scholarly journals Study of the threshold for the POT method based on hindcasted significant wave heights of tropical cyclone waves in the South China Sea

2019 ◽  
Author(s):  
Zhuxiao Shao ◽  
Bingchen Liang ◽  
Huajun Li ◽  
Ping Li ◽  
Dongyoung Lee
Keyword(s):  
Tropical Cyclone ◽  
South China Sea ◽  
Wave Height ◽  
South China ◽  
Significant Wave Height ◽  
Initial Sample ◽  
Significant Wave ◽  
Wave Heights ◽  
Significant Wave Heights ◽  
Assessing Method

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.

scholarly journals Extreme significant wave height of tropical cyclone waves in the South China Sea

2019 ◽  
Vol 19 (10) ◽  
pp. 2067-2077 ◽  
Author(s):  
Zhuxiao Shao ◽  
Bingchen Liang ◽  
Huajun Li ◽  
Ping Li ◽  
Dongyoung Lee
Keyword(s):  
Tropical Cyclone ◽  
South China Sea ◽  
Wave Height ◽  
South China ◽  
Significant Wave Height ◽  
Assessment Method ◽  
The South China Sea ◽  
Significant Wave ◽  
Wave Heights ◽  
Significant Wave Heights

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.

Wave Probabilities Consistent with Official Tropical Cyclone Forecasts

2016 ◽  
Vol 31 (6) ◽  
pp. 2035-2045 ◽  
Author(s):  
Charles R. Sampson ◽  
James A. Hansen ◽  
Paul A. Wittmann ◽  
John A. Knaff ◽  
Andrea Schumacher
Keyword(s):  
Surface Wave ◽  
Wind Speed ◽  
Tropical Cyclone ◽  
Wave Height ◽  
Significant Wave Height ◽  
Wave Model ◽  
Significant Wave ◽  
Wave Heights ◽  
Significant Wave Heights ◽  
Ocean Surface Wave

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.

Neural Networks Based Simulation of Significant Wave Height

2007 ◽  
Author(s):  
H. Bazargan ◽  
H. Bahai ◽  
A. Aminzadeh-Gohari ◽  
A. Bazargan
Keyword(s):  
Neural Networks ◽  
Wave Height ◽  
Significant Wave Height ◽  
Optimization Technique ◽  
Conditional Probability Density ◽  
Significant Wave ◽  
The Pacific ◽  
Wave Heights ◽  
Significant Wave Heights ◽  
Long Term Forecasting

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.

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 Long-Term Variability of Extreme Significant Wave Height in the South China Sea

2016 ◽  
Vol 2016 ◽  
pp. 1-21 ◽  
Author(s):  
Adekunle Osinowo ◽  
Xiaopei Lin ◽  
Dongliang Zhao ◽  
Zhifeng Wang
Keyword(s):  
South China Sea ◽  
Wave Height ◽  
South China ◽  
Significant Wave Height ◽  
The South China Sea ◽  
Wave Hindcast ◽  
Significant Wave ◽  
China Sea ◽  
Increasing Trend

This paper describes long-term spatiotemporal trends in extreme significant wave height (SWH) in the South China Sea (SCS) based on 30-year wave hindcast. High-resolution reanalysis wind field data sets are employed to drive a spectral wave model WAVEWATCH III™ (WW3). The wave hindcast information is validated using altimeter wave information (Topex/Poseidon). The model performance is satisfactory. Subsequently, the trends in yearly/seasonal/monthly mean extreme SWH are analyzed. Results showed that trends greater than 0.05 m yr−1are distributed over a large part of the central SCS. During winter, strong positive trends (0.07–0.08 m yr−1) are found in the extreme northeast SCS. Significant trends greater than 0.01 m yr−1are distributed over most parts of the central SCS in spring. In summer, significant increasing trends (0.01–0.05 m yr−1) are distributed over most regions below latitude 16°N. During autumn, strong positive trends between 0.02 and 0.08 m yr−1are found in small regions above latitude 12°N. Increasing positive trends are found to be generally significant in the central SCS in December, February, March, and July. Furthermore, temporal trend analysis showed that the extreme SWH exhibits a significant increasing trend of 0.011 m yr−1. The extreme SWH exhibits the strongest increasing trend of 0.03 m yr−1in winter and showed a decreasing trend of −0.0098 m yr−1in autumn.

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