• Journal of Environmental Science International
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• v.17 no.6
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• pp.657-669
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• 2008

Lately extreme weather event is occurring because of the global warming. Especially disaster due to the extreme heat are increasing but the definition and the standard of the extreme heat is obscure until now. So this study established the extreme heat standard by using the number of daily deaths. As a result, considering the climate of the megalopolis using daily maximum heat index and daily maximum temperature was the best for the standard of the extreme heat. And it showed that extreme heat lasted for 2 days affects the death toll the most. The regional incidence of the extreme heat is highest at August and July, September and June is following.

• Atmosphere
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• v.26 no.4
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• pp.697-709
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• 2016

This study estimates and evaluates the extreme value of 30 m-resolution daily maximum and minimum temperatures over South Korea, using inverse distance weighting (IDW), parameter-elevation regression on independent slopes model (PRISM) and generalized extreme value (GEV) method. The three experiments are designed and performed to find the optimal estimation strategy to obtain extreme value. First experiment (EXP1) applies GEV firstly to automated surface observing system (ASOS) to estimate extreme value and then applies IDW to produce high-resolution extreme values. Second experiment (EXP2) is same as EXP1, but using PRISM to make the high-resolution extreme value instead of IDW. Third experiment (EXP3) firstly applies PRISM to ASOS to produce the high-resolution temperature field, and then applies GEV method to make high resolution extreme value data. By comparing these 3 experiments with extreme values obtained from observation data, we find that EXP3 shows the best performance to estimate extreme values of maximum and minimum temperatures, followed by EXP1 and EXP2. It is revealed that EXP1 and EXP2 have a limitation to estimate the extreme value at each grid point correctly because the extreme values of these experiments with 30 m-resolution are calculated from only 60 extreme values obtained from ASOS. On the other hand, the extreme value of EXP3 is similar to observation compared to others, since EXP3 produces 30m-resolution daily temperature through PRISM, and then applies GEV to that result at each grid point. This result indicates that the quality of statistically produced high-resolution extreme values which are estimated from observation data is different depending on the combination and procedure order of statistical methods.

• Journal of the Korean earth science society
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• v.39 no.4
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• pp.327-341
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• 2018

Extreme temperatures and precipitations are expected to be more frequently occurring due to the ongoing global warming over the Korean Peninsula. However, few studies have analyzed the synoptic weather patterns associated with extreme events in a warming world. Here, the atmospheric patterns related to future extreme events are first analyzed using the HadGEM3-RA regional climate model. Simulations showed that the variability of temperature and precipitation will increase in the future (2051-2100) compared to the present (1981-2005), accompanying the more frequent occurrence of extreme events. Warm advection from East China and lower latitudes, a stagnant anticyclone, and local foehn wind are responsible for the extreme temperature (daily T>$38^{\circ}C$) episodes in Korea. The extreme precipitation cases (>$500mm\;day^{-1}$) were mainly caused by mid-latitude cyclones approaching the Korean Peninsula, along with the enhanced Changma front by supplying water vapor into the East China Sea. These future synoptic-scale features are similar to those of present extreme events. Therefore, our results suggest that, in order to accurately understand future extreme events, we should consider not only the effects of anthropogenic greenhouse gases or aerosol increases, but also small-scale topographic conditions and the internal variations of climate systems.

• Environmental Engineering Research
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• v.16 no.3
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• pp.175-183
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• 2011

The climate of the $21^{st}$ century is likely to be significantly different from that of the 20th century because of human-induced climate change. An extreme weather event is defined as a climate phenomenon that has not been observed for the past 30 years and that may have occurred by climate change and climate variability. The abnormal climate change can induce natural disasters such as floods, droughts, typhoons, heavy snow, etc. How will the frequency and intensity of extreme weather events be affected by the global warming change in the $21^{st}$ century? This could be a quite interesting matter of concern to the hydrologists who will forecast the extreme weather events for preventing future natural disasters. In this study, we establish the extreme indices and analyze the trend of extreme weather events using extreme indices estimated from the observed data of 66 stations controlled by the Korea Meteorological Administration (KMA) in Korea. These analyses showed that spatially coherent and statistically significant changes in the extreme events of temperature and rainfall have occurred. Under the global climate change, Korea, unlike in the past, is now being affected by extreme weather events such as heavy rain and abnormal temperatures in addition to changes in climate phenomena.

• Journal of Environmental Science International
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• v.2 no.3
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• pp.235-243
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• 1993

Extreme environments and freak wave characteristics in the coastal waters of Korean Peninsula are analyzed using the observed wave data. Freak wave has been intensely emphasized as an important environmental force parameter in several recent research works. However, the mechanism and occurrence probability of freak wave are not clarified. The aims of this study we: to summarize the distribution of extreme environment for wind waves, and to find occurrence probability of freak wave in the coastal waters of Korean Peninsula. These extreme sea conditions are discussed by applying extreme value analysis method, and the statistic characteristics are summarized which can be used to the design and analysis of coastal structures. The mechanism and the occurrence probability of freak wave are also discussed in detail using wave parameters in considered with wave deformation in the coastal waters. Key Words : extreme wave, freak wave, extreme analysis, design wave, probability density.

• The Korean Journal of Applied Statistics
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• v.20 no.2
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• pp.291-311
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• 2007

With a series of unexpected huge losses in the financial markets around the world recently, especially in the insurance market with extreme loss cases such as catastrophes, there is an increasing demand for risk management for extreme loss exposures due to high unpredictability of those risks. For extreme risk management, to make a maximum use of the information concerning the tail part of a loss distribution, EVT(Extreme Value Theory) modelling nay be the best to analyze extreme values. The Extreme Value Theory is widely used in practice and, especially in financal markets, EVT modelling is getting popular to analyBe the effects of extreme risks. This study is to review the significance of the Extreme Value Theory in risk management and, focusing on analyzing insurer's risk capital, extreme risk is measured using the real fire loss data and insurer's specific amount of risk capital is figured out to buffer the extreme risk.

• Journal of the Korean Institute of Intelligent Systems
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• v.26 no.1
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• pp.87-92
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• 2016

In this paper, optimization technique such as particle swarm optimization was used to optimize the parameters of fuzzy Extreme Learning Machine. While the learning speed of conventional neural networks is very slow, that of Extreme Learning Machine is very fast. Fuzzy Extreme Learning Machine is composed of the Extreme Learning Machine with very fast learning speed and fuzzy logic which can represent the linguistic information of the field experts. The general sigmoid function is used for the activation function of Extreme Learning Machine. However, the activation function of Fuzzy Extreme Learning Machine is the membership function which is defined in the procedure of fuzzy C-Means clustering algorithm. We optimize the parameters of the membership functions by using optimization technique such as Particle Swarm Optimization. In order to validate the classification capability of the proposed classifier, we make several experiments with the various machine learning datas.

• Proceedings of the Korea Water Resources Association Conference
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• 2016.05a
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• pp.201-201
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• 2016

The asymptotic extreme value distributions of maxima are a natural choice when designing against future extreme events like flood peaks or wave heights, given a stationary time series. The generalized extreme value distribution (GEV) is often utilised in this context because it is seen as a convenient single expression for extreme event analysis. However, the GEV has a drawback because the location of the distribution bound relative to the data is a discontinuous function of the GEV shape parameter. That is, for annual maxima approximated by the Gumbel distribution, the data is also consistent with a GEV distribution with an upper bound (no lower bound) or a GEV distribution with a lower bound (no upper bound). A more consistent single extreme value expression for design purposes is proposed as the Weibull distribution of smallest extremes, as applied to transformed annual maxima. The Weibull distribution limit holds here for sufficiently large sample sizes, irrespective of the extreme value domain of attraction applicable to the untransformed maxima. The Gumbel, Type 2, and Type 3 extreme value distributions thus become redundant, together with the GEV, because in reality there is only a single asymptotic extreme value distribution required for design purposes - the Weibull distribution of minima as applied to transformed maxima. An illustrative synthetic example is given showing transformed maxima from the normal distribution approaching the Weibull limit much faster than the untransformed sample maxima approach the normal distribution Gumbel limit. Some New Zealand examples are given with the Weibull distribution being applied to reciprocal transformations of annual flood maxima, where the untransformed maxima follow apparently different extreme value distributions.

• Communications for Statistical Applications and Methods
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• v.3 no.2
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• pp.249-273
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• 1996

This paper reviews and develops several statistical models for extreme values, based on threshold methodology. Extreme values of a time series are modeled in terms of tails which are defined as truncated forms of original variables, and Markov property is imposed on the tails. Tails of the generalized extreme value distribution and a multivariate extreme value distributively, of the tails of the series. These models are then applied to real ozone data series collected in the Chicago area. A major concern is given to detecting any possible trend in the extreme values.

• 한국방재학회:학술대회논문집
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• 2007.02a
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• pp.323-326
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• 2007

Recently, occurrence frequency of natural disasters decrease but scale of damage increase remarkably by the Climate change due to global warming. Especially, extreme heat become more critical weather problem in the Korean Peninsula. But, we don't have exact threshold about extreme heat. Extreme heat does not classify into natural disaster. Therefore, we have compared death count of the natural disaster with the one of extreme heat at Seoul, Korea. As a result, the number of death by extreme heat don't smaller than one by the natural disasters and we knew extreme heat have also to consider as natural disaster.