scholarly journals A NEW METHODOLOGY FOR EXTREME WAVES ANALYSIS BASED ON WEATHER-PATTERNS CLASSIFICATION METHODS

2017 ◽  
pp. 23 ◽  
Author(s):  
Sebastián Solari ◽  
Rodrigo Alonso
Keyword(s):  
Atlantic Coast ◽  
Extreme Value Distribution ◽  
Extreme Value ◽  
Extreme Value Analysis ◽  
Wave Direction ◽  
Value Analysis ◽  
Weather Patterns ◽  
Circulation Patterns ◽  
Alternative Approach ◽  
South Atlantic Coast

Extreme Value Analysis is usually based on the assumption that the data is independent and homogeneous. Historically the hypothesis of independence has received more attention than the hypothesis of homogeneity. The two most common ways of ensuring independence is to use annual maxima or peaks over threshold approaches. In wave and wind extreme analysis, the usual approaches to achieve homogeneous series have been to work to differentiate according to type of process generating the extreme value (e.g. differentiate between hurricanes and cyclones) and conduct directional analyzes. In this work an alternative approach is proposed, based on the use of cluster analysis methodologies to identify weather circulation patterns that results in extreme wave conditions. The proposed methodology is successfully applied to a case study in the Uruguayan South Atlantic coast. From the obtained results it seems that the proposed methodology is able to differentiate the data in homogenous subsets, not only in terms of the target variable (significant wave height) but also in terms of relevant covariables, like wave direction or sea level, and that the extreme value distribution of the whole data, obtained from the distributions fitted to each subset, is fairly insensitive to the number of weather patterns used in the analysis.

scholarly journals Extreme wave analysis based on atmospheric pattern classification: an application along the Italian coast

2020 ◽  
Vol 20 (5) ◽  
pp. 1233-1246
Author(s):  
Francesco De Leo ◽  
Sebastián Solari ◽  
Giovanni Besio
Keyword(s):  
Extreme Value Distribution ◽  
Extreme Value ◽  
Value Distribution ◽  
Extreme Value Analysis ◽  
Extreme Wave ◽  
Wind Fields ◽  
Value Analysis ◽  
Atmospheric Circulation Patterns ◽  
Circulation Patterns ◽  
The Mediterranean

Abstract. This paper provides a methodology for classifying samples of significant wave-height peaks in homogeneous subsets in terms of the atmospheric circulation patterns behind the observed extreme wave conditions. Then, a methodology is given for the computation of the overall extreme value distribution by starting from the distributions fitted to each single subset. To this end, the k-means clustering technique is used to classify the shape of the wind fields that occurred simultaneously to and prior to the occurrences of the extreme wave events. This results in a small number of characteristic circulation patterns related to as many subsets of extreme wave values. After fitting an extreme value distribution to each subset, bootstrapping is used to reconstruct the omni-circulation pattern's extreme value distribution. The methodology is applied to several locations along the Italian buoy network, and it is concluded from the obtained results that it yields a two-fold advantage: first, it is capable of identifying clearly differentiated subsets driven by homogeneous circulation patterns; second, it allows one to estimate high-return-period quantiles consistent with those resulting from the usual extreme value analysis. In particular, the circulation patterns highlighted are analyzed in the context of the Mediterranean Sea's atmospheric climatology and are shown to be due to well-known cyclonic systems typically crossing the Mediterranean basin.

scholarly journals Assessing the features of extreme smog in China and the differentiated treatment strategy

2018 ◽  
Vol 474 (2209) ◽  
pp. 20170511 ◽  
Author(s):  
Lu Deng ◽  
Zhengjun Zhang
Keyword(s):  
Control Strategies ◽  
Hazard Analysis ◽  
Extreme Value Distribution ◽  
Extreme Value ◽  
Return Level ◽  
Extreme Value Analysis ◽  
Value Analysis ◽  
Mean Values ◽  
And Control ◽  
Parameter Values

Extreme smog can have potentially harmful effects on human health, the economy and daily life. However, the average (mean) values do not provide strategically useful information on the hazard analysis and control of extreme smog. This article investigates China's smog extremes by applying extreme value analysis to hourly PM2.5 data from 2014 to 2016 obtained from monitoring stations across China. By fitting a generalized extreme value (GEV) distribution to exceedances over a station-specific extreme smog level at each monitoring location, all study stations are grouped into eight different categories based on the estimated mean and shape parameter values of fitted GEV distributions. The extreme features characterized by the mean of the fitted extreme value distribution, the maximum frequency and the tail index of extreme smog at each location are analysed. These features can provide useful information for central/local government to conduct differentiated treatments in cities within different categories and conduct similar prevention goals and control strategies among those cities belonging to the same category in a range of areas. Furthermore, hazardous hours, breaking probability and the 1-year return level of each station are demonstrated by category, based on which the future control and reduction targets of extreme smog are proposed for the cities of Beijing, Tianjin and Hebei as an example.

scholarly journals The Influence of Hindcast Modeling Uncertainty on the Prediction of High Return Period Wave Conditions

2004 ◽  
Author(s):  
Daniel C. Brooker ◽  
Geoffrey K. Cole ◽  
Jason D. McConochie
Keyword(s):  
Return Period ◽  
Extreme Value Distribution ◽  
Likelihood Estimation ◽  
Extreme Value ◽  
Extreme Value Analysis ◽  
Random Errors ◽  
Value Analysis ◽  
Simulation Techniques ◽  
Modeling Uncertainties ◽  
Wave Conditions

Extreme value analysis for the prediction of long return period met-ocean conditions is often based upon hindcast studies of wind and wave conditions. The random errors associated with hindcast modeling are not usually incorporated when fitting an extreme value distribution to hindcast data. In this paper, a modified probability distribution function is derived so that modeling uncertainties can be explicitly included in extreme value analysis. Maximum likelihood estimation is then used to incorporate hindcast uncertainty into return value estimates and confidence intervals. The method presented here is compared against simulation techniques for accounting for hindcast errors. The influence of random errors within modeled datasets on predicted 100 year return wave estimates is discussed.

scholarly journals The transformed-stationary approach: a generic and simplified methodology for non-stationary extreme value analysis

2016 ◽  
Vol 20 (9) ◽  
pp. 3527-3547 ◽  
Author(s):  
Lorenzo Mentaschi ◽  
Michalis Vousdoukas ◽  
Evangelos Voukouvalas ◽  
Ludovica Sartini ◽  
Luc Feyen ◽  
...  
Keyword(s):  
Time Series ◽  
Extreme Value Distribution ◽  
Extreme Value ◽  
Value Distribution ◽  
Stationary Time Series ◽  
Alternative Methods ◽  
Extreme Value Analysis ◽  
Accurate Estimation ◽  
Residual Water ◽  
Value Analysis

Abstract. Statistical approaches to study extreme events require, by definition, long time series of data. In many scientific disciplines, these series are often subject to variations at different temporal scales that affect the frequency and intensity of their extremes. Therefore, the assumption of stationarity is violated and alternative methods to conventional stationary extreme value analysis (EVA) must be adopted. Using the example of environmental variables subject to climate change, in this study we introduce the transformed-stationary (TS) methodology for non-stationary EVA. This approach consists of (i) transforming a non-stationary time series into a stationary one, to which the stationary EVA theory can be applied, and (ii) reverse transforming the result into a non-stationary extreme value distribution. As a transformation, we propose and discuss a simple time-varying normalization of the signal and show that it enables a comprehensive formulation of non-stationary generalized extreme value (GEV) and generalized Pareto distribution (GPD) models with a constant shape parameter. A validation of the methodology is carried out on time series of significant wave height, residual water level, and river discharge, which show varying degrees of long-term and seasonal variability. The results from the proposed approach are comparable with the results from (a) a stationary EVA on quasi-stationary slices of non-stationary series and (b) the established method for non-stationary EVA. However, the proposed technique comes with advantages in both cases. For example, in contrast to (a), the proposed technique uses the whole time horizon of the series for the estimation of the extremes, allowing for a more accurate estimation of large return levels. Furthermore, with respect to (b), it decouples the detection of non-stationary patterns from the fitting of the extreme value distribution. As a result, the steps of the analysis are simplified and intermediate diagnostics are possible. In particular, the transformation can be carried out by means of simple statistical techniques such as low-pass filters based on the running mean and the standard deviation, and the fitting procedure is a stationary one with a few degrees of freedom and is easy to implement and control. An open-source MATLAB toolbox has been developed to cover this methodology, which is available at https://github.com/menta78/tsEva/ (Mentaschi et al., 2016).

Estimation of Storm Peak and Intra-Storm Directional-Seasonal Design Conditions in the North Sea

2014 ◽  
Author(s):  
Graham Feld ◽  
David Randell ◽  
Yanyun Wu ◽  
Kevin Ewans ◽  
Philip Jonathan
Keyword(s):  
Wave Height ◽  
Significant Wave Height ◽  
Extreme Value ◽  
Extreme Value Analysis ◽  
Wave Direction ◽  
Value Analysis ◽  
Significant Wave ◽  
The North ◽  
Multiple Covariates ◽  
Design Values

Specification of realistic environmental design conditions for marine structures is of fundamental importance to their reliability over time. Design conditions for extreme waves and storm severities are typically estimated by extreme value analysis of time series of measured or hindcast significant wave height, HS. This analysis is complicated by two effects. Firstly, HS exhibits temporal dependence. Secondly, the characteristics of HSsp are non-stationary with respect to multiple covariates, particularly wave direction and season. We develop directional-seasonal design values for storm peak significant wave height (HSsp) by estimation of, and simulation under a non-stationary extreme value model for HSsp. Design values for significant wave height (HS) are estimated by simulating storm trajectories of HS consistent with the simulated storm peak events. Design distributions for individual maximum wave height (Hmax) are estimated by marginalisation using the known conditional distribution for Hmax given HS. Particular attention is paid to the assessment of model bias and quantification of model parameter and design value uncertainty using bootstrap resampling. We also outline existing work on extension to estimation of maximum crest elevation and total extreme water level.

Estimation of Storm Peak and Intrastorm Directional–Seasonal Design Conditions in the North Sea

2015 ◽  
Vol 137 (2) ◽  
Author(s):  
Graham Feld ◽  
David Randell ◽  
Yanyun Wu ◽  
Kevin Ewans ◽  
Philip Jonathan
Keyword(s):  
Wave Height ◽  
Significant Wave Height ◽  
Extreme Value ◽  
Extreme Value Analysis ◽  
Wave Direction ◽  
Value Analysis ◽  
Significant Wave ◽  
The North ◽  
Multiple Covariates ◽  
Design Values

Specification of realistic environmental design conditions for marine structures is of fundamental importance to their reliability over time. Design conditions for extreme waves and storm severities are typically estimated by extreme value analysis of time series of measured or hindcast significant wave height, HS. This analysis is complicated by two effects. First, HS exhibits temporal dependence. Second, the characteristics of HSsp are nonstationary with respect to multiple covariates, particularly wave direction, and season. We develop directional–seasonal design values for storm peak significant wave height (HSsp) by estimation of, and simulation under a nonstationary extreme value model for HSsp. Design values for significant wave height (HS) are estimated by simulating storm trajectories of HS consistent with the simulated storm peak events. Design distributions for individual maximum wave height (Hmax) are estimated by marginalization using the known conditional distribution for Hmax given HS. Particular attention is paid to the assessment of model bias and quantification of model parameter and design value uncertainty using bootstrap resampling. We also outline existing work on extension to estimation of maximum crest elevation and total extreme water level.

scholarly journals Statistics of Extreme Synoptic-Scale Wind Speeds in Ensemble Simulations of Current and Future Climate

2004 ◽  
Vol 17 (23) ◽  
pp. 4564-4574 ◽  
Author(s):  
H. W. van den Brink ◽  
G. P. Können ◽  
J. D. Opsteegh
Keyword(s):  
Climate Model ◽  
Extreme Value Distribution ◽  
Extreme Value ◽  
Extreme Value Analysis ◽  
Jet Stream ◽  
Return Periods ◽  
Value Analysis ◽  
Wind Speeds ◽  
Westerly Jet ◽  
Extreme Wind Speed

Abstract Statistical analysis of the wind speeds, generated by a climate model of intermediate complexity, indicates the existence of areas where the extreme value distribution of extratropical winds is double populated, the second population becoming dominant for return periods of order 103 yr. Meteorological analysis of the second population shows that it is caused when extratropical cyclones merge in an extremely strong westerly jet stream such that conditions are generated that are favorable for occurrence of strong diabatic feedbacks. Doubling of the greenhouse gas concentrations changes the areas of second population and increases its frequency. If these model results apply to the real world, then in the exit areas of the jet stream the extreme wind speed with centennial-to-millennial return periods is considerably larger than extreme value analysis of observational records implies.

scholarly journals EXTREME VALUE ANALYSIS FOR MODELING HIGH PM10 LEVEL IN JOHOR BAHRU

2015 ◽  
Vol 76 (1) ◽  
Author(s):  
Nor Azrita Mohd Amin ◽  
Mohd Bakri Adam ◽  
Ahmad Zaharin Aris
Keyword(s):  
Generalized Pareto Distribution ◽  
Pollution Index ◽  
Extreme Value Distribution ◽  
Value Theory ◽  
Extreme Value ◽  
Generalized Extreme Value Distribution ◽  
Return Level ◽  
Extreme Value Analysis ◽  
Value Analysis ◽  
Pm10 Level

Extreme value theory is a very well-known statistical analysis for modeling extreme data in environmental management. The main focus is to compare the generalized extreme value distribution (GEV) and the generalized Pareto distribution (GPD) for modeling extreme data in terms of estimated parameters and return levels. The maximum daily PM10 data for Johor Bahru monitoring station based on a 14 years database (1997-2010) were analyzed. It is found that the parameters estimated are more comparable if the extracted numbers of extreme series for both models are much more similar. The 10-years return value for GEV is  while for GPD is . Based on the threshold choice plot, threshold  is chosen and the corresponding 10-years return level is . According to the air pollution index in Malaysia, this value is categorized as hazardous.

Modeling European hot spells using extreme value analysis

2014 ◽  
Vol 58 (3) ◽  
pp. 193-207 ◽  
Author(s):  
C Photiadou ◽  
MR Jones ◽  
D Keellings ◽  
CF Dewes
Keyword(s):  
Extreme Value ◽  
Extreme Value Analysis ◽  
Value Analysis ◽  
Hot Spells
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