By Erik Vanem (auth.)
This ebook offers an instance of an intensive statistical remedy of ocean wave facts in house and time. It demonstrates how the versatile framework of Bayesian hierarchical space-time versions should be utilized to oceanographic strategies similar to major wave top as a way to describe dependence buildings and uncertainties within the data.
This monograph is a learn publication and it's partially cross-disciplinary. The method itself is firmly rooted within the statistical learn culture, in keeping with likelihood conception and stochastic methods. despite the fact that, that method has been utilized to an issue within the box of actual oceanography, interpreting information for major wave peak, that's of the most important value to ocean engineering disciplines. certainly, the statistical houses of important wave top are vital for the layout, building and operation of ships and different marine and coastal constructions. in addition, the ebook addresses the query of no matter if weather swap has an impression of the sea wave weather, and if that is so what that impact may be. therefore, this ebook is a crucial contribution to the continuing debate on weather swap, its implications and the way to evolve to a altering weather, with a selected specialise in the maritime industries and the marine atmosphere.
This e-book could be of price to somebody with an curiosity within the statistical modelling of environmental procedures, and specifically to these with an curiosity within the ocean wave weather. it's written on a degree that are meant to be comprehensible to everybody with a simple heritage in information or trouble-free arithmetic, and an advent to a couple uncomplicated ideas is equipped within the appendices for the uninitiated reader. The meant readership comprises scholars and execs all in favour of information, oceanography, ocean engineering, environmental examine, weather sciences and chance evaluation. furthermore, the book’s findings are appropriate for numerous stakeholders within the maritime industries resembling layout workplaces, type societies, send vendors, yards and operators, flag states and intergovernmental organisations resembling the IMO.
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Clim. change 2, 403–409 (2012) 7. : Multivariate return periods of sea storms for coastal erosion risk assessment. Nat. Hazards Earth Syst. Sci. 12, 2699–2708 (2012) 8. : Statistics for spatio-temporal data. Wiley, Hoboken (2011) 9. : Introduction to modern climate change. Cambridge University Press, Cambridge (2012) 10. : Climate change impact on extreme wave conditions in the North Sea: an ensemble study. Ocean Dyn. 58, 199–212 (2008) 11. : Wave modelling—the state of the art. Prog. Oceanogr.
A more recent reanalysis project, ERA-40 , was carried out by the European Center for Medium-Range Weather Forecasts (ECMWF) and covers a 45-year period from 1957 to 2002. 1 Wave Data and Data Sources 27 data contain six-hourly fields of global wave parameters such as significant wave height, mean wave direction and mean wave period as well as mean sea level pressure and wind fields and other meteorological parameters. 1 It has been reported that the ERA-40 dataset contains some inhomogeneities in time and that it underestimates high wave heights , but corrected datasets for the significant wave height have been produced .
This approach is extended to find return periods analytically for storms with two or more waves exceeding the threshold in [8, 10]. The basic idea behind the equivalent triangular storm model is that it, for a fixed location, associates a triangle to each actual storm and represents a significant wave height time series by means of a sequence of triangular storms. The triangle height is the maximum significant wave height during the actual storm and the triangle base is such that the maximum expected wave height in the actual storm equals the maximum expected wave height in the triangular storm model .
Bayesian Hierarchical Space-Time Models with Application to Significant Wave Height by Erik Vanem (auth.)