scholarly journals Wave Extremes in the Northeast Atlantic

2012 ◽  
Vol 25 (5) ◽  
pp. 1529-1543 ◽  
Author(s):  
Ole Johan Aarnes ◽  
Øyvind Breivik ◽  
Magnar Reistad
Keyword(s):  
Confidence Intervals ◽  
Order Statistic ◽  
Significant Wave Height ◽  
Extreme Value ◽  
Extreme Value Statistics ◽  
Gev Distribution ◽  
Northeast Atlantic ◽  
Generalized Pareto ◽  
Return Value ◽  
Meteorological Institute

The objective of this study is to compute 100-yr return value estimates of significant wave height using a new hindcast developed by the Norwegian Meteorological Institute. This regional hindcast covers the northeast Atlantic and spans the period 1958–2009. The return value estimates are based upon three different stationary models commonly applied in extreme value statistics: the generalized extreme value (GEV) distribution, the joint GEV distribution for the r largest-order statistic (rLOS), and the generalized Pareto (GP) distribution. Here, the qualitative differences between the models and their corresponding confidence intervals are investigated.

Modeling the Seasonality of Extreme Waves in the Gulf of Mexico

2010 ◽  
Vol 133 (2) ◽  
Author(s):  
Philip Jonathan ◽  
Kevin Ewans
Keyword(s):  
Gulf Of Mexico ◽  
Wave Height ◽  
Significant Wave Height ◽  
Poisson Model ◽  
Extreme Value ◽  
Significant Wave ◽  
Generalized Pareto ◽  
Seasonal Model ◽  
Pareto Model ◽  
Optimal Value

Statistics of storm peaks over threshold depend typically on a number of covariates including location, season, and storm direction. Here, a nonhomogeneous Poisson model is adopted to characterize storm peak events with respect to season for two Gulf of Mexico locations. The behavior of storm peak significant wave height over threshold is characterized using a generalized Pareto model, the parameters of which vary smoothly with season using a Fourier form. The rate of occurrence of storm peaks is also modeled using a Poisson model with rate varying with season. A seasonally varying extreme value threshold is estimated independently. The degree of smoothness of extreme value shape and scale and the Poisson rate with season are regulated by roughness-penalized maximum likelihood; the optimal value of roughness is selected by cross validation. Despite the fact that only the peak significant wave height event for each storm is used for modeling, the influence of the whole period of a storm on design extremes for any seasonal interval is modeled using the concept of storm dissipation, providing a consistent means to estimate design criteria for arbitrary seasonal intervals. The characteristics of the 100 year storm peak significant wave height, estimated using the seasonal model, are examined and compared with those estimated ignoring seasonality.

On the spectral shape of SEP events: An extreme value statistics approach

2013 ◽  
Author(s):  
M. Laurenza ◽  
G. Consolini ◽  
M. Storini ◽  
A. Damiani
Keyword(s):  
Extreme Value ◽  
Spectral Shape ◽  
Extreme Value Statistics

Extreme Storm Surge estimates and projection through the Metastatistical Extreme Value Distribution

2021 ◽  
Author(s):  
Maria Francesca Caruso ◽  
Marco Marani
Keyword(s):  
Sea Level ◽  
Extreme Value Distribution ◽  
Storm Surges ◽  
Extreme Value ◽  
Value Distribution ◽  
Generalized Extreme Value Distribution ◽  
Sample Sizes ◽  
Mean Sea Level ◽  
Gev Distribution ◽  
Calibration Sample

<p>Storm surges caused by extreme meteorological conditions are a major natural risk in coastal areas, especially in the context of global climate change. The increase of future sea-levels caused by continuing global warming, may endanger human lives and infrastructure through inundation, erosion and salinization.<br>Hence, the reliable estimation of the occurrence probability of these extreme events is crucial to quantify risk and to design adequate coastal defense structures. The probability of event occurrence is typically estimated by modelling observed sea-level records using one of a few statistical approaches.<br>The traditional Extreme Value Theory is based on the use of the Generalized Extreme Value distribution (GEV),  fitted either by considering block (typically yearly) maxima, or values exceeding a high threshold (POT). This approach does not make full use of all observational information, and thereby does not minimize estimation uncertainty.<br>The recently proposed Metastatistical Extreme Value Distribution (MEVD), instead, makes use of most of the available observations and has been shown to outperform the classical GEV distribution in several applications, including hourly and daily rainfall, flood peak discharge and extreme hurricane intensity.<br>Here, we comparatively apply the MEVD and the GEV distribution to long time series of sea-level observations distributed along European coastlines (Venice (IT), Hornbaek (DK), Marseille (FR), Newlyn (UK)). A cross-validation approach, dividing available data in separate calibration and test sub-samples, is used to compare their performances in high-quantile estimation.<br>The MEVD approach is based on the definition of an “ordinary values” distribution (here a Generalized Pareto distribution), whose parameters are estimated using the Probability Weighted Moments method on non-overlapping sub-samples of fixed size (5 years). To address the problems posed by observational samples of different sizes, we explore the effect on uncertainty of different calibration sample sizes, from 5 to 30 years. In this application, we find that the GEVD-POT and MEVD approaches perform similarly, once the above parameter choices are optimized. In particular, when considering short samples (5 years) and events with a high return time, the estimation errors show no significant differences in their median value across methods and sites, all approaches producing a similar underestimation of the actual quantile. When larger calibration sample sizes are considered (10-30 yrs), the median error of MEVD estimates tends to be closer to zero than those obtained from the traditional methods.<br>Future projections of sea-level rise until 2100 are also analyzed, with reference to intermediate and extreme representative concentration pathways (RCP 4.5 and RCP 8.5). The probability of future storm surges along European coastlines are then estimated assuming a changing mean sea-level and an unchanged storm regime. The projections indicate future changes in mean sea-level lead to increases in the height of storm surges for a fixed return period that are spatially heterogeneous across the coastal locations explored.</p>

scholarly journals Bestimmung seltener Starkniederschläge kurzer Dauer - Fallbeispiele im Vergleich mit den schweizerischen Starkniederschlagskarten | Determination of Rare Rainfall Intensities of Short-Duration - Case Studies Compared with Maps of Intense Rainfall in Switzerland

1999 ◽  
Vol 150 (6) ◽  
pp. 209-218 ◽  
Author(s):  
Felix Forster ◽  
Walter Baumgartner
Keyword(s):  
Case Studies ◽  
Short Duration ◽  
Extreme Value ◽  
Extreme Value Statistics ◽  
Intense Rainfall

The two maps of intense rainfall in the Hydrological Atlas of Switzerland (1992, 1997) are compared to data of an evaluation of extreme value statistics. The results are transferred to recommendations for practioners.

scholarly journals Estimating changes of temperatures and precipitation extremes in India using the Generalized Extreme Value (GEV) distribution

2018 ◽  
Author(s):  
Kishore Pangaluru ◽  
Isabella Velicogna ◽  
Tyler C. Sutterley ◽  
Yara Mohajerani ◽  
Enrico Ciraci ◽  
...  
Keyword(s):  
Extreme Temperature ◽  
Extreme Value ◽  
Precipitation Extremes ◽  
Temperature Extremes ◽  
Extreme Temperatures ◽  
Return Periods ◽  
Generalized Extreme Value ◽  
Gev Distribution ◽  
Cold Extreme ◽  
Temperature And Precipitation

Abstract. Changes in extreme temperature and precipitation may give some of the largest significant societal and ecological impacts. For changes in the magnitude of extreme temperature and precipitation over India, we used a statistical model of generalized extreme value (GEV) distribution. The GEV statistical distribution is a time-dependent distribution with different time scales of variability bounded by a precipitation, maximum (Tmax), and minimum (Tmin) temperature extremes and also assessed their possibility changes are evaluated and quantified over India is presented. The GEV-based method is applied on both precipitation and temperature extremes over India during the 20th and 21st centuries using multiple coupled climate models taking an interest in the Coupled Model Intercomparison Project Phase 5 (CMIP5) and observational datasets. The regional means of historical warm extreme temperatures are 34.89, 36.42, and 38.14 °C for three different (10, 20, and 50-year) periods, respectively; whereas the cold extreme mean temperatures are 7.75, 4.19, and −1.57 °C. It indicates that 20th century cold extreme temperatures have relatively larger variations than the warm extremes. As for the future, the CMIP5 models of warm extreme regional mean values increase from 0.33 to 0.75 °C in all return periods (10-, 20-, and 50-year periods), while in the case of cold extreme means values vary between 0.58 and 2.29 °C. In the future, cold extreme values have a larger increasing rate over the northwest, northeast, some parts of north-central, and Inter Peninsula regions. The CRU precipitation extremes are larger than the historical extreme precipitation in all three (10, 20, and 50-year) return-periods.

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