Synopses & Reviews
This book provides an example of a thorough statistical treatment of ocean wave data in space and time. It demonstrates how the flexible framework of Bayesian hierarchical space-time models can be applied to oceanographic processes such as significant wave height in order to describe dependence structures and uncertainties in the data. This monograph is a research book and it is partly cross-disciplinary. The methodology itself is firmly rooted in the statistical research tradition, based on probability theory and stochastic processes. However, that methodology has been applied to a problem in the field of physical oceanography, analyzing data for significant wave height, which is of crucial importance to ocean engineering disciplines. Indeed, the statistical properties of significant wave height are important for the design, construction and operation of ships and other marine and coastal structures. Furthermore, the book addresses the question of whether climate change has an effect of the ocean wave climate, and if so what that effect might be. Thus, this book is an important contribution to the ongoing debate on climate change, its implications and how to adapt to a changing climate, with a particular focus on the maritime industries and the marine environment. This book should be of value to anyone with an interest in the statistical modelling of environmental processes, and in particular to those with an interest in the ocean wave climate. It is written on a level that should be understandable to everyone with a basic background in statistics or elementary mathematics, and an introduction to some basic concepts is provided in the appendices for the uninitiated reader. The intended readership includes students and professionals involved in statistics, oceanography, ocean engineering, environmental research, climate sciences and risk assessment. Moreover, the book's findings are relevant for various stakeholders in the maritime industries such as design offices, classification societies, ship owners, yards and operators, flag states and intergovernmental agencies such as the IMO.
Synopsis
The monograph addresses modelling of ocean wave climate in space and time, with a focus on long-term temporal trends in the wave climate. Understanding of the ocean wave climate is of importance for design and operation of ships and other marine structures and should be of interest to many stakeholders within the maritime and offshore industries such as naval architects, ocean and coastal engineers, ship owners and yards, risk analysts, classification societies, etc. The models that are presented is an application of Bayesian hierarchical space-time models and should be of interest to people involved in stochastic modeling, statistics, environmental science, geostatistics. The models are concerned with the possible effects of climate change in the global wave climate, and should be of interest to everyone with an interest in climate research and the effects of climate change. The subject area is of academic interest within the area of stochastic modelling and statistics, but also of practical interest to naval architects and people working in design of ships and offshore structures as well as risk analysts and other stakeholders in the maritime industries. Any long-term trends in the wave climate need to be taken into account in the design and operation of marine structures, and knowledge of the operating environment is of paramount importance for e.g. maritime safety.
Table of Contents
Preface.- Acronyms.- 1.Introduction and Background.- 2.Literature Survey on StochasticWave Models.- 3.A Bayesian Hierarchical Space-Time Model for Significant Wave Height.- 4.Including a Log-Transform of the Data.- 6.Bayesian Hierarchical Modelling of the Ocean Windiness.- 7.Application: Impacts on Ship Structural Loads.- 8.Case study: Modelling the Effect of Climate Change on the World's Oceans.- 9.Summary and Conclusions.- A.Markov Chain Monte Carlo Methods.- B.Extreme Value Modelling.- C.Markov Random Fields.- D.Derivation of the Full Conditionals of the Bayesian Hierarchical Space-Time Model for Significant Wave Height.- E.Sampling from a Multi-normal Distribution.