Comparing Nearshore Wave Parameters in Amurang Bay location using MIKE-21 Spectral Wave Model

2019 ◽  
Vol 1 (2) ◽  
pp. 150-158
Author(s):  
Tommy Jansen
Keyword(s):  
Wave Height ◽  
Significant Wave Height ◽  
Wave Model ◽  
Google Earth ◽  
Coastal Development ◽  
Wave Direction ◽  
Wet Season ◽  
Significant Wave ◽  
Wave Parameters ◽  
Mike 21

Wave parameters as an accurate prediction in ocean environment are important thing for good coastal development. Spectral wind wave model as a tools in MIKE 21 SW based on unstructured mesh is used in this study which the model simulates the growth, decay and transformation of wind generated waves and swell in offshore and coastal areas. The Amurang Bay as the province of North Sulawesi Indonesia was selected as the study area which the geography position around 1012’16.16” N-124027’04.33” E to 1015’43.80” N-124032’01.06”E. The bathymetry and tide data used in this research from Indonesian Coastline Environmental map of year 1995 with scale 1:50.000 from BIG (Badan Informasi Geospasial) with a satellite data from Google earth of year 2018 and LANTAMAL Manado, the wind and current data was obtained from BMKG Manado. Time simulations are taken from 25 November to 23 December 2016 as a wet season and 25 Mei to 23 June 2016 as a dry season.The model computed the wave parameters using the forecast wind input. The synoptic map of significant wave height (Hs), wave period, wave direction are obtained from the result of simulation. During the dry and wet season conditions the predicted wave parameters as the result of the simulation with tide and wind show to be higher than with tide and no wind simulation. The average condition of significant wave height is higher in outside of bay than inside of bay.

Climatology, Variability and Extrema of Ocean Waves: The Web-Based KNMI/ERA-40 Wave Atlas

2006 ◽  
Author(s):  
Andreas Sterl ◽  
Sofia Caires
Keyword(s):  
Wave Height ◽  
Significant Wave Height ◽  
Wave Model ◽  
Ocean Waves ◽  
Wave Period ◽  
Web Based ◽  
Significant Wave ◽  
Wave Parameters ◽  
Wave Heights ◽  
The Web

The European Centre for Medium Range Weather Forecasts (ECMWF) has recently finished ERA-40, a reanalysis covering the period September 1957 to August 2002. One of the products of ERA-40 consists of 6-hourly global fields of wave parameters like significant wave height and wave period. These data have been generated with the Centre’s WAM wave model. From these results the authors have derived climatologies of important wave parameters, including significant wave height, mean wave period, and extreme significant wave heights. Particular emphasis is on the variability of these parameters, both in space and time. Besides for scientists studying climate change, these results are also important for engineers who have to design maritime constructions. This paper describes the ERA-40 data and gives an overview of the results derived. The results are available on a global 1.5° × 1.5° grid. They are accessible from the web-based KNMI/ERA-40 Wave Atlas at http://www.knmi.nl/waveatlas.

scholarly journals Assimilation of Directional Wave Spectra in the Wave Model WAM: An Impact Study from Synthetic Observations in Preparation for the SWIMSAT Satellite Mission

2006 ◽  
Vol 23 (3) ◽  
pp. 448-463 ◽  
Author(s):  
Lotfi Aouf ◽  
Jean-Michel Lefèvre ◽  
Danièle Hauser
Keyword(s):  
Wave Height ◽  
Significant Wave Height ◽  
Wave Model ◽  
Spectral Information ◽  
Wave Spectra ◽  
Satellite Mission ◽  
Significant Wave ◽  
Wave Parameters ◽  
The Impact ◽  
Assimilation Scheme

Abstract Within the framework of the Surface Waves Investigation and Monitoring from Satellite mission (SWIMSAT) proposed to the European Space Agency, an assimilation scheme has been implemented in the Wave Model (WAM) in order to estimate the impact of spectral information on wave prediction. The scheme uses an optimal interpolation and the “spectral partitioning” principle. The synthetic wave spectra are located along a SWIMSAT orbit track and are assimilated in a 4-day-period simulation. Random errors are included to simulate the uncertainties of SWIMSAT instrumentation. The sensitivity of the scheme to background and observational errors and the correlation length is examined. The assimilation impact is investigated for two cases of moderate and large errors of the first guess. The results show that the assimilation scheme works correctly and the rms errors of significant wave height, mean period, and direction are significantly reduced for both periods of analysis and forecast. The impact on significant wave height is noticeable during the period of analysis and stays efficient for 2-day forecasts. For a large error in the first guess, the impact increases and remains significant for 3-day forecasts. Statistical analysis of mean wave parameters clearly shows that the use of spectral information yields a better estimate of wave frequency, direction, and low-frequency wave height in comparison with the results based upon assimilation of wave heights only. However, total significant wave height is less sensitive to the addition of spectral information in the assimilation scheme. The use of correlation length depending on the latitude of grid points leads to a better spread of incremental observations and, hence, to better skills in terms of the rms errors of mean wave parameters. The use of several wavelength cutoffs concerning the SWIMSAT synthetic wave spectra suggests that the “assimilation index” of mean wave parameters decreases with the increasing wavelength cutoff.

Effect of Uni- and Bi-Directional Coupling of Ocean-Met Interaction on Significant Wave Height and Local Wind

2017 ◽  
Author(s):  
Adil Rasheed ◽  
Jakob Kristoffer Süld ◽  
Mandar Tabib
Keyword(s):  
Wave Height ◽  
Significant Wave Height ◽  
Surface Wind ◽  
Wave Model ◽  
Ocean Surface ◽  
Observation Data ◽  
Local Wind ◽  
Near Surface ◽  
Significant Wave ◽  
Bidirectional Coupling

Accurate prediction of near surface wind and wave height are important for many offshore activities like fishing, boating, surfing, installation and maintenance of marine structures. The current work investigates the use of different methodologies to make accurate predictions of significant wave height and local wind. The methodology consists of coupling an atmospheric code HARMONIE and a wave model WAM. Two different kinds of coupling methodologies: unidirectional and bidirectional coupling are tested. While in Unidirectional coupling only the effects of atmosphere on ocean surface are taken into account, in bidirectional coupling the effects of ocean surface on the atmosphere are also accounted for. The predicted values of wave height and local wind at 10m above the ocean surface using both the methodologies are compared against observation data. The results show that during windy conditions, a bidirectional coupling methodology has better prediction capability.

Gap-Filling and Predicting Wave Parameters Using Support Vector Regression Method

2011 ◽  
Author(s):  
Maziar Golestani ◽  
Mostafa Zeinoddini
Keyword(s):  
Support Vector Regression ◽  
Wave Height ◽  
Significant Wave Height ◽  
Rational Design ◽  
Wave Spectrum ◽  
Support Vector ◽  
Wave Spectra ◽  
Significant Wave ◽  
Wave Parameters ◽  
Data Gaps

Knowledge of relevant oceanographic parameters is of utmost importance in the rational design of coastal structures and ports. Therefore, an accurate prediction of wave parameters is especially important for safety and economic reasons. Recently, statistical learning methods, such as Support Vector Regression (SVR) have been successfully employed by researchers in problems such as lake water level predictions, and significant wave height prediction. The current study reports potential application of a SVR approach to predict the wave spectra and significant wave height. Also the capability of the model to fill data gaps was tested using different approaches. Concurrent wind and wave records (standard meteorological and spectral density data) from 4 stations in 2003, 2007, 2008 and 2009 were used both for the training the SVR system and its verification. The choice of these four locations facilitated the comparison of model performances in different geographical areas. The SVR model was then used to obtain predictions for the wave spectra and also time series of wave parameters (separately for each station) such as its Hs and Tp from spectra and wind records. New approach was used to predict wave spectra comparing to similar studies. Reasonably well correlation was found between the predicted and measured wave parameters. The SVR model was first trained and tested using various methods for selecting training data. Also different values for SVM parameters (e.g. tolerance of termination criterion, cost, and gamma in kernel function) were tested. The best possible results were obtained using a Unix shell script (in Linux) which automatically implements different values for different input parameters and finds the best regression by calculating statistical scores like correlation of coefficient, RMSE, bias and scatter index. Finally for a better understanding of the results, Quantile-Quantile plots were produced. The results show that SVR can be successfully used for prediction of Hs and wave spectrum out of a series of wind and spectral wave parameters inputs. Also it was noticed that SVR is an efficient tool to be used when data gaps are present in the data.

Uncertainty of Ocean Wave Hindcasts Due to Wind Modeling

1995 ◽  
Vol 117 (4) ◽  
pp. 294-297 ◽  
Author(s):  
J. C. Teixeira ◽  
M. P. Abreu ◽  
C. Guedes Soares
Keyword(s):  
Wind Speed ◽  
Relative Error ◽  
Wave Height ◽  
Significant Wave Height ◽  
Second Generation ◽  
Wave Model ◽  
Ocean Wave ◽  
Wind Fields ◽  
Significant Wave ◽  
Wind Input

Two wind models were developed and their results were compared with data gathered during the Wangara experiment, so as to characterize their uncertainty. One of the models was adopted to generate the wind fields used as input to a second generation wave model. The relative error in the wind speed was considered in order to assess the uncertainties of the predictions or the significant wave height. Different time steps for the wind input were also used to determine their effect on the predicted significant wave height.

scholarly journals Wave directional measurement in Patos Lagoon, RS, Brazil

RBRH ◽  
2017 ◽  
Vol 22 (0) ◽  
Author(s):  
Natália Lemke ◽  
◽  
Lauro Julio Calliari ◽  
José Antônio Scotti Fontoura ◽  
Déborah Fonseca Aguiar
Keyword(s):  
Wave Height ◽  
Significant Wave Height ◽  
Wave Climate ◽  
Coastal Protection ◽  
Peak Period ◽  
Patos Lagoon ◽  
Significant Wave ◽  
Wave Parameters ◽  
Wave Heights ◽  
Significant Wave Heights

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.

scholarly journals Evaluation of Assimilation in the MASNUM Wave Model Based on Jason-3 and CFOSAT

2021 ◽  
Vol 13 (19) ◽  
pp. 3833
Author(s):  
Meng Sun ◽  
Jianting Du ◽  
Yongzeng Yang ◽  
Xunqiang Yin
Keyword(s):  
Wave Height ◽  
Significant Wave Height ◽  
Wave Model ◽  
Ocean Waves ◽  
Validation Data ◽  
Engineering Construction ◽  
Significant Wave ◽  
Offshore Engineering ◽  
The Indian Ocean ◽  
Dynamic Sampling

Accurate numerical simulation of ocean waves is one of the most important measures to ensure shipping safety, offshore engineering construction, etc. The use of wave observations from satellite is an efficient way to correct model results. The goal of this paper is to assess the performance of assimilation in the MASNUM wave model for the Indian Ocean. The assimilation technique is based on Ensemble Adjusted Kalman Filter, with a variable ensemble constructed by the dynamic sampling method rather than ensemble members of wave model. Observations of significant wave height from satellites Jason-3 and CFOSAT are regarded as assimilation data and independent validation data, respectively. The results indicate good performance in terms of absolute mean error for significant wave height. Model error decreases by roughly 20–40% in high-sea conditions.

scholarly journals Characteristics of high monsoon wind-waves observed at multiple stations in the eastern Arabian Sea

2017 ◽  
Author(s):  
M. M. Amrutha ◽  
V. Sanil Kumar
Keyword(s):  
Wave Height ◽  
Significant Wave Height ◽  
Arabian Sea ◽  
Wind Waves ◽  
Surface Wind ◽  
Wave Model ◽  
Spatial Distance ◽  
Significant Wave ◽  
Eastern Arabian Sea ◽  
Crest Height

Abstract. The growth and decay of surface wind-waves during one-month period in a typical Indian summer monsoon is investigated based on the data collected at 9 to 15 m water depth at 4 locations in the nearshore waters of the eastern Arabian Sea covering a spatial distance of ~ 350 km. The significant wave height varied from 0.7 to 5.5 m during the data collection considered in the analysis. The heights of waves during the measurement period often exceed 3 m. The most extreme wave height is 1.50 to 1.62 times the significant wave height and the most extreme crest height of the wave is 1.23 to 1.35 times the significant wave height of the same 30-minutes record. The average ratio of crest height of the wave to the height of the same wave is 0.58 to 0.67. The height of waves having maximum crest height is smaller than the maximum wave height during 30 minutes period. Measured waves are predominantly swell, but since the majority of wave generation during the monsoon is adjacent to the study area and the wind–wave coupling is strong, wave periods are rarely above 15 s. The numerical wave model could estimate the wave height reasonably well during the wave growth compared to the wave decay period. Hovmöller diagrams show a considerable spatial variability in the wave and wind pattern in the Indian Ocean during the high wave event at the eastern Arabian Sea.

scholarly journals Atmospheric Cold Front-Generated Waves in the Coastal Louisiana

2020 ◽  
Vol 8 (11) ◽  
pp. 900
Author(s):  
Yuhan Cao ◽  
Chunyan Li ◽  
Changming Dong
Keyword(s):  
Wave Height ◽  
Significant Wave Height ◽  
Cold Front ◽  
Wave Spectrum ◽  
Low Frequency ◽  
Wave Direction ◽  
Significant Wave ◽  
Cold Fronts ◽  
Directional Wave ◽  
Coastal Louisiana

Atmospheric cold front-generated waves play an important role in the air–sea interaction and coastal water and sediment transports. In-situ observations from two offshore stations are used to investigate variations of directional waves in the coastal Louisiana. Hourly time series of significant wave height and peak wave period are examined for data from 2004, except for the summer time between May and August, when cold fronts are infrequent and weak. The intra-seasonal scale variations in the wavefield are significantly affected by the atmospheric cold frontal events. The wave fields and directional wave spectra induced by four selected cold front passages over the coastal Louisiana are discussed. It is found that significant wave height generated by cold fronts coming from the west change more quickly than that by other passing cold fronts. The peak wave direction rotates clockwise during the cold front events. The variability of the directional wave spectrum shows that the largest spectral density is distributed at low frequency in the postfrontal phase associated with migrating cyclones (MC storms) and arctic surges (AS storms).

Correlation Enhanced Machine Learning Approach based Wave Height Prediction

2018 ◽  
Vol 4 (5) ◽  
pp. 10
Author(s):  
Ruchi Shrivastava ◽  
Dr. Krishna Teerth Chaturvedi
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Wave Height ◽  
Significant Wave Height ◽  
Work Performance ◽  
Feature Reduction ◽  
Learning Approaches ◽  
Significant Wave ◽  
Wave Parameters ◽  
Height Prediction

The prediction of wave height is one of the major problems of coastal engineering and coastal structures. In recent years, advances in the prediction of significant wave height have been considerably developed using flexible calculation techniques. In addition to the traditional prediction of significant wave height, soft computing has explored a new way of predicting significant wave heights. This research was conducted in the direction of forecasting a significant wave height using machine learning approaches. In this paper, a problem of significant wave height prediction problem has been tackled by using wave parameters such as wave spectral density. This prediction of significant wave height helps in wave energy converters as well as in ship navigation system. This research will optimize wave parameters for a fast and efficient wave height prediction. For this Pearson’s, Kendall’s and Spearman’s Correlation Coefficients and Particle Swarm Optimization feature reduction techniques are used. So reduced features are taken into consideration for prediction of wave height using neural network. In this work, performance evaluation metrics such as MSE and RMSE values are decreased and gives better performance of classification that is compared with existing research’s implemented methodology. From the experimental results, it is observed that proposed algorithm gives the better prediction as compared to PSO feature reduction technique. So, it is also concluded that Co-relation enhanced neural network is better as compared to PSO based neural network with increased number of features.

Sign in / Sign up

Export Citation Format

Share Document