• The Korean Journal of Applied Statistics
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• v.28 no.2
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• pp.137-149
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• 2015

Flood planning needs to recognize trends for extreme precipitation events. Especially, the r-year return level is a common measure for extreme events. In this paper, we present a nonstationary temporal model for precipitation return levels using a hierarchical Bayesian modeling. For intensity, we model annual maximum daily precipitation measured in Korea with a generalized extreme value (GEV). The temporal dependence among the return levels is incorporated to the model for GEV model parameters and a linear model with autoregressive error terms. We apply the proposed model to precipitation data collected from various stations in Korea from 1973 to 2011.

• Journal of Korea Water Resources Association
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• v.41 no.5
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• pp.527-544
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• 2008

Generalized Pareto distribution (GPD) is frequently applied in hydrologic extreme value analysis. The main objective of statistics of extremes is the prediction of rare events, and the primary problem has been the estimation of the threshold and the exceedances which were difficult without an accurate method of calculation. In this paper, to obtain the threshold or the exceedances, four methods were considered. For this comparison a GPD model was used to estimate parameters and quantiles for the seven durations (1, 2, 3, 6, 12, 18 and 24 hours) and the ten return periods (2, 3, 5, 10, 20, 30, 50, 70, 80 and 100 years). The parameters and quantiles of the three-parameter generalized Pareto distribution were estimated with three methods (MOM, ML and PWM). To estimate the degree of fit, three methods (K-S, CVM and A-D test) were performed and the relative root mean squared error (RRMSE) was calculated for a Monte Carlo generated sample. Then the performance of these methods were compared with the objective of identifying the best method from their number.

• Proceedings of the Korea Water Resources Association Conference
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• 2018.05a
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• pp.154-154
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• 2018

To interpret the climate projections for the future as well as present, recognition of the consequences of the climate internal variability and quantification its uncertainty play a vital role. The Korean Peninsula belongs to the Far East Asian Monsoon region and its rainfall characteristics are very complex from time and space perspective. Its internal variability is expected to be large, but this variability has not been completely investigated to date especially using models of high temporal resolutions. Due to coarse spatial and temporal resolutions of General Circulation Models (GCM) projections, several studies adopted dynamic and statistical downscaling approaches to infer meterological forcing from climate change projections at local spatial scales and fine temporal resolutions. In this study, stochastic downscaling methodology was adopted to downscale daily GCM resolutions to hourly time scale using an hourly weather generator, the Advanced WEather GENerator (AWE-GEN). After extracting factors of change from the GCM realizations, these were applied to the climatic statistics inferred from historical observations to re-evaluate parameters of the weather generator. The re-parameterized generator yields hourly time series which can be considered to be representative of future climate conditions. Further, 30 ensemble members of hourly precipitation were generated for each selected station to quantify uncertainty. Spatial map was generated to visualize as separated zones formed through K-means cluster algorithm which region is more inconsistent as compared to the climatological norm or in which region the probability of occurrence of the extremes event is high. The results showed that the stations located near the coastal regions are more uncertain as compared to inland regions. Such information will be ultimately helpful for planning future adaptation and mitigation measures against extreme events.

• Journal of Korea Water Resources Association
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• v.50 no.1
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• pp.17-28
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• 2017

A recent increase in extreme weather events and flash floods associated with the enhanced climate variability results in an increase in climate-related disasters. For these reasons, various studies based on a high resolution weather radar system have been carried out. The weather radar can provide estimates of precipitation in real-time over a wide area, while ground-based rain gauges only provides a point estimate in space. Weather radar is thus capable of identifying changes in rainfall structure as it moves through an ungauged basin. However, the advantage of the weather radar rainfall estimates has been limited by a variety of sources of uncertainty in the radar reflectivity process, including systematic and random errors. In this study, we developed an ensemble radar rainfall estimation scheme using the multivariate copula method. The results presented in this study confirmed that the proposed ensemble technique can effectively reproduce the rainfall statistics such as mean, variance and skewness (more importantly the extremes) as well as the spatio-temporal structure of rainfall fields.

• The Korean Journal of Applied Statistics
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• v.25 no.1
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• pp.215-223
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• 2012

Response surface methodology(RSM) is a very useful statistical techniques for improving and optimizing the product process. By this reason, RSM has been utilized extensively in the industrial world, particularly in the circumstances where several product variables potentially influence some quality characteristic of the product. In order to estimate the optimal condition of product variables, an experiment is being conducted defining appropriate experimental region. However, this experimental region can vary with the experimental circumstances and choice of a researcher. Response surface designs can be classified, according to the shape of the experimental region, into spherical and cuboidal. In the spherical case, the design is either rotatable or very near-rotatable. The central composite design(CCD)s widely used in RSM is an example of 5-level and spherical design. The cuboidal CCDs(CCDs with ${\alpha}=1$) is appropriate when an experimental region is cuboidal but this design dose not satisfy the rotatability as it is not spherical. Practically, a 3-level spherical design is often required in the industrial world where various level of experiments are not available. Box-Behnken design(BBD)s are a most popular 3-level spherical designs for fitting second-order response surfaces and satisfy the rotatability but the experimental region does not vary with the number of variables. The new experimental design with expanded experimental region can be considered if the predicting response at the extremes are interested. This paper proposes a new 3-level spherical RSM which are constructed to expand the experimental region together with number of product variables.

• Journal of Korea Water Resources Association
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• v.47 no.9
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• pp.825-838
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• 2014

The stationary Markov chain model has been widely used as a daily rainfall simulation model. A main assumption of the stationary Markov model is that statistical characteristics do not change over time and do not have any trends. In other words, the stationary Markov chain model for daily rainfall simulation essentially can not incorporate any changes in mean or variance into the model. Here we develop a Non-stationary hidden Markov chain model (NHMM) based stochastic downscaling scheme for simulating the daily rainfall sequences, using general circulation models (GCMs) as inputs. It has been acknowledged that GCMs perform well with respect to annual and seasonal variation at large spatial scale and they stand as one of the primary sources for obtaining forecasts. The proposed model is applied to daily rainfall series at three stations in Nakdong watershed. The model showed a better performance in reproducing most of the statistics associated with daily and seasonal rainfall. In particular, the proposed model provided a significant improvement in reproducing the extremes. It was confirmed that the proposed model could be used as a downscaling model for the purpose of generating plausible daily rainfall scenarios if elaborate GCM forecasts can used as a predictor. Also, the proposed NHMM model can be applied to climate change studies if GCM based climate change scenarios are used as inputs.

• Journal of Korean Academy of Nursing
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• v.1 no.1
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• pp.85-94
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• 1970

This is a study of 21 tuberculosis patients receiving medical treatment at the Public Health Center in Kyongi Do, Pu Chun Gun and at the General Hospital. The results cover the findings of the period from May, 1969 to November 1970. The information obtained is based on personal interviews with the patients, and symptomatic diagnosis made from observations. The following statistics when not equalling 100% contain only the responses of the two extremes in each case. The findings of the research are as follows: 1. 52.3% of the patients in the study are males and 47.7% are females. 28.6% of the subjects are between 20 and 29 years of age and an equal percent are between 30 and 39 years. 2. 47.5% of the subjects had graduated from primary school, while only 4.8% had graduated from high school. 3. 57.1% of the patients said they had no religions beliefs, while 4.8% professed to being Buddhists or believing in superstition. 4. 47.3% of the people said they were unemployed, while 4.8% classified themselves as labourers. 5. In response to how tuberculosis was first detected in their respective cases, 52.6% became aware of their disease through X-ray results, while 4.8% were discovered to have tuberculosis when being treated for other diseases at the hospital. 6. When asked how many of the patients knew anything about their disease when treated, 57.1% knew nothing about tuberculosis when they received treatment, while 42.9% had some knowledge of the disease. 7. Of those who knew something about tuberculosis, 61.9% learned about from doctors and nurses, while 4.8% learned from other people. 8. 57.1% of the patients knew that tuberculosis is a communicable disease, while 42.9% did not know. 9. 52.4% of the patients did not know the cause of tuberculosis while 4.9% believed the disease was caused by a curse. 10. When asked about the extent of treatment, 52.4% responded that they had undergone continuous treatment, while 4.8% had not received treatment. 11.The reasons given for not continuing treatment were the following: economic factors 55.6%; side reactions to the treatment, lack of knowledge of how to get treatment, of the need for treatment, or of the positive effects of treatment 11.1%. 12. 61.9% of the subjects usually took the medical treatment at home, 9.5% took it in the mountains or at the beach. 13. 42.9% of the patients received drugs for treatment at the local public health center, while 4.8% received them at the hospital 14. 33.3% of the patients received P.A.S＋I.N.H.＋S.M. for treatment of tuberculosis, while 4.8% received P.A.S.＋S.M.. and some secondary drug. 15. Of the patients who took some extra medicine for tuberculosis, 38.1% took a Chinese drug, while 9.5% took herb medicine. 16. 38.1% of the patients had continued treatment for three years, 4.8% had interrupted the treatment. 17. When asked about the development of the disease after treatment, the patients gave the following information: after one month, 90.5% thought the treatment helped, while 9.5% weren't sure; after one year, 55.6% thought it was good, while 5.5% thought it was not; after three years, 63.6% had a very bad condition. while 4.8% didn't know. 18. 61.9% of the patients were unconcerned about covering their mouths when they coughed, while 38.1% covered their mouths. 19. 57.2% were unconcerned they spit, while 23.8% spit into a waste basket. 20. 66.7% were unconcerned about sterilizing tableware, while 9.5% handled it separately. 21. 66.7% were unconcerned about ventilating their room, while 9.5% ventilated the room twice a week.