• Atmosphere
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• v.29 no.1
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• pp.87-103
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• 2019

This study introduces the overall characteristics of LOVECLIM version 1.3, the earth system model of intermediate complexity (EMIC), including the installation and operation processes by conducting two kinds of past climate simulation. First climate simulation is the equilibrium experiment during the mid-Holocene (6,000 BP), when orbital parameters were different compared to those at present. The overall accuracy of simulated global atmospheric fields by LOVECLIM is relatively lower than that in Coupled Model Intercomparison Project phase 5 (CMIP5) and Paleoclimate modelling Intercomparison Project phase 3 (PMIP3) simulations. However, surface temperature over the globe, the 800 hPa meridional wind over the mid-latitude coastal region, and the 200 hPa zonal wind from LOVECLIM show similar spatial distribution to those multi-model mean of CMIP5/PMIP3 climate models. Second one is the transient climate experiment from mid-Holocene to present. LOVECLIM well captures the major differences in surface temperature between preindustrial and mid-Holocene simulations by CMIP5/PMIP3 multi-model mean, even though it was performed with short integration time (i.e., about four days in a single CPU environment). In this way, although the earth system model of intermediate complexity has a limit due to its relatively low accuracy, it can be a very useful tool in the specific research area such as paleoclimate.

• Atmosphere
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• v.25 no.2
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• pp.295-308
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• 2015

Based on the CMIP5 historical simulation datasets, we assessed the performance of state-of-the-art climate models in respect to the relationship between interannual variabilities of the North Pacific synoptic eddy (NPSE) and East Asian winter monsoon (EAWM). Observation (ERA-Interim) shows a high negative correlation (-0.73) between the interannual variabilities of East Asian winter monsoon (EAWM) intensity and North Pacific synoptic eddy (NPSE) activity during the period of 1979~2005. Namely, a stronger (weaker) EAWM is related to a weaker (stronger) synoptic eddy activities over the North Pacific. This strong reverse relationship can be well explained by latitudinal distributions of the surface temperature anomalies over East Asian continent, which leads the variation of local baroclinicity and significantly weakens the baroclinic wave activities over the northern latitudes of $40^{\circ}N$. This feature is supported by the distribution of the meridional heat flux (${\overline{{\nu}^{\prime}{\theta}^{\prime}}}$) anomalies, which have negative (positive) values along the latitudes $40{\sim}50^{\circ}N$ for strong(weak) EAWM years. In this study, the historical simulations by 11 CMIP5 climate models (BCC-CSM1.1, CanESM2, GFDL-ESM2G, GFDL-ESM2M, HadGEM2-AO, HadGEM2-CC, IPSL-CM5A-LR, MPI-ESM-LR, MPI-ESM-MR, MRI-CGCM3, and NorESM1-M) are analyzed for DJF of 1979~2005. Correlation coefficient between the two phenomena is -0.59, which is comparable to that of observation. Model-to-model variation in this relationship is relatively large as the range of correlation coefficient is between -0.76 (HadGEM2-CC and HadGEM2-AO) and -0.33 (MRI-CGCM3). But, these reverse relationships are shown in all models without any exception. We found that the multi-model ensemble is qualitatively similar to the observation in reasoning (that is, latitudinal distribution of surface temperature anomalies, variation of local baroclinicity and meridional heat flux by synoptic eddies) of the reverse relationship. However, the uncertainty for weak EAWM is much larger than strong EAWM. In conclusion, we suggest that CMIP5 models as an ensemble have a good performance in the simulation of EAWM, NPSE, and their relationship.

• Journal of Korea Water Resources Association
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• v.51 no.3
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• pp.221-233
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• 2018

Recently, the flood damage by the localized heavy rainfall and typhoon have been frequently occurred due to the climate change. Accurate rainfall forecasting and flood runoff estimates are needed to reduce such damages. However, the uncertainties are involved in guage rainfall, radar rainfall, and the estimated runoff hydrograph from rainfall-runoff models. Therefore, the purpose of this study is to identify the uncertainty of rainfall by generating a probabilistic radar rainfall ensemble and confirm the uncertainties of hydrological models through the analysis of the simulated runoffs from the models. The blending technique is used to estimate a single integrated or an optimal runoff hydrograph by the simulated runoffs from multi rainfall-runoff models. The radar ensemble is underestimated due to the influence of rainfall intensity and topography and the uncertainty of the rainfall ensemble is large. From the study, it will be helpful to estimate and predict the accurate runoff to prepare for the disaster caused by heavy rainfall.

• Proceedings of the Korean Society of Crop Science Conference
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• 2017.06a
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• pp.203-203
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• 2017

The Earth's climate is rapidly changing because of increasing carbon dioxide content in atmosphere so, climate prediction models anticipate that earth surface temperature will rise by 3 to $5^{\circ}C$ in next 50 to 100 years. Therefore, frequency of un-expected weather events such as drought, salinity, low or high temperature and flooding etc. will be increasing worldwide. Furthermore, increased atmosphere temperature can influence pests and pathogens spread as well. Therefore, to protect enormous grain loss from unexpected weather conditions, studies related with combine stress conditions like abiotic plus biotic stress condition are really required. Thus, our research focused on physiological responses under combined abiotic and biotic stress condition in rice plant. To induce uniform stress condition, we used NaCl (100 mM) and salicylic acid (0.5 and 1.0 mM SA) as each stress a stimulator. Each artificial abiotic and biotic stress inducer was applied to hydroponically grown rice seedlings alone or together for four day. The data were collected in a time-dependent manner [1, 2, 3 and 4 day(s) after treatment (DAT)] and were matched with our anticipation that shoot length and shoot fresh weight was decreased in solo and combined abiotic and biotic stress condition. The lipid peroxidation content was significantly increased ($1.5{\pm}0.2$ to $2.7{\pm}0.1mg$ mg of $MDA\;g^{-1}FW$) in the first two days in both stress exposed plants, and showed the opposite trend ($0.5{\pm}0.01$ to $0.1{\pm}0.001mg$ of $MDA\;g^{-1}FW$) in last two days under multi stress condition. Superoxide dismutase (SOD) activity did not showed difference in only biotic stress condition (alone 0.5 and 1.0 mM SA) as compared to control however, it was significantly increased in multi stress condition or solo abiotic stress condition whereas, catalase (CAT), and ascorbate peroxidase (APX) activities were significantly decreased in solo biotic and combined abiotic and biotic condition. In particular, both enzymes activities were more decreased in multi stress condition as compared to solo biotic stress condition. The results for relative mRNA expression level of CAT and APX enzymes were in agreement with results of spectrophotometric values. Correlation value between each stress condition and phenotypic data showed that biotic stress condition showed high correlation with activity of CAT and APX whilst, abiotic stress condition revealed significant correlation with SOD activity.

• Korean Journal of Remote Sensing
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• v.35 no.6_1
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• pp.999-1009
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• 2019

The importance of high-resolution sea ice maps of the Arctic Ocean is increasing due to the possibility of pioneering North Pole Routes and the necessity of precise climate prediction models. In this study,sea ice classification algorithms for two deep learning models were examined using Sentinel-1 A/B SAR data to generate high-resolution sea ice classification maps. Based on current ice charts, three classes (Open Water, First Year Ice, Multi Year Ice) of training data sets were generated by Arctic sea ice and remote sensing experts. Ten sea ice classification algorithms were generated by combing two deep learning models (i.e. Simple CNN and Resnet50) and five cases of input bands including incident angles and thermal noise corrected HV bands. For the ten algorithms, analyses were performed by comparing classification results with ground truth points. A confusion matrix and Cohen's kappa coefficient were produced for the case that showed best result. Furthermore, the classification result with the Maximum Likelihood Classifier that has been traditionally employed to classify sea ice. In conclusion, the Convolutional Neural Network case, which has two convolution layers and two max pooling layers, with HV and incident angle input bands shows classification accuracy of 96.66%, and Cohen's kappa coefficient of 0.9499. All deep learning cases shows better classification accuracy than the classification result of the Maximum Likelihood Classifier.

• Journal of Korean Society on Water Environment
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• v.29 no.2
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• pp.247-258
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• 2013

A grid-based configuration of Land Surface Models (LSMs) coupled with a climate model can be advantageous in impact assessment of climate change for a large scale area. We assessed the applicability of Common Land Model (CoLM) to runoff and land surface temperature (LST) simulations at the domain that encompasses the Nakdong river basin. To establish a high resolution model configuration of a $1km{\times}1km$ grid size, both surface boundary condition and atmospheric inputs from the observed weather data in 2009 were adjusted to the same resolution. The Leaf Area Index (LAI) was collected from MODerate esolution Imaging Spectroradiometer (MODIS) and the downward short wave flux was produced by a nonstationary multi-site weather state model. Compared with the observed runoffs at the stations on Nakdong river, simulated runoffs properly responded to rainfall. The spatial features and the seasonal variations of the domain fairly well were captured in the simulated LSTs as well. The monthly and seasonal trend of LST were described well compared to the observations, however, the monthly averaged simulated LST exceeded the observed up to $2^{\circ}C$ at the 24 stations. From the results of our study, it is shown that high resolution LSMs can be used to evaluate not only quantity but also quality of water resources as it can capture the geographical features of the area of interest and its rainfall-runoff response.

• Proceedings of the Korea Water Resources Association Conference
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• 2019.05a
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• pp.155-155
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• 2019

The Agricultural Policy/Environmental eXtender (APEX) models have been developed for assessing agricultural management efforts and their effects on soil and water at the field scale as well as more complex multi-subarea landscapes, whole farms, and watersheds. National Academy of Agricultural Sciences, Wanju, Korea, has modified a key component of APEX application, named APEX-Paddy for simulating water quality with considering appropriate paddy management practices, such as puddling and flood irrigation management. Calibration and validation are an anticipated step before any model application. Simple techniques are essential to assess whether or not a parameter should be adjusted for calibration. However, very few study has been done to evaluate the ability of APEX-Paddy to simulate the impact of multiple management scenarios on nutrients loss. In this study, the observation data from experimental fields at Iksan in South Kora was used in calibration and evaluation process during 2013-2015. The APEX auto- calibration tool (APEX-CUTE) was used for model calibration and sensitivity analysis. Four quantitative statistics, the coefficient of determination ($R^2$),Nash-Sutcliffe(NSE),percentbias(PBIAS)androotmeansquareerror(RMSE)were used in model evaluation. In this study, the hydrological process of the modified model, APEX-Paddy, is being calibrated and tested in predicting runoff discharge rate and nutrient yield. Field-scale calibration and validation processes are described with an emphasis on essential calibration parameters and direction regarding logical sequences of calibration steps. This study helps to understand the calibration and validation way is further provided for applications of APEX-Paddy at the field scales.

• Korean Journal of Remote Sensing
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• v.36 no.5_3
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• pp.1037-1051
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• 2020

The accurate monitoring and forecasting of the intensity of tropical cyclones (TCs) are able to effectively reduce the overall costs of disaster management. In this study, we proposed a multi-task learning (MTL) based deep learning model for real-time TC intensity estimation and forecasting with the lead time of 6-12 hours following the event, based on the fusion of geostationary satellite images and numerical forecast model output. A total of 142 TCs which developed in the Northwest Pacific from 2011 to 2016 were used in this study. The Communications system, the Ocean and Meteorological Satellite (COMS) Meteorological Imager (MI) data were used to extract the images of typhoons, and the Climate Forecast System version 2 (CFSv2) provided by the National Center of Environmental Prediction (NCEP) was employed to extract air and ocean forecasting data. This study suggested two schemes with different input variables to the MTL models. Scheme 1 used only satellite-based input data while scheme 2 used both satellite images and numerical forecast modeling. As a result of real-time TC intensity estimation, Both schemes exhibited similar performance. For TC intensity forecasting with the lead time of 6 and 12 hours, scheme 2 improved the performance by 13% and 16%, respectively, in terms of the root mean squared error (RMSE) when compared to scheme 1. Relative root mean squared errors(rRMSE) for most intensity levels were lessthan 30%. The lower mean absolute error (MAE) and RMSE were found for the lower intensity levels of TCs. In the test results of the typhoon HALONG in 2014, scheme 1 tended to overestimate the intensity by about 20 kts at the early development stage. Scheme 2 slightly reduced the error, resulting in an overestimation by about 5 kts. The MTL models reduced the computational cost about 300% when compared to the single-tasking model, which suggested the feasibility of the rapid production of TC intensity forecasts.

• Water Engineering Research
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• v.5 no.1
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• pp.47-54
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• 2004

Several computer programs have been developed to make stochastically generated weather data from observed daily data. But they require fully dataset to run WGEN. Mostly, meterological data frequently have sporadic missing data as well as totally missing data. The modified WGEN has data filling algorithm for incomplete meterological datasets. Any other WGEN models have not the function of data filling. Modified WGEN with data filling algorithm is processing from the equation of Matalas for first order autoregressive process on a multi dimensional state with known cross and auto correlations among state variables. The parameters of the equation of Matalas are derived from existing dataset and derived parameters are adopted to fill data. In case of WGEN (Richardson and Wright, 1984), it is one of most widely used weather generators. But it has to be modified and added. It uses an exponential distribution to generate precipitation amounts. An exponential distribution is easier to describe the distribution of precipitation amounts. But precipitation data with using exponential distribution has not been expressed well. In this paper, generated precipitation data from WGEN and Modified WGEN were compared with corresponding measured data as statistic parameters. The modified WGEN adopted a formula of CLIGEN for WEPP (Water Erosion Prediction Project) in USDA in 1985. In this paper, the result of other parameters except precipitation is not introduced. It will be introduced through study of verification and review soon

• Proceedings of the Korea Water Resources Association Conference
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• 2018.05a
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• pp.168-168
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• 2018

최근 기후변화로 인하여 전 세계적으로 과거 강우사상에서 확인되지 않는 극치사상이 빈번하게 관측되고 있으며 이에 따른 피해도 증가하고 있다. 미래의 기상학적 변동성 및 기후변화 영향은 지구순환모형 (General Circulation Models, GCM)을 통해 구체화되며 가장 일반적인 기후변화 전망자료로서 활용된다. 그러나 산정된 기후변화 시나리오마다 서로 그 특성에 차이가 있으며 이러한 이유로 다양한 원인으로 인해 큰 변동성을 가지는 미래 극치강우를 하나의 시나리오로 분석하기에는 무리가 있다. 또한 다양한 시나리오를 통해 분석한 결과값이 상이하며 이러한 시나리오별 산정 결과의 차이는 사용자에게 혼란을 야기할 수 있어 이를 하나의 결과로 나타낼 필요성이 있으나 정량적인 대푯값을 얻기 위해 특정 시나리오를 선택하는 것은 신뢰성에 문제가 있다. 본 연구에서는 시나리오들을 정량적 지표에 의거하여 혼합된 하나의 시나리오로 표출하고자 하였다. CORDEX-RCMs 시나리오 중 HadGEM3-RA, RegCM, SNU_WRF 및 GRIMs를 입력 자료로 하여 다중모형앙상블(Multi-Model Ensemble, MME)을 통해 낙동강 유역의 극치강우에 대한 하나의 최적 기후변화 시나리오를 도출하고자 하였으며 계층적 베이지안 (Hierarchical Bayesian Model, HBM) 기법을 통하여 기후변화 시나리오에 내제된 불확실성에 대한 정량적인 해석을 수행하였다.