• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.20 no.3
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• pp.59-64
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• 2020

In the Paris Climate Agreement, Korea submitted an ambitious goal of reducing the greenhouse gas emission forecast (BAU) by 37% by 2030. And as one of the countermeasures, a smart grid, an intelligent power grid, was presented. In order to apply the smart grid, EMS(Energy Management System) needs to be installed and operated in various fields, and the supply is delayed due to the lack of awareness of users and the limitations of system ROI. Therefore, recently, various data analysis and control technologies have been proposed to increase the efficiency of the installed EMS. In this study, we present a measurement control algorithm that analyzes and predicts big data collected by IoT using a SARIMA model to check and operate energy consumption of public sports facilities.

• Journal of the Korean Data and Information Science Society
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• v.28 no.5
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• pp.1133-1144
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• 2017

Cluster analysis with meteorological data allows to segment meteorological region based on meteorological characteristics. By the way, meteorological observed data are not adequate for cluster analysis because meteorological stations which observe the data are located not uniformly. Therefore the clustering of meteorological observed data cannot reflect the climate characteristic of South Korea properly. The clustering of $5km{\times}5km$ gridded data derived from a numerical model, on the other hand, reflect it evenly. In this study, we analyzed long-term grid data for temperatures and precipitation using cluster analysis. Due to the monthly difference of climate characteristics, clustering was performed by month. As the result of K-Means cluster analysis is so sensitive to initial values, we used initial values with Ward method which is hierarchical cluster analysis method. Based on clustering of gridded data, cluster of meteorological stations were determined. As a result, clustering of meteorological stations in South Korea has been made spatio-temporal segmentation.

• Journal of Climate Change Research
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• v.6 no.2
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• pp.61-72
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• 2015

In this study, we have investigated monthly changes in temperature extremes in South Korea for the past (1921~2010) and the future (2011~2100). We used seven stations' (Gangneung, Seoul, Incheon, Daegu, Jeonju, Busan, Mokpo) data from KMA (Korea Meteorological Administration) for the past. For the future we used the closest grid point values to observations from the RCP8.5 scenario of 1 km resolution. The Expert Team on Climate Change Detection and Indices (ETCCDI)'s climate extreme indices were employed to quantify the characteristics of temperature extremes change. Temperature extreme indices in summer have increased while those in winter have decreased in the past. The extreme indices are expected to change more rapidly in the future than in the past. The number of frost days (FD) is projected to decrease in the future, and the occurrence period will be shortened by two months at the end of the $21^{st}$ century (2071~2100) compared to the present (1981~2010). The number of hot days (HD) is projected to increase in the future, and the occurrence period is projected to lengthen by two months at the end of the $21^{st}$ century compared to the present. The annual highest temperature and its fluctuation is expected to increase. Accordingly, the heat damage is also expected to increase. The result of this study can be used as an information on damage prevention measures due to temperature extreme events.

• Journal of Korean Society for Atmospheric Environment
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• v.33 no.6
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• pp.544-553
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• 2017

There is an overall guideline of the installation of air quality monitoring stations in Korea, but specified steps for the selection of monitoring sites for hazardous air pollutants(HAPs) are not provided. In this study, we proposed a systematic method for the selection of monitoring sites for HAPs using geographic information system (GIS). As a case study, the Seoul metropolitan area (Seoul, Incheon, and Gyeonggi Province) was chosen, and 15 factors including population, vehicle registration, and emission data were compiled for each grid cell ($7km{\times}7km$). The number of factors above the top 30% of individual data for each grid cell was used to select priority monitoring sites for HAPs. In addition, several background sites were added for data comparison and source identification. Three scenarios were suggested: Scenario 1 with 7 sites, Scenario 2 with 17 sites, and Scenario 3 with 30 sites. This proposal is not the final result for an intensive monitoring program, but it is an example of method development for selecting appropriate sampling sites. These results can be applied not only to HAPs monitoring in megacities but also to the national HAPs monitoring network.

• Korean Journal of Agricultural and Forest Meteorology
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• v.14 no.3
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• pp.132-141
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• 2012

Climate change impact assessment of cereal crop production in South Korea was performed using land attributes and daily weather data at a farm scale as inputs to crop models. Farmlands in South Korea were grouped into 68 crop-simulation zone units (CZU) based on major mountains and rivers as well as existing land use information. Daily weather data at a 1-km grid spacing under the A1B- and RCP8.5 scenarios were generated stochastically to obtain decadal mean of daily data. These data were registered to the farmland grid cells and spatially averaged to represent climate conditions in each CZU. Monthly climate data for each decade in 2001~2100 were transformed to 30 sets of daily weather data for each CZU by using a stochastic weather generator. Soil data and crop management information for 68 CZU were used as inputs to the CERES-rice, CERE-barley and CROPGRO-soybean models calibrated to represent the genetic features of major domestic cultivars in South Korea. Results from the models suggested that the heading or flowering of rice, winter barley and soybean could be accelerated in the future. The grain-fill period of winter barley could be extended, resulting in much higher yield of winter barley in most CZUs than that of rice. Among the three major cereal grain crops in Korea, rice seems most vulnerable to negative impact of climate change, while little impact of climate change is expected on soybeans. Because a positive effect of climate change is projected for winter barley, policy in agricultural production should pay more attention to facilitate winter barley production as an adaptation strategy for the national food security.

• Journal of the Korean Society of Visualization
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• v.5 no.2
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• pp.9-13
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• 2007

Typhoon simulation based on dynamical forecasting results is demonstrated by utilizing geodesic model GME (operational global numerical weather prediction model of German Weather Service). It is based on uniform icosahedral-hexagonal grid. The GME gridpoint approach avoids the disadvantages of spectral technique as well as the pole problem in latitude-longitude grids and provides a data structure extremely well suited to high efficiency on distributed memory parallel computers. In this study we made an attempt to simulate typhoon 'NARI' that passed over the Korean Peninsula in 2007. GME has attributes of numerical weather prediction model and its high resolution can provide details on fine scale. High resolution of GME can play key role in the study of severe weather phenomenon such as typhoons. Simulation of future typhoon that is assumed to occur under the global warming situation shows that the life time of that typhoon will last for a longer time and the intensity will be extremely stronger.

• Journal of the Korean Society of Hazard Mitigation
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• v.7 no.5
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• pp.73-78
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• 2007

It has been increasing that significant loss of life and property due to global wanning and extreme weather, and the climate and temperature changes in Korea Peninsula are now greater than the global averages. Climate information from regional climate models(RCM) at a finer resolution than that of global climate models(GCM) is required to predictclimate and weather variability, changes, and impacts. The new surface boundary conditions(SBCs) development is motivated by the limitations and inconsistencies of existing SBCs that have influence on model predictability. A critical prerequisite in constructing SBCs is that the raw data should be accurate with physical consistency across all relevant parameters and must be appropriately filled for missing data if any. The aim of this study is to construct appropriate SBCs for the RCM in Asia domain which will be used for the prevention of disasters due to climate changes. As all SBCs have constructed onto the 30km grid-mesh of the RCM suitable for Asia applications, they can be also used for other distributed models for climate and hydrologic studies.

• Journal of The Korean Society of Agricultural Engineers
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• v.61 no.2
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• pp.1-11
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• 2019

Remote sensing products have long been used to monitor and forecast natural disasters. Satellite-derived rainfall products are becoming more accurate as space and time resolution improve, and are widely used in areas where measurement is difficult because of the periodic accumulation of images in large areas. In the case of North Korea, there is a limit to the estimation of precipitation for unmeasured areas due to the limited accessibility and quality of statistical data. CHIRPS (Climate Hazards Group InfraRed Precipitation with Stations) is global satellite-derived rainfall data of 0.05 degree grid resolution. It has been available since 1981 from USAID (U.S. Agency for International Development), NASA (National Aeronautics and Space Administration), NOAA (National Oceanic and Atmospheric Administration). This study evaluates the applicability of CHIRPS rainfall products for South Korea and North Korea by comparing CHIRPS data with ground observation data, and analyzing temporal and spatial drought trends using the Standardized Precipitation Index (SPI), a meteorological drought index available through CHIRPS. The results indicate that the data set performed well in assessing drought years (1994, 2000, 2015 and 2017). Overall, this study concludes that CHIRPS is a valuable tool for using data to estimate precipitation and drought monitoring in Korea.

• Journal of the Korean Association of Geographic Information Studies
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• v.17 no.3
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• pp.160-174
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• 2014

The purpose of this paper is to develop a meso-scale grid-based continuous hydrological model and apply to assess the future watershed hydrology by climate change. The model divides the watershed into rectangular cells, and the cell profile is divided into three layered flow components: a surface layer, a subsurface unsaturated layer, and a saturated layer. Soil water balance is calculated for each grid cell of the watershed, and updated daily time step. Evapotranspiration(ET) is calculated by Penman-Monteith method and the surface and subsurface flow adopts lag coefficients for multiple days contribution and recession curve slope for stream discharge. The model was calibrated and verified using 9 years(2001-2009) dam inflow data of two watersheds(Chungju Dam and Soyanggang Dam) with 1km spatial resolution. The average Nash-Sutcliffe model efficiency was 0.57 and 0.71, and the average determination coefficient was 0.65 and 0.72 respectively. For the whole Han river basin, the model was applied to assess the future climate change impact on the river bsain. Five IPCC SRES A1B scenarios of CSIRO MK3, GFDL CM2_1, CONS ECHO-G, MRI CGCM2_3_2, UKMO HADGEMI) showed the results of 7.0%~27.1 increase of runoff and the increase of evapotranspiration with both integrated and distributed model outputs.

• Journal of Korean Society of Forest Science
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• v.87 no.3
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• pp.358-365
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• 1998

To offer the basic information for sustainable production of forest resources and conservation of the global environment, change in potential natural vegetation (PNV) associated with climate change due to doubling atmospheric carbon dioxide ($2{\times}CO_2$) was estimated with the global natural vegetation mapping system based an K${\ddot{o}}$ppen scheme. The system interpolates climate data spherically to each grid cell, determines the vegetation types onto the grid cell, and produces potential vegetation map and area on the globe and continents. The climate data consist of the current, ($1{\times}CO_2$) climate prior to AD 1958 observed at some 2,000 stations and the doubling ($2{\times}CO_2$) climate estimated from Meteorological Research Institute of Japan. The vegetation zone under the $2{\times}CO_2$ climate scenario expanded mainly toward the poles due to the rise in temperature. The changed PNV area on the globe amounts to 1/3 (4.91 billion (G) ha) of the total land area (15.04 Gha). Kappa statistic for judging agreement between the patterns of vegetation distribution under $1{\times}CO_2$ climate and $2{\times}CO_2$ climates shows good agreement (0.63) for the globe as a whole. The most stable areas are desert and ice. The potential forest area (PFA) was estimated at 6.82 Gha of the land area in $2{\times}CO_2$ climate scenario. In terms of continental changes in PFA, North America and Asis are increased under the $2{\times}CO_2$ climate. However, the potential forest arms of the other continents are decreased by the climate. Europe has no change in the PFA. Especially, the expansion of desert area in Oceania would be accelerated by the $2{\times}CO_2$ climate.