• Proceedings of the Korean Institute Of Construction Engineering and Management
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• autumn
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• pp.373-378
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• 2003

Korea's overseas construction industry has been rather depressed by the weakened profitability as well as the sharp decrease of the market shares due to the lack of international competitiveness and the declined international market following the outbreak of Iraq war. There exist a lot of various risks in performing the overseas construction, and especially EPC projects, which entail complicated process from different parts, also require a sophisticated procurement and management skill. Subsequently, to survive in the competitive international market, we need to establish strategies to select potentially profitable projects at the initial stage of bidding process and to mitigate the high degree of risk exposures through contract negotiation and its adjustment. This research provides the profitability evaluation bases, with which overseas construction participants can forecast and analyze the risk more systematically, by eliciting profit-influencing factors from real overseas construction projects and structuring their cause-and-effect relationships. The profitability causal hierarchy structure describes the profitability factors' hierarchy in details and their interrelationships. It also enables us to find out critical factors directly related to profitability aggravation through a qualitative analysis. Ultimately, with this hierarchy structure as the base, the research will suggest how to develop the quantitative profitability forecasting model.

• Atmosphere
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• v.26 no.4
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• pp.617-633
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• 2016

Jangbogo station is located in Terra Nova Bay over the East Antarctica, which is often affected by individual storms moving along nearby storm tracks and a katabatic flow from the continental interior towards the coast. A numerical simulation for two strong wind events of maximum instantaneous wind speed ($41.17m\;s^{-1}$) and daily mean wind speed ($23.92m\;s^{-1}$) at Jangbogo station are conducted using the polar-optimized version of Weather Research and Forecasting model (Polar WRF). Verifying model results from 3 km grid resolution simulation against AWS observation at Jangbogo station, the case of maximum instantaneous wind speed is relatively simulated well with high skill in wind with a bias of $-3.3m\;s^{-1}$ and standard deviation of $5.4m\;s^{-1}$. The case of maximum daily mean wind speed showed comparatively lower accuracy for the simulation of wind speed with a bias of -7.0 m/s and standard deviation of $8.6m\;s^{-1}$. From the analysis, it is revealed that the each case has different origins for strong wind. The highest maximum instantaneous wind case is caused by the approach of the strong synoptic low pressure system moving toward Terra Nova Bay from North and the other daily wind maximum speed case is mainly caused by the katabatic flow from the interiors of Terra Nova Bay towards the coast. Our evaluation suggests that the Polar WRF can be used as a useful dynamic downscaling tool for the simulation and investigation of high wind events at Jangbogo station. However, additional efforts in utilizing the high resolution terrain is required to reduce the simulation error of high wind mainly caused by katabatic flow, which is received a lot of influence of the surrounding terrain.

• Atmosphere
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• v.24 no.4
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• pp.565-571
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• 2014

A long-range prediction system of tropical cyclone (TC) activity over the western North Pacific (WNP) has been operated in the National Typhoon Center of the Korea Meteorological Administration since 2012. The model forecasts the spatial distribution of TC tracks averaged over the period June~October. In this study, we separately developed TC prediction models for summer (June~August) and autumn (September~November) period based on the current operating system. To perform the three-month WNP TC activity prediction procedure readily, we modified the shell script calling in environmental variables automatically. The user can apply the model by changing these environmental variables of namelist parameter in consideration of their objective. The validations for the two seasons demonstrate the great performance of predictions showing high pattern correlations between hindcast and observed TC activity. In addition, we developed a post-processing script for deducing TC activity in the Korea emergency zone from final forecasting map and its skill is discussed.

• Atmosphere
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• v.25 no.4
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• pp.639-657
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• 2015

There is an increasing need to improve the air quality over South Korea to protect public health from local and remote anthropogenic pollutant emissions that are in an increasing trend. Here, we evaluate the performance of the WRF-Chem (Weather Research and Forecasting-Chemistry) model in simulating near-surface air quality of major Korean cities, and investigate the impacts of time-varying chemical initial and lateral boundary conditions (IC/BCs) on the air quality simulation using a chemical downscaling technique. The model domain was configured over the East Asian region and anthropogenic MICS-Asia 2010 emissions and biogenic MEGAN-2 emissions were applied with RACM gaseous chemistry and MADE/SORGAM aerosol mechanism. Two simulations were conducted for a 30-days period on April 2010 with chemical IC/BCs from the WRF-Chem default chemical species profiles ('WRF experiment') and the MOZART-4 (Model for OZone And Related chemical Tracers version 4) ('WRF_MOZART experiment'), respectively. The WRF_MOZART experiment has showed a better performance to predict near-surface CO, $NO_2$, $SO_2$, and $O_3$ mixing ratios at 7 major Korean cities than the WRF experiment, showing lower mean bias error (MBE) and higher index of agreement (IOA). The quantitative impacts of the chemical IC/BCs have depended on atmospheric residence time of the pollutants as well as the relative difference of chemical mixing ratios between the WRF and WRF_MOZART experiments at the lateral boundaries. Specifically, the WRF_MOZART experiment has reduced MBE in CO and O3 mixing ratios by 60~80 ppb and 5~10 ppb over South Korea than those in the WRF-Chem default simulation, while it has a marginal impact on $NO_2$ and $SO_2$ mixing ratios. Without using MOZART-4 chemical IC, the WRF simulation has required approximately 6-days chemical spin-up time for the East Asian model domain. Overall, the results indicate that realistic chemical IC/BCs are prerequisite in the WRF-Chem simulation to improve a forecast skill of local air quality over South Korea, even in case the model domain is sufficiently large to represent anthropogenic emissions from China, Japan, and South Korea.

• Korean Journal of Construction Engineering and Management
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• v.7 no.5
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• pp.64-76
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• 2006

Korea's overseas construction industry has been rather depressed by the weakened profitability as well as the sharp decrease of the market shares due to the lack of international competitiveness and the declined international market. There exist a lot of various risks in performing the overseas construction, and especially EPC projects, which entail complicated process from different parts, also require a sophisticated procurement and management skill. Subsequently, to survive in the competitive international market, we need to establish strategies to select potentially profitable projects at the initial stage of bidding process and to mitigate the high degree of risk exposure through contract negotiation and its adjustment. This research discusses the trend of environment in international construction markets. Then, it identifies the key factors that affect the profitability significantly through the structured surveys from 59 actual overseas projects, and it analyzes the key factors by using statistical methods. This research provides the profitability evaluation bases, with which overseas construction participants can forecast and analyze the risk more systematically, by eliciting profit-influencing factors using the result of statistical analysis, literature review and structuring their cause-and-effect relationships. The profitability casual hierarchy structure describes the profitability factors' hierarchy in details and their interrelationships. It also enables us to find out critical factors directly related to profitability aggravation through a qualitative and quantitative analysis. Ultimately, with this hierarchy structure as the base, the research will suggest how to develop the quantitative profitability forecasting model.

• Korean Journal of Applied Biomechanics
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• v.15 no.3
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• pp.143-152
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• 2005

The purpose of this study was to predict the failure or success of the Snatch-lifting trial as a consequence of the stand-up phase simulated in Kane's equation of motion that was effective for the dynamic analysis of multi-segment. This experiment was a case study in which one male athlete (age: 23yrs, height: 154.4cm, weight: 64.5kg) from K University was selected The system of a simulation included a multi-segment system that had one degree of freedom and one generalized coordinate for the shank segment angle. The reference frame was fixed by the Nonlinear Trans formation (NLT) method in order to set up a fixed Cartesian coordinate system in space. A weightlifter lifted a 90kg-barbell that was 75% of subject's maximum lifting capability (120kg). For this study, six cameras (Qualisys Proreflex MCU240s) and two force-plates (Kistler 9286AAs) were used for collecting data. The motion tracks of 11 land markers were attached on the major joints of the body and barbell. The sampling rates of cameras and force-plates were set up 100Hz and 1000Hz, respectively. Data were processed via the Qualisys Track manager (QTM) software. Landmark positions and force-plate amplitudes were simultaneously integrated by Qualisys system The coordinate data were filtered using a fourth-order Butterworth low pass filtering with an estimated optimum cut-off frequency of 9Hz calculated with Andrew & Yu's formula. The input data of the model were derived from experimental data processed in Matlab6.5 and the solution of a model made in Kane's method was solved in Matematica5.0. The conclusions were as follows; 1. The torque motor of the shank with 246Nm from this experiment could lift a maximum barbell weight (158.98kg) which was about 246 times as much as subject's body weight (64.5kg). 2. The torque motor with 166.5 Nm, simulated by angular displacement of the shank matched to the experimental result, could lift a maximum barbell weight (90kg) which was about 1.4 times as much as subject's body weight (64.5kg). 3. Comparing subject's maximum barbell weight (120kg) with a modeling maximum barbell weight (155.51kg) and with an experimental maximum barbell weight (90kg), the differences between these were about +35.7kg and -30kg. These results strongly suggest that if the maximum barbell weight is decided, coaches will be able to provide further knowledge and information to weightlifters for the performance improvement and then prevent injuries from training of weightlifters. It hopes to apply Kane's method to other sports skill as well as weightlifting to simulate its motion in the future study.

• Journal of Korea Water Resources Association
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• v.52 no.7
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• pp.475-482
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• 2019

Subseasonal-to-Seasonal (S2S) prediction information which have 2 weeks to 2 months lead time are expected to be used through many parts of industry fields, but utilizability is not reached to expectation because of lower predictability than weather forecast and mid- /long-term forecast. In this study, we used multi-layer perceptron (MLP) which is one of machine learning technique that was built for regression training in order to improve predictability of S2S precipitation data at South Korea through post-processing. Hindcast information of ECMWF was used for MLP training and the original data were compared with trained outputs based on dichotomous forecast technique. As a result, Bias score, accuracy, and Critical Success Index (CSI) of trained output were improved on average by 59.7%, 124.3% and 88.5%, respectively. Probability of detection (POD) score was decreased on average by 9.5% and the reason was analyzed that ECMWF's model excessively predicted precipitation days. In this study, we confirmed that predictability of ECMWF's S2S information can be improved by post-processing using MLP even the predictability of original data was low. The results of this study can be used to increase the capability of S2S information in water resource and agricultural fields.

• Atmosphere
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• v.31 no.5
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• pp.577-592
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• 2021

This study evaluates the predictability of the number of days of heat and cold damages by growth stages of rice in South Korea using the hindcast data (1986~2020) produced by Pusan National University Coupled General Circulation Model-Weather Research and Forecasting (PNU CGCM-WRF) model chain. The predictability is accessed in terms of Root Mean Square Error (RMSE), Normalized Standardized Deviations (NSD), Hit Rate (HR) and Heidke Skill Score (HSS). For the purpose, the model predictability to produce the daily maximum and minimum temperatures, which are the variables used to define heat and cold damages for rice, are evaluated first. The result shows that most of the predictions starting the initial conditions from January to May (01RUN to 05RUN) have reasonable predictability, although it varies to some extent depending on the month at which integration starts. In particular, the ensemble average of 01RUN to 05RUN with equal weighting (ENS) has more reasonable predictability (RMSE is in the range of 1.2~2.6℃ and NSD is about 1.0) than individual RUNs. Accordingly, the regional patterns and characteristics of the predicted damages for rice due to excessive high- and low-temperatures are well captured by the model chain when compared with observation, particularly in regions where the damages occur frequently, in spite that hindcasted data somewhat overestimate the damages in terms of number of occurrence days. In ENS, the HR and HSS for heat (cold) damages in rice is in the ranges of 0.44~0.84 and 0.05~0.13 (0.58~0.81 and -0.01~0.10) by growth stage. Overall, it is concluded that the PNU CGCM-WRF chain of 01RUN~05RUN and ENS has reasonable capability to predict the heat and cold damages for rice in South Korea.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.26 no.6B
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• pp.597-603
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• 2006

The purpose of this study is to improve the short term rainfall forecast skill using neural network model that can deal with the non-linear behavior between satellite data and ground observation, and minimize the flood damage. To overcome the geographical limitation of Korean peninsula and get the long forecast lead time of 3 to 6 hour, the developed rainfall forecast model took satellite imageries and wide range AWS data. The architecture of neural network model is a multi-layer neural network which consists of one input layer, one hidden layer, and one output layer. Neural network is trained using a momentum back propagation algorithm. Flood was estimated using rainfall forecasts. We developed a dynamic flood inundation model which is associated with 1-dimensional flood routing model. Therefore the model can forecast flood aspect in a protected lowland by levee failure of river. In the case of multiple levee breaks at main stream and tributaries, the developed flood inundation model can estimate flood level in a river and inundation level and area in a protected lowland simultaneously.

• Atmosphere
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• v.34 no.1
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• pp.55-67
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• 2024

This paper aims to provide a detailed introduction to the concept of the Ratio of Predictable Component (RPC) and the Signal-to-Noise Paradox. Then, we derive insights from them by exploring the paradoxical features by conducting a seasonal and regional analysis through ensemble expansion in KMA's climate prediction system (GloSea). We also provide an explanation of the ensemble generation method, with a specific focus on stochastic physics. Through this study, we can provide the predictability limits of our forecasting system, and find way to enhance it. On a global scale, RPC reaches a value of 1 when the ensemble is expanded to a maximum of 56 members, underlining the significance of ensemble expansion in the climate prediction system. The feature indicating RPC paradoxically exceeding 1 becomes particularly evident in the winter North Atlantic and the summer North Pacific. In the Siberian Continent, predictability is notably low, persisting even as the ensemble size increases. This region, characterized by a low RPC, is considered challenging for making reliable predictions, highlighting the need for further improvement in the model and initialization processes related to land processes. In contrast, the tropical ocean demonstrates robust predictability while maintaining an RPC of 1. Through this study, we have brought to attention the limitations of potential predictability within the climate prediction system, emphasizing the necessity of leveraging predictable signals with high RPC values. We also underscore the importance of continuous efforts aimed at improving models and initializations to overcome these limitations.