A Bayesian hierarchical spatio-temporal model for significant wave height in the North Atlantic

Vanem, Erik; Huseby, Arne Bang; Natvig, Bent

doi:10.1007/s00477-011-0522-4

Cited by 42 publications

(32 citation statements)

References 43 publications

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“…As demonstrated for SWH by Vanem et al (2011) the Bayesian models allow predicting climate changes in met-ocean conditions based on historical data and forcing scenarios, thus avoiding running climate models. Vanem and BitnerGregersen (2012) have applied successfully the Bayesian hierarchical model of Vanem et al (2011) to monthly maxima at one location in the North Atlantic and predicted increase of extreme SWH. The model has been recently extended to account for different forcing scenarios, Vanem (2012).…”

Section: Effect Of Extreme Value Modelmentioning

confidence: 99%

Uncertainties

Bitner‐Gregersen

Eide

Hørte

et al. 2013

SpringerBriefs in Climate Studies

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The oceanographic community has always been concerned with providing environmental models and data which approximate the physics of the ocean in the most accurate way. Industry, on the other hand, needs accurate data and models for design purposes. Although uncertainties of data and models were discussed before the 1980s, they were not systematically quantified. Further development of the reliability methods (Madsen et al. 1986) and their implementation by some parts of the industry in the 1980s has brought much focus onto the uncertainties associated with environmental description. The PROBabilistic Analysis program PROBAN Ò developed by Det Norske Veritas at the end of the 1980s, and continuously improved since then (DNV 2002), is still one of the leading software packages for reliability calculations and is used by academia as well as industry. Reliability methods allow quantification, in a probabilistic way, of the uncertainties in the different parameters that govern structural integrity.

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Section: Effect Of Extreme Value Modelmentioning

confidence: 99%

Uncertainties

Bitner‐Gregersen

Eide

Hørte

et al. 2013

SpringerBriefs in Climate Studies

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“…A brief discussion of the prior distribu-280 tions applied to the model parameters will also be given, and this fully specifies the model. It is noted that the derivation of the full conditionals used in the Gibbs sampler is completely analogous to the significant wave height model, and reference is made to Vanem et al (2012b) for details; see also 285 Natvig and Tvete (2007).…”

Section: The Stochastic Modelmentioning

confidence: 99%

“…The Bayesian hierarchical space-time model resembles the 270 model for significant wave height (Vanem et al, 2012b) and the spatio-temporal data is indexed in a similar way by two indices; an index x to denote the spatial location, with x = 1, 2, ..., X = 66 and an index t to denote time (i.e. month), where t = 1, 2, ..., T = 540, and t = 1 corresponds to Septem-275 ber 1957 and t = T = 540 corresponds to August 2002.…”

Section: The Stochastic Modelmentioning

confidence: 99%

“…In order to investigate the impact of climate change on the significant wave height, one of the most important sea state parameters for ship operations, the framework of Bayesian hierarchical space-time models (Wikle et al, 1998(Wikle et al, , 2001) was applied to significant wave height data in Vanem et al (2012b). Extensions of this model including a logarithmic transformation of the data (Vanem et al, 2012d) and a regression component on atmospheric levels of CO 2 (Vanem et al, 2012a) have also been investigated.…”

Section: Introductionmentioning

confidence: 99%

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Bayesian hierarchical modelling of North Atlantic windiness

Vanem

Breivik

2013

Nat. Hazards Earth Syst. Sci.

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Abstract. Extreme weather conditions represent serious natural hazards to ship operations and may be the direct cause or contributing factor to maritime accidents. Such severe environmental conditions can be taken into account in ship design and operational windows can be defined that limits hazardous operations to less extreme conditions. Nevertheless, possible changes in the statistics of extreme weather conditions, possibly due to anthropogenic climate change, represent an additional hazard to ship operations that is less straightforward to account for in a consistent way. Obviously, there are large uncertainties as to how future climate change will affect the extreme weather conditions at sea and there is a need for stochastic models that can describe the variability in both space and time at various scales of the environmental conditions. Previously, Bayesian hierarchical space-time models have been developed to describe the variability and complex dependence structures of significant wave height in space and time. These models were found to perform reasonably well and provided some interesting results, in particular, pertaining to long-term trends in the wave climate. In this paper, a similar framework is applied to oceanic windiness and the spatial and temporal variability of the 10-m wind speed over an area in the North Atlantic ocean is investigated. When the results from the model for North Atlantic windiness is compared to the results for significant wave height over the same area, it is interesting to observe that whereas an increasing trend in significant wave height was identified, no statistically significant long-term trend was estimated in windiness. This may indicate that the increase in significant wave height is not due to an increase in locally generated wind waves, but rather to increased swell. This observation is also consistent with studies that have suggested a poleward shift of the main storm tracks.

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“…Similar approaches are also adopted in purely statistical models used for the estimation of wave height (see, for example, Vanem 2011; Vanem et al 2011). At this point a critical simplification is usually made: The ''distance'' between observed and modeled values or distributions is measured by means of classical Euclidean geometry tools-using, for example, least square methods.…”

Section: Introductionmentioning

confidence: 99%

Wave height characteristics in the north Atlantic ocean: a new approach based on statistical and geometrical techniques

Galanis

Chu

Kallos

et al. 2011

Stoch Environ Res Risk Assess

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The main characteristics of the significant wave height in an area of increased interest, the north Atlantic ocean, are studied based on satellite records and corresponding simulations obtained from the numerical wave prediction model WAM. The two data sets are analyzed by means of a variety of statistical measures mainly focusing on the distributions that they form. Moreover, new techniques for the estimation and minimization of the discrepancies between the observed and modeled values are proposed based on ideas and methodologies from a relatively new branch of mathematics, information geometry. The results obtained prove that the modeled values overestimate the corresponding observations through the whole study period. On the other hand, 2-parameter Weibull distributions fit well the data in the study. However, one cannot use the same probability density function for describing the whole study area since the corresponding scale and shape parameters deviate significantly for points belonging to different regions. This variation should be taken into account in optimization or assimilation procedures, which is possible by means of information geometry techniques.

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A Bayesian hierarchical spatio-temporal model for significant wave height in the North Atlantic

Cited by 42 publications

References 43 publications

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Bayesian hierarchical modelling of North Atlantic windiness

Wave height characteristics in the north Atlantic ocean: a new approach based on statistical and geometrical techniques

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