• Atmosphere
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• v.31 no.2
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• pp.143-156
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• 2021

This study investigates the impacts of land cover change due to urbanization on the Urban Heat Island Intensity (UHII) and the Heat Index (HI) over the Seoul metropolitan area using the Unified Model (UM) with the Met Office Reading Urban Surface Exchange Scheme (MORUSES) during the heat wave from 16, July to 5, August 2018. Two simulations are performed with the late 1980s land-use (EXP1980) and the late 2000s land-use (EXP2000). EXP2000 is verified using Automatic Weather Station (AWS) data from 85 points in the study area, and observation sites are classified into two categories according to the urban fraction change over 20 years; Category A is 0.2 or less increase, and Category B is 0.2 or more increase. The 1.5-m temperature and relative humidity in Category B increase by up to 1.1℃ and decreased by 7% at 1900 LST and 2000 LST, respectively. This means that the effect of the urban fraction changes is higher at night. UHII increases by up to 0.3℃ in Category A and 1.3℃ in Category B at 1900 LST. Analysis of the surface energy balance shows that the heat store for a short time during the daytime and release at nighttime with upward sensible heat flux. As a result of the HI, there is no significant difference between the two experiments during the daytime, but it increases 1.6℃ in category B during the nighttime (2200 LST). The results indicate that the urbanization increase both UHII, and HI, but the times of maximum difference between EXP1980 and EXP2000 are different.

• Atmosphere
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• v.32 no.3
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• pp.179-189
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• 2022

The purpose of this study is to build and evaluate a high-resolution (50 m) KMAPP (Korea Meteorological Administration Post Processing) reflecting building data. KMAPP uses LDAPS (Local Data Assimilation and Prediction System) data to detail ground wind speed through surface roughness and elevation corrections. During the detailing process, we improved the vegetation roughness data to reflect the impact of city buildings. AWS (Automatic Weather Station) data from a total of 48 locations in the metropolitan area including Seoul in 2019 were used as the observation data used for verification. Sensitivity analysis was conducted by dividing the experiment according to the method of improving the vegetation roughness length. KMAPP has been shown to improve the tendency of LDAPS to over simulate surface wind speeds. Compared to LDAPS, Root Mean Square Error (RMSE) is improved by approximately 23% and Mean Bias Error (MBE) by about 47%. However, there is an error in the roughness length around the Han River or the coastline. Accordingly, the surface roughness length was improved in KMAPP and the building information was reflected. In the sensitivity experiment of improved KMAPP, RMSE was further improved to 6% and MBE to 3%. This study shows that high-resolution KMAPP reflecting building information can improve wind speed accuracy in urban areas.

• Proceedings of the Korea Water Resources Association Conference
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• 2021.06a
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• pp.383-383
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• 2021

강수는 수자원 분석 및 지리학적 연구에 가장 핵심적으로 쓰이는 수문인자이며, 최근 기후변화와 방재 관련한 다양한 연구에서 정확한 강수자료의 중요성이 부각되고 있다. 특히, 강수는 지표에서의 유출, 침투, 증발 등 다양한 수문현상으로 이어지므로, 수문순환, 물수지 분석에 있어 강우강도 등 강수 발생 양상과 유형에 대한 정확한 자료는 필수불가결하다. 강수량은 Automatic Weather Station (AWS)을 통해 비교적 정확하게 측정되고 있으나, 이러한 계측자료는 기상학적, 지형적 영향을 크게 받으며 대표성이 좁다는 단점을 가지고 있어 유출 및 기후 등 공간적 범위를 대상으로 한 연구에 활용하기에 한계점을 가지고 있다. 이러한 한계점을 극복하기 위해 지상강우레이더를 통한 국지적 강수자료 및 인공위성 기반 전 지구적 강수 관측 자료가 활용되고 있다. 특히 인공위성을 활용한 강우 측정방법은 미계측 유역에서 수자원 측정 및 관리 계획을 세우거나 전 지구적으로 장기적 변화를 분석하는데 있어 가장 활용도가 높다. National Aeronautics and Space Administration (NASA)의 Tropical Rainfall Measuring Mission (TRMM)을 포함한 기존 강수측정 보조 위성에 더하여 2014년 Global Precipitation Measurement (GPM) 핵심 위성이 발사된 이후 다양한 기관에서 여러 인공위성을 결합한 강수 산출물들을 제공하고 있다(NASA-IMERG, JAXA-GSMAP, NOAA-CMORPH). 본 연구에서는 세 가지 위성 기반 강수 자료의 산출 알고리즘을 비교□분석하고, 강우강도에 따른 산출물들의 정확도를 평가하였다. 본 연구결과는 높은 강우강도 발생 시 나타나는 위성 강수자료의 불확실성을 개선하는 데 기여할 수 있을 것으로 판단되며, 이후 신뢰도 높은 다중 위성 융합 강수 산출물을 구현하기 위한 바탕이 될 것으로 기대된다.

• Korean Journal of Environment and Ecology
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• v.32 no.3
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• pp.303-312
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• 2018

This study was conducted to investigate the relationship between the decrease of forest biomass by forest thinning and the change of temperature in the natural forest by measuring forest biomass and temperature before and after forest thinning in the Pusan National University forest where afforestation had been carried out. We intended to investigate the relationship between the forest biomass, estimated by calculating the Basal area, Crown area and Crown volume using the same formula to the same quadrat before and after forest thinning, and the forest temperature. Temperature measurement was carried out on April 20, 2016 through 28 before forest thinning, July 26, 2016 through November 4 around the time of forest thinning, and April 15, 2017 through May 8 after forest thinning. A temperature data logger was installed to point north at the height of 2.0 m above the ground in the center of the quadrat to record data every 10 minutes during the measurement periods. We used the AWS (Automatic Weather Station) data of the Dongnae-gu area located in the nearby city because it was difficult to set the control group since the whole forest was the subject to the forest thinning. The analysis of the relationship between forest biomass change and temperature showed that the change in temperature inside the forest was the greatest in the midday (12:00 - 15: 00) and was highly correlated with the Crown volume in the forest biomass. The temperature increase was much larger (average $1.91^{\circ}C$) 1 year after forest thinning than immediately after forest thinning (average $0.74^{\circ}C$). The comparison of the decrease rate of Crown volume and the increase in temperature showed that the Pitch pine community, which showed the highest decrease of Crown volume by 15.4%, recorded the highest temperature rise of $1.06^{\circ}C$ immediately after forest thinning and $2.49^{\circ}C$ 1 year after forest thinning. The Pitch pine-Korean red pine community, which showed the lowest Crown volume reduction rates with 5.0%, recorded no significant difference immediately after forest thinning but a temperature rise of $0.92^{\circ}C$ 1 year after forest thinning. The results confirmed that the decrease of forest biomass caused by forest thinning led to a rapid increase of the internal temperature. The fact that the temperature increase was more severe after 1 year than immediately after forest thinning confirmed that the microclimate changes due to the removed biomass cannot be recovered in a short time.

• The Journal of Engineering Geology
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• v.26 no.2
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• pp.197-205
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• 2016

Rainfall-induced landslide disaster case histories are typically required to establish critical lines based on the decrease coefficient for judging the likelihood of slope collapse or failure; however, reliably setting critical lines is difficult because the number of nationwide disaster case histories is insufficient and not well distributed across the region. In this study, we propose a method for setting the critical area to judge the risk of slope collapse without disaster case history information. Past 10 years rainfall data based on decrease coefficient are plotted as points, and a reference line is established by connecting the outermost points. When realtime working rainfall cross the reference line, warning system is operating and this system can be utilized nationwide through setting of reference line for each AWS (Automatic Weather Station). Warnings were effectively predicted at 10 of the sites, and warnings could have been issued 30 min prior to the landslide movement at eight of the sites. These results indicate a reliability of about 67%. To more fully utilize this model, it is necessary to establish nationwide rainfall databases and conduct further studies to develop regional critical areas for landslide disaster prevention.

• Journal of Environmental Impact Assessment
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• v.33 no.1
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• pp.9-17
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• 2024

The purpose of this study is to select and predict optimal heatwave indices for describing and predicting heat-related illnesses. Regression analysis was conducted using Heat-related illness surveillance system data for a number of heat-related illnesses and meteorological data from the Korea Meteorological Administration's Automatic Weather Station (AWS) for the period from 2021 to 2023. Daily average temperature, daily maximum temperature, daily average Wet Bulb Globe Temperature (WBGT), and daily maximum WBGT values were calculated and analyzed. The results indicated that among the four indicators, the daily maximum WBGT showed the highest suitability with an R² value of 0.81 and RMSE of 0.98, with a threshold of 29.94 Celsius. During the entire analysis period, there were a total of 91 days exceeding this threshold, resulting in 339 cases of heat-related illnesses. Predictions of heat-related illness cases from 2021 to 2023 using the regression equation for daily maximum WBGT showed an accuracy with less than 10 cases of error annually, demonstrating a high level of precision. Through continuous research and refinement of data and analysis methods, it is anticipated that this approach could contribute to predicting and mitigating the impact of heatwaves.

• Korean Journal of Agricultural and Forest Meteorology
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• v.8 no.1
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• pp.36-44
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• 2006

In this study, we constructed a forest fire danger map in the Yeongdong area of Gangwon-do and Northeastern area of Gyeongsangbuk-do using a forest fire rating model and geographical information system (GIS). We investigated the appropriate positions of the automatic weather station (AWS) and a comprehensive network solution (a system including measurement, communication and data processing) for the establishment of an optimum mountain meteorological observation network system (MMONS). Also, we suggested a possible plan for combining the MMONS with unmanned monitoring camera systems and wireless relay towers operated by local governments and the Korea Forest Service for prevention of forest fire.

• Korean Journal of Agricultural and Forest Meteorology
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• v.22 no.2
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• pp.92-101
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• 2020

To prevent and mitigate damage to farms due to heavy snowfall, snow weight information should be provided in addition to snow depth. This study reviews four formulae regarding snow density and weight used in extant studies and applies them in Suwon area to estimate snow weight in Korea. We investigated the observed snow depth of 94 meteorological stations and automatic weather stations (AWS) data over the past 30 years (1988-2017). Based on the spatial distribution of snow depth by area in Korea, much of the fresh snow cover, due to heavy snowfall, occurred in Jeollabuk-do and Gangwon-do. Record snowfalls occurred in Gyeongsangbuk-do and Gangwon-do. However, the most recent heavy snowfall in winter occurred in Gyeonggi-do, Gyeongsangbuk-do, and Jeollanam-do. This implies that even if the snow depth is high, there is no significant damage unless the snow weight is high. The estimation of snow weight in Suwon area yielded different results based on the calculation method of snow density. In general, high snow depth is associated with heavy snow weight. However, maximum snow weight and maximum snow depth do not necessarily occur on the same day. The result of this study can be utilized to estimate the snow weight at other locations in Korea and to carry out snow weight prediction based on a numerical model. Snow weight information is expected to aid in establishing standards for greenhouse design and to reduce the economic losses incurred by farms.

• Journal of the Korea Society of Computer and Information
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• v.29 no.9
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• pp.125-136
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• 2024

This study presents an automated Python algorithm for analyzing rainfall characteristics to establish critical rainfall thresholds as part of a landslide early warning system. Rainfall data were sourced from the Korea Meteorological Administration's Automatic Weather System (AWS) and the Korea Forest Service's Automatic Mountain Observation System (AMOS), while landslide data from 2020 to 2023 were gathered via the Life Safety Map. The algorithm involves three main steps: 1) processing rainfall data to correct inconsistencies and fill data gaps, 2) identifying the nearest observation station to each landslide location, and 3) conducting statistical analysis of rainfall characteristics. The analysis utilized power law and nonlinear regression, yielding an average R² of 0.45 for the relationships between rainfall intensity-duration, effective rainfall-duration, antecedent rainfall-duration, and maximum hourly rainfall-duration. The critical thresholds identified were 0.9-1.4 mm/hr for rainfall intensity, 68.5-132.5 mm for effective rainfall, 81.6-151.1 mm for antecedent rainfall, and 17.5-26.5 mm for maximum hourly rainfall. Validation using AUC-ROC analysis showed a low AUC value of 0.5, highlighting the limitations of using rainfall data alone to predict landslides. Additionally, the algorithm's speed performance evaluation revealed a total processing time of 30 minutes, further emphasizing the limitations of relying solely on rainfall data for disaster prediction. However, to mitigate loss of life and property damage due to disasters, it is crucial to establish criteria using quantitative and easily interpretable methods. Thus, the algorithm developed in this study is expected to contribute to reducing damage by providing a quantitative evaluation of critical rainfall thresholds that trigger landslides.

• Journal of the Korean Society of Environmental Restoration Technology
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• v.6 no.3
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• pp.79-85
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• 2003

Human beings have pursued development and economic betterment; thus, enhancing convenience and prosperity. A flourish of human civilization upon the industrialization results a massive urbanization. However, human beings have connived the environmental importance in the course of rapid urbanization. The environmental quality now becomes one of the most important factors that determine the quality of life in a city. Many studies were proceeded about the heat island effect in large cities. In general, most studies have been done to investigate urban microclimate phenomena using meteorological network or AWS (automatic weather station) data. Those preceding studies do not seem to sufficiently reflect the and thus, failed to show regional representative. In this study, temporal Landsat TM satellite imageries of May 20, 1987 and May 21, 1999 were 뻐d in order to detect the surface temperature of the study area using the band 6 ($10.4{\mu}m{\sim}12.5{\mu}m$). The surface temperature distribution detected by the band 6 of Landsat TM was over layed with the land cover classification data in order to investigate the temperature difference of the paved road and the riparian areas of the stream. As a result, a surface temperature difference as much as $3^{\circ}C$ between the paved road and the riparian areas with vegetation was observed. This study concludes that the land cover change is one of the main causes of urban heat island effect which may be closely affected by the paved areas and roads. Besides, the change of the atmospheric temperature followed by the urban secular change could have been confirmed. In the case of Yangjaecheon stream which underwent a heavy environmental restoration in 1995, the temperature was decreased as much as $0.6^{\circ}C$ after the restoration. The results of this study is expected to contribute to develop an urban space in harmony with the healthy human life and the environment respecting the crucial role of vegetation to stabilize the urban environmental dynamics.