• Journal of Korea Water Resources Association
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• v.54 no.12
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• pp.1317-1328
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• 2021

This study aims to provide a predictive model based on climate models for simulating continuous daily rainfall sequences by combining bias-correction and spatio-temporal downscaling approaches. For these purposes, this study proposes a combined modeling system by applying conditional Copula and Multisite Non-stationary Hidden Markov Model (MNHMM). The GloSea5 system releases the monthly rainfall prediction on the same day every week, however, there are noticeable differences in the updated prediction. It was confirmed that the monthly rainfall forecasts are effectively updated with the use of the Copula-based bias-correction approach. More specifically, the proposed bias-correction approach was validated for the period from 1991 to 2010 under the LOOCV scheme. Several rainfall statistics, such as rainfall amounts, consecutive rainfall frequency, consecutive zero rainfall frequency, and wet days, are well reproduced, which is expected to be highly effective as input data of the hydrological model. The difference in spatial coherence between the observed and simulated rainfall sequences over the entire weather stations was estimated in the range of -0.02~0.10, and the interdependence between rainfall stations in the watershed was effectively reproduced. Therefore, it is expected that the hydrological response of the watershed will be more realistically simulated when used as input data for the hydrological model.

• Atmosphere
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• v.29 no.3
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• pp.257-267
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• 2019

This study is to assess the applicability of the Extreme Forecast Index (EFI) algorithm of the ECMWF seasonal forecast system to the Global Seasonal Forecasting System version 5 (GloSea5), operational seasonal forecast system of the Korea Meteorological Administration (KMA). The EFI is based on the difference between Cumulative Distribution Function (CDF) curves of the model's climate data and the current ensemble forecast distribution, which is essential to diagnose the predictability in the extreme cases. To investigate its applicability, the experiment was conducted during the heat-wave cases (the year of 1994 and 2003) and compared GloSea5 hindcast data based EFI with anomaly data of ERA-Interim. The data also used to determine quantitative estimates of Probability Of Detection (POD), False Alarm Ratio (FAR), and spatial pattern correlation. The results showed that the area of ERA-Interim indicating above 4-degree temperature corresponded to the area of EFI 0.8 and above. POD showed high ratio (0.7 and 0.9, respectively), when ERA-Interim anomaly data were the highest (on Jul. 11, 1994 (> $5^{\circ}C$) and Aug. 8, 2003 (> $7^{\circ}C$), respectively). The spatial pattern showed a high correlation in the range of 0.5~0.9. However, the correlation decreased as the lead time increased. Furthermore, the case of Korea heat wave in 2018 was conducted using GloSea5 forecast data to validate EFI showed successful prediction for two to three weeks lead time. As a result, the EFI forecasts can be used to predict the probability that an extreme weather event of interest might occur. Overall, we expected these results to be available for extreme weather forecasting.

• Atmosphere
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• v.28 no.2
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• pp.113-121
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• 2018

In order to monitor greenhouse gases including $CO_2$, various types of surface-, aircraft-, and satellite-based measurement projects have been conducted. These data help understand the variations of greenhouse gases and are used in atmospheric inverse modeling systems to simulate surface fluxes for greenhouse gases. CarbonTracker is a system for estimating surface $CO_2$ flux, using an atmospheric inverse modeling method, based on only surface observation data. Because of the insufficient surface observation data available for accurate estimation of the surface $CO_2$ flux, additional observations would be required. In this study, a system that assimilates aircraft $CO_2$ measurement data in CarbonTracker (CT2013B) is developed, and the estimated results from this data assimilation system are evaluated. The aircraft $CO_2$ measurement data used are obtained from the Comprehensive Observation Network for Trace gases by the Airliner (CONTRAIL) project. The developed system includes the preprocessor of the raw observation data, the observation operator, and the ensemble Kalman filter (EnKF) data assimilation process. After preprocessing the raw data, the modeled value corresponding spatially and temporally to each observation is calculated using the observation operator. These modeled values and observations are then averaged in space and time, and used in the EnKF data assimilation process. The modeled values are much closer to the observations and show smaller biases and root-mean-square errors, after the assimilation of the aircraft $CO_2$ measurement data. This system could also be used to assimilate other aircraft $CO_2$ measurement data in CarbonTracker.

• Earthquakes and Structures
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• v.15 no.5
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• pp.453-462
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• 2018

Damages to buildings affected by a near-fault strong ground motion are largely attributed to the vertical component of the earthquake resulting in column failures, which could lead to disproportionate building catastrophic collapse in a progressive fashion. Recently, considerable interests are awakening to study effects of earthquake vertical components on structural responses. In this study, detailed modeling and time-history analyses of a 12-story code-conforming reinforced concrete moment frame building carrying the gravity loads, and exposed to once only the horizontal component of, and second time simultaneously the horizontal and vertical components of an ensemble of far-field and near-field earthquakes are conducted. Structural responses inclusive of tension, compression and its fluctuations in columns, the ratio of shear demand to capacity in columns and peak mid-span moment demand in beams are compared with and without the presence of the vertical component of earthquake records. The influences of the existence of earthquake vertical component in both exterior and interior spans are separately studied. Thereafter, the correlation between the increase of demands induced by the vertical component of the earthquake and the ratio of a set of earthquake record characteristic parameters is investigated. It is shown that uplift initiation and the magnitude of tensile forces developed in corner columns are relatively more critical. Presence of vertical component of earthquake leads to a drop in minimum compressive force and initiation of tension in columns. The magnitude of this reduction in the most critical case is recorded on average 84% under near-fault ground motions. Besides, the presence of earthquake vertical components increases the shear capacity required in columns, which is at most 31%. In the best case, a direct correlation of 95% between the increase of the maximum compressive force and the ratio of vertical to horizontal 'effective peak acceleration (EPA)' is observed.

• Journal of The Korean Astronomical Society
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• v.49 no.5
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• pp.193-198
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• 2016

Early-type galaxies (ETGs) are supposed to follow the virial relation $M=k_e{\sigma}^2R_e/G$, with M being the mass, σ_* being the stellar velocity dispersion, R_e being the effective radius, G being Newton's constant, and k_e being the virial factor, a geometry factor of order unity. Applying this relation to (a) the ATLAS^3D sample of Cappellari et al. (2013) and (b) the sample of Saglia et al. (2016) gives ensemble-averaged factors 〈k_e〉 = 5.15 ± 0.09 and 〈k_e〉 = 4.01 ± 0.18, respectively, with the difference arising from different definitions of effective velocity dispersions. The two datasets reveal a statistically significant tilt of the empirical relation relative to the theoretical virial relation such that $M{\propto}({\sigma}^2_*R_e)^{0.92}$. This tilt disappears when replacing R_e with the semi-major axis of the projected half-light ellipse, a. All best-fit scaling relations show zero intrinsic scatter, implying that the mass plane of ETGs is fully determined by the virial relation. Whenever a comparison is possible, my results are consistent with, and confirm, the results by Cappellari et al. (2013). The difference between the relations using either a or R_e arises from a known lack of highly elliptical high-mass galaxies; this leads to a scaling (1 - ϵ ) ∝ M^0.12, with ϵ being the ellipticity and $R_e=a\sqrt[]{1-{\epsilon}}$. Accordingly, a, not R_e, is the correct proxy for the scale radius of ETGs. By geometry, this implies that early-type galaxies are axisymmetric and oblate in general, in agreement with published results from modeling based on kinematics and light distributions.

• Journal of the Korean Institute of Gas
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• v.24 no.6
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• pp.91-97
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• 2020

Lithium-ion batteries (LIBs) have a longer lifespan, higher energy density, and lower self-discharge rates than other batteries, therefore, they are preferred as an Energy Storage System (ESS). However, during years 2017-2019, 28 ESS fire accidents occurred in Korea, and accurate capacity estimation of LIB is essential to ensure safety and reliability during operations. In this study, data-driven modeling that predicts capacity changes according to the charging cycle of LIB was conducted, and developed models were compared their performance for the selection of the optimal machine learning model, which includes the Decision Tree, Ensemble Learning Method, Support Vector Regression, and Gaussian Process Regression (GPR). For model training, lithium battery test data provided by NASA was used, and GPR showed the best prediction performance. Based on this study, we will develop an enhanced LIB capacity prediction and remaining useful life estimation model through additional data training, and improve the performance of anomaly detection and monitoring during operations, enabling safe and stable ESS operations.

• Proceedings of the Korea Water Resources Association Conference
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• 2021.06a
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• pp.392-392
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• 2021

자료동화(data assimilation) 기법은 관측 자료와 예측 모형의 정보를 동시에 활용, 모형의 상태량(state variables)이나 매개변수(model parameters)를 실시간으로 업데이트하는 Bayesian 필터링 이론에 근거한 방법으로, 최근 이를 활용한 수문 모의 정확도 향상 기술이 빠르게 발전하고 있다. 본 연구에서는 앙상블 자료동화의 정확성을 향상시키기 위한 세부 방법인 along-the-stream localization과 inflation 기법의 분포형 수문 모형에 대한 적용성을 대규모 지역 단위(regional-scale) 모의를 통해 검토한다. 분포형 수문모형과 자료동화 framework로는 WRF-Hydro(Weather Research and Forecasting Model Hydrological Modeling System)와 DART(Data Assimilation Research Testbed)를 각각 적용한다. WRF-Hydro는 미국의 전 대륙지역(CONUS; continental United States)에 대한 수문 모델링 framework인 National Water Model의 핵심엔진이고, DART는 미국 National Center for Atmospheric Research(NCAR) 연구소에서 개발한 범용 자료동화 도구이다. 본 연구에서는 지표수 수문모형의 자료동화를 위해 개발된 기법인 along-the-stream localization과 inflation 기법이 하도 추적에 미치는 영향을 분석한다. along-the stream localization 기법은 공간적 근접도 외에 하도의 수문학적 연관관계를 고려하는 localization 기법으로, 상대적으로 수문학적 상관도가 떨어지는 하도에 대한 과도한 자료동화를 줄여줄 수 있다. inflation 기법은 앙상블의 다양성을 증가시키는 기법으로, 칼만 필터(Kalman filter)에 의한 업데이트의 이전이나 이후 적용하여 앙상블 예측의 정확도를 추가적으로 향상시킬 수 있다. 본 고에서는 앙상블 자료동화 기법을 지표수 수문 모의에 적용할 경우 남아 있는 난제와 적용 가능한 방법에 대해 중점적으로 논의한다.

• Proceedings of the Korea Water Resources Association Conference
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• 2021.06a
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• pp.140-140
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• 2021

Urban flood management is a crucial and challenging task, particularly in developed cities. Therefore, accurate prediction of urban flooding under heavy precipitation is critically important to address such a challenge. In recent years, machine learning techniques have received considerable attention for their strong learning ability and suitability for modeling complex and nonlinear hydrological processes. Moreover, a survey of the published literature finds that hybrid computational intelligent methods using nature-inspired algorithms have been increasingly employed to predict or simulate the streamflow with high reliability. The present study is aimed to propose a novel approach, an ensemble tree, Bayesian Additive Regression Trees (BART) model incorporating a nature-inspired algorithm to predict hourly multi-step ahead streamflow. For this reason, a hybrid intelligent model was developed, namely GA-BART, containing BART model integrating with Genetic algorithm (GA). The Jungrang urban basin located in Seoul, South Korea, was selected as a case study for the purpose. A database was established based on 39 heavy rainfall events during 2003 and 2020 that collected from the rain gauges and monitoring stations system in the basin. For the goal of this study, the different step ahead models will be developed based in the methods, including 1-hour, 2-hour, 3-hour, 4-hour, 5-hour, and 6-hour step ahead streamflow predictions. In addition, the comparison of the hybrid BART model with a baseline model such as super vector regression models is examined in this study. It is expected that the hybrid BART model has a robust performance and can be an optional choice in streamflow forecasting for urban basins.

• Journal of Korea Water Resources Association
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• v.47 no.3
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• pp.257-267
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• 2014

The impact of the combination of changes in temperature and rainfall due to climate change on surface water resources is important in hydro-meteorological research. In this study, 4 hydro-meteorological (HM) models from the Rainfall Runoff Library in the Catchment Modeling Toolkit were used to model the impact of climate change on runoff in streams for 5 river basins in the Republic of Korea. Future projections from 2021 to 2040 (2030s), 2051 to 2070 (2060s) and 2081 to 2099 (2090s), were derived from 12 General Circulation Models (GCMs) and 3 representative concentration pathways (RCPs). GCM outputs were statistically adjusted and downscaled using Long-Ashton Research Station Weather Generator (LARS-WG) and the HM models were well calibrated and verified for the period from 1999 to 2009. The study showed that there is substantial spatial, temporal and HM uncertainty in the future runoff shown by the interquartile range, range and coefficient of variation. In summary, the aggregated runoff will increase in the future by 10~24%, 7~30% and 11~30% of the respective baseline runoff for the RCP2.6, RCP4.5 and RCP8.5, respectively. This study presents a method to model future stream-flow taking into account the HM model and climate based uncertainty.

• The Journal of Bigdata
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• v.6 no.2
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• pp.71-84
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• 2021

Since small and medium-sized enterprises fell short of the securement of technological competitiveness in the field of big data and artificial intelligence (AI) field-core technologies of the Fourth Industrial Revolution, it is important to strengthen the competitiveness of the overall industry through technology commercialization. In this study, we aimed to propose a priority related to technology transfer and commercialization for practical use of public research results. We utilized public research performance information, improving missing values of 6T classification by deep learning model with an ensemble method. Then, we conducted topic modeling to derive the converging fields of big data and AI. We classified the technology fields into four different segments in the technology portfolio based on technology activity and technology efficiency, estimating the potential of technology commercialization for those fields. We proposed a priority of technology commercialization for 10 detailed technology fields that require long-term investment. Through systematic analysis, active utilization of technology, and efficient technology transfer and commercialization can be promoted.