Regression quantile models for estimating trends in extreme significant wave heights

Muraleedharan, G.; Lucas, C.; Soares, C. Guedes

doi:10.1016/j.oceaneng.2016.04.009

Cited by 26 publications

(10 citation statements)

References 35 publications

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“…Therefore, the location parameter x 0 and the scale parameter β provide information on the mean value and the variance, respectively. A quantile regression-a particular type of VGAM [58,59]-is selected to estimate the location parameters x 0 of the GPD in the non-stationary statistical model. It estimates the 100τ% conditional quantile yτ (x) of a response variable Y as a function…”

Section: Generalized Pareto Distribution: Univariate Distribution-funmentioning

confidence: 99%

Multivariate Hybrid Modelling of Future Wave-Storms at the Northwestern Black Sea

Lin-Ye

Garcı́a-León

Gràcia

et al. 2018

Water

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The characterization of future wave-storms and their relationship to large-scale climate can provide useful information for environmental or urban planning at coastal areas. A hybrid methodology (process-based and statistical) was used to characterize the extreme wave-climate at the northwestern Black Sea. The Simulating WAve Nearshore spectral wave-model was employed to produce wave-climate projections, forced with wind-fields projections for two climate change scenarios: Representative Concentration Pathways (RCPs) 4.5 and 8.5. A non-stationary multivariate statistical model was built, considering significant wave-height and peak-wave-period at the peak of the wave-storm, as well as storm total energy and storm-duration. The climate indices of the North Atlantic Oscillation, East Atlantic Pattern, and Scandinavian Pattern have been used as covariates to link to storminess, wave-storm threshold, and wave-storm components in the statistical model. The results show that, first, under both RCP scenarios, the mean values of significant wave-height and peak-wave-period at the peak of the wave-storm remain fairly constant over the 21st century. Second, the mean value of storm total energy is more markedly increasing in the RCP4.5 scenario than in the RCP8.5 scenario. Third, the mean value of storm-duration is increasing in the RCP4.5 scenario, as opposed to the constant trend in the RCP8.5 scenario. The variance of each wave-storm component increases when the corresponding mean value increases under both RCP scenarios. During the 21st century, the East Atlantic Pattern and changes in its pattern have a special influence on wave-storm conditions. Apart from the individual characteristics of each wave-storm component, wave-storms with both extreme energy and duration can be expected in the 21st century. The dependence between all the wave-storm components is moderate, but grows with time and, in general, the severe emission scenario of RCP8.5 presents less dependence between storm total energy and storm-duration and among wave-storm components.

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Section: Generalized Pareto Distribution: Univariate Distribution-funmentioning

confidence: 99%

Multivariate Hybrid Modelling of Future Wave-Storms at the Northwestern Black Sea

Lin-Ye

Garcı́a-León

Gràcia

et al. 2018

Water

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“…Box-plot test [10] is the commonly used outlier test for normal distributions. It is a useful type of graph used to show the shape of distribution by using the five major attributes: smallest non-outlier observation (Min), lower quartile (Q1), median, upper quartile (Q3), and largest non-outlier observation (Max).…”

Section: Box-plot (Bp)mentioning

confidence: 99%

Outlier Detection in Ocean Wave Measurements by Using Unsupervised Data Mining Methods

Mahmoodi

Ghassemi

2018

Polish Maritime Research

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Outliers are considerably inconsistent and exceptional objects in the data set that do not adapt to expected normal condition. An outlier in wave measurements may be due to experimental and configuration errors, technical defects in equipment, variability in the measurement conditions, rare or unknown conditions such as tsunami, windstorm and etc. To improve the accuracy and reliability of an built ocean wave model, or to extract important and valuable information from collected wave data, detecting of outlying observations in wave measurements is very important. In this study, three typical outlier detection algorithms:Box-plot (BP), Local Distance-based Outlier Factor (LDOF), and Local Outlier Factor (LOF) methods are used to detect outliers in significant wave height (Hs) records. The historical wave data are taken from National Data Buoy Center (NDBC). Finally, those data points are considered as outlier identified by at least two methods which are presented and discussed. Then, Hsprediction has been modelled with and without the presence of outliers by using Regression trees (RTs).

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“…A comprehensive understanding of the ocean surface waves arising from extreme weather conditions is of great interest to coastal and ocean engineers and oceanographers. Many studies about predicting, simulating, or hindcasting typhoon-generated storm waves and storm surges have been carried out through numerical models because wave buoys cannot be deployed throughout an entire marine area [10][11][12][13][14]. A third-generation spectral wind wave model is mainly driven by wind fields and has been widely used to predict, simulate, and hindcast wave heights.…”

Section: Introductionmentioning

confidence: 99%

Numerical Simulation of Large Wave Heights from Super Typhoon Nepartak (2016) in the Eastern Waters of Taiwan

Hsiao

Chen

et al. 2020

JMSE

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Super Typhoon Nepartak (2016) was used for this case study because it is the most intense typhoon that made landfall in Taiwan in the past decade. Winds extracted from the Climate Forecast System version 2 (CFSV2) and ERA5 datasets and merged with a parametric typhoon model using two hybrid techniques served as the meteorological conditions for driving a coupled wave-circulation model. The computed significant wave heights were compared with the observations recorded at three wave buoys in the eastern waters of Taiwan. Model performance in terms of significant wave height was also investigated by employing the CFSV2 winds under varying spatial and temporal resolutions. The results of the numerical experiments reveal that the simulated storm wave heights tended to decrease significantly due to the lower spatial resolution of the hourly winds from the CFSV2 dataset; however, the variations in the storm wave height simulations were less sensitive to the temporal resolution of the wind field. Introducing the combination of the CFSV2 and the parametric typhoon winds greatly improved the storm wave simulations, and similar phenomena can be found in the exploitation of the ERA5 dataset blended into the parametric wind field. The overall performance of the hybrid winds derived from ERA5 was better than that from the CFSV2, especially in the outer region of Super Typhoon Nepartak (2016).

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Regression quantile models for estimating trends in extreme significant wave heights

Cited by 26 publications

References 35 publications

Multivariate Hybrid Modelling of Future Wave-Storms at the Northwestern Black Sea

Multivariate Hybrid Modelling of Future Wave-Storms at the Northwestern Black Sea

Outlier Detection in Ocean Wave Measurements by Using Unsupervised Data Mining Methods

Numerical Simulation of Large Wave Heights from Super Typhoon Nepartak (2016) in the Eastern Waters of Taiwan

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