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Slope distribution in front-back asymmetric stochastic Lagrange time waves

Lindgren, Georg LU (2010) In Advances in Applied Probability 42(2). p.489-508
Abstract
The stochastic Lagrange wave model is a realistic alternative to the Gaussian linear wave model, which has been successfully used in ocean engineering for more than half a century. This paper presents the slope distributions and other characteristic distributions at level crossings

for asymmetric Lagrange time waves, i.e. what can be observed at a fixed measuring station, thereby extending results previously given for space waves. The distributions are given as expectations in a multivariate normal distribution, and they have to be evaluated by simulation or numerical integration. Interesting characteristic variables are: slope in time, slope in space, and vertical particle velocity when the waves are observed close to instances... (More)
The stochastic Lagrange wave model is a realistic alternative to the Gaussian linear wave model, which has been successfully used in ocean engineering for more than half a century. This paper presents the slope distributions and other characteristic distributions at level crossings

for asymmetric Lagrange time waves, i.e. what can be observed at a fixed measuring station, thereby extending results previously given for space waves. The distributions are given as expectations in a multivariate normal distribution, and they have to be evaluated by simulation or numerical integration. Interesting characteristic variables are: slope in time, slope in space, and vertical particle velocity when the waves are observed close to instances when the water level crosses a predetermined level.

The theory has been made possible by recent generalizations of

Rice's formula for the expected number of marked crossings in random

fields. (Less)
Please use this url to cite or link to this publication:
author
organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
Palm distribution, Crossing theory, Rice formula, Slepian model, wave steepness, Gaussian process
in
Advances in Applied Probability
volume
42
issue
2
pages
489 - 508
publisher
Applied Probability Trust
external identifiers
  • wos:000278796800011
  • scopus:77955889280
ISSN
0001-8678
DOI
10.1239/aap/1275055239
project
MERGE
language
English
LU publication?
yes
id
99acf710-fc4b-4889-9bb6-f10350e93907 (old id 1545560)
date added to LUP
2010-02-23 12:18:29
date last changed
2018-05-29 10:58:47
@article{99acf710-fc4b-4889-9bb6-f10350e93907,
  abstract     = {The stochastic Lagrange wave model is a realistic alternative to the Gaussian linear wave model, which has been successfully used in ocean engineering for more than half a century. This paper presents the slope distributions and other characteristic distributions at level crossings <br/><br>
for asymmetric Lagrange time waves, i.e. what can be observed at a fixed measuring station, thereby extending results previously given for space waves. The distributions are given as expectations in a multivariate normal distribution, and they have to be evaluated by simulation or numerical integration. Interesting characteristic variables are: slope in time, slope in space, and vertical particle velocity when the waves are observed close to instances when the water level crosses a predetermined level. <br/><br>
The theory has been made possible by recent generalizations of <br/><br>
Rice's formula for the expected number of marked crossings in random <br/><br>
fields.},
  author       = {Lindgren, Georg},
  issn         = {0001-8678},
  keyword      = {Palm distribution,Crossing theory,Rice formula,Slepian model,wave steepness,Gaussian process},
  language     = {eng},
  number       = {2},
  pages        = {489--508},
  publisher    = {Applied Probability Trust},
  series       = {Advances in Applied Probability},
  title        = {Slope distribution in front-back asymmetric stochastic Lagrange time waves},
  url          = {http://dx.doi.org/10.1239/aap/1275055239},
  volume       = {42},
  year         = {2010},
}