Modelling Sea Surface Dynamics Using Crossing Distributions
(2004) Abstract
 The thesis deals mainly with modelling sea surface dynamics. We consider two different scales. The shortterm scale,known as sea state, in which the sea surface over a restricted time period and space can be modelled as a stationary random field, and the longterm scale in which we study the evolution of wave characteristics like the significant wave height $H_s$, over long periods of time and at great geographic regions.
The main statistical tools are crossing distributions, which are given by a generalisation of Rice's formula which is valid under mild conditions.
In the shortterm scale, we consider the sea surface as a Gaussian stationary random field. Study of the motion of such a surface should... (More)  The thesis deals mainly with modelling sea surface dynamics. We consider two different scales. The shortterm scale,known as sea state, in which the sea surface over a restricted time period and space can be modelled as a stationary random field, and the longterm scale in which we study the evolution of wave characteristics like the significant wave height $H_s$, over long periods of time and at great geographic regions.
The main statistical tools are crossing distributions, which are given by a generalisation of Rice's formula which is valid under mild conditions.
In the shortterm scale, we consider the sea surface as a Gaussian stationary random field. Study of the motion of such a surface should include the notion of velocity. Different velocities, that capture different aspects of the sea dynamics, are defined and their statistical distributions are obtained. Also of interest is the effect the wave kinematics have on the distribution of global maximum. It is observed that taking into account time dynamics of spatial characteristics results in distributions different than those obtained for the static case.
Satellites orbiting around the earth provide with global spatial coverage of the ocean surfaces. The logarithmic values of $H_s$ are modelled as a locally stationary Gaussian random field. The mean value varies seasonally and geographically and the covariance structure is modelled as a sum of two independent sources, one in a coarser and one in a finer scale. To capture the temporal variability velocities, that enter the covariance structure as parameters, are used. Wave climate of $H_s$ is of importance for different applications, like for example estimation of the fatigue accumulated by a vessel sailing a certain route. (Less)
Please use this url to cite or link to this publication:
http://lup.lub.lu.se/record/467080
 author
 Baxevani, Anastassia ^{LU}
 opponent

 Prof Leadbetter, Ross
 organization
 publishing date
 2004
 type
 Thesis
 publication status
 published
 subject
 keywords
 Statistik, actuarial mathematics, programmering, operations research, Statistics, velocities, global maximum, Rice formula, level crossings, Gaussian random fields, programming, aktuariematematik, operationsanalys
 pages
 186 pages
 publisher
 KFS AB
 defense location
 Matematikcentrum,SÃ¶lvegatan 18, sal MH:C
 defense date
 20040528 10:15
 ISSN
 14040034
 ISBN
 9162860968
 language
 English
 LU publication?
 yes
 id
 7361a7ff3f8b4c30b6d468379d52d1ef (old id 467080)
 date added to LUP
 20070925 20:08:08
 date last changed
 20160919 08:44:57
@phdthesis{7361a7ff3f8b4c30b6d468379d52d1ef, abstract = {The thesis deals mainly with modelling sea surface dynamics. We consider two different scales. The shortterm scale,known as sea state, in which the sea surface over a restricted time period and space can be modelled as a stationary random field, and the longterm scale in which we study the evolution of wave characteristics like the significant wave height $H_s$, over long periods of time and at great geographic regions.<br/><br> <br/><br> The main statistical tools are crossing distributions, which are given by a generalisation of Rice's formula which is valid under mild conditions.<br/><br> <br/><br> In the shortterm scale, we consider the sea surface as a Gaussian stationary random field. Study of the motion of such a surface should include the notion of velocity. Different velocities, that capture different aspects of the sea dynamics, are defined and their statistical distributions are obtained. Also of interest is the effect the wave kinematics have on the distribution of global maximum. It is observed that taking into account time dynamics of spatial characteristics results in distributions different than those obtained for the static case.<br/><br> <br/><br> Satellites orbiting around the earth provide with global spatial coverage of the ocean surfaces. The logarithmic values of $H_s$ are modelled as a locally stationary Gaussian random field. The mean value varies seasonally and geographically and the covariance structure is modelled as a sum of two independent sources, one in a coarser and one in a finer scale. To capture the temporal variability velocities, that enter the covariance structure as parameters, are used. Wave climate of $H_s$ is of importance for different applications, like for example estimation of the fatigue accumulated by a vessel sailing a certain route.}, author = {Baxevani, Anastassia}, isbn = {9162860968}, issn = {14040034}, keyword = {Statistik,actuarial mathematics,programmering,operations research,Statistics,velocities,global maximum,Rice formula,level crossings,Gaussian random fields,programming,aktuariematematik,operationsanalys}, language = {eng}, pages = {186}, publisher = {KFS AB}, school = {Lund University}, title = {Modelling Sea Surface Dynamics Using Crossing Distributions}, year = {2004}, }