Applied Data Analytics to Buoy Records for Weather Window Evaluation

2021 ◽  
Author(s):  
Tirtharaj Bhaumik ◽  
Shiladitya Basu
Keyword(s):  
Wave Height ◽  
Significant Wave Height ◽  
Data Analytics ◽  
Time Series Data ◽  
Weather Data ◽  
Series Data ◽  
Wave Direction ◽  
Significant Wave ◽  
Using Data ◽  
Future Work

This paper analyzes weather data recorded by typical oceanographic buoys using data analytics and regression techniques. Time series data over a period of more than four decades (1976 – 2020) are reviewed and profiled. A set of key variables including seasonality, wind speed, wind direction, wave period, wave direction, etc., are screened from the buoy measurements to build a predictive model based on multiple linear regression for significant wave height prediction. A sensitivity analysis is then conducted for the available weather window corresponding to specified threshold operational limits of the significant wave height. Key insights are presented along with suggestions for future work to assist marine operators in planning and derisking offshore operations. Utilizing the algorithms and workflows presented in this paper, a user can increase confidence in weather window prediction, and develop safer, efficient offshore operation plans.

Estimation of Wave Height Return Periods Using a Nonstationary Time Series Modelling

2006 ◽  
Author(s):  
Christos N. Stefanakos ◽  
Vale´rie Monbet
Keyword(s):  
Time Series ◽  
Wave Height ◽  
Significant Wave Height ◽  
Time Series Data ◽  
Nonstationary Time Series ◽  
Series Data ◽  
Dependence Structure ◽  
Return Periods ◽  
Significant Wave ◽  
Time Series Modelling

A new method for calculating return periods of various level values from nonstationary time series data is presented. The key-idea of the method is a new definition of the return period, based on the Mean Number of Upcrossings of the level x* (MENU method). The whole procedure is numerically implemented and applied to long-term measured time series of significant wave height. The method is compared with other more classical approaches that take into acount the time dependance for time series of significant wave height. Estimates of the extremal index are given and for each method bootstrap confidence intervals are computed. The predictions obtained by means of MENU method are lower than the traditional predictions. This is in accordance with the results of other methods that take also into account the dependence structure of the examined time series.

Uncertainties in Extreme Wave Height Estimates for Hurricane-Dominated Regions

2007 ◽  
Vol 129 (4) ◽  
pp. 300-305 ◽  
Author(s):  
Philip Jonathan ◽  
Kevin Ewans
Keyword(s):  
Wave Height ◽  
Significant Wave Height ◽  
Small Data ◽  
Extreme Wave ◽  
Significant Wave ◽  
Wave Characteristics ◽  
Height Estimates ◽  
Wave Heights ◽  
Using Data ◽  
High Percentile

Inherent uncertainties in estimation of extreme wave heights in hurricane-dominated regions are explored using data from the GOMOS Gulf of Mexico hindcast for 1900–2005. In particular, the effect of combining correlated values from a neighborhood of 72 grid locations on extreme wave height estimation is quantified. We show that, based on small data samples, extreme wave heights are underestimated and site averaging usually improves estimates. We present a bootstrapping approach to evaluate uncertainty in extreme wave height estimates. We also argue in favor of modeling supplementary indicators for extreme wave characteristics, such as a high percentile (95%) of the distribution of 100-year significant wave height, in addition to its most probable value, especially for environments where the distribution of 100-year significant wave height is strongly skewed.

scholarly journals Significant Wave Height Prediction with the CRBM-DBN Model

2019 ◽  
Vol 36 (3) ◽  
pp. 333-351 ◽  
Author(s):  
Xining Zhang ◽  
Hao Dai
Keyword(s):  
Wave Height ◽  
Significant Wave Height ◽  
Restricted Boltzmann Machine ◽  
Series Data ◽  
Model Parameters ◽  
Boltzmann Machine ◽  
Short Term ◽  
Prediction Ability ◽  
Significant Wave ◽  
Height Prediction

AbstractIn recent years, deep learning technology has been gradually used for time series data prediction in various fields. In this paper, the restricted Boltzmann machine (RBM) in the classical deep belief network (DBN) is substituted with the conditional restricted Boltzmann machine (CRBM) containing temporal information, and the CRBM-DBN model is constructed. Key model parameters, which are determined by the particle swarm optimization (PSO) algorithm, are used to predict the significant wave height. Observed data in 2016, which are from nearshore and offshore buoys (i.e., 42020 and 42001) belonging to the National Data Buoy Center (NDBC), are taken to train the model, and the corresponding data in 2017 are used for testing with lead times of 1–24 h. In addition, we trained the data of 42040 in 2003 and tested the data in 2004 in order to investigate the prediction ability of the CRBM-DBN model for the extreme event. The prediction ability of the model is evaluated by the Nash–Sutcliffe coefficient of efficiency (CE) and root-mean-square error (RMSE). Experiments demonstrate that for the short-term (≤9 h) prediction, the RMSE and CE for the significant wave height prediction are <10 cm and >0.98, respectively. Moreover, the relative error of the short-term prediction for the maximum wave height is less than 26%. The excellent short-term and extreme events forecasting ability of the CRBM-DBN model is vital to ocean engineering applications, especially for designs of ocean structures and vessels.

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).

Comparative wave measurements at a wave energy site with a recently developed low-cost wave buoy (Spotter), ADCP and pressure loggers

2021 ◽  
Author(s):  
Orrin Lancaster ◽  
Remo Cossu ◽  
Sebastien Boulay ◽  
Scott Hunter ◽  
Tom E. Baldock
Keyword(s):  
Wave Height ◽  
Wave Energy ◽  
Significant Wave Height ◽  
Low Cost ◽  
Wave Period ◽  
Wave Direction ◽  
Mean Values ◽  
Significant Wave ◽  
Wave Measurements ◽  
The Mean

AbstractWave measurements from a new, low-cost, real-time wave buoy (Spotter) are investigated in a comparative study as part of a site characterization study at a wave energy candidate site at King Island, Tasmania, Australia. Measurements from the Sofar Ocean Spotter buoy are compared with concurrent measurements from a Teledyne RD Instrument (RDI) 1200 kHz Work Horse ADCP and two RBRsolo3 D wave16 pressure loggers. The comparison period between 8th August – 12th October 2019 provides both the shallowest and longest continuous published comparison undertaken with the Spotter buoy.Strong agreement was evident between the Spotter buoy and RDI ADCP of key wave parameters including the significant wave height, peak wave period, and mean wave direction, with the mean values of those parameters across the full deployment period agreeing within 3%. Surface wave spectra and directional spectra are also analyzed with good agreement observed over the majority of the frequency domain, although the Spotter buoy records approximately 17% less energy within a narrow frequency band near the peak frequency when compared to the RDI ADCP. Measurements derived from the pressure loggers routinely underestimated the significant wave height and overestimated the mean wave period over the deployment period. The comparison highlights the suitability of the Spotter buoy for low-cost wave resource studies, with accurate measurements of key parameters and spectra observed.

Estimation of Storm Peak and Intra-Storm Directional-Seasonal Design Conditions in the North Sea

2014 ◽  
Author(s):  
Graham Feld ◽  
David Randell ◽  
Yanyun Wu ◽  
Kevin Ewans ◽  
Philip Jonathan
Keyword(s):  
Wave Height ◽  
Significant Wave Height ◽  
Extreme Value ◽  
Extreme Value Analysis ◽  
Wave Direction ◽  
Value Analysis ◽  
Significant Wave ◽  
The North ◽  
Multiple Covariates ◽  
Design Values

Specification of realistic environmental design conditions for marine structures is of fundamental importance to their reliability over time. Design conditions for extreme waves and storm severities are typically estimated by extreme value analysis of time series of measured or hindcast significant wave height, HS. This analysis is complicated by two effects. Firstly, HS exhibits temporal dependence. Secondly, the characteristics of HSsp are non-stationary with respect to multiple covariates, particularly wave direction and season. We develop directional-seasonal design values for storm peak significant wave height (HSsp) by estimation of, and simulation under a non-stationary extreme value model for HSsp. Design values for significant wave height (HS) are estimated by simulating storm trajectories of HS consistent with the simulated storm peak events. Design distributions for individual maximum wave height (Hmax) are estimated by marginalisation using the known conditional distribution for Hmax given HS. Particular attention is paid to the assessment of model bias and quantification of model parameter and design value uncertainty using bootstrap resampling. We also outline existing work on extension to estimation of maximum crest elevation and total extreme water level.

Estimation of Storm Peak and Intrastorm Directional–Seasonal Design Conditions in the North Sea

2015 ◽  
Vol 137 (2) ◽  
Author(s):  
Graham Feld ◽  
David Randell ◽  
Yanyun Wu ◽  
Kevin Ewans ◽  
Philip Jonathan
Keyword(s):  
Wave Height ◽  
Significant Wave Height ◽  
Extreme Value ◽  
Extreme Value Analysis ◽  
Wave Direction ◽  
Value Analysis ◽  
Significant Wave ◽  
The North ◽  
Multiple Covariates ◽  
Design Values

Specification of realistic environmental design conditions for marine structures is of fundamental importance to their reliability over time. Design conditions for extreme waves and storm severities are typically estimated by extreme value analysis of time series of measured or hindcast significant wave height, HS. This analysis is complicated by two effects. First, HS exhibits temporal dependence. Second, the characteristics of HSsp are nonstationary with respect to multiple covariates, particularly wave direction, and season. We develop directional–seasonal design values for storm peak significant wave height (HSsp) by estimation of, and simulation under a nonstationary extreme value model for HSsp. Design values for significant wave height (HS) are estimated by simulating storm trajectories of HS consistent with the simulated storm peak events. Design distributions for individual maximum wave height (Hmax) are estimated by marginalization using the known conditional distribution for Hmax given HS. Particular attention is paid to the assessment of model bias and quantification of model parameter and design value uncertainty using bootstrap resampling. We also outline existing work on extension to estimation of maximum crest elevation and total extreme water level.

scholarly journals Surrogate Modeling of Time-Dependent Metocean Conditions during Hurricanes

2021 ◽  
Author(s):  
Chi Qiao ◽  
Andrew T. Myers
Keyword(s):  
Wave Height ◽  
Significant Wave Height ◽  
Time History ◽  
Surrogate Modeling ◽  
Offshore Structures ◽  
Surrogate Models ◽  
Time Dependent ◽  
Wave Direction ◽  
Persistent Problem ◽  
Significant Wave

Abstract Metocean conditions during hurricanes are defined by multiple parameters (e.g., significant wave height and surge height) that vary in time with significant auto- and cross-correlation. In many cases, the nature of the variation of these characteristics in time is important to design and assess the risk to offshore structures, but a persistent problem is that measurements are sparse and time history simulations using metocean models are computationally onerous. Surrogate modeling is an appealing approach to ease the computational burden of metocean modeling, however, modeling the time-dependency of metocean conditions using surrogate models is challenging because the conditions at one time instant are dependent on not only the conditions at that instant but also on the conditions at previous time instances. In this paper, time-dependent surrogate modeling of significant wave height, peak wave period, peak wave direction, and storm surge is explored using a database of metocean conditions at an offshore site. Three types of surrogate models, including Kriging, Multilayer Perceptron (MLP), and Recurrent Neural Network with Gated Recurrent Unit (RNN-GRU), are evaluated, with two different time-dependent structures considered for the Kriging model and two training set sizes for the MLP model, resulting in a total of five models evaluated in this paper. The performance of the models is compared in terms of accuracy and sensitivity towards hyperparameters, and the MLP and RNN-GRU models are demonstrated to have extraordinary prediction performance in this context.

scholarly journals The Effect of the Difference in Intensity and Track of Tropical Cyclone on Significant Wave Height and Wave Direction in the Southeast Indian Ocean

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Yosafat Donni Haryanto ◽  
Nelly Florida Riama ◽  
Dendi Rona Purnama ◽  
Aurel Dwiyana Sigalingging
Keyword(s):  
Tropical Cyclone ◽  
Indian Ocean ◽  
Tropical Cyclones ◽  
Wave Height ◽  
Significant Wave Height ◽  
Surface Wind ◽  
Ocean Waves ◽  
Reanalysis Data ◽  
Wave Direction ◽  
Significant Wave

This study aims to analyze the effect of the differences in intensity and track of tropical cyclones upon significant wave heights and direction of ocean waves in the southeast Indian Ocean. We used the tropical cyclone data from Japan Aerospace Exploration Agency (JAXA) starting from December 1997 to November 2017. The significant wave height and wave direction data are reanalysis data from Copernicus Marine Environment Monitoring Service (CMEMS), and the mean sea level pressure, surface wind speed, and wind direction data are reanalysis data from European Center for Medium-Range Weather Forecasts (ECMWF) from December 1997 to November 2017. The results show that the significant wave height increases with the increasing intensity of tropical cyclones. Meanwhile, the direction of the waves is influenced by the presence of tropical cyclones when tropical cyclones enter the categories of 3, 4, and 5. Tropical cyclones that move far from land tend to have higher significant wave height and wider affected areas compared to tropical cyclones that move near the mainland following the coastline

Uncertainties in Extreme Wave Height Estimates for Hurricane Dominated Regions

2006 ◽  
Author(s):  
Philip Jonathan ◽  
Kevin Ewans
Keyword(s):  
Wave Height ◽  
Significant Wave Height ◽  
Small Data ◽  
Extreme Wave ◽  
Significant Wave ◽  
Wave Characteristics ◽  
Height Estimates ◽  
Wave Heights ◽  
Using Data ◽  
High Percentile

The inherent uncertainties in estimation of extreme wave heights in hurricane-dominated regions are explored using data from the GOMOS Gulf of Mexico hindcast for the period 1900–2005. In particular, the effect of combining correlated values from a neighbourhood of 72 grid locations on extreme wave height estimation is quantified. We show that, based on small data samples, extreme wave heights can be underestimated and that site averaging usually improves estimates. We present a bootstrapping approach to evaluate the uncertainty in extreme wave height estimates. We also argue in favour of modelling supplementary indicators for extreme wave characteristics, such as a high percentile (95%) of the distribution of 100-year significant wave height, in addition to its most probable value, especially for environments where the distribution of 100-year significant wave height may be skewed.

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