• 한국재난정보학회 논문집
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• 제8권4호
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• pp.419-431
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• 2012

우주기상 극대기인 2003년은 지구상에 크나큰 재앙을 초래할 것으로 예견되고 있다. 특히 근년에 들어 지자기 폭풍에 의한 손상과 가시화 될 수 있는 대 폭풍피해 사례를 보이고 있다. 본 연구에서는 이상에서 제시된 문제점에 대한 피해분석에 따른 궁극적인 우주기상정보시스템 모델 구축으로 피해 저감하고 대비방안을 설정하는 것이다. 구현방법으로는 uIT기반과 GIS기반의 우주기상 정보시스템 구축으로 우주폭풍에서 방사되는 우주복사폭풍(flare), 우주입자폭풍(solar proton event), 우주자기폭풍(geomagnetic storm) 등에 의한 분야별 폭풍피해를 분석하여 유형별 피해 대응에 대비할 수 있도록 하였다. 이로써 공간정보기반의 우주폭풍 전기전자 피해대비 운영관리시스템 구현은 GIS기법에 의한 의사결정지원 시스템으로 피해예측 및 방재환경을 스마트 IT환경과 융합한 첨단 정보시스템으로 구현하여 인명과 재산을 보전할 수 있는 방안으로 활용될 수 있을 것이다.

• 대기
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• 제18권3호
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• pp.159-169
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• 2008

The characteristics of the typhoon's extratropical transition (ET) over the western North Pacific area were investigated using the cyclone phase space (CPS) diagram method suggested by Hart (2003). The data used in this study were the global data assimilation prediction system (GDAPS) and NCEP data set. The number of typhoons selected were 75 cases during 2002 to 2007, and the three parameters were analyzed : the motion relative thickness asymmetry of the storm (B), the upper thermal wind shear and the lower thermal wind shear. Comparing the best-track data provided by the Regional Specialized Meteorological Center /Tokyo, the time of the ET based on CPS was 2~6 hours earlier than the best-track data. And it was shown that the 400- km and 30 kt wind radius of storm for the CPS method were better agreement than the previous suggested radius 500- km.

• 한국물환경학회지
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• 제23권1호
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• pp.38-45
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• 2007

A one-dimensional kinematic wave model was used to calculate temporal and spatial changes of the highway runoff. Infiltration into pavement was considered using Darcy's law, as a function of flow depth and pavement hydraulic conductivity ($K_p$). The model equation was calculated using the method of characteristics (MOC), which provided stable solutions for the model equation. 22 storm events monitored in a highway runoff monitoring site in west Los Angeles in the U.S. were used for the model calculation and evaluation. Using three different values of $K_p$ ($5{\times}10^{-6}$, $10^{-5}$, and $2{\times}10^{-5}cm/sec$), total runoff volume and peak flow rate were calculated and then compared with the measured data for each storm event. According to the calculation results, $10^{-5}cm/sec$ was considered a site representative value of $K_p$. The study suggested a one-dimensional method to predict hydrodynamic behavior of highway runoff, which is required for the water quality prediction.

• 한국항해항만학회:학술대회논문집
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• 한국항해항만학회 2006년도 International Symposium on GPS/GNSS Vol.2
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• pp.47-51
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• 2006

Among Solar activities' events, the geomagnetic storms are believed to cause the largest atmospheric effects. The geomagnetic storm is a complex process of solar wind/magnetospheric origin. It is well known to affect severely on the ionosphere. However, this effect of this complex process will maybe act at various altitudes in the atmosphere, even including the lower layer and the neutral middle atmosphere, particularly the stratosphere. Nowadays, the GPS-derived ZTD (zenith tropospheric delay) can be transformed into the precipitable water vapor (PWV) through a function relation, and further has been widely used in meteorology, especially in improving the precision of Numerical Weather Prediction (NWP) models. However, such geomagnetic effects on the atmosphere are ignored in GPS meteorology applications. In this paper, we will investigate the geomagnetic storms' effects on the middle atmosphere and troposphere (0-100km) by GPS observations and other data. It has found that geomagnetic storms' effect on the atmosphere also appears in the troposphere, but the mechanism to interpret correlations in the troposphere need be further studied.

• 농업과학연구
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• 제50권4호
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• pp.799-808
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• 2023

Globally, abnormal climate phenomena have led to an increase in rainfall intensity, consequently causing a rise in flooding-related damages. Agricultural areas, in particular, experience significant annual losses every year due to a lack of research on flooding in these regions. This study presents a comprehensive analysis of the flood event that occurred on July 16, 2017, in the agricultural area situated in Sindaedong, Heungdeok-gu, Cheongju-si. To achieve this, the EPA (United States Environmental Protection Agency) Storm Water Management Model (SWMM) was employed to generate runoff data by rainfall information. The produced runoff data facilitated the identification of flood occurrence points, and the analysis results exhibited a strong correlation with inundation trace maps provided by the Ministry of the Interior and Safety (MOIS). The detailed output of the SWMM model enabled the extraction of time-specific runoff information at each inundation point, allowing for a detailed understanding of the inundation status in the agricultural area over different time frames. This research underscores the significance of utilizing the SWMM model to simulate inundation in agricultural areas, thereby validating the efficacy of flood alerts and risk management plans. In particular, the integration of rainfall data and the SWMM model in flood prediction methodologies is expected to enhance the formulation of preventative measures and response strategies against flood damages in agricultural areas.

• 천문학회보
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• 제35권1호
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• pp.32.1-32.1
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• 2010

KASI's Solar and Space Weather Research Group (SSWRG) is actively involved in solar and space weather research. Since its inception, the SSWRG has been utilizing ground-based assets for its research, such as the Solar Flare Telescope, Solar Imaging Spectrograph, and Sunspot Telescope. In 2007 SSWRG initiated the Korean Space Weather Prediction Center (KSWPC). The goal of KSWPC is to extend the current ground observation capabilities, construct space weather database and networking, develop prediction models, and expand space weather research. Beginning in 2010, SSWRG plans to expand its research activities by collaborating with new international partners, continuing the development of space weather prediction models and forecast system, and phasing into developing and launching space-based assets. In this talk, we will report on KASI's recent activities of international collaborations with NASA for STEREO (Solar Terrestrial Relations Observatory), SDO (Solar Dynamic Observatory), and Radiation Belt Storm Probe (RBSP).

• 한국산학기술학회논문지
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• 제20권4호
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• pp.69-75
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• 2019

전 세계적으로 발생하고 있는 이상기후현상으로 자연재해의 발생빈도와 피해규모가 증가하고 있는 추세이다. 특히, 일본의 대지진, 미국의 허리케인 카트리나, 한국의 태풍 매미 등 세계적으로 연안지역에서 발생하는 자연재해에 의한 피해는 막대하다. 재해대응 차원에서 피해 규모를 예측할 수 있다면 신속하게 대응하여 피해를 저감할 수 있다고 판단된다. 따라서, 본 연구에서는 여러 가지 자연재해 중 해풍과 파랑에 의해 발생하는 풍랑에 관한 피해예측함수를 개발하였다. 국내의 연안지역을 대상으로 재해연보('1991~'2017)의 풍랑 및 태풍피해 이력을 수집하였으며, 물가상승률을 반영하기 위해 2017년을 기준으로 피해액을 환산하였다. 또한, 풍랑 및 태풍피해가 발생했을 때의 해양기상인자 자료를 수집하였다. 수집된 자료를 통하여 회귀분석을 실시하였으며, 최종적으로, 연안의 지역특성을 반영하여 전국 74개 지역의 해역별 풍랑 피해예측함수를 개발하였다. 개발된 풍랑피해 예측함수를 통하여 사전대비 차원의 피해예측이 가능할 것으로 판단되며, 재해통계관련 법 제도 개선에 활용 될 것으로 기대된다.

• 한국항해항만학회:학술대회논문집
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• 한국항해항만학회 2004년도 추계학술대회
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• pp.227-234
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• 2004

최근 초대형 태풍의 내습으로 한국 연안해역에 치명적인 타격을 가져왔고 이로 인해 연안구조물의 설계기준을 재고해야 한다는 논의가 되고 있다. 극한파랑을 입력으로 한 파랑변환의 계산은 특히 해안역의 계획 및 건설 작업 분야에서 의미가 있다 본 연구에서 태풍으로 인한 수위의 변화예측은 물리모델에서나 실제현장에서 다루기가 매우 어렵기 때문에 수치모델에 주로 근거를 두었다. 복잡한 해안선 및 구조물 해역에 대한 파랑변환 수치모델을 다루고 극한파랑의 입력을 위해 광역에서의 태풍매미의 도래시 관측과 수치모텔의 분석에서 도입하였다. 최종적으로 한국 남해안의 부산항에서 폭풍고조 및 극한파랑의 문제에 위에서 기술한 모델을 적용하여 수위상승의 효과와 극한파랑의 변환을 해안역 침수와 관련하여 분석하였다. 아울러 관심대상역에서 기초적인 재해위험지도를 제시해 보았다.

• 대기
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• 제25권1호
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• pp.67-83
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• 2015

Predicting the location and intensity of precipitation still remains a main issue in numerical weather prediction (NWP). Resolution is a very important component of precipitation forecasts in NWP. Compared with a lower resolution model, a higher resolution model can predict small scale (i.e., storm scale) precipitation and depict convection structures more precisely. In addition, an ensemble technique can be used to improve the precipitation forecast because it can estimate uncertainties associated with forecasts. Therefore, NWP using both a higher resolution model and ensemble technique is expected to represent inherent uncertainties of convective scale motion better and lead to improved forecasts. In this study, the limited area ensemble prediction system for the convective-scale (i.e., high resolution) operational Unified Model (UM) in Korea Meteorological Administration (KMA) was developed and evaluated for the ensemble forecasts during August 2012. The model domain covers the limited area over the Korean Peninsula. The high resolution limited area ensemble prediction system developed showed good skill in predicting precipitation, wind, and temperature at the surface as well as meteorological variables at 500 and 850 hPa. To investigate which combination of horizontal resolution and ensemble member is most skillful, the system was run with three different horizontal resolutions (1.5, 2, and 3 km) and ensemble members (8, 12, and 16), and the forecasts from the experiments were evaluated. To assess the quantitative precipitation forecast (QPF) skill of the system, the precipitation forecasts for two heavy rainfall cases during the study period were analyzed using the Fractions Skill Score (FSS) and Probability Matching (PM) method. The PM method was effective in representing the intensity of precipitation and the FSS was effective in verifying the precipitation forecast for the high resolution limited area ensemble prediction system in KMA.

• 한국물환경학회지
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• 제38권6호
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• pp.292-305
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• 2022

The Yeongsan River, one of the four largest rivers in South Korea, has been facing difficulties with water quality management with respect to algal bloom. The algal bloom menace has become bigger, especially after the construction of two weirs in the mainstream of the Yeongsan River. Therefore, the prediction and factor analysis of Chlorophyll-a (Chl-a) concentration is needed for effective water quality management. In this study, Chl-a prediction model was developed, and the performance evaluated using machine and deep learning methods, such as Deep Neural Network (DNN), Random Forest (RF), and eXtreme Gradient Boosting (XGBoost). Moreover, the correlation analysis and the feature importance results were compared to identify the major factors affecting the concentration of Chl-a. All models showed high prediction performance with an R² value of 0.9 or higher. In particular, XGBoost showed the highest prediction accuracy of 0.95 in the test data.The results of feature importance suggested that Ammonia (NH₃-N) and Phosphate (PO₄-P) were common major factors for the three models to manage Chl-a concentration. From the results, it was confirmed that three machine learning methods, DNN, RF, and XGBoost are powerful methods for predicting water quality parameters. Also, the comparison between feature importance and correlation analysis would present a more accurate assessment of the important major factors.