• Atmosphere
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• v.32 no.2
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• pp.149-162
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• 2022

This study investigated the prediction skill of the Asian dust seasonal forecasting model (GloSea5-ADAM) on the Asian dust and meteorological variables related to the dust generation for the period of 1991~2016. Additionally, we evaluated the prediction skill of those variables depending on the combination of the initial dates in the sub-seasonal scale for the dust source region affecting South Korea. The Asian dust and meteorological variables (10 m wind speed, 1.5 m relative humidity, and 1.5 m air temperature) from GloSea5-ADAM were compared to that from Synoptic observation and European Centre for medium range weather forecasts reanalysis v5, respectively, based on Mean Bias Error (MBE), Root Mean Square Error (RMSE), and Anomaly Correlation Coefficient (ACC) as evaluation criteria. In general, the Asian dust and meteorological variables in the source region showed high ACC in the prediction scale within one month. For all variables, the use of the initial dates closest to the prediction month led to the best performances based on MBE, RMSE, and ACC, and the performances could be improved by adjusting the number of ensembles considering the combination of the initial date. ACC was as high as 0.4 in Spring when using the closest two initial dates. In particular, the GloSea5-ADAM shows the best performance of Asian dust generation with an ACC of 0.60 in the occurrence frequency of Asian dust in March when using the closest initial dates for initial conditions.

• Communications for Statistical Applications and Methods
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• v.19 no.3
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• pp.507-516
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• 2012

We study time series models for seasonal time series data with a covariance structure that depends on time and the periodic autocorrelation at various lags $k$. In this paper, we introduce an ARMA model with periodically varying coefficients(PARMA) and analyze Arosa ozone data with a periodic correlation in the practical case study. Finally, we use a PARMA model and a seasonal ARIMA model for data analysis and show the performance of a PARMA model with a comparison to the SARIMA model.

• Asian Journal of Atmospheric Environment
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• v.7 no.2
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• pp.85-94
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• 2013

Methane concentrations have been monitored at the Anmyeon-do Observatory, Korea, since 1999. In recent years, the methane concentration has increased, but the sources of this increase have yet to be identified. This study was designed to identify the major source contributing to the increase by using World Data Centre for Greenhouse Gases (WDCGG) data and the Greenhouse Gases Emission Presumption (GEP) method. The data were collected at Anmyeon-do between 2003 and 2009 (except 2008), and the analyses showed that the increase in methane concentration originated mainly in rice paddies around the observation point. The annual average methane concentration at Anmyeon-do was 1894 ppb, of which 100-103 ppb (5.3-5.4%) was shown to originate mainly from rice paddies. The seasonal fluctuation in methane concentration from May to October estimated by the GEP method was compared with experimental data of previous research conducted on rice paddies. The close match obtained through this comparison shows that the GEP method is effective. The difference in methane concentration was also analyzed in terms of land use and land cover. It was shown that although rice paddies account for only 14.7% of the area surveyed, they accounted for between 69 and 90% of the total increase in methane concentration. These results confirm that rice paddies are the main source of the increase in methane concentration observed at Anmyeon-do.

• Ocean Science Journal
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• v.41 no.4
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• pp.235-244
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• 2006

Determining 'photosynthetically active radiation' (PAR) is a key part of calculating phytoplankton productivity in a biogeochemical model. We explore the daily and seasonal variability in the ratio of PAR irradiance to total irradiance that occurred at Ieodo Ocean Research Station (IORS) in the East China Sea under clear-sky conditions in 2004 using a simple radiative transfer model (RTM). Meteorological data observed at IORS and aerosol optical properties derived from Aerosol Robotic Network observations at Gosan are used for the RTM. Preliminary results suggest that the use of simple PAR irradiance-ratio values is appropriate in calculating phytoplankton productivity as follows: an average of $0.44\;({\pm}0.01)$ in January to an average of $0.48\;({\pm}0.01)$ in July, with average daily variabilities over these periods of about $0.016\;({\pm}0.008)$ and $0.025\;({\pm}0.008)$, respectively. The model experiments demonstrate that variations in the major controlling input parameters (i.e. solar zenith angle, precipitable water vapor and aerosol optical thickness) cause PAR irradiance ratio variation at daily and seasonal timescales. Further, increases (>0.012) in the PAR irradiance ratio just below the sea-surface are positively correlated with high solar zenith angles and strong wind stresses relative to those just above the sea-surface.

• Journal of Astronomy and Space Sciences
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• v.27 no.2
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• pp.161-172
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• 2010

In this paper, we investigate the sundials made by the Kang Family of Jinju in the later period of the Joseon dynasty in terms of their characteristics, functions and the manufacturing technique. One of the characteristics of these sundials is that the value of polar height (i.e., the latitude of Seoul), the name of manufacturer, 24 seasonal subdivisions and so forth are written on the surface. In particular, polar height is expressed as '$37^{\circ}$ 39' 15"' in all 12 sundials examined in this study. To investigate the manufacturing technology in terms of astronomy, we analyze the positions of gnomon and of the lines corresponding to 24 seasonal subdivisions (season lines) and to each hour (hour lines). To verify the accuracy of the positions, we use a planar projection method. That is, we obtain 2D images of the sundials using a camera or scanner, and compare these with astronomical calculations for the positions of gnomon and season/hour lines. We believe that this method will be very useful for the study of sundials preserved elsewhere.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.16 no.1
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• pp.91-100
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• 2004

Energy efficiency evaluation method for a multi-type air conditioning system still has not been developed. In this study, analysis on capacity range and evaluating method of standards for air conditioners was conducted with world-wide Standards. It is not a proper approach to use the standards for residential air conditioner to multi type air conditioners. Some difficulties and problems are commented in this study with overview of the standards. Through the analytic research, an evaluating method for multi type air conditioner was suggested with Integrated Part Load Value (IPLV). The suggested concept for evaluating energy efficiency during part load condition considers building load pattern and operating hours of the system at different locations. Load was weighted in IPLV to consider not only the concept of occurrence of outdoor temperature such as bin method but also operation hours of the system. An experiment about the IPLV was conducted with variable air volume ducted type air conditioning system and multi-type system through modified code tester to give a glance at quantitative value of the IPLV.

• Journal of Environmental Science International
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• v.11 no.12
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• pp.1281-1289
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• 2002

This study was conducted to evaluate water quality in the Keum River using multivariate analysis. The analysis data in Keum river made use of surveyed data by the ministry of environment from January 1994 to December 2001. Thirteen water quality parameter were determined on each sample. The results was summarized as follow; Water quality in the Keum River could be explained up to 71.39% by four factors which were included in loading of organic matter and nutrients by the tributaries (32.88%), seasonal variation (16.09%), loading of pathogenic bacteria by domestic sewage of Gapcheon (13.39%) and internal metabolism in estuary as lakes(9.03%). For spatial variation of factor score, four group was classified by each factor characterization. Station 1 and 2 was influenced by Daechung dam, station 3 was affected by domestic sewage of Gapcheon, station 10~12 was affected by estuary dyke and the rest station. The result of cluster analysis by station was classified into four group that has different water quality characteristics. In monthly cluster analysis, three group was classified according to seasonal characteristic. Also, in yearly cluster analysis, three group was classified. It is necessary to control the pollutant loadings by Gapcheon inflow domestic sewage in Daejeon city for the sake of water quality management of Keum river.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.13 no.3
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• pp.69-78
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• 1993

A testing system has been developed to evaluate the seasonal variation of the resilient modulus of granular subgrade soils. Two sites were successfully instrumented with soil moisture-temperature cells to monitor over a period of one year, the field temperature and moisture content underneath the pavement. Multiple regression equations were developed to determine the resilient modulus under environmental conditions. It is noted that the use of the effective resilient modulus at the location of the Average Depth of Significant Stress (ADSS) provides a reasonable basis for determining subgrade properties. In addition, a theoretical model has been developed to predict the resilient modulus due to the change of temperature and moisture condition.

• Proceedings of the KSRS Conference
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• 2007.10a
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• pp.286-289
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• 2007

This study is to develop a cloud detection algorit1un for COMS and it is currently tested by using MODIS level 2B and MTSAT-1R satellite radiance data. Unlike many existing cloud detection schemes which use a threshold method and traditional statistical methods, in this study a feed-forward neural network method with back-propagation algorit1un is used. MODIS level 2B products are matched with feature information of five-band MTSAT 1R image data to form the training dataset. The neural network is trained over the global region for the period of January to December in 2006 with 5 km spatial resolution. The main results show that this model is capable to detect complex cloud phenomena. And when it is applied to seasonal images, it shows reliable results to reflect seasonal characteristics except for snow cover of winter. The cloud detection by the neural network method shows 90% accuracy compared to the MODIS products.

• Journal of Korean Society of Rural Planning
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• v.19 no.1
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• pp.43-54
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• 2013

School closings in rural area continue to occur due to drops in population in those areas. Many studies have been done in order to effectively utilize such closed schools. However, there is a lack of studies targeting operators of the closed facilities for revitalization. Therefore, the goal of this research is understanding the real operational status of and finding solutions for the closed schools after studying the current operational status of the closed schools by conducting surveys with their operators. The survey was performed with closed-school-operators in Chungcheongnam-Do as the target; and the analysis was conducted regarding the acquisition type of the closed schools (purchase, lease), building condition, repair and construction status, annual visitors, operational days, operational fees, seasonal ups and downs, operational profits, and satisfaction of local government support. The results showed that there were differences with visitors and operational days along acquisition types as well as low operational profits and seasonal ups and downs including dissatisfaction of local government support. In this study, institutional and political improvement measurements were suggested for the effective operation of the closed facilities based on the main results.