• Journal of the Korean earth science society
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• v.24 no.1
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• pp.7-21
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• 2003

The state-of-art third generation wave prediction model WAM was applied to the Korean seas for a winter monsoon period of January 1997. The wind field used in the present study is the global NSCAT-ERS/NCEP blended winds, which was further interpolated using a bi-cubic spline interpolator to fine grid limited area shallow water regime surrounding the Korean seas. To evaluate and investigate the accuracy of WAM, the hindcasted wave heights are compared with observed data from two shallow water buoys off Chil-Bal and Duk-Juk. A detailed study has been carried with the various meteorological parameters in observed buoy data and its inter-dependency on model computed wave fields was also investigated. The RMS error between the observation and model computed wave heights results to 0.489 for Chil-Bal and 0.417 for Duk-Juk. A similar comparison between the observation and interpolated winds off Duk-Juk show RMS error of 2.28 which suggest a good estimate for wave modelling studies.

• Journal of the Korean earth science society
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• v.39 no.1
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• pp.1-22
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• 2018

Eight different data sets are examined in order to gain insight into the surface heat flux traits of the East Asian marginal seas. In the case of solar radiation of the East Sea (Japan Sea), Coordinated Ocean-ice Reference Experiments ver. 2 (CORE2) and the Objectively Analyzed Air-Sea Fluxes (OAFlux) are similar to the observed data at meteorological stations. A combination is sought by averaging these as well as the Climate Forecast System Reanalysis (CFSR) and the National Centers for Environmental Prediction (NCEP)-1 data to acquire more accurate surface heat flux for the East Asian marginal seas. According to the Combination Data, the annual averages of net heat flux of the East Sea, Yellow Sea, and East China Sea are -61.84, -22.42, and $-97.54Wm^{-2}$, respectively. The Kuroshio area to the south of Japan and the southern East Sea were found to have the largest upward annual mean net heat flux during winter, at -460- -300 and at $-370--300Wm^{-2}$, respectively. The long-term fluctuation (1984-2004) of the net heat flux shows a trend of increasing transport of heat from the ocean into the atmosphere throughout the study area.

• Journal of Korean Academy of Nursing
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• v.37 no.4
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• pp.453-458
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• 2007

Purpose: The purpose of this study was to compare metabolic syndrome (MS) risk factor prevalence by obesity and age in middle-aged women. Method: Two hundred and fifty-one subjects were recruited from the health promotion center of a tertiary care hospital in an urban city. MS was defined by the third report of the national cholesterol education program (NCEP) expert panel on detection, evaluation, and treatment of high blood cholesterol in adults(Adult Treatment Panel III)(ATP III), and obesity was determined by body mass $index(BMI){\geq}25kg/m^2$. Results: The mean blood pressure, fasting glucose, total cholesterol, and triglyceride were significantly higher in the obese group than in the non-obese group. The prevalence of MS, hypertension, and impaired fasting glucose were significantly higher in the obese group than in the non-obese group. In the forties, blood pressure was significantly higher in the obese group than in the non-obese group. In the fifties, body fat, systolic blood pressure, fasting glucose, total cholesterol, and triglyceride were significantly higher in the obese group than in the non-obese group. Conclusions: These results show that the nurse should focus on the obese fifty year old female patients for improvement of the MS risk factors.

• Korean Journal of Health Education and Promotion
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• v.29 no.1
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• pp.97-108
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• 2012

Objectives: We assessed the feasibility of health coaching for health coaching program on metabolic syndrome. Methods: We developed a 6 month health coaching program on metabolic Syndrome. We recruited people with metabolic syndrome according to modified NCEP-ATP III. The participants were 9 men over 30 years of age who had taken a health screening at general hospital. We collected data such as demographics, BMI, body fat, blood pressure, HDL-cholesterol blood sugar and triglyceride. The program was analyzed by using Wilcoxon signed rank test. Results: Participants showed significantly decreased BMI, weight, waist circumference, body fat after 6 month program. They talked the awareness about their own behavior. They changed into better for eating habits, physical activities, and self management. Their discipline increased and eating habits became regular. They were satisfied to this program and showed strong confidence about their own change. Conclusions: Coaching did not direct certain behavioral change but guided self awareness and practice. Health coaching program showed long maintained effect to participants. We suggested health coaching as a helpful individual program to intervene risky health behavior especially for metabolic Syndrome.

• Journal of muscle and joint health
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• v.19 no.2
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• pp.152-160
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• 2012

Purpose: The purpose of this study was to test the effects of the Tai Chi exercise on metabolic syndrome and health-related quality of life in middle-aged women. Methods: A quasi-experimental design was used. Subjects were sixty middle-aged women with metabolic syndrome. All of the subjects were met the criteria of the National Cholesterol Education Program-Adult Treatment Panel III (NCEP-ATP III). The subjects were divided into the experiment group (n=33) trained the Tai Chi for 12 weeks and the control group (n=27). Metabolic syndrome risk factors including blood pressure, waist circumference, glucose, triglyceride (TG), and high density lipoprotein cholesterol (HDL-C) were measured before and after the 12-week period. Euro Quality of Life Questionnaire 5-Dimensional Classification (EQ-5D) was used to evaluate the health-related quality of life. Results: The experiment group showed significant decreases in diastolic blood pressure, waist circumference, glucose, and TG; and increase in HDL-C compared to the control group. For the health-related quality of life evaluation, the experiment group showed significant improvement more than the control group. Conclusion: The Tai Chi exercise may be effective intervention in preventing cardiovascular disease caused by metabolic syndrome in middle-aged women.

• Journal of Korean Society for Atmospheric Environment
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• v.19 no.3
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• pp.249-261
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• 2003

A three-dimensional mesoscale atmospheric dispersion modeling system consisting of the Lagrangian particle dispersion model (LPDM) and the meteorological mesoscale model (MM5) was employed to simulate the transport and dispersion of non-reactive pollutant during the nuclear spill event occurred from Sep. 31 to Oct. 3, 1999 in Tokaimura city, Japan. For the comparative analysis of numerical experiment, two more sets of foreign mesoscale modeling system; NCEP (National Centers for Environmental Prediction) and DWD (Deutscher Wetter Dienst) were also applied to address the applicability of air pollution dispersion predictions. We noticed that the simulated results of horizontal wind direction and wind velocity from three meteorological modeling showed remarkably different spatial variations, mainly due to the different horizontal resolutions. How-ever, the dispersion process by LPDM was well characterized by meteorological wind fields, and the time-dependent dilution factors ($\chi$/Q) were found to be qualitatively simulated in accordance with each mesocale meteorogical wind field, suggesting that LPDM has the potential for the use of the real time control at optimization of the urban air pollution provided detailed meteorological wind fields. This paper mainly pertains to the mesoscale modeling approaches, but the results imply that the resolution of meteorological model and the implementation of the relevant scale of air quality model lead to better prediction capabilities in local or urban scale air pollution modeling.

• The Sea:JOURNAL OF THE KOREAN SOCIETY OF OCEANOGRAPHY
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• v.18 no.1
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• pp.21-31
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• 2013

Ecosystem models are mathematical representations of underlying mechanistic relationships among ecological components and processes. Ecosystem modeling is a useful tool to visualize inherent complexities of ecological relationships among components and the characteristic variability in ecological systems, and to quantitatively predict effects of modification of systems due to human activities and/or climate change. A number of interdisciplinary programs in recent 20 to 30 years motivated oceanographic communities to explore and employ systematic and holistic approaches, and as an outcome of these efforts, synthesis and modeling became a popular and important way of integrating lessons learned from many on-going projects. This is a brief review that includes: background information of ecosystem dynamics model; what needs to be considered in building a model framework; biologically-physically coupled processes; end-to-end modeling efforts; and parameterization and related issues.

• Korean Journal of Remote Sensing
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• v.27 no.6
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• pp.729-741
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• 2011

Wind speeds have long been estimated from C-band VV-polarized SAR data by using the CMOD algorithms such as CMOD4, CMOD5, and CMOD_IFR2. Some SAR data with HH-polarization without any observations in VV-polarization mode should be converted to VV-polarized value in order to use the previous algorithms based on VV-polarized observation. To satisfy the necessity of polarization ratio (PR) for the conversion, we retrieved the conversion parameter from full-polarized SIR-C SAR image off the east coast of Korea. The polarization ratio for SIR-C SAR data was estimated to 0.47. To assess the accuracy of the polarization ratio coefficient, pseudo VV-polarized normalized radar cross section (NRCS) values were calculated and compared with the original VV-polarized ones. As a result, the estimated psudo values showed a good agreement with the original VV-polarized data with an root mean square error by 0.99 dB. We applied the psudo NRCS to the estimation of wind speeds based on the CMOD wind models. Comparison of the retrieved wind field with the ECMWF and NCEP/NCAR reanalysis wind data showed relatively small rms errors of 1.88 and 1.91 m/s, respectively. SIR-C HH-polarized SAR wind retrievals met the requirement of the scatterometer winds in overall. However, the polarization ratio coefficient revealed dependence on NRCS value, wind speed, and incident angle.

• Proceedings of the Korea Water Resources Association Conference
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• 2009.05a
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• pp.112-115
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• 2009

The research of climate change impact in hydrometeorology often relies on climate change information. In this paper, neural networks models such as support vector machine neural networks model (SVM-NNM) and multilayer perceptron neural networks model (MLP-NNM) are proposed statistical downscaling of the monthly precipitation. The input nodes of neural networks models consist of the atmospheric meteorology and the atmospheric pressure data for 2 grid points including $127.5^{\circ}E/35^{\circ}N$ and $125^{\circ}E/35^{\circ}N$, which produced the best results from the previous study. The output node of neural networks models consist of the monthly precipitation data for Seoul station. For the performances of the neural networks models, they are composed of training and test performances, respectively. From this research, we evaluate the impact of SVM-NNM and MLP-NNM performances for the downscaling of the monthly precipitation data. We should, therefore, construct the credible monthly precipitation data for Seoul station using statistical downscaling method. The proposed methods can be applied to future climate prediction/projection using the various climate change scenarios such as GCMs and RCMs.

• Proceedings of the Korea Water Resources Association Conference
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• 2009.05a
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• pp.125-128
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• 2009

The research of climate change impact in hydrometeorology often relies on climate change information. In this paper, neural networks models such as generalized regression neural networks model (GRNNM) and multilayer perceptron neural networks model (MLP-NNM) are proposed statistical downscaling of the daily precipitation. The input nodes of neural networks models consist of the atmospheric meteorology and the atmospheric pressure data for 4 grid points including $127.5^{\circ}E/37.5^{\circ}N$, $127.5^{\circ}E/35^{\circ}N$, $125^{\circ}E/37.5^{\circ}N$ and $125^{\circ}E/35^{\circ}N$, respectively. The output node of neural networks models consist of the daily precipitation data for Seoul station. For the performances of the neural networks models, they are composed of training and test performances, respectively. From this research, we evaluate the impact of GRNNM and MLP-NNM performances for the downscaling of the daily precipitation data. We should, therefore, construct the credible daily precipitation data for Seoul station using statistical downscaling method. The proposed methods can be applied to future climate prediction/projection using the various climate change scenarios such as GCMs and RCMs.