• Korean Journal of Agricultural and Forest Meteorology
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• v.19 no.3
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• pp.93-101
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• 2017

The distribution of inter-annual variation in temperature would help evaluate the likelihood of a climatic risk and assess suitable zones of crops under climate change. In this study, we evaluated two methods to estimate the standard deviation of temperature in the areas where weather information is limited. We calculated the monthly standard deviation of temperature by collecting temperature at 0600 and 1500 local standard time from 10 automated weather stations (AWS). These weather stations were installed in the range of 8 to 1,073m above sea level within a mountainous catchment for 2011-2015. The observed values were compared with estimates, which were calculated using a geospatial correction scheme to derive the site-specific temperature. Those estimates explained 88 and 86% of the temperature variations at 0600 and 1500 LST, respectively. However, it often underestimated the temperatures. In the spring and fall, it tended to had different variance (e.g., increasing or decreasing pattern) from lower to higher elevation with the observed values. A regression analysis was also conducted to quantify the relationship between the standard deviation in temperature and the topography. The regression equation explained a relatively large variation of the monthly standard deviation when lapse-rate corrected temperature, basic topographical variables (e.g., slope, and aspect) and topographical variables related to temperature (e.g., thermal belt, cold air drainage, and brightness index) were used. The coefficient of determination for the regression analysis ranged between 0.46 and 0.98. It was expected that the regression model could account for 70% of the spatial variation of the standard deviation when the monthly standard deviation was predicted by using the minimum-maximum effective range of topographical variables for the area.

• Environmental and Resource Economics Review
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• v.27 no.2
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• pp.287-313
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• 2018

This paper measures and analyzes cooling and heating demand in Korean electricity demand using time-varying temperature response functions and cooling and heating temperature effects. We fit the model to Korean data for residential and commercial sector over 1999:01~2016:12 and the estimation results show that the growth rate of heating demand is much higher than that of base and cooling demand, and especially the growth rate of heating demand in commercial sector is much higher. And we define the temperature-normalized demand conditioning that monthly temperatures are assumed as average monthly temperatures. The growth rate of heating demand in the estimated temperature-normalized demand is higher than that in the real demand. Our results are expected to be a base data for Winter Demand Management and short-term electricity demand forecasting.

• Ecology and Resilient Infrastructure
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• v.2 no.3
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• pp.198-205
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• 2015

Impacts of climate change on aquatic ecosystems range from changes in physiological processes of aquatic organisms to species distribution. In this study, MaxEnt that has high prediction power without nonoccurrence data was used to simulate fish distribution changes in the Geum river watershed according to climate change. The fish distribution in 2050 and 2100 was predicted with RCP 8.5 climate change scenario using fish occurrence data (a total of 47 species, including 17 endemic species) from 2007 to 2009 at 134 survey points and 9 environmental variables (monthly lowest, highest and average air temperature, monthly precipitation, monthly lowest, highest and average water temperature, altitude and slope). The fitness of MaxEnt modeling was successful with the area under the relative operating characteristic curve (AUC) of 0.798, and environmental variables that showed a high level of prediction were as follows: altitude, monthly average precipitation and monthly lowest water temperature. As climate change proceeds until 2100, the probability of occurrence for Odontobutis interrupta and Acheilognathus yamatsuatea (endemic species) decreases whereas the probability of occurrence for Microphysogobio yaluensis and Lepomis macrochirus (exotic species) increases. In particular, five fish species (Gnathopogon strigatus, Misgurnus mizolepis, Erythroculter erythropterus, A. yamatsuatea and A. koreensis) were expected to become extinct in the Geum river watershed in 2100. In addition, the species rich area was expected to move to the northern part of the Geum river watershed. These findings suggest that water temperature increase caused by climate change may disturb the aquatic ecosystem of Geum river watershed significantly.

• Ocean and Polar Research
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• v.34 no.2
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• pp.201-218
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• 2012

The purpose of this study is to investigate climatological variations from the sea surface temperature (SST), chlorophyll-a concentration (Chl-a), and phytoplankton size class (PSC), using NOAA AVHRR, SeaWiFS, and MODIS data in the South Sea of Korea (SSK) and East China Sea (ECS). 26-year monthly SST and 13-year monthly Chl-a and PSC data, separated by whole and nine-different areas, were used to understand seasonal and inter-annual variations. SST and Chl-a clearly showed seasonal variations: higher SST and Chl-a were observed during the summer and spring, and lower values occurred during the winter and summer. The annual and monthly SST over 26 years increased by $0.2{\sim}1.0^{\circ}C$. The annual and monthly Chl-a concentration over 13 years decreased by $0.2{\sim}1.1mg/m^3$. To determine more detailed spatial and temporal variations, we used the combined data with monthly SST, Chl-a, and PSC. Between 1998 and 2010, the inter-annual trend of Chl-a decreased, with decreasing micro- and nano-size plankton, and increasing pico-size plankton. In regional analysis, the west region of the study area was spatially and temporally correlated with the area dominated by decreasing micro-size plankton; while the east region was less sensitive to coastal and land effects, and was dominated by increasing pico-size plankton. This phenomenon is better related to one or more forcing factors: the increased stratification of ocean driven by changes occurring in spatial variations of the SST caused limited contributions of nutrients and changed marine ecosystems in the study area.

• Magazine of the Korean Society of Agricultural Engineers
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• v.42 no.4
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• pp.67-75
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• 2000

A simulation model for reservoir sizing was developed to be applied in a region with insufficient hydrological data. Reservoir storage balance equation was formulated on a monthly basis. Gajiyama equation was generalized to estimate monthly reservoir inflow more accurately. Monthly evaporation equation on a reservoir water surface was introduced , which was functioned with monthly mean temperature. Generalized Gajiyama equation was applied to estmate reservoir inflow of the Sayeon dam. Nash-Sutcliffe's model efficiency was 0.793. Using developed model for reservoir sizing, water supply capacity was analyzed with 118.000㎥/day on the Sayeon dam. This showed a reasonable result as compared with 110000㎥/day in other technical report. For general application of developed model, a virtual reservoir was considered and its dta of surface area and volume by elevation was prepared using DEM. Using the model, size of reservoir was determined and water supply capacity was anlayzed on a virtual reservoir.

• Korean Journal of Agricultural and Forest Meteorology
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• v.20 no.3
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• pp.252-261
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• 2018

This study was conducted to develop a method to generate daily maximum and minimum temperatures using monthly data. We analyzed 30-year daily weather data of the 23 meteorological stations in South Korea and elucidated the parameters for predicting annual trend (center value ($\hat{U}$), amplitude (C), deviation (T)) and daily fluctuation (A, B) of daily maximum and minimum temperature. We use national average values for C, T, A and B parameters, but the center value is derived from the annual average data on each stations. First, daily weather data were generated according to the occurrence of rainfall, then calibrated using monthly data, and finally, daily maximum and minimum daily temperatures were generated. With this method, we could generate daily weather data with more than 95% similar distribution to recorded data for all 23 stations. In addition, this method was able to generate Growing Degree Day(GDD) similar to the past data, and it could be applied to areas not subject to survey. This method is useful for generating daily data in case of having monthly data such as climate change scenarios.

• Korean Journal of Agricultural and Forest Meteorology
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• v.4 no.4
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• pp.203-212
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• 2002

This study was conducted to remove the urban heat island effects embedded in the interpolated surfaces of daily minimum temperature in the Korean Peninsula. Fifty six standard weather stations are usually used to generate the gridded temperature surface in South Korea. Since most of the weather stations are located in heavily populated and urbanized areas, the observed minimum temperature data are contaminated with the so-called urban heat island effect. Without an appropriate correction, temperature estimates over rural area or forests might deviate significantly from the actual values. We simulated the spatial pattern of population distribution within any single population reporting district (city or country) by allocating the reported population to the "urban" pixels of a land cover map with a 30 by 30 m spacing. By using this "digital population model" (DPM), we can simulate the horizontal diffusion of urban effect, which is not possible with the spatially discontinuous nature of the population statistics fer each city or county. The temperature estimation error from the existing interpolation scheme, which considers both the distance and the altitude effects, was regressed to the DPMs smoothed at 5 different scales, i.e., the radial extent of 0.5, 1.5, 2.5, 3.5 and 5.0 km. Optimum regression models were used in conjunction with the distance-altitude interpolation to predict monthly normals of daily minimum temperature in South Korea far 1971-2000 period. Cross validation showed around 50% reduction in terms of RMSE and MAE over all months compared with those by the conventional method.conventional method.

• Journal of Korean Society for Atmospheric Environment
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• v.25 no.4
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• pp.304-315
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• 2009

Numerical simulations were carried out to understand the effect of Sea Surface Temperature (SST) spatial distribution on regional circulation. A three-dimensional non-hydrostatic atmospheric model RAMS, version 6.0, was applied to examine the impact of SST forcing on regional circulation. New Generation Sea Surface Temperature (NGSST) data were implemented to RAMS to compare the results of modeling with default SST data. Several numerical experiments have been undertaken to evaluate the effect of SST for initialization. First was the case with NGSST data (Case NG), second was the case with RAMS monthly data (Case RM) and third was the case with seasonally averaged RAMS monthly data (Case RS). Case NG showed accurate spatial distributions of SST but, the results of RM and RS were $3{\sim}4^{\circ}C$ lower than buoy observation data. By analyzing practical sea surface conditions, large difference in horizontal temperature and wind field for each run were revealed. Case RM and Case RS showed similar horizontal and vertical distributions of temperature and wind field but, Case NG estimated the intensity of sea breeze weakly and land breeze strongly. These differences were due to the difference of the temperature gradient caused by different spatial distributions of SST. Diurnal variations of temperature and wind speed for Case NG indicated great agreement with the observation data and statistics such as root mean squared error, index of agreement, regression were also better than Case RM and Case RS.

• Proceedings of the Korea Water Resources Association Conference
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• 2021.06a
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• pp.373-373
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• 2021

Rainfall forecasting is an important issue that is applied in many areas, such as agriculture, flood warning, and water resources management. In this context, this study proposed a statistical and machine learning-based forecasting model for monthly rainfall. The Bayesian Gaussian process was chosen to optimize the hyperparameters of the Stacked Long Short-term memory (SLSTM) model. The proposed SLSTM model was applied for predicting monthly precipitation of Seoul station, South Korea. Data were retrieved from the Korea Meteorological Administration (KMA) in the period between 1960 and 2019. Four schemes were examined in this study: (i) prediction with only rainfall; (ii) with deseasonalized rainfall; (iii) with rainfall and minimum temperature; (iv) with deseasonalized rainfall and minimum temperature. The error of predicted rainfall based on the root mean squared error (RMSE), 16-17 mm, is relatively small compared with the average monthly rainfall at Seoul station is 117mm. The results showed scheme (iv) gives the best prediction result. Therefore, this approach is more straightforward than the hydrological and hydraulic models, which request much more input data. The result indicated that a deep learning network could be applied successfully in the hydrology field. Overall, the proposed method is promising, given a good solution for rainfall prediction.

• Journal of Korean Society of Forest Science
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• v.98 no.6
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• pp.733-739
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• 2009

This study was carried out to analyze the relationship between major climatic factors (monthly temperature, precipitation) and radial growth of Korean Pine (Pinus koraiensis) in Inje-gun(Mt. Seorak), Sanchung-gun and Namwon-si(Mt. Jiri). Radial growth measurements were crossdated and correlated with climate variables. The climatic factors used were monthly mean temperature and monthly precipitation from August of previous year to September of the current year. In Inje-gun, radial growth was negatively correlated with monthly mean temperature in April, May, June, and August of the current year and was positively correlated with precipitation in August, October of the previous year and in April, August of the current year. Sanchung-gun showed a negative correlation with monthly mean temperature in September of the previous year and August of the current year but positive correlation in January of the current year. In contrast, precipitation in September of the previous year and August of the current year showed a positive influence. The results suggest that high temperature together with low precipitation may thus cause water stress and thereby limit radial growth in this region. In Namwon-si, the climategrowth relationships show that radial growth was negatively correlated with monthly mean temperature in August, October of the previous year and June of the current year. But August of the previous year and May, August of the current year in precipitation was positively correlated with radial growth. In the study areas, precipitation plays an important role for the radial growth of Kroean Pine (Pinus koraiensis). The results suggest that precipitation is important in the radial growth because water deficit in trees would take place in response to high temperature in the study area. Also there was some differences with study areas in the effects of both climatic variables probably as a result of different stand structure and geographical conditions and micro-climate.