Bayesian Hierarchical Space-Time Models with Application to by Erik Vanem (auth.)

By Erik Vanem (auth.)

This publication 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 types will be utilized to oceanographic procedures corresponding to major wave peak on the way to describe dependence constructions and uncertainties within the data.

This monograph is a examine booklet and it truly is in part cross-disciplinary. The technique itself is firmly rooted within the statistical examine culture, in response to chance concept and stochastic tactics. despite the fact that, that technique has been utilized to an issue within the box of actual oceanography, studying facts for major wave top, that's of the most important value to ocean engineering disciplines. certainly, the statistical houses of important wave top are very important for the layout, development and operation of ships and different marine and coastal buildings. in addition, the e-book addresses the query of no matter if weather swap has an impact of the sea wave weather, and if that is so what that impact will be. therefore, this publication is a crucial contribution to the continuing debate on weather switch, its implications and the way to evolve to a altering weather, with a specific specialize in the maritime industries and the marine setting.

This publication will be of price to an individual with an curiosity within the statistical modelling of environmental tactics, and specifically to these with an curiosity within the ocean wave weather. it really is written on a degree that are supposed to be comprehensible to every body with a simple heritage in facts or simple arithmetic, and an advent to a couple easy recommendations is equipped within the appendices for the uninitiated reader. The meant readership comprises scholars and pros excited about facts, oceanography, ocean engineering, environmental learn, weather sciences and chance review. additionally, the book’s findings are suitable for varied stakeholders within the maritime industries reminiscent of layout workplaces, type societies, send vendors, yards and operators, flag states and intergovernmental businesses comparable to the IMO.

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Additional resources for Bayesian Hierarchical Space-Time Models with Application to Significant Wave Height

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Possibly, trends in the wind speed could be investigated separately and compared to the trends in the wave climate in order to gain insight. Correlated trends in wind speed and wave height over an area could indicate that the increasing waves are due to locally generated winds whereas increases in wave heights that are not 5 Often attributed to the physicist Niels Boer, but the actual origin of this quote is unknown. 4 Some Identified Areas for Further Research 23 accompanied by increased wind would possibly be due to increasing swells, most likely due to changes in wind tracks and increasing winds in other areas.

For the purpose of modeling the ocean wave climate in space and time, space-time data of wave climate parameters such as the significant wave height are needed. Furthermore, when covariates are to be included in the model, data for such explanatory variables are needed. Basically, two sets of data have been needed in the modeling of ocean wave climate presented in this monograph: ocean wave data with sufficient resolution in both space and time, and data on levels of atmospheric greenhouse gases.

Philos. Trans. R. Soc. A 365, 2053–2075 (2007) 30. : Identifying trends in the ocean wave climate by time series analyses of significant wave height data. Ocean Eng. 61, 148–160 (2012) 31. : The representative concentration pathways: an overview. Clim. Change 109, 5–31 (2011) 32. : Bayesian forecasting and dynamic models, 2nd edn. Springer, Heidelberg (1997) 33. : Hierarchical Bayesian models for predicting the spread of ecological processes. Ecology 84, 1382–1392 (2003) 34. : Hierarchical models in environmental science.

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