Statistical Uncertainty in Long-Term Distributions of Significant Wave Height

1996 ◽  
Vol 118 (4) ◽  
pp. 284-291 ◽  
Author(s):  
C. Guedes Soares ◽  
A. C. Henriques
Keyword(s):  
Probability Distribution ◽  
Wave Height ◽  
Significant Wave Height ◽  
Data Sets ◽  
Monthly Basis ◽  
Significant Wave ◽  
Fitting Procedure ◽  
The One ◽  
Homogeneous Data

This work examines some aspects involved in the estimation of the parameters of the probability distribution of significant wave height, in particular the homogeneity of the data sets and the statistical methods of fitting a distribution to data. More homogeneous data sets are organized by collecting the data on a monthly basis and by separating the simple sea states from the combined ones. A three-parameter Weibull distribution is fitted to the data. The parameters of the fitted distribution are estimated by the methods of maximum likelihood, of regression, and of the moments. The uncertainty involved in estimating the probability distribution with the three methods is compared with the one that results from using more homogeneous data sets, and it is concluded that the uncertainty involved in the fitting procedure can be more significant unless the method of moments is not considered.

scholarly journals Long-Term and Seasonal Trends in Global Wave Height Extremes Derived from ERA-5 Reanalysis Data

2020 ◽  
Vol 8 (12) ◽  
pp. 1015
Author(s):  
Alicia Takbash ◽  
Ian R. Young
Keyword(s):  
Southern Hemisphere ◽  
Wave Height ◽  
Significant Wave Height ◽  
Reanalysis Data ◽  
Extreme Value Analysis ◽  
Winter Storms ◽  
Value Analysis ◽  
Weather Forecasts ◽  
Significant Wave

A non-stationary extreme value analysis of 41 years (1979–2019) of global ERA5 (European Centre for Medium-Range Weather Forecasts Reanalysis) significant wave height data is undertaken to investigate trends in the values of 100-year significant wave height, Hs100. The analysis shows that there has been a statistically significant increase in the value of Hs100 over large regions of the Southern Hemisphere. There have also been smaller decreases in Hs100 in the Northern Hemisphere, although the related trends are generally not statistically significant. The increases in the Southern Hemisphere are a result of an increase in either the frequency or intensity of winter storms, particularly in the Southern Ocean.

scholarly journals ON LONG-TERM STATISTICS FOR OCEAN AND COASTAL WAVES

1978 ◽  
Vol 1 (16) ◽  
pp. 2 ◽  
Author(s):  
Michel K. Ochi
Keyword(s):  
Wave Height ◽  
Significant Wave Height ◽  
Contingency Table ◽  
Statistical Properties ◽  
Significant Wave ◽  
Coastal Waves ◽  
Normal Probability ◽  
Log Normal ◽  
Confidence Domain

This paper discusses the statistical properties of long-term ocean and coastal waves derived from analysis of available data. It was found from the results of the analysis that the statistical properties of wave height and period obey the bi-variate log-normal probability law. The method to determine the confidence domain for a specified confidence coefficient is presented so that reliable information in severe seas where data are always sparse can be obtained from a contingency table. Estimation of the extreme significant wave height expected in the long-term is also discussed.

scholarly journals Projected sea level rise and changes in extreme storm surge and wave events during the 21st century in the region of Singapore

2015 ◽  
Vol 12 (6) ◽  
pp. 2955-3001
Author(s):  
H. Cannaby ◽  
M. D. Palmer ◽  
T. Howard ◽  
L. Bricheno ◽  
D. Calvert ◽  
...  
Keyword(s):  
Sea Level Rise ◽  
Sea Level ◽  
Wave Height ◽  
21St Century ◽  
Significant Wave Height ◽  
Quantitative Assessment ◽  
Climate Model ◽  
Mean Sea Level ◽  
Significant Wave

Abstract. Singapore is an island state with considerable population, industries, commerce and transport located in coastal areas at elevations less than 2 m making it vulnerable to sea-level rise. Mitigation against future inundation events requires a quantitative assessment of risk. To address this need, regional projections of changes in (i) long-term mean sea level and (ii) the frequency of extreme storm surge and wave events have been combined to explore potential changes to coastal flood risk over the 21st century. Local changes in time mean sea level were evaluated using the process-based climate model data and methods presented in the IPCC AR5. Regional surge and wave solutions extending from 1980 to 2100 were generated using ~ 12 km resolution surge (Nucleus for European Modelling of the Ocean – NEMO) and wave (WaveWatchIII) models. Ocean simulations were forced by output from a selection of four downscaled (~ 12 km resolution) atmospheric models, forced at the lateral boundaries by global climate model simulations generated for the IPCC AR5. Long-term trends in skew surge and significant wave height were then assessed using a generalised extreme value model, fit to the largest modelled events each year. An additional atmospheric solution downscaled from the ERA-Interim global reanalysis was used to force historical ocean model simulations extending from 1980–2010, enabling a quantitative assessment of model skill. Simulated historical sea surface height and significant wave height time series were compared to tide gauge data and satellite altimetry data respectively. Central estimates of the long-term mean sea level rise at Singapore by 2100 were projected to be 0.52 m (0.74 m) under the RCP 4.5 (8.5) scenarios respectively. Trends in surge and significant wave height 2 year return levels were found to be statistically insignificant and/or physically very small under the more severe RCP8.5 scenario. We conclude that changes to long-term mean sea level constitute the dominant signal of change to the projected inundation risk for Singapore during the 21st century. We note that the largest recorded surge residual in the Singapore Strait of ~ 84 cm lies between the central and upper estimates of sea level rise by 2100, highlighting the vulnerability of the region.

Joint probability of storm surge and wave along Hainan Island based on bi-variate copulas analysis

2020 ◽  
Author(s):  
Meng Cheng ◽  
Weihua Fang
Keyword(s):  
Probability Distribution ◽  
Storm Surge ◽  
Wave Height ◽  
Significant Wave Height ◽  
Probability Distributions ◽  
Joint Probability ◽  
Hainan Island ◽  
Joint Probability Distribution ◽  
Significant Wave ◽  
Surge Height

<p>Tropical cyclones (TCs) often bring multiple hazards to offshore and onshore areas, including wind, rainfall, riverine flood, wave and storm surge. These hazards usually interact with each other and cause greater amplified hazard intensity. In the coastal areas, wave may damage coastal defense system like sea walls and dykes, and overtopping storm surge could hence become severe flooding due to the breach of the dykes. The probability distributions of wave and surge, as univariate respectively, have been studies and used in the design in various research. However, far less investigations on their joint probability distribution have been carried out in the past.</p><p>In this study, the dataset of hourly surge height, and significant wave height of 89 TC events impacting along Hainan Island during 1949~2013 was obtained, which are simulated numerically with ADCIRC and SWAN respectively. Following that, 4 types of probability distributions for univariate were used to fit the marginal distribution of storm surge and wave. Secondly, Frank, Clayton and Gumbel Copula were tried to construct the joint probability distribution of wave and surge, and the optimal Copula was determined by K-S test and AIC, BIC criteria. Based on the optimal Copula selected for each area of interest, the joint return period of wave and surge was estimated.</p><p>The results show that, 1) the annual maximum value of the storm surge height and significant wave height of Hainan Island has a relatively obvious geographical distribution regularity. 2) GEV and Gumbel are the most optimal distribution for storm surge height and significant wave height respectively. 3) Clayton Copula is the best model for fitting joint probability of storm surge and wave. The estimated joining probability distribution can help the determination of design standard, and typical TC disaster scenario development.</p>

Wave Power Statistics for Sea States

2011 ◽  
Vol 133 (4) ◽  
Author(s):  
Dag Myrhaug ◽  
Bernt J. Leira ◽  
Håvard Holm
Keyword(s):  
Wave Height ◽  
Significant Wave Height ◽  
Wave Climate ◽  
Bivariate Distribution ◽  
Wave Power ◽  
Power Devices ◽  
Characteristic Wave ◽  
Significant Wave ◽  
Wave Power Potential

This paper provides a bivariate distribution of wave power and significant wave height, as well as a bivariate distribution of wave power and a characteristic wave period for sea states, and the statistical aspects of wave power for sea states are discussed. This is relevant for, e.g., making assessments of wave power devices and their potential for converting energy from waves. The results can be applied to compare systematically the wave power potential at different locations based on long term statistical description of the wave climate.

scholarly journals Projected sea level rise and changes in extreme storm surge and wave events during the 21st century in the region of Singapore

Ocean Science ◽  
2016 ◽  
Vol 12 (3) ◽  
pp. 613-632 ◽  
Author(s):  
Heather Cannaby ◽  
Matthew D. Palmer ◽  
Tom Howard ◽  
Lucy Bricheno ◽  
Daley Calvert ◽  
...  
Keyword(s):  
Sea Level Rise ◽  
Sea Level ◽  
Wave Height ◽  
21St Century ◽  
Significant Wave Height ◽  
Quantitative Assessment ◽  
Climate Model ◽  
Mean Sea Level ◽  
Significant Wave

Abstract. Singapore is an island state with considerable population, industries, commerce and transport located in coastal areas at elevations less than 2 m making it vulnerable to sea level rise. Mitigation against future inundation events requires a quantitative assessment of risk. To address this need, regional projections of changes in (i) long-term mean sea level and (ii) the frequency of extreme storm surge and wave events have been combined to explore potential changes to coastal flood risk over the 21st century. Local changes in time-mean sea level were evaluated using the process-based climate model data and methods presented in the United Nations Intergovernmental Panel on Climate Change Fifth Assessment Report (IPCC AR5). Regional surge and wave solutions extending from 1980 to 2100 were generated using  ∼  12 km resolution surge (Nucleus for European Modelling of the Ocean – NEMO) and wave (WaveWatchIII) models. Ocean simulations were forced by output from a selection of four downscaled ( ∼  12 km resolution) atmospheric models, forced at the lateral boundaries by global climate model simulations generated for the IPCC AR5. Long-term trends in skew surge and significant wave height were then assessed using a generalised extreme value model, fit to the largest modelled events each year. An additional atmospheric solution downscaled from the ERA-Interim global reanalysis was used to force historical ocean model simulations extending from 1980 to 2010, enabling a quantitative assessment of model skill. Simulated historical sea-surface height and significant wave height time series were compared to tide gauge data and satellite altimetry data, respectively. Central estimates of the long-term mean sea level rise at Singapore by 2100 were projected to be 0.52 m (0.74 m) under the Representative Concentration Pathway (RCP)4.5 (8.5) scenarios. Trends in surge and significant wave height 2-year return levels were found to be statistically insignificant and/or physically very small under the more severe RCP8.5 scenario. We conclude that changes to long-term mean sea level constitute the dominant signal of change to the projected inundation risk for Singapore during the 21st century. We note that the largest recorded surge residual in the Singapore Strait of  ∼  84 cm lies between the central and upper estimates of sea level rise by 2100, highlighting the vulnerability of the region.

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.

Sign in / Sign up

Export Citation Format

Share Document