• Journal of the Korean Association of Geographic Information Studies
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• v.23 no.4
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• pp.83-100
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• 2020

This study evaluated the accuracy of soil moisture and evapotranspiration by calculating the hydrological parameters in Korean peninsula using Land Information System(LIS) developed by US NASA. We used Noah-MP surface model to calculate hydrological parameters, and used MERRA2(Modern-Era Retrospective analysis for Research and Applications, Version 2) for hydrological forcing data. And, International Geosphere-Biosphere Program(IGBP) and University of Maryland(UMD) land cover maps were applied to compare the output accuracy, and Automated Synoptic Observing System(ASOS) of KMA was used as ground observation data. In order to evaluate the accuracy of the output data, the correlation coefficient(CC), BIAS, and efficiency factor (NSE, Nash-Sutcliffe Efficiency) were analyzed with soil moisture and evapotranspiration by ASOS ground observation data. As a result, the correlation coefficient of soil moisture using IGBP was 0.56 on average, and evapotranspiration was about 0.71. On the other hand, soil moisture using UMD was 0.68 on average and evapotranspiration was about 0.72, and the correlation coefficient by UMD was evaluated as high accuracy compared to the results by using IGBP. The correlation coefficient of soil moisture was an average of 0.68 and evapotranspiration was an average of 0.72 when MERRA2 was used as hydrological forcing data. On the other hand, the soil moisture applied with ASOS was an average of 0.66, and evapotranspiration was an average of 0.72. It is judged that the ASOS point data was reanalyzed as 0.65°× 0.5°grids, which is the same spatial resolution with MERRA2, resulting in differences in accuracy depending on the region.

• Journal of the Korean Association of Geographic Information Studies
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• v.20 no.4
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• pp.102-114
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• 2017

The purpose of this study was the assessment of the accuracy of Global Precipitation Measurement (GPM) Integrated Multi-Satellite Retrievals for GPM (IMERG), a rainfall data source derived from satellite images, for evaluation of its applicability to use in ungauged or inaccessible areas. The study area was the overall area of the Korean peninsula divided into six regions. Automated Surface Observing System (ASOS) rainfall data from the Korean Meteorological Administration and IMERG satellite rainfall were used. Their average correlation coefficient was 0.46 for a 1-h temporal resolution, and it increased to 0.69 for a 24-h temporal resolution. The IMERG data quantitatively estimated less than the rainfall totals from ground gauges, and the bias decreased as the temporal resolution was decreased. The correlation coefficients of the two rainfall events, which had relatively greater rainfall amounts, were 0.68 and 0.69 for a 1-h temporal resolution. Additionally, the spatial distributions of the ASOS and IMERG data were similar to each other. The study results showed that the IMERG data were very useful in the assessment of the hydro-meteorological characteristics of ungauged or inaccessible areas. In a future study, verification of the accuracy of satellite-derived rainfall data will be performed by expanding the analysis periods and applying various statistical techniques.

• Journal of Environmental Science International
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• v.25 no.4
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• pp.603-613
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• 2016

Fog may have a significant impact on road conditions. In an attempt to improve fog predictability in Jeju, we conducted machine learning with various data mining techniques such as tree models, conditional inference tree, random forest, multinomial logistic regression, neural network and support vector machine. To validate machine learning models, the results from the simulation was compared with the fog data observed over Jeju(184 ASOS site) and Gosan(185 ASOS site). Predictive rates proposed by six data mining methods are all above 92% at two regions. Additionally, we validated the performance of machine learning models with WRF (weather research and forecasting) model meteorological outputs. We found that it is still not good enough for operational fog forecast. According to the model assesment by metrics from confusion matrix, it can be seen that the fog prediction using neural network is the most effective method.

• Journal of Environmental Science International
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• v.27 no.7
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• pp.509-518
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• 2018

Weather is the most influential factor for crop cultivation. Weather information for cultivated areas is necessary for growth and production forecasting of agricultural crops. However, there are limitations in the meteorological observations in cultivated areas because weather equipment is not installed. This study tested methods of predicting the daily mean temperature in onion fields using geostatistical models. Three models were considered: inverse distance weight method, generalized additive model, and Bayesian spatial linear model. Data were collected from the AWS (automatic weather system), ASOS (automated synoptic observing system), and an agricultural weather station between 2013 and 2016. To evaluate the prediction performance, data from AWS and ASOS were used as the modeling data, and data from the agricultural weather station were used as the validation data. It was found that the Bayesian spatial linear regression performed better than other models. Consequently, high-resolution maps of the daily mean temperature of Jeonnam were generated using all observed weather information.

• Proceedings of the Korea Water Resources Association Conference
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• 2018.05a
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• pp.187-187
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• 2018

최근 우리나라에서는 기후변화에 따른 이상기후로 인하여 홍수와 가뭄 등 자연 재해의 빈도가 증가하는 등 사회, 경제, 환경 등 다양한 분야에 영향을 받고 있다. 또한 우리나라는 기후변화와 더불어 강우의 계절적 특징상 강우량의 편중현상이 발생하므로 물 관리의 어려움이 크다. 이러한 상황에서 수자원의 미래 변화에 관한 연구는 필수적이며, 본 연구에서는 안동댐 유역을 대상 유역으로 하여 댐유입량과 유사량의 RCP 시나리오에 따른 변화 분석을 실시하였다. 댐유입량은 수자원의 이용에 있어서 직접적인 연관이 있는 중요한 요소이며, 유사량 또한 수자원의 효용가치를 증가시키기 위해 제어해야할 중요한 요소이다. 경상북도 안동시에 위치한 다목적댐인 안동댐은 유역면적이 $1,584km^2$이고, 총 저수용량은 $1,248\;10^6m^3$으로 용수공급 및 발전, 관광지 등으로 이용되고 있다. 안동댐 유역의 RCP 시나리오에 따른 변화 분석을 실시하기 위해, 분포형 수문모형인 SWAT(Soil and Water Assessment Tool) 모형을 이용하였다. RCP 시나리오를 적용하기 전, ASOS 관측자료를 이용하여 2010-2017 기간을 모의하고 2010년을 모형의 안정화 기간으로 두고, 2011-2017년에 대해 검보정을 실시한 후, RCP 시나리오에 따른 모의를 실시하였다. RCP 시나리오는 기후변화센터에서 제공하는 RCP 4.5와 RCP 8.5 시나리오를 이용하였으며, 2010-2099 기간에 관하여 SWAT 모형을 모의하고, 2010년을 모형의 안정화 기간, 2011-2040 기간을 2025s, 2041-2070 기간을 2055s, 2071-2099 기간을 2085s로 두어 결과를 ASOS 관측자료를 이용한 결과와 비교 분석하였다.

• Proceedings of the Korea Water Resources Association Conference
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• 2018.05a
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• pp.180-180
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• 2018

가능최대강수량(PMP)은 대규모 수공구조물의 설계 시 기준이 되는 강수량으로, 최근 대규모 거대재난에 대비한 대피계획수립에 PMP를 활용하려는 움직임이 있으며 PMP에 대한 국내 연구가 활발히 수행되고 있다. PMP를 추정하기 위해 Hershfield의 통계적 방법에 대한 간단한 대안이 제안되었다. PMP는 물리적인 강우량 상한계로, 확률론적 개념과는 모순적이다. 또한, Hershfield의 PMP는 연 최대 시계열 평균의 선형함수로 주어지는 모양 매개변수를 가지는 GEV 분포의 약 60,000년 빈도임이 밝혀졌다. 따라서 본 연구에서는 Hershfield의 방법을 확률론적으로 해석하는 것이 바람직할 것으로 판단하였고, 기상청 ASOS 및 AWS 자료를 이용하여 우리나라 각 지점자료 중 10년 이상의 자료를 사용하여 Hershfield 방법을 적용하여 PMP를 산정하였다. 각 지점의 빈도계수를 구하여 우리나라 자료에 적합한 확률분포의 형태를 적용하였고, 분포형의 매개변수 값을 추정하였다. 또한, Hershfield의 빈도계수와, 우리나라 자료에 해당하는 빈도계수가 몇 년 빈도로 계산되는지 각각 확인해 보았다. ASOS 및 AWS 자료를 이용하여 연 최대 강수량 시계열 평균과 모양 매개변수의 관계 공식 또한 구성하였다. 본 연구의 방법을 검증하기 위하여 우리나라에서 제일 오래된 자료(57년)인 서울지점 자료를 이용하여 경험적인 분포함수와 본 연구에서 제안하고 있는 방법을 비롯한 다양한 방법을 통하여 구한 분포함수를 비교하여 도시하였다.

• Proceedings of the Korea Water Resources Association Conference
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• 2021.06a
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• pp.40-40
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• 2021

수재해 방지를 위한 수문해석 모형에서 정량적인 강수자료의 역할은 매우 중요하다. 최근에는 기후변화로 인한 국지성 집중호우 등 돌발 강수의 빈도가 증가하고 있어 지상에 설치된 우량계보다 시·공간적 변동성을 반영할 수 있는 격자형 위성 강수자료의 활용성이 커지고 있다. 하지만 위성강수자료는 관측 시에 대기의 상태 또는 위성별 관측 센서, 공간적 스케일 차이 등에 의해 실제 내린 강수와의 편의가 존재한다. 이를 해결하기 위해 지점 강수자료를 이용한 통계적, 지형정보학적 상세화 기법이 적용되고 있으나, 대부분의 연구에서 강수자료의 양적 보정만을 목적으로 수행되었다. 본 연구에서는 머신러닝 기반의 랜덤포레스트(random forest) 모델을 사용하여 다중위성 강수자료(CHIRPSv2, CMORPH, GSMaP, TRMMv7)와 기상청에서 제공하는 AWS, ASOS 지점 강수를 사용하여 최적 위성강수자료를 생성 후 각 위성강수자료와 비교·분석하였다. 2003년에서 2017년까지의 각 위성강수자료를 수집하여 같은 공간 스케일로 전처리한 뒤 모델에 입력하였으며 AWS 강수자료는 훈련, ASOS 강수자료는 검증에 이용되었다. 그 결과, 생성된 최적 위성강수자료는 각 위성강수자료보다 지점강수와의 편의가 줄고 높은 상관관계를 나타내고 있다. 이는 앞으로 사용될 위성강수자료의 시·공간적 보정 및 단기예측에 활용할 수 있으며, 특히 원격탐사자료의 의존도가 높은 미계측 대유역 수문해석에 정량적인 강수자료를 제공할 수 있을 것으로 판단된다.

• Journal of Environmental Health Sciences
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• v.42 no.2
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• pp.85-91
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• 2016

Objectives: The objective of this study was to assess heat stress, compare heat stress indices, and evaluate the usefulness of wet bulb globe temperature (WBGT) among outdoor workers exposed to heat during the summer season. Methods: WBGT, dry temperature, and heat index were measured using WBGT measurers (QUESTemp 32 model and QUESTemp 34 model, QUEST, WI, USA) by industrial hygienists from August 27 to September 16, 2015. Heat stress indices were measured at the workplaces of a shipbuilder in Ulsan and a construction site in Daegu. The dry temperature observed by the Automated Synoptic Observing System (ASOS) of the Korea Meteorological Administration was also compared. Results: Dry temperature measured by WBGT is different from that by ASOS. The temperature obtained from ASOS was less than $33^{\circ}C$, above which point a heat wave is forecast by the Korea Meteorological Administration. A heat index above $32.8^{\circ}C$ as a moderate risk was not observed during measurement. WBGT was consistently higher than $22^{\circ}C$, above which the risk of heat-related illness is increased in unacclimated workers involved in work with a high metabolic rate. WBGT was sometimes higher than $28^{\circ}C$, above which the risk of heat-related illness is increased in acclimated workers involved in work with a moderate metabolic rate in September. Conclusion: According to the measurement of heat stress indices, WBGT was more sensitive than heat index and temperature. Thus, general measures to prevent heat-related diseases should be implemented in workplaces during the summer season according to WBGT.

• Korean Journal of Remote Sensing
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• v.36 no.3
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• pp.487-501
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• 2020

As a viable option for retrieval of LST (Land Surface Temperature), this paper presents a DNN (Deep Neural Network) based approach using 148 Landsat 8 images for South Korea. Because the brightness temperature and emissivity for the band 10 (approx. 11-㎛ wavelength) of Landsat 8 are derived by combining physics-based equations and empirical coefficients, they include uncertainties according to regional conditions such as meteorology, climate, topography, and vegetation. To overcome this, we used several land surface variables such as NDVI (Normalized Difference Vegetation Index), land cover types, topographic factors (elevation, slope, aspect, and ruggedness) as well as the T₀ calculated from the brightness temperature and emissivity. We optimized four seasonal DNN models using the input variables and in-situ observations from ASOS (Automated Synoptic Observing System) to retrieve the LST, which is an advanced approach when compared with the existing method of the bias correction using a linear equation. The validation statistics from the 1,728 matchups during 2013-2019 showed a good performance of the CC=0.910~0.917 and RMSE=3.245~3.365℃, especially for spring and fall. Also, our DNN models produced a stable LST for all types of land cover. A future work using big data from Landsat 5/7/8 with additional land surface variables will be necessary for a more reliable retrieval of LST for high-resolution satellite images.

• Korean Journal of Remote Sensing
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• v.37 no.6_2
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• pp.1837-1847
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• 2021

This study investigated wind corridors for the entire Suwon-city area using a geographic information system and a computational fluid dynamics model. We conducted numerical simulations for 16 inflow wind directions using the average wind speeds measured at the Suwon automated synoptic observation system (ASOS) for recent ten years. We analyzed the westerly (dominant wind direction) and easterly cases (not dominant but strong wind speed) in detail and investigated the characteristics of a wind speed distribution averaged using the frequencies of 16 wind directions as weighting factors. The characteristics of the wind corridors in Suwon city can be summarized as; (1) In the northern part of Suwon, complicated flows were formed by the high mountainous terrain, and strong (weak) winds and updrafts (downdrafts) were simulated on the windward (leeward) mountain slope. (2) On the leeward mountain slope, a wind corridor was formed along a valley, and relatively strong airflow flowed into the residential area. (3) The strong winds were simulated in a wide and flat area in the west and south part of Suwon city. (4) Due to the friction and flow blocking by buildings, wind speeds decreased, and airflows became complicated in the downtown area. (5) Wind corridors in residential areas were formed along wide roads and areas with few obstacles, such as rivers, lakes, and reservoirs.