• Bulletin of the Korean Space Science Society
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• 2009.10a
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• pp.26.2-26.2
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• 2009

The Solar and Space Weather Research Group (SOS) in Korea Astronomy and Space Science Institute (KASI) is constructing the Space Weather Prediction Center since 2007. As a part of the project, we are developing automatic real-time system of the global 3-D magnetohydrodynamics (MHD) simulation. The MHD simulation model of earth's magnetosphere is designed as modified leap-frog scheme by T. Ogino, and it was parallelized by using message passing interface (MPI). Our work focuses on the automatic processing about simulation of 3-D MHD model and visualization of the simulation results. We used PC cluster to compute, and virtual reality modeling language (VRML) file format to visualize the MHD simulation. The system can show the variation of earth's magnetosphere by the solar wind in quasi real time. For data assimilation we used four parameters from ACE data; density, pressure, velocity of solar wind, and z component of interplanetary magnetic field (IMF). In this paper, we performed some initial tests and made a animation. The automatic real-time system will be valuable tool to understand the configuration of the solar-terrestrial environment for space weather research.

• Journal of Korean Academy of Fundamentals of Nursing
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• v.17 no.2
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• pp.274-281
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• 2010

Purpose: The purpose of this study was to identify the correlation between level of professional autonomy and clinical decision making abilities in clinical nurses, and to provide basic information for promoting competency nurses in making independent decisions. Method: Data were collected from July 1 to July 18, 2008, and participants were 202 clinical nurses in general hospitals. Collected data were analyzed using descriptive statistics: frequency and percentage and Pearson correlation coefficients with the SPSS WIN 14.0 program. Results: The professional autonomy index for the nurses was 159.63 points. The clinical decision making ability index was 119.79 points. The most highly ranked factor in clinical decision making was search for information and unbiased assimilation of new information. There was a statistically significant difference in professional autonomy according to age, clinical experience, and type of duty. Relation between level of professional autonomy and clinical decision making showed a positive correlation. Conclusion: As a results show a significant correlation between professional autonomy and clinical decision making in clinical nurses, improvement in professional autonomy of clinical nurses, would be promoted through continuous support and training.

• Industrial Engineering and Management Systems
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• v.9 no.4
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• pp.312-322
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• 2010

An enterprise must achieve both a sound organizational management and effective development of individual members while maintaining a balanced approach. Relative to this, there is a research field dubbed as Organizational Activation. However, there is a necessity to clarify various factors required for organizational activation and to propose a methodology for organization management. This study is an attempt to create a model from an organization-a University Seminar Class-and agents therein-the Students and the Teacher-with an assumed goal of advancing together toward the students' self-growth. The model is expressed on two dimensional planes with vectors through a computer. Vectors are composed of the growth, demand, member, hindrance and student vectors. These vectors provide data to the mathematical model for a simulation. Each agent provided the individual information from the questionnaire-conducted to 169 university students. From the analysis data and extrapolations, this study was able to craft a guideline for future seminar activity. It also examines the possibility of assimilation of the Questionnaire Approach and that of the Computational Organization Theory approach. Finally, this study discusses the future possibility of application of the Assimilated Method for research and development, and for project management.

• Proceedings of the Korea Water Resources Association Conference
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• 2018.05a
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• pp.177-177
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• 2018

유역의 홍수량 산정하기 위해서 데이터기반 모형, 개념적 모형, 프로세스 기반 모형과 같은 다양한 개념의 수문학적 모형이 개발되고 적용성이 검토되고 있다. 물리기반 강우-유출관계 모형의 경우, 이론적으로 강우유출응답의 연속 모의에 적합하다고 알려져 있으나 모형 구성에 필요한 수문자료 확보의 한계성 때문에 실절적인 적용에 어려움이 있다. 또한 수문 자료가 충분하지 않거나, 없는 미계측 유역에서 홍수량을 산정하기 위해서는 기존의 수문 관측 시스템의 데이터를 이용하기 어렵기 때문에 레이더 및 위성 등을 이용한 다양한 기상수문데이터 도입이 필요하다. 이에 본 연구에서는 관측된 자료와 함께 모델기반 수문기상 시스템인 GLDAS(Global Land Data Assimilation System, GLDAS)의 자료를 이용하여 개념적 강우-유출관계 모형인 PMD(Probaility Distributed Model, PMD)을 통해 홍수량을 모의하는 방법을 적용하였다. 이를 위해 개념적 강우유출관계 모형을 구성하고, 공간적 토양저류(soil moisture storage)분포를 산정하기 위해 토양의 함수상태를 산출하였다. 이같은 접근법은 수문 자료의 제한, 모형 검정의 문제와 같은 어려움을 해결하기 위한 대안으로 제시할 수 있으며, 분석 결과로부터 모델기반 수문기상 자료와 개념적 강우-유출관계 모형의 활용가능성을 검증할 계획이다.

• Atmosphere
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• v.30 no.4
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• pp.319-333
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• 2020

Aircraft observations constitute one of the major sources of temperature observations which provide three-dimensional information. But it is well known that the aircraft temperature data have warm bias against sonde observation data, and therefore, the correction of aircraft temperature bias is important to improve the model performance. In this study, the algorithm of the bias correction modified from operational KMA (Korea Meteorological Administration) global model is adopted in the preprocessing of aircraft observations, and the effect of the bias correction of aircraft temperature is investigated by conducting the two experiments. The assimilation with the bias correction showed better consistency in the analysis-forecast cycle in terms of the differences between observations (radiosonde and GPSRO (Global Positioning System Radio Occultation)) and 6h forecast. This resulted in an improved forecasting skill level of the mid-level temperature and geopotential height in terms of the root-mean-square error. It was noted that the benefits of the correction of aircraft temperature bias was the upper-level temperature in the midlatitudes, and this affected various parameters (winds, geopotential height) via the model dynamics.

• Journal of Korea Water Resources Association
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• v.56 no.9
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• pp.543-551
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• 2023

Snow is an essential climate factor that affects the climate system and surface energy balance, and it also has a crucial role in water balance by providing solid water stored during the winter for spring runoff and groundwater recharge. In this study, statistical analysis of Local Data Assimilation and Prediction System (LDAPS), Modern.-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2), and ERA5-Land snow depth data were used to evaluate the applicability in South Korea. The statistical analysis between the Automated Synoptic Observing System (ASOS) ground observation data provided by the Korea Meteorological Administration (KMA) and the reanalysis data showed that LDAPS and ERA5-Land were highly correlated with a correlation coefficient of more than 0.69, but LDAPS showed a large error with an RMSE of 0.79 m. In the case of MERRA-2, the correlation coefficient was lower at 0.17 because the constant value was estimated continuously for some periods, which did not adequately simulate the increase and decrease trend between data. The statistical analysis of LDAPS and ASOS showed high and low performance in the nearby Gangwon Province, where the average snowfall is relatively high, and in the southern region, where the average snowfall is low, respectively. Finally, the error variance between the four independent snow depth data used in this study was calculated through triple collocation (TC), and a merged snow depth data was produced through weighting factors. The reanalyzed data showed the highest error variance in the order of LDAPS, MERRA-2, and ERA5-Land, and LDAPS was given a lower weighting factor due to its higher error variance. In addition, the spatial distribution of ERA5-Land snow depth data showed less variability, so the TC-merged snow depth data showed a similar spatial distribution to MERRA-2, which has a low spatial resolution. Considering the correlation, error, and uncertainty of the data, the ERA5-Land data is suitable for snow-related analysis in South Korea. In addition, it is expected that LDAPS data, which is highly correlated with other data but tends to be overestimated, can be actively utilized for high-resolution representation of regional and climatic diversity if appropriate corrections are performed.

• Atmosphere
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• v.29 no.4
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• pp.355-365
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• 2019

Based on in-situ monitoring data produced by National Institute of Meteorological Sciences, we evaluated the performance of Joint UK Land Environment Simulator (JULES) on the surface energy balance for rice-paddy and cropland in Korea with the operational ancillary data used for Unified Model (UM) Local Data Assimilation and Prediction System (LDAPS) (CTL) and the high-resolution ancillary data from external sources (EXP). For these experiments, we employed the one-year (March 2015~February 2016) observations of eddy-covariance fluxes and soil moisture contents from a double-cropping rice-paddy in BoSeong and a cropland in AnDong. On the rice-paddy site the model performed better in the CTL experiment except for the sensible heat flux, and the latent heat flux was underestimated in both of experiments which can be inferred that the model represents flood-irrigated surface poorly. On the cropland site the model performance of the EXP experiment was worse than that of CTL experiment related to unrealistic surface type fractions. The pattern of the modeled soil moisture was similar to the observation but more variable in time. Our results shed a light on that 1) the improvement of land scheme for the flood-irrigated rice-paddy and 2) the construction of appropriate high-resolution ancillary data should be considered in the future research.

• Journal of Environmental Impact Assessment
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• v.27 no.5
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• pp.447-474
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• 2018

The purpose of this study is to build a new dataset of spatially interpolated climate data of South Korea by performing various geo-statistical interpolation techniques for comparison with the LDAPS grid data of KMA. Among 595 observation data in 2017, 80 % of the total points and remaining 117 points were used for spatial mapping and quantification,respectively. IDW, cokriging, and kriging were performed via the ArcGIS10.3.1 software and Python3.6.4, and each result was then divided into three clusters and four watersheds for statistical verification. As a result, cokriging produced the most suitable grid climate data for instantaneous temperature. For 1-hr accumulated precipitation, IDW was most suitable for expressing local rainfall effects.

• 한국신재생에너지학회:학술대회논문집
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• 2011.11a
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• pp.56.1-56.1
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• 2011

The solar and meteorological resources map is calculated using by one-layer solar radiation model (GWNU model), satellites data and numerical model output on the Korean peninsula. The Meteorological input data to perform the GWNU model are retrieved aerosol optical thickness from MODIS (TERA/AQUA), total ozone amount from OMI (AURA), cloud fraction from geostationary satellites (MTSAT-1R) and temperature, pressure and total precipitable water from output of RDAPS (Regional Data Assimilation and Prediction System) and KLAPS (Korea Local Analysis and Prediction System) model operated by KMA (Korea Meteorological Administration). The model is carried out every hour using by the meteorological data (total ozone amount, aerosol optical thickness, temperature, pressure and cloud amount) and the basic data (surface albedo and DEM). And the result is analyzed the distribution in time and space and validated with 22 meteorological solar observations. The solar resources map is used to the solar energy-related industries and assessment of the potential resources for solar plant. The National Institute of Meteorological Research in KMA released $4km{\times}4km$ solar map in 2008 and updated solar map with $1km{\times}1km$ resolution and topological effect in 2010. The meteorological resources map homepage (http://www.greenmap.go.kr) is provided the various information and result for the meteorological-solar resources map.

• Journal of Ocean Engineering and Technology
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• v.35 no.4
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• pp.273-286
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• 2021

In recent years, as human casualties and property damage caused by hazardous waves have increased in the East Sea, precise wave prediction skills have become necessary. In this study, the Simulating WAves Nearshore (SWAN) third-generation numerical wave model was calibrated and optimized to enhance the accuracy of winter storm wave prediction in the East Sea. We used Source Term 6 (ST6) and physical observations from a large-scale experiment conducted in Australia and compared its results to Komen's formula, a default in SWAN. As input wind data, we used Korean Meteorological Agency's (KMA's) operational meteorological model called Regional Data Assimilation and Prediction System (RDAPS), the European Centre for Medium Range Weather Forecasts' newest 5th generation re-analysis data (ERA5), and Japanese Meteorological Agency's (JMA's) meso-scale forecasting data. We analyzed the accuracy of each model's results by comparing them to observation data. For quantitative analysis and assessment, the observed wave data for 6 locations from KMA and Korea Hydrographic and Oceanographic Agency (KHOA) were used, and statistical analysis was conducted to assess model accuracy. As a result, ST6 models had a smaller root mean square error and higher correlation coefficient than the default model in significant wave height prediction. However, for peak wave period simulation, the results were incoherent among each model and location. In simulations with different wind data, the simulation using ERA5 for input wind datashowed the most accurate results overall but underestimated the wave height in predicting high wave events compared to the simulation using RDAPS and JMA meso-scale model. In addition, it showed that the spatial resolution of wind plays a more significant role in predicting high wave events. Nevertheless, the numerical model optimized in this study highlighted some limitations in predicting high waves that rise rapidly in time caused by meteorological events. This suggests that further research is necessary to enhance the accuracy of wave prediction in various climate conditions, such as extreme weather.