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.

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 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.

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 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.

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.

Development of Operational Limit Diagrams for Offshore Lifting Procedures

2015 ◽  
Author(s):  
Leonardo Roncetti ◽  
Fabrício Nogueira Corrêa ◽  
Carl Horst Albrecht ◽  
Breno Pinheiro Jacob
Keyword(s):  
Wave Height ◽  
Significant Wave Height ◽  
Technological Development ◽  
Automatic Generation ◽  
Dynamic Responses ◽  
Offshore Platforms ◽  
Peak Period ◽  
Significant Wave ◽  
Wave Heights ◽  
Offshore Crane

Lifting operations with offshore cranes are fundamental for proper functioning of a platform. Despite the great technological development, offshore cranes load charts only consider the significant wave height as parameter of environmental load, neglecting wave period, which may lead to unsafe or overestimated lifting operations. This paper aims to develop a method to design offshore crane operational limit diagrams for lifting of personnel and usual loads, in function of significant wave height and wave peak period, using time domain dynamic analysis, for a crane installed on a floating unit. The lifting of personnel with crane to transfer between a floating unit and a support vessel is a very used option in offshore operations, and this is in many cases, the only alternative beyond the helicopter. Due to recent fatal accidents with lifting operations in offshore platforms, it is essential the study about this subject, contributing to the increase of safety. The sea states for analysis were chosen covering usual significant wave heights and peak periods limits for lifting operations. The methodology used the SITUA / Prosim software to obtain the dynamic responses of the personnel transfer basket lifting and container loads on a typical FPSO. Through program developed by the author, it was implemented the automatic generation of diagrams as a function of operational limits. It is concluded that using this methodology, it is possible to achieve greater efficiency in the design and execution of personnel and routine load lifting, increasing safety and a wider weather window available.

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 Ocean State Forecast along Ship Routes: Evaluation Using ESSO-INCOIS Real-Time Ship-Mounted Wave Height Meter and Satellite Observations

2015 ◽  
Vol 32 (11) ◽  
pp. 2211-2222 ◽  
Author(s):  
R. Harikumar ◽  
N. K. Hithin ◽  
T. M. Balakrishnan Nair ◽  
P. Sirisha ◽  
B. Krishna Prasad ◽  
...  
Keyword(s):  
Indian Ocean ◽  
Correlation Coefficient ◽  
Wave Height ◽  
Significant Wave Height ◽  
Satellite Observations ◽  
Southern Indian Ocean ◽  
Significant Wave ◽  
Safe Navigation ◽  
Wave Heights ◽  
The Indian Ocean

AbstractOcean state forecast (OSF) along ship routes (OAS) is an advisory service of the Indian National Centre for Ocean Information Services (INCOIS) of the Earth System Science Organization (ESSO) that helps mariners to ensure safe navigation in the Indian Ocean in all seasons as well as in extreme conditions. As there are many users who solely depend on this service for their decision making, it is very important to ensure the reliability and accuracy of the service using the available in situ and satellite observations. This study evaluates the significant wave height (Hs) along the ship track in the Indian Ocean using the ship-mounted wave height meter (SWHM) on board the Oceanographic Research Vessel Sagar Nidhi, and the Cryosat-2 and Jason altimeters. Reliability of the SWHM is confirmed by comparing with collocated buoy and altimeter observations. The comparison along the ship routes using the SWHM shows very good agreement (correlation coefficient > 0.80) in all three oceanic regimes, [the tropical northern Indian Ocean (TNIO), the tropical southern Indian Ocean (TSIO), and extratropical southern Indian Ocean (ETSI)] with respect to the forecasts with a lead time of 48 h. However, the analysis shows ~10% overestimation of forecasted significant wave height in the low wave heights, especially in the TNIO. The forecast is found very reliable and accurate for the three regions during June–September with a higher correlation coefficient (average = 0.88) and a lower scatter index (average = 15%). During other months, overestimation (bias) of lower Hs is visible in the TNIO.

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