scholarly journals Evaluation of extreme wave probability on the basis of long-term data analysis

2018 ◽  
Author(s):  
Kirill Bulgakov ◽  
Vadim Kuzmin ◽  
Shilov Dmitry
Keyword(s):  
Data Analysis ◽  
Wave Height ◽  
Significant Wave Height ◽  
Extreme Wave ◽  
Method Of Calculation ◽  
Significant Wave ◽  
Wave Heights ◽  
Term Data

Abstract. A method of calculation of wave height probability based on the significant wave height probability is described. An application of the method on the basis of long-term data analysis is presented. Examples of averaged annual and seasonal fields of extreme wave heights obtained by the above method are given.

scholarly journals Evaluation of extreme wave probability on the basis of long-term data analysis

Ocean Science ◽  
2018 ◽  
Vol 14 (5) ◽  
pp. 1321-1327 ◽  
Author(s):  
Kirill Bulgakov ◽  
Vadim Kuzmin ◽  
Dmitry Shilov
Keyword(s):  
Wave Height ◽  
Wind Wave ◽  
Cumulative Probability ◽  
Extreme Waves ◽  
Model Data ◽  
Extreme Wave ◽  
Method Of Calculation ◽  
Wave Heights ◽  
Term Data

Abstract. A method of calculation of wind wave height probability based on the significant wave height probability is described (Chalikov and Bulgakov, 2017). The method can also be used for estimation of the height of extreme waves of any given cumulative probability. The application of the method on the basis of long-term model data is presented. Examples of averaged annual and seasonal fields of extreme wave heights obtained using the above method are given. Areas where extreme waves can appear are shown.

scholarly journals Wave climatology in the Arkona Basin, the Baltic Sea

2011 ◽  
Vol 8 (6) ◽  
pp. 2237-2270 ◽  
Author(s):  
T. Soomere ◽  
R. Weisse ◽  
A. Behrens
Keyword(s):  
Baltic Sea ◽  
Wave Height ◽  
Significant Wave Height ◽  
Wave Climate ◽  
Extreme Wave ◽  
The Baltic Sea ◽  
Significant Wave ◽  
Wave Heights ◽  
The Baltic

Abstract. The basic features of the wave climate in the South-Eastern Baltic Sea are studied based on available long-term measurements and simulations. The analysis of average, typical and extreme wave conditions, frequency of occurrence of different wave parameters, variations in wave heights from weekly to decadal scales, etc., is performed based on waverider measurements at the Darss Sill since 1991. The measured climatology is compared against numerical simulations with the WAM wave model driven by downscaled reanalysis of wind fields for 1958–2002 and by adjusted geostrophic winds for 1970–2007. The wave climate in this region is typical for semi-enclosed basins of the Baltic Sea. The maximum wave heights are about half of those in the Baltic Proper. The overall reliably recorded maximum significant wave height HS =4.46 m occurred during a severe S-SW storm in 1993 when the 10-min average wind speed reached 28 m s−1. The long-term average significant wave height (0.75 m) shows modest interannual (about 12 % of the long-term mean) and substantial seasonal variation. The wave periods are mostly concentrated in a narrow range of 2.5–4 s and their distribution is almost constant over decades. The role of remote swell is very small. The annual wave properties show large interannual variability but no long-term trends in average and extreme wave heights can be observed.

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.

Modelling significant wave height distributions with quantile functions for estimation of extreme wave heights

2012 ◽  
Vol 54 ◽  
pp. 119-131 ◽  
Author(s):  
G. Muraleedharan ◽  
Cláudia Lucas ◽  
C. Guedes Soares ◽  
N. Unnikrishnan Nair ◽  
P.G. Kurup
Keyword(s):  
Wave Height ◽  
Significant Wave Height ◽  
Extreme Wave ◽  
Significant Wave ◽  
Wave Heights ◽  
Quantile Functions

scholarly journals Uncertainties of Estimating Extreme Significant Wave Height for Engineering Applications Depending on the Approach and Fitting Technique—Adriatic Sea Case Study

2020 ◽  
Vol 8 (4) ◽  
pp. 259 ◽  
Author(s):  
Marko Katalinić ◽  
Joško Parunov
Keyword(s):  
Wave Height ◽  
Significant Wave Height ◽  
Adriatic Sea ◽  
Least Square Method ◽  
Initial Distribution ◽  
Least Square ◽  
Likelihood Method ◽  
Extreme Wave ◽  
Significant Wave

Studies on the extrapolation of extreme significant wave height, based on long-term databases, are extensively covered in literature. An engineer, working in the field of naval architecture, marine engineering, or maritime operation planning, when tackling the problem of extreme wave prediction, would typically follow relevant codes and standards. Currently, authorities in the field of offshore operation within its guidelines propose several methods: the initial-distribution, extreme value, and peak-over threshold approaches. Furthermore, for each proposed method, different mathematical fitting techniques are applicable to optimize the candidate distribution parameters: the least-square method, the method of moments, and the maximum likelihood method. A comprehensive analysis was done to determine the difference in the results depending on the choice of method and fitting technique. All combinations were tested on a long-term database for a location in the Adriatic Sea. The variability of the results and trends of extreme wave height estimates for long return periods are presented, and the limitations of certain methods and techniques are noted.

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.

scholarly journals Estimation of Significant Wave Heights from ASCAT Scatterometer Data via Deep Learning Network

2021 ◽  
Vol 13 (2) ◽  
pp. 195
Author(s):  
He Wang ◽  
Jingsong Yang ◽  
Jianhua Zhu ◽  
Lin Ren ◽  
Yahao Liu ◽  
...  
Keyword(s):  
Deep Learning ◽  
Wave Height ◽  
Significant Wave Height ◽  
Incidence Angle ◽  
Ocean Waves ◽  
Global Scale ◽  
Learning Technology ◽  
Sea State ◽  
Significant Wave ◽  
Wave Heights

Sea state estimation from wide-swath and frequent-revisit scatterometers, which are providing ocean winds in the routine, is an attractive challenge. In this study, state-of-the-art deep learning technology is successfully adopted to develop an algorithm for deriving significant wave height from Advanced Scatterometer (ASCAT) aboard MetOp-A. By collocating three years (2016–2018) of ASCAT measurements and WaveWatch III sea state hindcasts at a global scale, huge amount data points (>8 million) were employed to train the multi-hidden-layer deep learning model, which has been established to map the inputs of thirteen sea state related ASCAT observables into the wave heights. The ASCAT significant wave height estimates were validated against hindcast dataset independent on training, showing good consistency in terms of root mean square error of 0.5 m under moderate sea condition (1.0–5.0 m). Additionally, reasonable agreement is also found between ASCAT derived wave heights and buoy observations from National Data Buoy Center for the proposed algorithm. Results are further discussed with respect to sea state maturity, radar incidence angle along with the limitations of the model. Our work demonstrates the capability of scatterometers for monitoring sea state, thus would advance the use of scatterometers, which were originally designed for winds, in studies of ocean waves.

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.

Sign in / Sign up

Export Citation Format

Share Document