Response of a CALM Buoy Moored Vessel in Squall Condition

2016 ◽  
Author(s):  
Mark Paalvast ◽  
Jelte Kymmell ◽  
Ward Gorter ◽  
Alison Brown
Keyword(s):  
Extreme Values ◽  
Generalized Pareto Distribution ◽  
Generalized Pareto ◽  
Benchmark Database ◽  
Peak Over Threshold ◽  
Vessel Response ◽  
Design Loads ◽  
Novel Method ◽  
Selection For ◽  
Reliable Design

This paper reviews the response of a hawser moored vessel to squalls and addresses a novel method for obtaining statistically reliable design loads. Industry paradigms related to squall selection for analysis input are reviewed and renewed. A benchmark database consisting of more than 15,000 unique squall-wave-current induced extreme values enables the validation of a range of less computationally demanding analysis and squall selection methods. Extreme values are extrapolated to a design value using a Peak Over Threshold (POT) method to fit a Generalized Pareto Distribution (GPD). The influence of associated metocean conditions and squall characteristics on the vessel response is presented. By means of bootstrapping a satisfactory population size for design purposes is studied. The findings challenge common design practices currently employed throughout the industry.

scholarly journals ON THE TWO STEP THRESHOLD SELECTION FOR OVER-THRESHOLD MODELLING

2012 ◽  
Vol 1 (33) ◽  
pp. 42
Author(s):  
Pietro Bernardara ◽  
Franck Mazas ◽  
Jérôme Weiss ◽  
Marc Andreewsky ◽  
Xavier Kergadallan ◽  
...  
Keyword(s):  
Pareto Distribution ◽  
Extreme Values ◽  
Generalized Pareto Distribution ◽  
Value Theory ◽  
Water Levels ◽  
Distributed Data ◽  
Threshold Selection ◽  
The Past ◽  
Generalized Pareto ◽  
Selection For

In the general framework of over-threshold modelling (OTM) for estimating extreme values of met-ocean variables, such as waves, surges or water levels, the threshold selection logically requires two steps: the physical declustering of time series of the variable in order to obtain samples of independent and identically distributed data then the application of the extreme value theory, which predicts the convergence of the upper part of the sample toward the Generalized Pareto Distribution. These two steps were often merged and confused in the past. A clear framework for distinguishing them is presented here. A review of the methods available in literature to carry out these two steps is given here together with the illustration of two simple and practical examples.

scholarly journals A Holistic Modelling Approach for the Estimation of Return Levels of Peak Flows in Bavaria

Water ◽  
2020 ◽  
Vol 12 (9) ◽  
pp. 2349
Author(s):  
Florian Willkofer ◽  
Raul R. Wood ◽  
Fabian von Trentini ◽  
Jens Weismüller ◽  
Benjamin Poschlod ◽  
...  
Keyword(s):  
Climate Change ◽  
Generalized Pareto Distribution ◽  
Holistic Approach ◽  
Peak Flows ◽  
Modelling Framework ◽  
Generalized Pareto ◽  
Peak Over Threshold ◽  
Robust Modelling ◽  
Change Impact ◽  
Modelling Approach

This study introduces a holistic approach for the hydrological modelling of peak flows for the major Bavarian river basins, referred to as Hydrological Bavaria. This approach, intended to develop a robust modelling framework to support water resources management under climate change conditions, comprises a regionalized parameterization of the water balance simulation model (WaSiM) for 98 catchments in high temporal (3 h) and spatial (500 m) resolution using spatially coherent information and an automatized calibration (dynamically dimensioned search–simulated annealing, DDS-SA) for storage components. The performance of the model was examined using common metrics (Nash & Sutcliffe Efficiency (NSE), Kling-Gupta Efficiency (KGE)). The simulations provided the means for the calculation of a level of trust (LOT) by comparing observed and simulated high flows with a five, ten, and 20-year return period. These estimates were derived by the Generalized Pareto Distribution (GPD) applying the peak over threshold (POT) sampling method. Results show that the model overall performs well with regard to the selected objective measures, but also exhibits regional disparities mainly due to the availability of meteorological inputs or water management data. For most catchments, the LOT shows moderate to high confidence in the estimation of return periods with the hydrological model. Therefore, we consider the holistic modelling approach applicable for climate change impact studies concerned with dynamic alterations in peak flows.

scholarly journals GENERALIZED PARETO DISTRIBUTION UNTUK PENGUKURAN VALUE AT RISK PADA PORTOFOLIO SAHAM SYARIAH DAN APLIKASINYA MENGGUNAKAN GUI MATLAB

2018 ◽  
Vol 7 (3) ◽  
pp. 224-235
Author(s):  
Desi Nur Rahma ◽  
Di Asih I Maruddani ◽  
Tarno Tarno
Keyword(s):  
At Risk ◽  
Capital Market ◽  
Value At Risk ◽  
Pareto Distribution ◽  
Extreme Values ◽  
Generalized Pareto Distribution ◽  
Generalized Pareto ◽  
Heavy Tailed ◽  
Is Value

The capital market is one of long-term investment alternative. One of the traded products is stock, including sharia stock. The risk measurement is an important thing for investor in other that can decrease investment loss. One of the popular methods now is Value at Risk (VaR). There are many financial data that have heavy tailed, because of extreme values, so Value at Risk Generalized Pareto Distribution is used for this case. This research also result a Matlab GUI programming application that can help users to measure the VaR. The purpose of this research is to analyze VaR with GPD approach with GUI Matlab for helping the computation in sharia stock. The data that is used in this case are PT XL Axiata Tbk, PT Waskita Karya (Persero) Tbk, dan PT Charoen Pokphand Indonesia Tbk on January, 2nd 2017 until May, 31st 2017. The results of VaRGPD are: EXCL single stock VaR 8,76% of investment, WSKT single stock VaR 4% of investment, CPIN single stock VaR 5,86% of investment, 2 assets portfolio (EXCL and WSKT) 4,09% of investment, 2 assets portfolio (EXCL and CPIN) 5,28% of investment, 2 assets portfolio (WSKT and CPIN) 3,68% of investment, and 3 assets portfolio (EXCL, WSKT, and CPIN) 3,75% of investment. It can be concluded that the portfolios more and more, the risk is smaller. It is because the possibility of all stocks of the company dropped together is small. Keywords: Generalized Pareto Distribution, Value at Risk, Graphical User Interface, sharia stock

Design Approach for CALM Buoy Moored Vessel in Squall Conditions

2016 ◽  
Author(s):  
Alison Brown ◽  
Ward Gorter ◽  
Mark Paalvast ◽  
Jelte Kymmell
Keyword(s):  
Pareto Distribution ◽  
Generalized Pareto Distribution ◽  
Mesoscale Convective Systems ◽  
Design Parameters ◽  
Convective Systems ◽  
Extreme Loads ◽  
Tropical Environments ◽  
Generalized Pareto ◽  
Peak Over Threshold ◽  
Mesoscale Convective

This paper focuses on examining the response of a vessel moored to a Catenary Anchor Leg Mooring (CALM) buoy in squall conditions. This type of mooring arrangement is typically a temporary mooring used for loading and offloading product or a temporary arrangement used during construction and typically selected for shallow water locations, often in tropical environments when conditions are otherwise relatively benign. Squalls are mesoscale convective systems that cause rapid increases in wind speed and are often associated with large changes in wind direction and also occur mostly in tropical environments. Hence for some locations squall events are the design drivers for this mooring arrangement and are particularly important due to the imperfect squall forecasts available to the industry. To understand the risks in a squall environment the vessel-CALM buoy system is modelled for a range of both squall conditions and associated environmental conditions, covering typical associated wave and current conditions by season and direction. A response-based approach is used to determine the design parameters for the extreme loads, extrapolated using a peak over threshold (POT) approach and using a Generalized Pareto Distribution (GPD), for the vessel-CALM buoy system. The method for this approach is described in detail and contrasted with previous industry approaches.

Uncertainties in extreme surge level estimates from observational records

2005 ◽  
Vol 363 (1831) ◽  
pp. 1377-1386 ◽  
Author(s):  
H.W van den Brink ◽  
G.P Können ◽  
J.D Opsteegh
Keyword(s):  
Wind Speed ◽  
Pareto Distribution ◽  
Climate Model ◽  
Generalized Pareto Distribution ◽  
Optimal Choice ◽  
Ensemble Simulations ◽  
The North ◽  
Generalized Pareto ◽  
Peak Over Threshold ◽  
The North Sea

Ensemble simulations with a total length of 7540 years are generated with a climate model, and coupled to a simple surge model to transform the wind field over the North Sea to the skew surge level at Delfzijl, The Netherlands. The 65 constructed surge records, each with a record length of 116 years, are analysed with the generalized extreme value (GEV) and the generalized Pareto distribution (GPD) to study both the model and sample uncertainty in surge level estimates with a return period of 10 4 years, as derived from 116-year records. The optimal choice of the threshold, needed for an unbiased GPD estimate from peak over threshold (POT) values, cannot be determined objectively from a 100-year dataset. This fact, in combination with the sensitivity of the GPD estimate to the threshold, and its tendency towards too low estimates, leaves the application of the GEV distribution to storm-season maxima as the best approach. If the GPD analysis is applied, then the exceedance rate, λ , chosen should not be larger than 4. The climate model hints at the existence of a second population of very intense storms. As the existence of such a second population can never be excluded from a 100-year record, the estimated 10 4 -year wind-speed from such records has always to be interpreted as a lower limit.

scholarly journals Assessing the adequacy of bias corrected IMERG satellite precipitation estimates using extended mixture distribution mapping method over Yangtze River basin

2018 ◽  
Vol 246 ◽  
pp. 01096
Author(s):  
Qiumei Ma ◽  
Lihua Xiong ◽  
Chong-Yu Xu ◽  
Shenglian Guo
Keyword(s):  
River Basin ◽  
Yangtze River ◽  
Extreme Values ◽  
Yangtze River Basin ◽  
Mixture Distribution ◽  
Mapping Method ◽  
Generalized Pareto ◽  
Precipitation Estimates ◽  
Distribution Mapping ◽  
Emd Method

Satellite precipitation estimates (SPE) product with high spatiotemporal resolution is a potential alternative to traditional ground-based gauge precipitation. However, SPE is frequently biased due to its indirect measurement, and thus bias correction is necessary before applying to a specific region. An improved distribution mapping method, i.e., Extended Mixture Distribution (EMD) of censored Gamma and generalized Pareto distributions, was established. The advantage of EMD method is that it describes both moderate and extreme values well and carries on the traditional censored, shifted Gamma distribution to combine the precipitation occurrence/non-occurrence events together. Then the EMD method was applied to the Integrated Multi-satellitE Retrievals for GPM product (IMERG) as statistical post-processing over Yangtze River basin. The Version-2 Gridded dataset of daily Surface Precipitation from China Meteorological Administration (GSP-CMA) was taken as reference. The adequacy of bias corrected IMERG precipitation was assessed and the results showed that (1) the Root Mean Squared Error and Relative Bias between bias-corrected IMERG precipitation and reference are significantly reduced relative to the raw IMERG estimates; (2) the performance of extreme values of IMERG in Yangtze River basin is enhanced since both the under- and over-estimation of the raw IMERG are compromised, due to the generalized Pareto distribution introduced in EMD which is enable to describe the extreme value distribution. This highlights the improved distribution mapping method, EMD is flexible and robust to bias correct the IMERG precipitation to obtain higher accuracy of SPE despite the coarse resolution of reference.

Inference on P(Y

2020 ◽  
Vol 72 (2) ◽  
pp. 89-110
Author(s):  
Manoj Chacko ◽  
Shiny Mathew
Keyword(s):  
Maximum Likelihood ◽  
Pareto Distribution ◽  
Generalized Pareto Distribution ◽  
Maximum Likelihood Estimators ◽  
Record Values ◽  
Asymptotic Distributions ◽  
Bayes Estimators ◽  
Generalized Pareto ◽  
Pareto Distributions ◽  
Generalized Pareto Distributions

In this article, the estimation of [Formula: see text] is considered when [Formula: see text] and [Formula: see text] are two independent generalized Pareto distributions. The maximum likelihood estimators and Bayes estimators of [Formula: see text] are obtained based on record values. The Asymptotic distributions are also obtained together with the corresponding confidence interval of [Formula: see text]. AMS 2000 subject classification: 90B25

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