A Bayesian Hierarchical Space-Time Model for Significant Wave Height

2013 ◽  
pp. 65-105 ◽  
Author(s):  
Erik Vanem
Keyword(s):  
Wave Height ◽  
Significant Wave Height ◽  
Space Time ◽  
Time Model ◽  
Bayesian Hierarchical ◽  
Significant Wave ◽  
Space Time Model

A Bayesian-Hierarchical Space-Time Model for Significant Wave Height Data

2011 ◽  
Author(s):  
Erik Vanem ◽  
Arne Bang Huseby ◽  
Bent Natvig
Keyword(s):  
Wave Height ◽  
Significant Wave Height ◽  
North Atlantic Ocean ◽  
Space Time ◽  
Maritime Transportation ◽  
Time Model ◽  
Bayesian Hierarchical ◽  
Significant Wave ◽  
The North ◽  
Future Return

Bad weather and rough seas contribute significantly to the risk to maritime transportation. This stresses the importance of taking severe sea state conditions adequately into account, with due treatment of the uncertainties involved, in ship design and operation. Hence, there is a need for appropriate stochastic models describing the variability of sea states. This paper presents a Bayesian hierarchical space-time stochastic model for significant wave height. The model has been fitted by data for an area in the North Atlantic ocean and aims at describing the temporal and spatial variability of significant wave height in this area. It could also serve as foundation for further extensions used for long-term prediction of significant wave height and future return periods of extreme significant wave heights. The model will be outlined in this paper, and the results will be discussed. Furthermore, a discussion of possible model extensions will be presented.

Statistical modeling of the space-time relation between wind and significant wave height

2022 ◽  
Author(s):  
Said Obakrim ◽  
Pierre Ailliot ◽  
Valerie Monbet ◽  
Nicolas Raillard
Keyword(s):  
Statistical Modeling ◽  
Wave Height ◽  
Significant Wave Height ◽  
Space Time ◽  
Significant Wave ◽  
Time Relation

Long-term trends in ocean chlorophyll: update from a Bayesian hierarchical space-time model

2020 ◽  
Author(s):  
Claudie Beaulieu ◽  
Matthew Hammond ◽  
Stephanie Henson ◽  
Sujit Sahu
Keyword(s):  
Climate Change ◽  
Coupled Model ◽  
Climate Change Impacts ◽  
Space Time ◽  
Time Model ◽  
Bayesian Hierarchical ◽  
Color Sensors ◽  
Long Term Trends ◽  
Space Time Model

<p>Assessing ongoing changes in marine primary productivity is essential to determine the impacts of climate change on marine ecosystems and fisheries. Satellite ocean color sensors provide detailed coverage of ocean chlorophyll in space and time, now with a combined record length of just over 20 years. Detecting climate change impacts is hindered by the shortness of the record and the long timescale of memory within the ocean such that even the sign of change in ocean chlorophyll is still inconclusive from time-series analysis of satellite data. Here we use a Bayesian hierarchical space-time model to estimate long-term trends in ocean chlorophyll. The main advantage of this approach comes from the principle of ”borrowing strength” from neighboring grid cells in a given region to improve overall detection. We use coupled model simulations from the CMIP5 experiment to form priors to provide a “first guess” on observational trend estimates and their uncertainty that we then update using satellite observations. We compare the results with estimates obtained with the commonly used vague prior, reflecting the case where no independent knowledge is available.  A global average net positive chlorophyll trend is found, with stronger regional trends that are typically positive in high and mid latitudes, and negative at low latitudes outside the Atlantic. The Bayesian hierarchical model used here provides a framework for integrating different sources of data for detecting trends and estimating their uncertainty in studies of global change.</p>

scholarly journals Space-time models for moving fields with an application to significant wave height fields

2010 ◽  
Vol 22 (3) ◽  
pp. 354-369 ◽  
Author(s):  
Pierre Ailliot ◽  
Anastassia Baxevani ◽  
Anne Cuzol ◽  
Valerie Monbet ◽  
Nicolas Raillard
Keyword(s):  
Wave Height ◽  
Significant Wave Height ◽  
Space Time ◽  
Significant Wave ◽  
Height Fields

scholarly journals Statistical modeling of the space-time relation between wind and significant wave height

2022 ◽  
Author(s):  
Said Obakrim ◽  
Pierre Ailliot ◽  
Valerie Monbet ◽  
Nicolas Raillard
Keyword(s):  
Statistical Modeling ◽  
Wave Height ◽  
Significant Wave Height ◽  
Space Time ◽  
Significant Wave ◽  
Time Relation

scholarly journals Bayesian hierarchical modelling of North Atlantic windiness

2013 ◽  
Vol 13 (3) ◽  
pp. 545-557 ◽  
Author(s):  
E. Vanem ◽  
O. N. Breivik
Keyword(s):  
Climate Change ◽  
Wave Height ◽  
North Atlantic ◽  
Environmental Conditions ◽  
Significant Wave Height ◽  
Weather Conditions ◽  
Extreme Weather ◽  
Bayesian Hierarchical ◽  
Significant Wave

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.

scholarly journals A space–time Bayesian hierarchical modeling framework for projection of seasonal maximum streamflow

2022 ◽  
Vol 26 (1) ◽  
pp. 149-166
Author(s):  
Álvaro Ossandón ◽  
Manuela I. Brunner ◽  
Balaji Rajagopalan ◽  
William Kleiber
Keyword(s):  
Lead Time ◽  
Hierarchical Modeling ◽  
Space Time ◽  
Bayesian Hierarchical Modeling ◽  
Modeling Framework ◽  
Time Model ◽  
Bayesian Hierarchical ◽  
Streamflow Extremes ◽  
Seasonal Streamflow ◽  
Space Time Model

Abstract. Timely projections of seasonal streamflow extremes can be useful for the early implementation of annual flood risk adaptation strategies. However, predicting seasonal extremes is challenging, particularly under nonstationary conditions and if extremes are correlated in space. The goal of this study is to implement a space–time model for the projection of seasonal streamflow extremes that considers the nonstationarity (interannual variability) and spatiotemporal dependence of high flows. We develop a space–time model to project seasonal streamflow extremes for several lead times up to 2 months, using a Bayesian hierarchical modeling (BHM) framework. This model is based on the assumption that streamflow extremes (3 d maxima) at a set of gauge locations are realizations of a Gaussian elliptical copula and generalized extreme value (GEV) margins with nonstationary parameters. These parameters are modeled as a linear function of suitable covariates describing the previous season selected using the deviance information criterion (DIC). Finally, the copula is used to generate streamflow ensembles, which capture spatiotemporal variability and uncertainty. We apply this modeling framework to predict 3 d maximum streamflow in spring (May–June) at seven gauges in the Upper Colorado River basin (UCRB) with 0- to 2-month lead time. In this basin, almost all extremes that cause severe flooding occur in spring as a result of snowmelt and precipitation. Therefore, we use regional mean snow water equivalent and temperature from the preceding winter season as well as indices of large-scale climate teleconnections – El Niño–Southern Oscillation, Atlantic Multidecadal Oscillation, and Pacific Decadal Oscillation – as potential covariates for 3 d spring maximum streamflow. Our model evaluation, which is based on the comparison of different model versions and the energy skill score, indicates that the model can capture the space–time variability in extreme streamflow well and that model skill increases with decreasing lead time. We also find that the use of climate variables slightly enhances skill relative to using only snow information. Median projections and their uncertainties are consistent with observations, thanks to the representation of spatial dependencies through covariates in the margins and a Gaussian copula. This spatiotemporal modeling framework helps in the planning of seasonal adaptation and preparedness measures as predictions of extreme spring streamflows become available 2 months before actual flood occurrence.

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