristic parameters. In this context, the sea storm definition commonly used for storm identification from significant wave height data is adapted for wind storm, by processing wind speed data. A sensitivity analysis is proposed as function of the storm thresholds aiming at identifying optimal combination of wind speed and significant wave height thresholds allowing the association of relatively large number of events ensuring high correlation between wind and wave storm parameters. The analysis is carried out using as input data wind speeds and significant wave heights from four meteorological (buoys and anemometers) stations of the National Data Buoy Center moored off the East Coast of the United States. Results reveal that an optimal threshold combination is achieved assuming both wind speed and significant wave height threshold as 1.5 time their respective averages.


2021 ◽  
Vol 13 (2) ◽  
pp. 195
Author(s):  
He Wang ◽  
Jingsong Yang ◽  
Jianhua Zhu ◽  
Lin Ren ◽  
Yahao Liu ◽  
...  

Sea state estimation from wide-swath and frequent-revisit scatterometers, which are providing ocean winds in the routine, is an attractive challenge. In this study, state-of-the-art deep learning technology is successfully adopted to develop an algorithm for deriving significant wave height from Advanced Scatterometer (ASCAT) aboard MetOp-A. By collocating three years (2016–2018) of ASCAT measurements and WaveWatch III sea state hindcasts at a global scale, huge amount data points (>8 million) were employed to train the multi-hidden-layer deep learning model, which has been established to map the inputs of thirteen sea state related ASCAT observables into the wave heights. The ASCAT significant wave height estimates were validated against hindcast dataset independent on training, showing good consistency in terms of root mean square error of 0.5 m under moderate sea condition (1.0–5.0 m). Additionally, reasonable agreement is also found between ASCAT derived wave heights and buoy observations from National Data Buoy Center for the proposed algorithm. Results are further discussed with respect to sea state maturity, radar incidence angle along with the limitations of the model. Our work demonstrates the capability of scatterometers for monitoring sea state, thus would advance the use of scatterometers, which were originally designed for winds, in studies of ocean waves.


Author(s):  
Leonardo Roncetti ◽  
Fabrício Nogueira Corrêa ◽  
Carl Horst Albrecht ◽  
Breno Pinheiro Jacob

Lifting operations with offshore cranes are fundamental for proper functioning of a platform. Despite the great technological development, offshore cranes load charts only consider the significant wave height as parameter of environmental load, neglecting wave period, which may lead to unsafe or overestimated lifting operations. This paper aims to develop a method to design offshore crane operational limit diagrams for lifting of personnel and usual loads, in function of significant wave height and wave peak period, using time domain dynamic analysis, for a crane installed on a floating unit. The lifting of personnel with crane to transfer between a floating unit and a support vessel is a very used option in offshore operations, and this is in many cases, the only alternative beyond the helicopter. Due to recent fatal accidents with lifting operations in offshore platforms, it is essential the study about this subject, contributing to the increase of safety. The sea states for analysis were chosen covering usual significant wave heights and peak periods limits for lifting operations. The methodology used the SITUA / Prosim software to obtain the dynamic responses of the personnel transfer basket lifting and container loads on a typical FPSO. Through program developed by the author, it was implemented the automatic generation of diagrams as a function of operational limits. It is concluded that using this methodology, it is possible to achieve greater efficiency in the design and execution of personnel and routine load lifting, increasing safety and a wider weather window available.


2020 ◽  
Vol 12 (20) ◽  
pp. 3367
Author(s):  
Kaoru Ichikawa ◽  
Xi-Feng Wang ◽  
Hitoshi Tamura

Satellite altimetry is a unique system that provides repeated observations of significant wave height (SWH) globally, but its measurements could be contaminated by lands, slicks, or calm water with smooth surface. In this study, capability of subwaveform retrackers against 20 Hz Jason-2 measurements is examined in the calm Celebes Sea. Distances between contamination sources and Jason-2 observation points can be determined using sequentially assembled adjacent waveforms (radargram). When no contamination sources are present within a Jason-2 footprint, subwaveform retrackers are in excellent agreement with the Sensor Geophysical Data Records (SGDR) MLE4 retracker that uses full-length waveforms, except that Adaptive Leading Edge Subwaveform (ALES) retracker has a positive bias in a calm sea state (SWH < 1 m), which is not unusual in the Celebes Sea. Meanwhile, when contamination sources exist within 4.5 km from Jason-2 observation points, SGDR occasionally estimates unrealistically large SWH values, although they could be partly eliminated by sigma0 filters. These datasets are then compared with WAVEWATCH III model, resulting in good agreement. The agreement becomes worse if swells from the Pacific is excluded in the model, suggesting constant presence of swells despite the semi-enclosed nature. In addition, outliers are found related with locally-confined SWH events, which could be inadequately represented in the model.


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