• Journal of the Korean association of regional geographers
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• v.17 no.4
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• pp.432-442
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• 2011

Climatic variables such as temperature and precipitation tend to vary both in space and in time simultaneously. Thus, it is necessary to include space-time autocorrelation into conventional spatial interpolation methods for reliable time-series mapping. This paper introduces and applies space-time variogram modeling and space-time kriging to generate time-series temperature maps using hourly Automatic Weather System(AWS) temperature observation data for a one-month period. First, temperature observation data are decomposed into deterministic trend and stochastic residual components. For trend component modeling, elevation data which have reasonable correlation with temperature are used as secondary information to generate trend component with topographic effects. Then, space-time variograms of residual components are estimated and modelled by using a product-sum space-time variogram model to account for not only autocorrelation both in space and in time, but also their interactions. From a case study, space-time kriging outperforms both conventional space only ordinary kriging and regression-kriging, which indicates the importance of using space-time autocorrelation information as well as elevation data. It is expected that space-time kriging would be a useful tool when a space-poor but time-rich dataset is analyzed.

• Journal of the Korean association of regional geographers
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• v.16 no.4
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• pp.339-354
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• 2010

In this study, diurnal and intra-seasonal variations of summertime temperature lapse rate (TLR) by synoptic weather conditions in a mountainous basin are examined based on hourly temperature data observed in 2009 summer at an Automatic Weather Station (AWS) network deployed in Haean basin (called Punch Bowl), Yanggu in the Republic of Korea. Summertime average TLR between the top and bottom of the basin is $-0.53^{\circ}C$/100m. Due to its diurnal variations, TLR shows the lowest by $-0.25^{\circ}C$/100m at 6AM, while it maximizes up to $-0.85^{\circ}C$/100m between 4PM~5PM. Comparisons of daily average TLRs by synoptic weather patterns reveal that the magnitude of TLRs is greatest in the order of rainy days ($-0.63^{\circ}C$/100m), heavy rainfall days ($-0.53^{\circ}C$/100m), partly cloudy days ($-0.47^{\circ}C$/100m), and sunny days ($-0.39^{\circ}C$/100m). At dawn on sunny days in summer, strong cooling pools accompanying temperature inversion layers are formed within the basin, while on heavy rainfall days, warming pools are observed due to relatively low TLRs associated with the reduction of surface radiation cooling by clouds.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.18 no.2
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• pp.112-123
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• 2006

Double-peak normal distribution function was suggested as the probability density function of the non-tidal components (NTC) data in Korean coastal zone. Frequency distribution analysis of the NTC data was carried out using hourly tidal elevation data of the ten tidal gauging stations, i.e., Incheon, Gunsan, Mokpo, Jeju, Yeosu, Masan, Gadeokdo, Busan, Pohang, and Sokcho which were served through the Internet Homepage by the National Ocean Research Institute. NTC data is defined as the difference between the measured tidal elevation data and the astronomical tidal elevation data using 64 tidal constituents information. Based on the RMS error and R2 value comparison analysis, it was found that this suggested function as the probability density function of the NTC data was found to be more appropriate than the normal distribution function. The parameters of the double-peak function were estimated optimally using Levenberg-Marquardt method which was modified from the Newton method. The standard deviation and skewness coefficient were highly correlated with the non-tidal constants of the tidal gauging stations except Mokpo, Jeju and Sokcho stations.

• Journal of Energy Engineering
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• v.28 no.4
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• pp.82-93
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• 2019

In this study, a new model is proposed to improve the problem of the decline of predict rate of heat demand on a particular date, such as a public holiday for the conventional heat demand forecasting system. The proposed model was the Four Season Mixed Heat Demand Prediction Neural Network Model, which showed an increase in the forecast rate of heat demand, especially for each type of forecast date (weekday/weekend/holiday). The proposed model was selected through the following process. A model with an even error for each type of forecast date in a particular season is selected to form the entire forecast model. To avoid shortening learning time and excessive learning, after each of the four different models that were structurally simplified were learning and a model that showed optimal prediction error was selected through various combinations. The output of the model is the hourly 24-hour heat demand at the forecast date and the total is the daily total heat demand. These forecasts enable efficient heat supply planning and allow the selection and utilization of output values according to their purpose. For daily heat demand forecasts for the proposed model, the overall MAPE improved from 5.3~6.1% for individual models to 5.2% and the forecast for holiday heat demand greatly improved from 4.9~7.9% to 2.9%. The data in this study utilized 34 months of heat demand data from a specific apartment complex provided by the Korea District Heating Corp. (January 2015 to October 2017).

• Journal of Energy Engineering
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• v.28 no.4
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• pp.1-7
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• 2019

In response to the recent increase in demand for hydrogen stations, the Ministry of Trade and Industry has enacted and promulgated special notifications to enable the installation of hydrogen stations in the form of the combined complex in existing automotive fuel supply facilities such as LPG, CNG, and gas stations. Hydrogen multi energy filling stations haven't been operated yet in Korea till the establishment of special standards, so it is necessary create special standards by considering all Korean environmental characteristics such as four seasons and daily crossings. In this study, we collected and analyzed the charging data of Ulsan LPG-Hydrogen Multi Fueling Station installed for the first time in Korea. The data are hourly temperature and pressure data from compressors, storage vessels and dispensers. We used the data collected for a year, including the highest temperature and the lowest temperature in Ulsan to compare seasonal characteristics. As a result, it was found that the change of the outside temperature affects the initial temperature of the vehicle's container of the hydrogen car, which finally affects the charging time and the charging speed of the vehicle. There was no effect on vehicle containers because the limit temperature suggested by the Korean Hydrogen Station Standard(KGS FP217) and the US Filling Protocol(SAE J2601) was not exceeded.

• Horticultural Science & Technology
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• v.28 no.6
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• pp.937-941
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• 2010

This study investigated the effect of global warming on full bloom date (FBD) of 'Niitaka' pear ($Pyrus$ $pyrifolia$ Nakai) tree by calculating the development stage index by hourly temperatures recorded at Pear Research Station, estimating the distribution of average FBD and the change of FBD according to temperature rising by integrating development rate at 67 locations in Korea Meteorological Administration site. Development stage index of 'Niitaka' pear tree was 0.9593 at Naju location. Differences between full bloom dates observed at Cheonan region and predictions by development stage index were 0-7 days, and matched year was 35.3%. FBDs of 67 locations were distributed from April 4 to May 28. When yearly temperature was raised 1, 2, 3, 4, and $5^{\circ}C$ at 67 locations, predicted FBD was accelerated at most of the locations. However, FBD decelerated at south coast locations from $3^{\circ}C$ rise and did not bloom at 'Gosan', 'Seogwipo', and 'Jeju' locations from $4^{\circ}C$ rise. When monthly temperature was raised 1, 3, and $5^{\circ}C$ at 67 locations, predicted FBD was the most accelerated at March temperature rise, and followed by April, February, January and December. Therefore, global warming will cause acceleration of the full bloom date at pear production areas in Korea.

• Journal of Korean Society for Atmospheric Environment
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• v.22 no.5
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• pp.661-678
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• 2006

Products developed in this research is a software which can transfer the type of shape(.shp) into the type of ascii using the land cover data and the topography data in the metropolitan area of Seoul. In addition, it can calculate the $CO_2$ flux according to distribution of plants within the land cover data. The $CO_2$ flux is calculated by the experimental equation which is compose of the meteorological parameters such as the solar radiation and the air temperature. The net flux was shown in about $-19ton/km^2$ by removing $CO_2$ through the photosynthesis during daytime, and in 2 ton/km2 by producing it through the respiration during nighttime on 10 August 2004, the maximum day of air temperature during the period of 3yr(2001 to 2004), in the metropolitan area of Seoul. Spatial distribution of the air temperature and the wind field is simulated by substituting the middle classification of the land cover map data, upgraded by the Korean Ministry of Environment(KME), for the land-use data of the United States Geological Survey(USGS) within the Meteorological Mesoscale Model Version 5(MM5) on 10 August 2006 in the metropolitan area of Seoul. Difference of the air temperature between both data was shown in the maximum range of $-2^{\circ}C\;to\;2.9^{\circ}C$, and the air temperature due to the land use data of KME was higher than that of USGS in average $0.4^{\circ}C$. Also, those of wind vectors were meanly lower than that of USGS in daytime and nighttime. Furthermore, the hourly time series of Volatile Organic Components(VOCs) is calculated by using the Biosphere Emission and Interaction System Version 2(BEIS2) including the new land cover data and the meteorological parameters such as the air temperature and so]ar insolation. It is possible to calculate the concentration of ozone due to the biogenic emission of VOCs.

• Journal of Environmental Impact Assessment
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• v.24 no.2
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• pp.111-118
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• 2015

This study was conducted for the establishment of statistical method to determine the representative air quality monitoring station representing long-term ozone trends of Seoul. In this study, hourly ozone concentrations from 2002 to 2011 were used for further analysis. KZ-filter, correlation matrix, cluster analysis, and Kriging method were applied to select the representative station. The analysis based on correlation matrix found that long-term trend of ozone concentrations measured at Sinjung, Sadang, and Bun-dong showed a high correlation. The cluster analysis found that the former three stations belonged to the same cluster. The analysis based on Kriging method also showed that the former three stations were highly correlated with other stations in spatial distribution. Considering these results and the highest correlation coefficient of Sinjung station, the Sinjung station was the most suitable as the representative station used to understand the long-term ozone trend of Seoul. This result could be applied to understand long-term trend of other pollutants. Furthermore, this result can also be used to assess the appropriacy of spatial distribution of national air quality monitoring stations.

• Horticultural Science & Technology
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• v.29 no.3
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• pp.181-186
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• 2011

To predict the bud bursting date of 'Campbell Early' grapevines, the bud developmental rate (DVR) models were constructed. The DVRs for bud bursting were calculated from the demanded times at controlled air temperatures. The DVRs were examined on the 'Campbell Early' grapevines incubated in three different temperatures at 4.6, 11.8, and $16.6^{\circ}C$. The DVR increased exponentially or linearly on the air temperature with a slope of about 0.0019. The DVR equations were computed as $DVR=0.0249+0.0020e^{0.1654x}$ or DVR = 0.0019x + 0.0187. These DVR equations offered developmental indices and predicted dates for bud bursting with air temperature data. The DVR equations were validated to the bud bursting data observed in the field. When bud bursting dates were calculated with daily temperature data, the root mean squared error (RMSE) between the observed and the predicted dates was less than 4 days. When those were calculated with hourly temperature data, on the other hand, the RMSE was less than 3 days. These results suggest that the DVR models are useful to predict bud bursting date of 'Campbell Early' grapevines.

• Journal of the Korean GEO-environmental Society
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• v.14 no.7
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• pp.51-56
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• 2013

Recently, a top record of hourly-based rainfall has been changed annually and flood damages of road have increased. To solve this problem, pavements for drainage were developed and practically constructed but there was no considerations on sub-base. In this research, we proposed standard for distribution of particle size of sub-base to consider strength characteristic and drainage property. We focused to compare coefficients strength and permeability by laboratory tests. Prior to tests, 4 samples were selected under the consideration on the international or domestic design guideline. In the tests, strength characteristics were compared with resilient modulus. Also, permeability characteristics were compared with coefficient of upward and downward permeability. Resilient modulus was determined with MR test using cyclic triaxial testing system. Two permeability tests were carried out. One is variable head permeability test for downward drainage and the other is Rowe Cell test for upward drainage. In the case of Rowe Cell test, middle-sized sampler with 150mm diameter was used for this study. Consequentially, we tried to find the optimum distribution of particle size to satisfy both of strength and permeability characteristics for sub-base.