scholarly journals Basin-wide spatial conditional extremes for severe ocean storms

Extremes ◽  
2020 ◽  
Author(s):  
Rob Shooter ◽  
Jonathan Tawn ◽  
Emma Ross ◽  
Philip Jonathan
Keyword(s):  
Spatial Dependence ◽  
Piecewise Linear ◽  
Asymptotic Independence ◽  
Model Parameters ◽  
Asymptotic Dependence ◽  
Remote Locations ◽  
The North ◽  
Linear Representations ◽  
Dependent Parameter ◽  
Conditional Extremes

Abstract Physical considerations and previous studies suggest that extremal dependence between ocean storm severity at two locations exhibits near asymptotic dependence at short inter-location distances, leading to asymptotic independence and perfect independence with increasing distance. We present a spatial conditional extremes (SCE) model for storm severity, characterising extremal spatial dependence of severe storms by distance and direction. The model is an extension of Shooter et al. 2019 (Environmetrics 30, e2562, 2019) and Wadsworth and Tawn (2019), incorporating piecewise linear representations for SCE model parameters with distance and direction; model variants including parametric representations of some SCE model parameters are also considered. The SCE residual process is assumed to follow the delta-Laplace form marginally, with distance-dependent parameter. Residual dependence of remote locations given conditioning location is characterised by a conditional Gaussian covariance dependent on the distances between remote locations, and distances of remote locations to the conditioning location. We apply the model using Bayesian inference to estimates extremal spatial dependence of storm peak significant wave height on a neighbourhood of 150 locations covering over 200,000 km2 in the North Sea.

scholarly journals Optimizing models of the North Atlantic spring bloom using physical, chemical and bio-optical observations from a Lagrangian float

2010 ◽  
Vol 7 (6) ◽  
pp. 8477-8520 ◽  
Author(s):  
W. Bagniewski ◽  
K. Fennel ◽  
M. J. Perry ◽  
E. A. D'Asaro
Keyword(s):  
North Atlantic ◽  
Spring Bloom ◽  
Deep Ocean ◽  
Complex Model ◽  
Optical Observations ◽  
Model Parameters ◽  
Carbon Export ◽  
The North ◽  
Chemical Measurements ◽  
The North Atlantic

Abstract. The North Atlantic spring bloom is one of the main events that lead to carbon export to the deep ocean and drive oceanic uptake of CO2 from the atmosphere. Here we use a suite of physical, bio-optical and chemical measurements made during the 2008 spring bloom to optimize and compare three different models of biological carbon export. The observations are from a Lagrangian float that operated south of Iceland from early April to late June, and were calibrated with ship-based measurements. The simplest model is representative of typical NPZD models used for the North Atlantic, while the most complex model explicitly includes diatoms and the formation of fast sinking diatom aggregates and cysts under silicate limitation. We carried out a variational optimization and error analysis for the biological parameters of all three models, and compared their ability to replicate the observations. The observations were sufficient to constrain most phytoplankton-related model parameters to accuracies of better than 15%. However, the lack of zooplankton observations leads to large uncertainties in model parameters for grazing. The simulated vertical carbon flux at 100 m depth is similar between models and agrees well with available observations, but at 600 m the simulated flux is much larger for the model with diatom aggregation. While none of the models can be formally rejected based on their misfit with the available observations, the model that includes export by diatom aggregation has slightly better fit to the observations and more accurately represents the mechanisms and timing of carbon export based on observations not included in the optimization. Thus models that accurately simulate the upper 100 m do not necessarily accurately simulate export to deeper depths.

scholarly journals Exploitation of a Productive Asset in the Presence of Strategic Behavior and Pollution Externalities

Mathematics ◽  
2020 ◽  
Vol 8 (10) ◽  
pp. 1682
Author(s):  
N. Baris Vardar ◽  
Georges Zaccour
Keyword(s):  
Strategic Behavior ◽  
Piecewise Linear ◽  
Growth Function ◽  
Piecewise Linear Approximation ◽  
Stable Steady State ◽  
Model Parameters ◽  
State Variables ◽  
Long Run ◽  
Productive Asset ◽  
Asset Growth

We study the strategic behavior of firms competing in the exploitation of a common-access productive asset, in the presence of pollution externalities. We consider a differential game with two state variables (asset stock and pollution stock), and by using a piecewise-linear approximation of the nonlinear asset growth function, we provide a tractable characterization of the symmetric feedback–Nash equilibrium with asymptotically stable steady state(s). The results show that the firm’s strategy takes three forms depending on the pair of state variables and that different options for the model parameters lead to contrasting outcomes in both the short- and long-run equilibria.

Integrated application of heliborne and ground electromagnetic surveys for mapping EM conductor for uranium exploration and its subsurface validation, North Delhi Fold Belt, Rajasthan, India: A case study

Geophysics ◽  
2013 ◽  
Vol 78 (1) ◽  
pp. B13-B24 ◽  
Author(s):  
A. K. Chaturvedi ◽  
Cas Lotter ◽  
Shailesh Tripathi ◽  
A. K. Maurya ◽  
Indrajit Patra ◽  
...  
Keyword(s):  
Time Domain ◽  
Uranium Mineralization ◽  
Model Parameters ◽  
Fold Belt ◽  
The North ◽  
Geophysical Surveys ◽  
Uranium Exploration ◽  
Time Domain Electromagnetic ◽  
Conducting Bodies ◽  
Electromagnetic Surveys

A fracture-controlled uranium deposit was identified in Proterozoic Ajabgarh metasediments of the North Delhi Fold Belt within the Khetri subbasin at Rohil, Sikar district, Rajasthan, India. Uranium mineralization in the area is associated with geologic structures, albitization, and pyroxenization of metasediments and conductors such as metallic sulfides and carbonaceous phyllites/graphitic schists. To locate uranium mineralization akin to Rohil in nearby thick soil covered areas, this association was targeted through heliborne geophysical surveys. High-resolution heliborne magnetic and time domain electromagnetic (TEM) surveys were conducted around Rohil. The survey delineated several targets with favorable geologic structures and conductors such as graphitic schist for further uranium exploration. One favorable target near Chappar village was taken up for follow-up exploration work. The EM conductor mapped from heliborne survey was subsequently validated through ground time-domain electromagnetic surveys and subsurface exploration. Modeling of heliborne and ground-based electromagnetic data revealed the presence of subsurface conducting bodies with comparable model parameters. Drilling established the presence of a subsurface conductor up to a depth of 300 m, which was attributed to the presence of graphite and sulfides (pyrrhotite) along foliation plane of carbon phyllite/graphitic schist/quartz-biotite schist and calc-silicate rock. Further detailed laboratory investigations (petrology/X-ray diffraction) of selected core samples from the conductive zones confirmed the presence of pyrrhotite and graphite responsible for EM signature. This study, carried out by using multiparameter data sets, proved the efficacy of heliborne surveys in locating favorable targets for uranium exploration in Ajabgarh group of rocks.

Spatial Probabilistic Modeling of Corrosion in Ship Structures

2017 ◽  
Vol 3 (3) ◽  
Author(s):  
Jesus Luque ◽  
Rainer Hamann ◽  
Daniel Straub
Keyword(s):  
Spatial Variability ◽  
Spatial Dependence ◽  
Measurement Data ◽  
Corrosion Process ◽  
Model Parameters ◽  
Plate Element ◽  
Structural Element ◽  
Ship Structures ◽  
Operational Conditions ◽  
Corrosion Model

Corrosion in ship structures is influenced by a variety of factors that are varying in time and space. Existing corrosion models used in practice only partially address the spatial variability of the corrosion process. Typical estimations of corrosion model parameters are based on averaging measurements for one ship type over structural elements from different ships and operational conditions. Most models do not explicitly predict the variability and correlation of the corrosion process among multiple locations in the structure. This correlation is of relevance when determining the necessary inspection coverage, and it can influence the reliability of the ship structure. In this paper, we develop a probabilistic spatiotemporal corrosion model based on a hierarchical approach, which represents the spatial variability and correlation of the corrosion process. The model includes as hierarchical levels vessel–compartment–frame–structural element–plate element. At all levels, variables representing common influencing factors (e.g., coating life) are introduced. Moreover, at the lowest level, which is the one of the plate element, the corrosion process can be modeled as a spatial random field. For illustrative purposes, the model is trained through Bayesian analysis with measurement data from a group of tankers. In this application, the spatial dependence among corrosion processes in different parts of the ships is identified and quantified using the proposed hierarchical model. Finally, how this spatial dependence can be exploited when making inference on the future condition of the ships is demonstrated.

Hierarchical Bayesian lithology/fluid prediction: A North Sea case study

Geophysics ◽  
2012 ◽  
Vol 77 (2) ◽  
pp. B69-B85 ◽  
Author(s):  
Kjartan Rimstad ◽  
Per Avseth ◽  
Henning Omre
Keyword(s):  
North Sea ◽  
Rock Physics ◽  
Model Parameters ◽  
Gas Oil ◽  
Clear Fluid ◽  
Prior Model ◽  
Spatial Coupling ◽  
The North ◽  
Likelihood Model ◽  
Physics Model

Seismic 3D amplitude variation with offset (AVO) data from the Alvheim field in the North Sea are inverted into lithology/fluid classes, elastic properties, and porosity. Lithology/fluid maps over hydrocarbon prospects provide more reliable estimates of gas/oil volumes and improve the decision concerning further reservoir assessments. The Alvheim field is of turbidite origin with complex sand-lobe geometry and appears without clear fluid contacts across the field. The inversion is phrased in a Bayesian setting. The likelihood model contains a convolutional, linearized seismic model and a rock-physics model that capture vertical trends due to increased sand compaction and possible cementation. The likelihood model contains several global model parameters that are considered to be stochastic to adapt the model to the field under study and to include model uncertainty in the uncertainty assessments. The prior model on the lithology/fluid classes is a Markov random field that captures local vertical/horizontal continuity and vertical sorting of fluids. The predictions based on the posterior model are validated by observations in five wells used as blind tests. Hydrocarbon volumes with reliable gas/oil distributions are predicted. The spatial coupling provided by the prior model is crucial for reliable predictions; without the coupling, hydrocarbon volumes are severely underestimated. Depth trends in the rock-physics likelihood model improve the gas versus oil predictions. The porosity predictions reproduce contrasts observed in the wells, and mean square error is reduced by one-third compared to Gauss-linear predictions.

Simulation of Nitrate Concentration Affected from Land Use Changes in the Lower Part of Yom River Basin,Thailand: A Preliminary Study

2014 ◽  
Vol 931-932 ◽  
pp. 738-743
Author(s):  
Satika Boonkaewwan ◽  
Srilert Chotpantarat
Keyword(s):  
Land Use ◽  
River Basin ◽  
Swat Model ◽  
Land Use Changes ◽  
Sediment Concentration ◽  
Model Parameters ◽  
The North ◽  
Optimal Values ◽  
Preliminary Study ◽  
Year 2000

The Lower Yom River Basin is located in the north of Thailand. This study carried out to calibrate and validate using SWAT model in terms of streamflow and sediment concentration hydrographs (Year 2000-2012) for 3 RID streamflow gauging stations (the Royal Irrigation Department). The nitrates concentrations simulate have been influenced of land use changes during last ten years. Optimal values of model parameters derived from calibration and validation processes, which showed well fitted between observed and simulated results. In the last decade, particular in Lower Yom River, the land use change gradually transformed to be more paddy field and has been increased 127.48 km2 (approx. 0.87% increase), followed by urban area, which has been increased 196.66 km2 (approx. 1.35% increase), respectively. Average monthly concentration of nitrate increased 38.28 mg/l (approx.13.40 % increase), 43.17 mg/l (approx.12.00% increase), 43.02 mg/l (approx. 8.60% increase) at station Y.6, Y.4 and Y.17, respectively. Accordingly, on the basis of the results presented in this study, land use changes can significantly affect on concentrations of nitrate.

scholarly journals SYSTEMIC RISK: AN ASYMPTOTIC EVALUATION

2017 ◽  
Vol 48 (02) ◽  
pp. 673-698 ◽  
Author(s):  
Alexandru V. Asimit ◽  
Jinzhu Li
Keyword(s):  
Systemic Risk ◽  
Financial Distress ◽  
Mathematical Formulation ◽  
Tail Dependence ◽  
Value Theory ◽  
Asymptotic Independence ◽  
Regulatory Capital ◽  
Asymptotic Dependence ◽  
Asymptotic Evaluation ◽  
Mathematical Definitions

AbstractSystemic risk (SR) has been shown to play an important role in explaining the financial turmoils in the last several decades and understanding this source of risk has been a particular interest amongst academics, practitioners and regulators. The precise mathematical formulation of SR is still scrutinised, but the main purpose is to evaluate the financial distress of a system as a result of the failure of one component of the financial system in question. Many of the mathematical definitions of SR are based on evaluating expectations in extreme regions and therefore, Extreme Value Theory (EVT) represents the key ingredient in producing valuable estimates of SR and even its decomposition per individual components of the entire system. Without doubt, the prescribed dependence model amongst the system components has a major impact over our asymptotic approximations. Thus, this paper considers various well-known dependence models in the EVT literature that allow us to generate SR estimates. Our findings reveal that SR has a significant impact under asymptotic dependence, while weak tail dependence, known as asymptotic independence, produces an insignificant loss over the regulatory capital.

scholarly journals Evaluation of two ozone air quality modelling systems

2004 ◽  
Vol 4 (2) ◽  
pp. 1855-1885 ◽  
Author(s):  
S. Ortega ◽  
M. R. Soler ◽  
J. Beneito ◽  
D. Pino
Keyword(s):  
Air Quality ◽  
Mesoscale Model ◽  
Nonlocal Boundary ◽  
Three Dimensional ◽  
Base Stations ◽  
Model Parameters ◽  
The North ◽  
Forecasting System ◽  
North Of Spain ◽  
Ozone Episode

Abstract. The aim of this paper is to evaluate and compare two models of ozone air quality used in the north of Spain near the metropolitan area of Barcelona. As the focus of the paper is the comparison of the two systems, we do not attempt to improve the agreement by adjusting the emission inventory or model parameters. The first model, or forecasting system, is made up of three modules. The first module is a mesoscale model (MASS). This provides the initial condition for the second module, which is a nonlocal boundary layer model based on the transilient turbulence scheme. The third module is a photochemical box model (OZIPR), which is applied in Eulerian and Lagrangian modes and receives suitable information from the two previous modules. The model forecast is evaluated against ground base stations during summer 2001. The second model is the MM5/UAM-V. This is a grid model designed to predict the hourly three-dimensional ozone concentration fields. The model is applied during an ozone episode that occurred between 21 and 23 June 2001. Our results reflect the good performance of the two modelling systems when they are used in a specific episode.

scholarly journals International Trade and the Convergence in Youth Technological Awareness and Expectations within NAFTA: The Case of GMOs and Nuclear Power Technologies

2019 ◽  
Author(s):  
Ibrahim Niankara ◽  
Lee C. Adkins
Keyword(s):  
International Trade ◽  
Free Trade ◽  
North American ◽  
Nuclear Power ◽  
Genetically Modified Organisms ◽  
Situational Factors ◽  
Model Parameters ◽  
Probit Regression ◽  
The North ◽  
The Usa

Relying on the USA, Canada and Mexico extract from the cross-national data sample on the environmental affection and cognition of adolescent students (Niankara, 2019), along with seemingly unrelated bivariate weighted ordered probit regression modeling (Niankara and Zoungrana, 2018), this study reports on the convergence of technological awareness and expectations within the context of international trade. We achieve this by adopting a regional perspective in investigating the effects of affective, cognitive and situational factors on youth's awareness and expectations about genetically modified organisms (GMOs) and nuclear power technology (NPT) within the North American free trade block. Identification of model parameters is achieved using maximum simulated likelihood methods. The findings show that although it has been over 20 years as of 2015 that USA, Canada, and Mexico ratified the north American free trade agreement (NAFTA), the diffusion of technology and information within the trade block has not succeeded in homogenizing awareness and expectations about GMOs and Nuclear power technology, as observed in the youth population across the three countries. Indeed, with regards to technological awareness, compared to youth from the USA, those from Canada show 15% (GMOs) and 7.1% (NPT) more awareness respectively; while those in Mexico are respectively 34.4% and 19.5% less aware about GMOs and NPT. With respect to technological expectations, compared to youth from the USA, those from Canada and Mexico are respectively 34.4% and 39.9% more optimistic about GMOs, while 15% and 49.7% more optimistic about NPT. Overall, youth within NAFTA country members are respectively 2.5% and 6.7% more optimistic about GMOs and NPT for every level increase in their awareness about the two technologies.

scholarly journals Generalized Odd Power Cauchy Family and Its Associated Heteroscedastic Regression Model

2021 ◽  
Vol 9 (3) ◽  
pp. 516-528
Author(s):  
Emrah Altun EA ◽  
Morad Alizadeh ◽  
Thiago Ramires ◽  
Edwin Ortega
Keyword(s):  
Monte Carlo ◽  
Regression Model ◽  
Data Structures ◽  
Generating Functions ◽  
Shape Parameter ◽  
Model Parameters ◽  
Data Sets ◽  
Complex Data ◽  
Linear Representations ◽  
Heteroscedastic Regression

This study introduces a generalization of the odd power Cauchy family by adding one more shape parameter togain more flexibility modeling the complex data structures. The linear representations for the density, moments, quantile,and generating functions are derived. The model parameters are estimated employing the maximum likelihood estimationmethod. The Monte Carlo simulations are performed under different parameter settings and sample sizes for the proposedmodels. In addition, we introduce a new heteroscedastic regression model based on the special member of the proposedfamily. Three data sets are analyzed with competitive and proposed models.

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