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Jeong-Soo Park
Department of Mathematics and Statistics, Chonnam National University, Gwangju 61186, Korea

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Journal article
Published: 16 August 2021 in Atmosphere
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Scientists who want to know future climate can use multimodel ensemble (MME) methods that combine projections from individual simulation models. To predict the future changes of extreme rainfall in Iran, we examined the observations and 24 models of the Coupled Model Inter-Comparison Project Phase 6 (CMIP6) over the Middle East. We applied generalized extreme value (GEV) distribution to series of annual maximum daily precipitation (AMP1) data obtained from both of models and the observations. We also employed multivariate bias-correction under three shared socioeconomic pathway (SSP) scenarios (namely, SSP2-4.5, SSP3-7.0, and SSP5-8.5). We used a model averaging method that takes both performance and independence of model into account, which is called PI-weighting. Return levels for 20 and 50 years, as well as the return periods of the AMP1 relative to the reference years (1971–2014), were estimated for three future periods. These are period 1 (2021–2050), period 2 (2046–2075), and period 3 (2071–2100). From this study, we predict that over Iran the relative increases of 20-year return level of the AMP1 in the spatial median from the past observations to the year 2100 will be approximately 15.6% in the SSP2-4.5, 23.2% in the SSP3-7.0, and 28.7% in the SSP5-8.5 scenarios, respectively. We also realized that a 1-in-20 year (or 1-in-50 year) AMP1 observed in the reference years in Iran will likely become a 1-in-12 (1-in-26) year, a 1-in-10 (1-in-22) year, and a 1-in-9 (1-in-20) year event by 2100 under the SSP2-4.5, SSP3-7.0, and SSP5-8.5 scenarios, respectively. We project that heavy rainfall will be more prominent in the western and southwestern parts of Iran.

ACS Style

Juyoung Hong; Khadijeh Javan; Yonggwan Shin; Jeong-Soo Park. Future Projections and Uncertainty Assessment of Precipitation Extremes in Iran from the CMIP6 Ensemble. Atmosphere 2021, 12, 1052 .

AMA Style

Juyoung Hong, Khadijeh Javan, Yonggwan Shin, Jeong-Soo Park. Future Projections and Uncertainty Assessment of Precipitation Extremes in Iran from the CMIP6 Ensemble. Atmosphere. 2021; 12 (8):1052.

Chicago/Turabian Style

Juyoung Hong; Khadijeh Javan; Yonggwan Shin; Jeong-Soo Park. 2021. "Future Projections and Uncertainty Assessment of Precipitation Extremes in Iran from the CMIP6 Ensemble." Atmosphere 12, no. 8: 1052.

Journal article
Published: 22 April 2021 in Atmosphere
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In recent decades, extremely cold winters have occurred repeatedly throughout the Northern Hemisphere, including the Korean Peninsula (hereafter, Korea). Typically, cold winter temperatures in Korea can be linked to the strengthening of the Siberian High (SH). Although previous studies have investigated the typical relationship between the SH and winter temperatures in Korea, this study uniquely focused on a change in the relationship, which reflects the influence of the Arctic Oscillation (AO) and El Niño–Southern Oscillation (ENSO). A significant change in the 15-year moving correlation between the SH and the surface air temperature average in Korea (K-tas) was observed in January. The correlation changed from −0.80 during 1971–1990 to −0.16 during 1991–2010. The mean sea-level pressure pattern regressed with the temperature, and a singular value decomposition analysis that incorporated the temperature and pressure supports that the negative high correlation during 1971–1990 was largely affected by AO. This connection with AO is substantiated by empirical orthogonal function (EOF) analysis with an upper-level geopotential height at 300 hPa. In the second mode of the EOF, the temperature and pressure patterns were primarily affected by ENSO during 1991–2010. Consequently, the interdecadal change in correlation between K-tas and the SH in January can be attributed to the dominant effect of AO from 1971–1990 and of ENSO from 1991–2010. Our results suggest that the relative importance of these factors in terms of the January climate in Korea has changed on a multidecadal scale.

ACS Style

Jae-Seung Yoon; Il-Ung Chung; Ho-Jeong Shin; Kunmn-Yeong Jang; Maeng-Ki Kim; Jeong-Soo Park; Doo-Sun Park; Kyung-On Boo; Young-Hwa Byun; Hyun-Min Sung. Non-Stationary Effects of the Arctic Oscillation and El Niño–Southern Oscillation on January Temperatures in Korea. Atmosphere 2021, 12, 538 .

AMA Style

Jae-Seung Yoon, Il-Ung Chung, Ho-Jeong Shin, Kunmn-Yeong Jang, Maeng-Ki Kim, Jeong-Soo Park, Doo-Sun Park, Kyung-On Boo, Young-Hwa Byun, Hyun-Min Sung. Non-Stationary Effects of the Arctic Oscillation and El Niño–Southern Oscillation on January Temperatures in Korea. Atmosphere. 2021; 12 (5):538.

Chicago/Turabian Style

Jae-Seung Yoon; Il-Ung Chung; Ho-Jeong Shin; Kunmn-Yeong Jang; Maeng-Ki Kim; Jeong-Soo Park; Doo-Sun Park; Kyung-On Boo; Young-Hwa Byun; Hyun-Min Sung. 2021. "Non-Stationary Effects of the Arctic Oscillation and El Niño–Southern Oscillation on January Temperatures in Korea." Atmosphere 12, no. 5: 538.

Research article
Published: 12 January 2021 in Journal of Applied Statistics
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The four-parameter kappa distribution (K4D) is a generalized form of some commonly used distributions such as generalized logistic, generalized Pareto, generalized Gumbel, and generalized extreme value (GEV) distributions. Owing to its flexibility, the K4D is widely applied in modeling in several fields such as hydrology and climatic change. For the estimation of the four parameters, the maximum likelihood approach and the method of L-moments are usually employed. The L-moment estimator (LME) method works well for some parameter spaces, with up to a moderate sample size, but it is sometimes not feasible in terms of computing the appropriate estimates. Meanwhile, using the maximum likelihood estimator (MLE) with small sample sizes shows substantially poor performance in terms of a large variance of the estimator. We therefore propose a maximum penalized likelihood estimation (MPLE) of K4D by adjusting the existing penalty functions that restrict the parameter space. Eighteen combinations of penalties for two shape parameters are considered and compared. The MPLE retains modeling flexibility and large sample optimality while also improving on small sample properties. The properties of the proposed estimator are verified through a Monte Carlo simulation, and an application case is demonstrated taking Thailand’s annual maximum temperature data.

ACS Style

Nipada Papukdee; Jeong-Soo Park; Piyapatr Busababodhin. Penalized likelihood approach for the four-parameter kappa distribution. Journal of Applied Statistics 2021, 1 -15.

AMA Style

Nipada Papukdee, Jeong-Soo Park, Piyapatr Busababodhin. Penalized likelihood approach for the four-parameter kappa distribution. Journal of Applied Statistics. 2021; ():1-15.

Chicago/Turabian Style

Nipada Papukdee; Jeong-Soo Park; Piyapatr Busababodhin. 2021. "Penalized likelihood approach for the four-parameter kappa distribution." Journal of Applied Statistics , no. : 1-15.

Journal article
Published: 11 January 2021 in Atmosphere
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Scientists occasionally predict projected changes in extreme climate using multi-model ensemble methods that combine predictions from individual simulation models. To predict future changes in precipitation extremes in the Korean peninsula, we examined the observed data and 21 models of the Coupled Model Inter-Comparison Project Phase 6 (CMIP6) over East Asia. We applied generalized extreme value distribution (GEVD) to a series of annual maximum daily precipitation (AMP1) data. Multivariate bias-corrected simulation data under three shared socioeconomic pathway (SSP) scenarios—namely, SSP2-4.5, SSP3-7.0, and SSP5-8.5—were used. We employed a model weighting method that accounts for both performance and independence (PI-weighting). In calculating the PI-weights, two shape parameters should be determined, but usually, a perfect model test method requires a considerable amount of computing time. To address this problem, we suggest simple ways for selecting two shape parameters based on the chi-square statistic and entropy. Variance decomposition was applied to quantify the uncertainty of projecting the future AMP1. Return levels spanning over 20 and 50 years, as well as the return periods relative to the reference years (1973–2010), were estimated for three overlapping periods in the future, namely, period 1 (2021–2050), period 2 (2046–2075), and period 3 (2071–2100). From these analyses, we estimated that the relative increases in the observations for the spatial median 20-year return level will be approximately 18.4% in the SSP2-4.5, 25.9% in the SSP3-7.0, and 41.7% in the SSP5-8.5 scenarios, respectively, by the end of the 21st century. We predict that severe rainfall will be more prominent in the southern and central parts of the Korean peninsula.

ACS Style

Yonggwan Shin; Yire Shin; Juyoung Hong; Maeng-Ki Kim; Young-Hwa Byun; Kyung-On Boo; Il-Ung Chung; Doo-Sun Park; Jeong-Soo Park. Future Projections and Uncertainty Assessment of Precipitation Extremes in the Korean Peninsula from the CMIP6 Ensemble with a Statistical Framework. Atmosphere 2021, 12, 97 .

AMA Style

Yonggwan Shin, Yire Shin, Juyoung Hong, Maeng-Ki Kim, Young-Hwa Byun, Kyung-On Boo, Il-Ung Chung, Doo-Sun Park, Jeong-Soo Park. Future Projections and Uncertainty Assessment of Precipitation Extremes in the Korean Peninsula from the CMIP6 Ensemble with a Statistical Framework. Atmosphere. 2021; 12 (1):97.

Chicago/Turabian Style

Yonggwan Shin; Yire Shin; Juyoung Hong; Maeng-Ki Kim; Young-Hwa Byun; Kyung-On Boo; Il-Ung Chung; Doo-Sun Park; Jeong-Soo Park. 2021. "Future Projections and Uncertainty Assessment of Precipitation Extremes in the Korean Peninsula from the CMIP6 Ensemble with a Statistical Framework." Atmosphere 12, no. 1: 97.

Journal article
Published: 31 December 2020 in Entropy
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The approximated non-linear least squares (ALS) tunes or calibrates the computer model by minimizing the squared error between the computer output and real observations by using an emulator such as a Gaussian process (GP) model. A potential defect of the ALS method is that the emulator is constructed once and it is no longer re-built. An iterative method is proposed in this study to address this difficulty. In the proposed method, the tuning parameters of the simulation model are calculated by the conditional expectation (E-step), whereas the GP parameters are updated by the maximum likelihood estimation (M-step). These EM-steps are alternately repeated until convergence by using both computer and experimental data. For comparative purposes, another iterative method (the max-min algorithm) and a likelihood-based method are considered. Five toy models are tested for a comparative analysis of these methods. According to the toy model study, both the variance and bias of the estimates obtained from the proposed EM algorithm are smaller than those from the existing calibration methods. Finally, the application to a nuclear fusion simulator is demonstrated.

ACS Style

Yun Seo; Jeong-Soo Park. Expectation-Maximization Algorithm for the Calibration of Complex Simulator Using a Gaussian Process Emulator. Entropy 2020, 23, 53 .

AMA Style

Yun Seo, Jeong-Soo Park. Expectation-Maximization Algorithm for the Calibration of Complex Simulator Using a Gaussian Process Emulator. Entropy. 2020; 23 (1):53.

Chicago/Turabian Style

Yun Seo; Jeong-Soo Park. 2020. "Expectation-Maximization Algorithm for the Calibration of Complex Simulator Using a Gaussian Process Emulator." Entropy 23, no. 1: 53.

Journal article
Published: 07 December 2020 in Mathematics
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Maximum likelihood estimation (MLE) of the four-parameter kappa distribution (K4D) is known to be occasionally unstable for small sample sizes and to be very sensitive to outliers. To overcome this problem, this study proposes Bayesian analysis of the K4D. Bayesian estimators are obtained by virtue of a posterior distribution using the random walk Metropolis–Hastings algorithm. Five different priors are considered. The properties of the Bayesian estimators are verified in a simulation study. The empirical Bayesian method turns out to work well. Our approach is then compared to the MLE and the method of the L-moments estimator by calculating the 20-year return level, the confidence interval, and various goodness-of-fit measures. It is also compared to modeling using the generalized extreme value distribution. We illustrate the usefulness of our approach in an application to the annual maximum wind speeds in Udon Thani, Thailand, and to the annual maximum sea-levels in Fremantle, Australia. In the latter example, non-stationarity is modeled through a trend in time on the location parameter. We conclude that Bayesian inference for K4D may be substantially useful for modeling extreme events.

ACS Style

Palakorn Seenoi; Piyapatr Busababodhin; Jeong-Soo Park. Bayesian Inference in Extremes Using the Four-Parameter Kappa Distribution. Mathematics 2020, 8, 2180 .

AMA Style

Palakorn Seenoi, Piyapatr Busababodhin, Jeong-Soo Park. Bayesian Inference in Extremes Using the Four-Parameter Kappa Distribution. Mathematics. 2020; 8 (12):2180.

Chicago/Turabian Style

Palakorn Seenoi; Piyapatr Busababodhin; Jeong-Soo Park. 2020. "Bayesian Inference in Extremes Using the Four-Parameter Kappa Distribution." Mathematics 8, no. 12: 2180.

Journal article
Published: 04 September 2020 in Entropy
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The approximated nonlinear least squares (ALS) method has been used for the estimation of unknown parameters in the complex computer code which is very time-consuming to execute. The ALS calibrates or tunes the computer code by minimizing the squared difference between real observations and computer output using a surrogate such as a Gaussian process model. When the differences (residuals) are correlated or heteroscedastic, the ALS may result in a distorted code tuning with a large variance of estimation. Another potential drawback of the ALS is that it does not take into account the uncertainty in the approximation of the computer model by a surrogate. To address these problems, we propose a generalized ALS (GALS) by constructing the covariance matrix of residuals. The inverse of the covariance matrix is multiplied to the residuals, and it is minimized with respect to the tuning parameters. In addition, we consider an iterative version for the GALS, which is called as the max-minG algorithm. In this algorithm, the parameters are re-estimated and updated by the maximum likelihood estimation and the GALS, by using both computer and experimental data repeatedly until convergence. Moreover, the iteratively re-weighted ALS method (IRWALS) was considered for a comparison purpose. Five test functions in different conditions are examined for a comparative analysis of the four methods. Based on the test function study, we find that both the bias and variance of estimates obtained from the proposed methods (the GALS and the max-minG) are smaller than those from the ALS and the IRWALS methods. Especially, the max-minG works better than others including the GALS for the relatively complex test functions. Lastly, an application to a nuclear fusion simulator is illustrated and it is shown that the abnormal pattern of residuals in the ALS can be resolved by the proposed methods.

ACS Style

Youngsaeng Lee; Jeong-Soo Park. Generalized Nonlinear Least Squares Method for the Calibration of Complex Computer Code Using a Gaussian Process Surrogate. Entropy 2020, 22, 985 .

AMA Style

Youngsaeng Lee, Jeong-Soo Park. Generalized Nonlinear Least Squares Method for the Calibration of Complex Computer Code Using a Gaussian Process Surrogate. Entropy. 2020; 22 (9):985.

Chicago/Turabian Style

Youngsaeng Lee; Jeong-Soo Park. 2020. "Generalized Nonlinear Least Squares Method for the Calibration of Complex Computer Code Using a Gaussian Process Surrogate." Entropy 22, no. 9: 985.

Article
Published: 27 August 2020
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Projected changes in extreme climate are occasionally predicted through multi-model ensemble methods using a weighted averaging that combines predictions from individual simulation models. To predict future changes in precipitation extremes, observed data and 21 of the Coupled Model Inter-comparison Project Phase 6 (CMIP6) models are examined for 46 grids over the Korean peninsula. We apply the generalized extreme value distribution (GEVD) to the series of annual maximum daily precipitation (AMP1) data. Simulation data under three shared socioeconomic pathway (SSP) scenarios, namely, SSP2-4.5, SSP3-7.0, and SSP5-8.5, are used. A multivariate bias correction technique that considers the spatial dependency between nearby grids is applied to these simulation data. In addition, a model weighting approach that accounts for both performance and independence (PI-weighting) is employed. In this study, we estimate the future changes in precipitation extremes in the Korean peninsula using the multiple CMIP6 models and PI-weighting method. In applying the PI-weighting, we suggest simple ways for selecting two shape 1 parameters based on the chi-square statistic and entropy. Variance decomposition with the interaction term between the CMIP6 model and the SSP scenario is applied to quantify the uncertainty of projecting the future AMP1. Return levels spanning over 20 and 50 years, as well as the return periods relative to the reference years (1973-2014), are estimated for three future overlapping periods, namely, period 1 (2021-2050), period 2 (2046-2075), and period 3 (2071-2100). From these analyses, we estimate that relative increases in the observations for the spatial median 20-year (50-year) return level will be approximately 16.4% (16.5%) in the SSP2-4.5, 22.9% (22.8%) in the SSP3-7.0, and 37.6% (35.4%) in the SSP5-8.5 scenarios, respectively, by the end of the 21st century. The expected frequency of the reoccurring years, particularly for the AMP1 from 150 mm to 300 mm under the SSP5-8.5 scenario, are projected to increase by approximately 1.4 times that of the past 30 years for period 1, approximately 2.3 times that for period 2, and approximately 3.5 times that for period 3. From the analysis based on latitude, severe rainfall was found to be more prominent in the southern and central parts of the Korean peninsula.

ACS Style

Jeong-Soo ParkiD; Yonggwan ShiniD; Yire Shin; Juyoung Hong; Maeong-Ki KimiD; Young-Hwa Byun; Kyung-On Boo; Il-Ung Chung; Doo-Sun R Park. Future projections and uncertainty assessment of precipitation extremes in the Korean peninsula from the CMIP6 ensemble. 2020, 1 .

AMA Style

Jeong-Soo ParkiD, Yonggwan ShiniD, Yire Shin, Juyoung Hong, Maeong-Ki KimiD, Young-Hwa Byun, Kyung-On Boo, Il-Ung Chung, Doo-Sun R Park. Future projections and uncertainty assessment of precipitation extremes in the Korean peninsula from the CMIP6 ensemble. . 2020; ():1.

Chicago/Turabian Style

Jeong-Soo ParkiD; Yonggwan ShiniD; Yire Shin; Juyoung Hong; Maeong-Ki KimiD; Young-Hwa Byun; Kyung-On Boo; Il-Ung Chung; Doo-Sun R Park. 2020. "Future projections and uncertainty assessment of precipitation extremes in the Korean peninsula from the CMIP6 ensemble." , no. : 1.

Journal article
Published: 23 July 2020 in Atmosphere
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A model weighting scheme is important in multi-model climate ensembles for projecting future changes. The climate model output typically needs to be bias corrected before it can be used. When a bias-correction (BC) is applied, equal model weights are usually derived because some BC methods cause the observations and historical simulation to match perfectly. This equal weighting is sometimes criticized because it does not take into account the model performance. Unequal weights reflecting model performance may be obtained from raw data before BC is applied. However, we have observed that certain models produce excessively high weights, while the weights generated in all other models are extremely low. This phenomenon may be partly due to the fact that some models are more fit or calibrated to the observations for a given applications. To address these problems, we consider, in this study, a hybrid weighting scheme including both equal and unequal weights. The proposed approach applies an “imperfect” correction to the historical data in computing their weights, while it applies ordinary BC to the future data in computing the ensemble prediction. We employ a quantile mapping method for the BC and a Bayesian model averaging for performance-based weighting. Furthermore, techniques for selecting the optimal correction rate based on the chi-square test statistic and the continuous ranked probability score are examined. Comparisons with ordinary ensembles are provided using a perfect model test. The usefulness of the proposed method is illustrated using the annual maximum daily precipitation as observed in the Korean peninsula and simulated by 21 models from the Coupled Model Intercomparison Project Phase 6.

ACS Style

Yonggwan Shin; Youngsaeng Lee; Jeong-Soo Park. A Weighting Scheme in A Multi-Model Ensemble for Bias-Corrected Climate Simulation. Atmosphere 2020, 11, 775 .

AMA Style

Yonggwan Shin, Youngsaeng Lee, Jeong-Soo Park. A Weighting Scheme in A Multi-Model Ensemble for Bias-Corrected Climate Simulation. Atmosphere. 2020; 11 (8):775.

Chicago/Turabian Style

Yonggwan Shin; Youngsaeng Lee; Jeong-Soo Park. 2020. "A Weighting Scheme in A Multi-Model Ensemble for Bias-Corrected Climate Simulation." Atmosphere 11, no. 8: 775.

Original paper
Published: 07 April 2020 in Theoretical and Applied Climatology
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We analyze annual extremes of daily maximum and minimum surface air temperature and of daily rainfall in East Asia and the Korean peninsula. This study made intensive use of the simulation data available from the CMIP5 (Coupled Model Intercomparison Project Phase 5) multimodels in historical and future experiments up to the year 2100, employing three different radiative forcings: RCP2.6, RCP4.5, and RCP8.5 (representative concentration pathways). Several reanalysis datasets are used to compare and evaluate the simulated climate extremes in the late twentieth century. We estimate the future changes in precipitation and temperature extremes in East Asia and Korea, and compare them to the global result, for the reference period 1986–2005. The rising rate of future cold extremes over East Asia and Korea is faster than that of warm extremes. This phenomenon appears more distinctly in Korea as a local scale, indicating more sensitivity of the Korean peninsula to global warming. The increase of the 20-year return level of maximum precipitation in the CMIP5 over East Asia by the end of twenty-first century is about 7% in the RCP2.6, 15% in the RCP4.5, and 35% in the RCP8.5 experiments, which exceed the corresponding global values. We also estimate the changes in precipitation extremes across East Asia as a function of the annual mean temperature variation at the same location. The CMIP5 sensitivity in maximum precipitation across East Asia is 5.5%/∘C, which is lower than the global figure (5.8%/∘C). The sensitivity for the Korean peninsula is 7.38%/∘C, indicating the strong impact of global warming to Korea. The results will be important in mitigating the detrimental effects of variations of climatic extremes and in improving the regional strategy for water resource and eco-environmental management, particularly for such areas in East Asia under significant changes in temperature and rainfall extremes.

ACS Style

Youngsaeng Lee; Jayeong Paek; Jeong-Soo Park; Kyung-On Boo. Changes in temperature and rainfall extremes across East Asia in the CMIP5 ensemble. Theoretical and Applied Climatology 2020, 141, 143 -155.

AMA Style

Youngsaeng Lee, Jayeong Paek, Jeong-Soo Park, Kyung-On Boo. Changes in temperature and rainfall extremes across East Asia in the CMIP5 ensemble. Theoretical and Applied Climatology. 2020; 141 (1-2):143-155.

Chicago/Turabian Style

Youngsaeng Lee; Jayeong Paek; Jeong-Soo Park; Kyung-On Boo. 2020. "Changes in temperature and rainfall extremes across East Asia in the CMIP5 ensemble." Theoretical and Applied Climatology 141, no. 1-2: 143-155.

Articles
Published: 20 February 2020 in Communications in Statistics - Simulation and Computation
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Tuning a complex simulation code refers to the process of improving the agreement of a code calculation with respect to a set of experimental data by adjusting parameters implemented in the code. This process belongs to the class of inverse problems or model calibration. For this problem, the approximated nonlinear least squares (ANLS) method based on a Gaussian process (GP) metamodel has been employed by some researchers. A potential drawback of the ANLS method is that the metamodel is built only once and not updated thereafter. To address this difficulty, we propose an iterative algorithm in this study. In the proposed algorithm, the parameters of the simulation code and GP metamodel are alternatively re-estimated and updated by maximum likelihood estimation and the ANLS method. This algorithm uses both computer and experimental data repeatedly until convergence. A study using toy-models including inexact computer code with bias terms reveals that the proposed algorithm performs better than the ANLS method and the conditional-likelihood-based approach. Finally, an application to a nuclear fusion simulation code is illustrated.

ACS Style

Yun Am Seo; Youngsaeng Lee; Jeong-Soo Park. Iterative method for tuning complex simulation code. Communications in Statistics - Simulation and Computation 2020, 1 -18.

AMA Style

Yun Am Seo, Youngsaeng Lee, Jeong-Soo Park. Iterative method for tuning complex simulation code. Communications in Statistics - Simulation and Computation. 2020; ():1-18.

Chicago/Turabian Style

Yun Am Seo; Youngsaeng Lee; Jeong-Soo Park. 2020. "Iterative method for tuning complex simulation code." Communications in Statistics - Simulation and Computation , no. : 1-18.

Research article
Published: 19 January 2020 in Atmospheric Science Letters
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Projections of changes in extreme climate are sometimes predicted by multimodel ensemble methods that combine forecasts from individual simulation models using weighted averaging. One method to assign weight to each model is the Bayesian model averaging (BMA) in which posterior probability is used. For the cases of extreme climate, the generalized extreme value distribution (GEVD) is typically used. We applied the approach of GEV‐embedded BMA to a series of 35 years of the annual maximum daily precipitation data (both historical data and data gathered from simulation experiments for future periods) over the Korean peninsula as simulated by the models in the Coupled Model Intercomparison Project Phase 5 (CMIP5). Simulation data under two Representative Concentration Pathway (RCP) scenarios, namely RCP4.5 and RCP8.5, were used. Observed data and 17 CMIP5 models for 12 gird cells in Korea have been examined to predict future changes in precipitation extremes. A simple regional frequency analysis of pooling observations from three stations in each cell was employed to reduce the estimation variance and local fluctuations. A bias correction technique using the regression‐type transfer function was applied to these simulation data. Return levels spanning over 20 and 50 years, as well as the return periods relative to the reference years (1971–2005), were estimated for two future periods, namely Period 1 (2021–2050) and Period 2 (2066–2095). From these analyses, relative increase observed in the spatially averaged 20‐year (50‐year) return level was approximately 23% (16%) and 45% (36%) in the RCP4.5 and RCP8.5 experiments, respectively, by the end of the 21st century. We concluded that extreme rainfalls will likely occur two times and four times more frequently in the RCP4.5 and RCP8.5 scenarios, respectively, as compared to in the reference years by the end of the 21st century.

ACS Style

Youngsaeng Lee; Younggwan Shin; Kyung‐On Boo; Jeong‐Soo Park. Future projections and uncertainty assessment of precipitation extremes in the Korean peninsula from the CMIP5 ensemble. Atmospheric Science Letters 2020, 21, 1 .

AMA Style

Youngsaeng Lee, Younggwan Shin, Kyung‐On Boo, Jeong‐Soo Park. Future projections and uncertainty assessment of precipitation extremes in the Korean peninsula from the CMIP5 ensemble. Atmospheric Science Letters. 2020; 21 (2):1.

Chicago/Turabian Style

Youngsaeng Lee; Younggwan Shin; Kyung‐On Boo; Jeong‐Soo Park. 2020. "Future projections and uncertainty assessment of precipitation extremes in the Korean peninsula from the CMIP5 ensemble." Atmospheric Science Letters 21, no. 2: 1.

Original paper
Published: 11 November 2018 in Stochastic Environmental Research and Risk Assessment
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Projections of changes in extreme climate are sometimes predicted by using multi-model ensemble methods such as Bayesian model averaging (BMA) embedded with the generalized extreme value (GEV) distribution. BMA is a popular method for combining the forecasts of individual simulation models by weighted averaging and characterizing the uncertainty induced by simulating the model structure. This method is referred to as the GEV–embedded BMA. It is, however, based on a point-wise analysis of extreme events, which means it overlooks the spatial dependency between nearby grid cells. Instead of a point-wise model, a spatial extreme model such as the max-stable process (MSP) is often employed to improve precision by considering spatial dependency. We propose an approach that integrates the MSP into BMA, which is referred to as the MSP–BMA herein. The superiority of the proposed method over the GEV–embedded BMA is demonstrated by using extreme rainfall intensity data on the Korean peninsula from Coupled Model Intercomparison Project Phase 5 (CMIP5) multi-models. The reanalysis data called Asian precipitation highly-resolved observational data integration towards evaluation, v1101 and 17 CMIP5 models are examined for 10 grid boxes in Korea. In this example, the MSP–BMA achieves a variance reduction over the GEV–embedded BMA. The bias inflation by MSP–BMA over the GEV–embedded BMA is also discussed. A by-product technical advantage of the MSP–BMA is that tedious ‘regridding’ is not required before and after the analysis while it should be done for the GEV–embedded BMA.

ACS Style

Yonggwan Shin; Youngsaeng Lee; Juntae Choi; Jeong-Soo Park. Integration of max-stable processes and Bayesian model averaging to predict extreme climatic events in multi-model ensembles. Stochastic Environmental Research and Risk Assessment 2018, 33, 47 -57.

AMA Style

Yonggwan Shin, Youngsaeng Lee, Juntae Choi, Jeong-Soo Park. Integration of max-stable processes and Bayesian model averaging to predict extreme climatic events in multi-model ensembles. Stochastic Environmental Research and Risk Assessment. 2018; 33 (1):47-57.

Chicago/Turabian Style

Yonggwan Shin; Youngsaeng Lee; Juntae Choi; Jeong-Soo Park. 2018. "Integration of max-stable processes and Bayesian model averaging to predict extreme climatic events in multi-model ensembles." Stochastic Environmental Research and Risk Assessment 33, no. 1: 47-57.

Journal article
Published: 31 October 2018 in Journal of the Korean Society of Supply Chain Management
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ACS Style

Yeonggil Kim; Jeong Soo Park. Moderate Effect of SCM Strategy on Relationship between Environmental Management Practice and Corporate Performance in China. Journal of the Korean Society of Supply Chain Management 2018, 18, 9 -16.

AMA Style

Yeonggil Kim, Jeong Soo Park. Moderate Effect of SCM Strategy on Relationship between Environmental Management Practice and Corporate Performance in China. Journal of the Korean Society of Supply Chain Management. 2018; 18 (2):9-16.

Chicago/Turabian Style

Yeonggil Kim; Jeong Soo Park. 2018. "Moderate Effect of SCM Strategy on Relationship between Environmental Management Practice and Corporate Performance in China." Journal of the Korean Society of Supply Chain Management 18, no. 2: 9-16.

Journal article
Published: 01 October 2018 in Journal of Environmental Management
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Erigeron annuus is one of the major invasive, alien plants in Korea, and therefore research to manage (control) this invasive plant is essential. In this research, studies were conducted to determine the mechanisms by which E. annuus became the dominant plant at a landfill site and to develop management strategies for this alien plant. Because the seeds and seedling stage did not have superior adaptations to disturbed soil, demonstrate allelopathy, outcompete other species, or show rapid growth, the disturbance from mowing was likely the primary reason for the dominance of E. annuus. The areas without mowing showed a significant decrease in the coverage of E. annuus, whereas the mowed (managed) areas showed a significant increase. Additionally, mowing once increased the weight of reproductive organs by 50% and suppressed the growth of native species. Thus, the primary factor in the invasion of the alien species E. annuus was mowing, and, to control such an invasion, areas should be protected from mowing. Additionally, with selective mowing that targeted only E. annuus, mowing three times produced only approximately 10% of the reproductive organ biomass compared with that of the control. Because the flower stalk of E. annuus was relatively tall compared with that of native species in early summer, selective mowing might also provide a solution to control invasions of E. annuus. Therefore, with improved ecological understanding of the site and species, mowing of the right target during the optimal season and at an appropriate frequency is an environmental friendly solution to the management of E. annuus.

ACS Style

Uhram Song; Deokjoo Son; Changku Kang; Eun Ju Lee; Kyoo Lee; Jeong Soo Park. Mowing: A cause of invasion, but also a potential solution for management of the invasive, alien plant species Erigeron annuus (L.) Pers. Journal of Environmental Management 2018, 223, 530 -536.

AMA Style

Uhram Song, Deokjoo Son, Changku Kang, Eun Ju Lee, Kyoo Lee, Jeong Soo Park. Mowing: A cause of invasion, but also a potential solution for management of the invasive, alien plant species Erigeron annuus (L.) Pers. Journal of Environmental Management. 2018; 223 ():530-536.

Chicago/Turabian Style

Uhram Song; Deokjoo Son; Changku Kang; Eun Ju Lee; Kyoo Lee; Jeong Soo Park. 2018. "Mowing: A cause of invasion, but also a potential solution for management of the invasive, alien plant species Erigeron annuus (L.) Pers." Journal of Environmental Management 223, no. : 530-536.

Journal article
Published: 01 August 2018 in Journal of Investigative Dermatology
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Many itch mediators activate G-protein coupled receptor (GPCR), and trigger itch via activation of GPCR-mediated signaling pathways. GPCRs are desensitized by G protein-coupled receptor kinases (GRKs). The aim of this study is to explore the role of GRKs in itch response and the linkage between GRKs and glutamine (Gln), an amino acid previously demonstrated as an itching reliever. Itch responses were evoked by histamine, chloroquine (CQ), and dinitrochlorobenzene (DNCB)-induced contact dermatitis (CD). Phosphorylation and protein expression were detected by immunofluorescent staining and Western blotting. GRK2 knockdown using siRNA enhanced itch responses evoked by histamine, CQ, and DNCB-induced CD, whereas GRK2 overexpression using GRK2 expressing adenovirus reduced the itch responses. Gln reduced all itch evoked by histamine, CQ, and DNCB-induced CD. Gln-mediated inhibition of itch was abolished by GRK2 knockdown. Gln application resulted in a rapid and strong expression GRK2 in not only DNCB-induced CD (within 10 min), but also cultured rat dorsal root ganglion cells, F11 (within 1 min). ERK inhibitor abrogates Gln-mediated GRK2 expression and inhibition of itch in DNCB-induced CD. Our data indicate that GRK2 is a key negative regulator of itch and Gln attenuates itch via a rapid induction of GRK2 in an ERK-dependent way.

ACS Style

Yu-Na Im; Yu-Dong Lee; Jeong-Soo Park; Hae-Kyoung Kim; Suhn-Young Im; Hwa-Ryung Song; Hern-Ku Lee; Myung-Kwan Han. GPCR Kinase (GRK)-2 Is a Key Negative Regulator of Itch: l-Glutamine Attenuates Itch via a Rapid Induction of GRK2 in an ERK-Dependent Way. Journal of Investigative Dermatology 2018, 138, 1834 -1842.

AMA Style

Yu-Na Im, Yu-Dong Lee, Jeong-Soo Park, Hae-Kyoung Kim, Suhn-Young Im, Hwa-Ryung Song, Hern-Ku Lee, Myung-Kwan Han. GPCR Kinase (GRK)-2 Is a Key Negative Regulator of Itch: l-Glutamine Attenuates Itch via a Rapid Induction of GRK2 in an ERK-Dependent Way. Journal of Investigative Dermatology. 2018; 138 (8):1834-1842.

Chicago/Turabian Style

Yu-Na Im; Yu-Dong Lee; Jeong-Soo Park; Hae-Kyoung Kim; Suhn-Young Im; Hwa-Ryung Song; Hern-Ku Lee; Myung-Kwan Han. 2018. "GPCR Kinase (GRK)-2 Is a Key Negative Regulator of Itch: l-Glutamine Attenuates Itch via a Rapid Induction of GRK2 in an ERK-Dependent Way." Journal of Investigative Dermatology 138, no. 8: 1834-1842.

Journal article
Published: 01 June 2018 in Journal of Hydro-environment Research
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To assess the remediation capacity of a leachate channel, we monitored basic environmental parameters such as bathymetry, leachate, and soil characteristics and vegetation coverage. Based on our results, we designed a series of experiments to determine the most suitable remediating plant species for sustainable wastewater treatment. We found that adaptability to water depth may be a prime driver of reduced remediation capacity. Large portions of the leachate channel were deeper than the maximum tolerance range of many candidate emergent macrophytes, resulting in only 16% total vegetation coverage. Among tested species, Typha angustifolia showed the most promising potential for remediation, reaching the highest aboveground biomass (3300 g/m2) and demonstrating maximum concentrations in tissues (34600 mg/kg of Na, 4013 mg/kg of Mg, 904 mg/kg of P, 639 mg/kg of Mn, 191 mg/kg of Fe and 62 mg/kg of Zn) when grown in leachate filled tank for six months. Typha angustifolia also showed greater tolerance of water depth than Phragmites australis, which previously was planted in leachate channels. Thus, T. angustifolia should be more suitable for the actual water depth of the channel. Additional planting of T. angustifolia will improve the vegetation coverage, the total remediation capacity and sustainability of the leachate channel. Considering water depths of target wetlands when selecting remediation plant will improve remediation ability and sustainability of remediation wetlands.

ACS Style

Uhram Song; Bruce Waldman; Jeong Soo Park; Kyoo Lee; Soo-Je Park; Eun Ju Lee. Improving the remediation capacity of a landfill leachate channel by selecting suitable macrophytes. Journal of Hydro-environment Research 2018, 20, 31 -37.

AMA Style

Uhram Song, Bruce Waldman, Jeong Soo Park, Kyoo Lee, Soo-Je Park, Eun Ju Lee. Improving the remediation capacity of a landfill leachate channel by selecting suitable macrophytes. Journal of Hydro-environment Research. 2018; 20 ():31-37.

Chicago/Turabian Style

Uhram Song; Bruce Waldman; Jeong Soo Park; Kyoo Lee; Soo-Je Park; Eun Ju Lee. 2018. "Improving the remediation capacity of a landfill leachate channel by selecting suitable macrophytes." Journal of Hydro-environment Research 20, no. : 31-37.

Journal article
Published: 01 June 2018 in Korean Journal of Clinical Oncology
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ACS Style

Jeong-Soo Park; Jeong-Heum Baek; Won-Suk Lee; Jun-Young Yang; Woon-Kee Lee; Kun-Kuk Kim; Yeon-Ho Park. Long-term oncologic outcomes in pathologic tumor response after neoadjuvant chemoradiation for locally advanced rectal cancer. Korean Journal of Clinical Oncology 2018, 14, 37 -42.

AMA Style

Jeong-Soo Park, Jeong-Heum Baek, Won-Suk Lee, Jun-Young Yang, Woon-Kee Lee, Kun-Kuk Kim, Yeon-Ho Park. Long-term oncologic outcomes in pathologic tumor response after neoadjuvant chemoradiation for locally advanced rectal cancer. Korean Journal of Clinical Oncology. 2018; 14 (1):37-42.

Chicago/Turabian Style

Jeong-Soo Park; Jeong-Heum Baek; Won-Suk Lee; Jun-Young Yang; Woon-Kee Lee; Kun-Kuk Kim; Yeon-Ho Park. 2018. "Long-term oncologic outcomes in pathologic tumor response after neoadjuvant chemoradiation for locally advanced rectal cancer." Korean Journal of Clinical Oncology 14, no. 1: 37-42.

Journal article
Published: 31 May 2018 in Journal of the Korean Society of Supply Chain Management
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ACS Style

Yeonggil Kim; Jeong Soo Park; Jae Yeul Lee; Soowook Kim. A Study on Effect of Collaborative SCM on Corporate Performance : Moderate Effect of Environmental Management Practices. Journal of the Korean Society of Supply Chain Management 2018, 18, 103 -110.

AMA Style

Yeonggil Kim, Jeong Soo Park, Jae Yeul Lee, Soowook Kim. A Study on Effect of Collaborative SCM on Corporate Performance : Moderate Effect of Environmental Management Practices. Journal of the Korean Society of Supply Chain Management. 2018; 18 (1):103-110.

Chicago/Turabian Style

Yeonggil Kim; Jeong Soo Park; Jae Yeul Lee; Soowook Kim. 2018. "A Study on Effect of Collaborative SCM on Corporate Performance : Moderate Effect of Environmental Management Practices." Journal of the Korean Society of Supply Chain Management 18, no. 1: 103-110.

Original article
Published: 27 February 2018 in Journal of Plant Biology
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We investigated multivariate relationships among snowmelt, soil physicochemical properties and the distribution patterns of Arctic tundra vegetation. Seven dominant species were placed in three groups (Veg-1, 2, 3) based on niche overlap (Pianka’s Index) and ordination method, and a partial least squares path model was applied to estimate complex multivariate relationships of four latent variables on the abundance and richness of plant species. The abundance of Veg-1 (Luzula confusa and Salix polaris) was positively correlated with early snowmelt time, high soil nutrients and dense moss cover, but the abundance of Veg-2 (Saxifraga oppositifolia, Bistorta vivipara and Silene acaulis) was negatively correlated with these three variables. Plant richness was positively associated with early snowmelt and hydrological properties. Our results indicate that the duration of the snowpack can directly influence soil chemical properties and plant distribution. Furthermore, plant species richness was significantly affected by snow melt time in addition to soil moisture and moss cover. We predict that L. confusa and S. polaris may increase in abundance in response to early snowmelt and increased soil moisture-nutrient availability, which may be facilitated by climate change. Other forb species in dry and infertile soil may decrease in abundance in response to climate change, due to increasingly unfavourable environmental conditions and competition with mosses.

ACS Style

Jeong Soo Park; Deokjoo Son; Yoo Kyung Lee; Jong Hak Yun; Eun Ju Lee. Multivariate Relationships between Snowmelt and Plant Distributions in the High Arctic Tundra. Journal of Plant Biology 2018, 61, 33 -39.

AMA Style

Jeong Soo Park, Deokjoo Son, Yoo Kyung Lee, Jong Hak Yun, Eun Ju Lee. Multivariate Relationships between Snowmelt and Plant Distributions in the High Arctic Tundra. Journal of Plant Biology. 2018; 61 (1):33-39.

Chicago/Turabian Style

Jeong Soo Park; Deokjoo Son; Yoo Kyung Lee; Jong Hak Yun; Eun Ju Lee. 2018. "Multivariate Relationships between Snowmelt and Plant Distributions in the High Arctic Tundra." Journal of Plant Biology 61, no. 1: 33-39.