• Water Engineering Research
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• 제3권2호
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• pp.113-122
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• 2002

Accurate quantitative forecasting of rainfall for basins with a short response time is essential to predict flash floods. In this study, a Quantitative Flood Forecasting (QFF) model was developed by incorporating the evolving structure and frequency of intense weather systems and by using neural network approach. Besides using radiosonde and rainfall data, the model also used the satellite-derived characteristics of storm systems such as tropical cyclones, mesoscale convective complex systems and convective cloud clusters as input. The convective classification and tracking system (CCATS) was used to identify and quantify storm properties such as lifetime, area, eccentricity, and track. As in standard expert prediction systems, the fundamental structure of the neural network model was learned from the hydroclimatology of the relationships between weather system, rainfall production and streamflow response in the study area. All these processes stretched leadtime up to 18 hours. The QFF model will be applied to the mid-Atlantic region of United States in a forthcoming paper.

• 한국수자원학회논문집
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• 제36권3호
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• pp.495-505
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• 2003

Meteosat-5 IR 위성영상을 사용하여 1999년과 2000년 여름몬순기간 동안 발생한 네팔과 인디아 북쪽 히말라야 산악지역에 발생하는 여러 형태의 대류계 즉, 중규모 대류계들 (Mesoscale Convective Complex, MCC and Convective Cloud Clusters, CCC) 와 보다 약한 Disorganized Short-lived Convection (DSL)의 이동특성 및 시공간적인 생성특성 등을 조사하였다. 대상지역에 발생하는 중규모 대류계의 전형적인 지속시간은 약 11시간이며 크기는 약 $300,000km^2$ 이다. 중규모 대류계의 중심은 히말라야산맥으로부터 원거리에 위치함에도 불구하고 집중강운-는 위도 $25^{\circ}-30^{\circ}N$ 사이의 히말라야 하단에 발생하는 중규모 대류계와 직접적 상관관계를 가진다. 결과는 히말라야 고도 500-4000m에 설치된 강우계로부터 획득된 강우자료의 변화 특성과 대류계 거동 특성이 유사함을 보여주었다(Barros et al. 2000). 집중호우의 강력한 야간발생과 Gangetic Plains에서 발생한 중규모 대류계와의 연관성을 보여주었다(Barros et al. 2000).

• Water Engineering Research
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• 제3권2호
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• pp.123-134
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• 2002

A developed Quantitative Flood Forecasting (QFF) model was applied to the mid-Atlantic region of the United States. The model incorporated the evolving structure and frequency of intense weather systems of the study area for improved flood forecasting. Besides using radiosonde and rainfall data, the model also used the satellite-derived characteristics of storm systems such as tropical cyclones, mesoscale convective complex systems and convective cloud clusters associated with synoptic atmospheric conditions as Input. Here, we present results from the application of the Quantitative Flood Forecasting (QFF) model in 2 small watersheds along the leeward side of the Appalachian Mountains in the mid-Atlantic region. Threat scores consistently above 0.6 and close to 0.8 ∼ 0.9 were obtained fur 18 hour lead-time forecasts, and skill scores of at least 40% and up to 55 % were obtained.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2002년도 학술발표회 논문집(I)
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• pp.43-50
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• 2002

Accurate quantitative forecasting of rainfall for basins with a short response time is essential to predict streamflow and flash floods. Previously, neural networks were used to develop a Quantitative Precipitation Forecasting (QPF) model that highly improved forecasting skill at specific locations in Pennsylvania, using both Numerical Weather Prediction (NWP) output and rainfall and radiosonde data. The objective of this study was to improve an existing artificial neural network model and incorporate the evolving structure and frequency of intense weather systems in the mid-Atlantic region of the United States for improved flood forecasting. Besides using radiosonde and rainfall data, the model also used the satellite-derived characteristics of storm systems such as tropical cyclones, mesoscale convective complex systems and convective cloud clusters as input. The convective classification and tracking system (CCATS) was used to identify and quantify storm properties such as life time, area, eccentricity, and track. As in standard expert prediction systems, the fundamental structure of the neural network model was learned from the hydroclimatology of the relationships between weather system, rainfall production and streamflow response in the study area. The new Quantitative Flood Forecasting (QFF) model was applied to predict streamflow peaks with lead-times of 18 and 24 hours over a five year period in 4 watersheds on the leeward side of the Appalachian mountains in the mid-Atlantic region. Threat scores consistently above .6 and close to 0.8 ∼ 0.9 were obtained fur 18 hour lead-time forecasts, and skill scores of at least 4％ and up to 6％ were attained for the 24 hour lead-time forecasts. This work demonstrates that multisensor data cast into an expert information system such as neural networks, if built upon scientific understanding of regional hydrometeorology, can lead to significant gains in the forecast skill of extreme rainfall and associated floods. In particular, this study validates our hypothesis that accurate and extended flood forecast lead-times can be attained by taking into consideration the synoptic evolution of atmospheric conditions extracted from the analysis of large-area remotely sensed imagery While physically-based numerical weather prediction and river routing models cannot accurately depict complex natural non-linear processes, and thus have difficulty in simulating extreme events such as heavy rainfall and floods, data-driven approaches should be viewed as a strong alternative in operational hydrology. This is especially more pertinent at a time when the diversity of sensors in satellites and ground-based operational weather monitoring systems provide large volumes of data on a real-time basis.

• 대기
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• 제15권3호
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• pp.155-165
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• 2005

The characteristics of six precipitation systems occurred around Cheongju in 2002 are analyzed after the convective/stratiform radar echo classification using radar reflectivity from the Meteorological Research Institute"s X-band Doppler weather radar. The Biggerstaff and Listemaa (2000) algorithm is applied for the classification and reveals a physical characteristics of the convective and stratiform rain diagnosed from the three-dimensional structure of the radar reflectivity. The area satisfying the vertical profile of radar reflectivity is well classified, while the area near the radar site and the topography-shielded area show a mis-classification. The seasonal characteristics of the precipitation system are also analyzed using the contoured frequency by altitude diagrams (CFADs). The heights of maximum reflectivity are 4 km and 5.5 km in spring and summer, respectively, and the vertical gradient of radar reflectivity from 1.5 km to the melting layer in spring is larger than in summer.

• 한국지구과학회지
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• 제33권5호
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• pp.377-394
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• 2012

To elucidate the mechanism associated with the development of heavy precipitation system, a field experiment was carried out in Jejudo (or Jeju Island) and Marado, Korea from 22 June to 12 July 2006. The synoptic atmospheric conditions were analyzed using the National Centers for Environmental Prediction-National Center for Atmospheric Research's (NCEP/NCAR) reanalyzed data, weather maps, and sounding data. The kinematic characteristics of each precipitation system were investigated by dual Doppler radar analysis. During the field experiment, data of four precipitation events with more than 20 mm rainfall were collected. In F case (frontal precipitation), a typical Changma front was dominant and the observation field was fully saturated. However there was no convective instability near the surface. LF case (low pressure accompanied with Changma front) showed strong convective instability near the surface, while a strong convergence corresponded to the low pressure from China accompanied with Changma front. In FT case (Changma front indirectly influenced by typhoon), the presence of a convective instability indicated the transport of near surface, strong additional moisture from the typhoon 'EWINIAR'. The convergence wind field was ground to be located at a low level. The convective instability was not significant in T case (precipitation of the typhoon 'EWINIAR'), since the typhoon passed through Jejudo and the Changma front was disappeared toward the northeastern region of the Korean peninsula. The kinematic (convergence and divergence) characteristics of wind fields, convective instability, and additional moisture inflow played important roles in the formation and development of heavy precipitation.

• 대기
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• 제26권1호
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• pp.97-109
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• 2016

Heavy rainfall (over $80mm\;hr^{-1}$) system associated with unstable atmospheric conditions occurred over the Seoul metropolitan area on 27 July 2011. To investigate the heavy rainfall system, we used three-dimensional data from Korea Local Analysis and Prediction System (KLAPS) reanalysis data and analysed the structure of the precipitation system, kinematic characteristics, thermodynamic properties, and Meteorological condition. The existence of Upper-Level Jet (ULJ) and Low-Level Jet (LLJ) are accelerated the heavy rainfall. Convective cloud developed when a strong southwesterly LLJ and strong moisture convergence occurring around the time of the heavy rainfall is consistent with the results of previous studies on such continuous production. Environmental conditions included high equivalent potential temperature of over 355 K at low levels, and low equivalent potential temperature of under 330 K at middle levels, causing vertical instability. The tip of the band shaped precipitation system was made up of line-shaped convective systems (LSCSs) that caused flooding and landslides, and the LSCSs were continuously enhanced by merging between new cells and the pre-existing cell. Difference of wind direction between low and middle levels has also been considered an important factor favouring the occurrence of precipitation systems similar to LSCSs. Development of LSCs from the wind direction difference at heights of the severe precipitation occurrence area was also identified. This study can contribute to the identification of production and development mechanisms of heavy rainfall and can be used in applied research for prediction of severe weather.

• 한국지구과학회지
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• 제36권6호
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• pp.553-566
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• 2015

수도권 지역의 고해상도 수치실험에 있어 연직 해상도와 대기경계층 모수화 방안의 효과를 조사하였다. WRF 모델을 이용하여 2013년 10월 25일 0000 UTC 부터 10월 26일 0000 UTC까지 수치 적분을 수행하였다. 수치 결과는 서울 남부에 위치한 선릉지역에서 관측된 6시간 간격의 라디오존데 자료와 서울지역의 43개 자동 기상 관측소 자료를 이용하여 검증하였다. 대기 하층의 연직해상도 비교 실험은 연직 44, 50, 60개의 층으로 구성되었으며, 특히 약 2 km고도 이하의 층을 세분화하였다. 연직 해상도가 가장 높은 60개층 실험에서 대기경계층 고도의 일 변동이 가장 뚜렷하게 나타났고, 특히 산악 지형과 같은 고지대에서는 대기경계층 고도와 10 m 바람장에서 연직해상도 실험 별 차이가 크게 나타났다. WRF 모델 내 ACM2, YSU, MYJ 대기경계층 모수화 방안에 따른 온도의 민감도 실험에서는 모든 실험수행 시간대에서 수치 모델 결과가 라디오존데 관측에 비교하여 온도를 과소 모의하였다. 지상 온도는 YSU 방안과 ACM2 방안이 MYJ 방안에 비해 상대적으로 편차가 낮게 나타났다.

• 한국지구과학회지
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• 제42권5호
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• pp.496-503
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• 2021

한반도 남해안 지역의 여름철 대기 안정도 특성을 분석함으로써, 한반도 특성에 맞는 강수 예측을 위한 대기 안정도 지수의 정량적인 임계값을 도출하고자 하였다. 보성 표준기상관측소에서 관측한 2019년도 여름철 라디오존데 집중관측자료를 분석에 사용하였으며, 총 관측자료는 243개 이다. 강수 유무 및 중규모 대기 현상에 대한 대기 안정도를 분석하기 위해서, 대류가용잠재에너지(Convective Available Potential Energy, CAPE)와 폭풍지수(Storm Relative Helicity, SRH)를 비교하였으며 특히 SRH 분석은 고도 별로 총 4개의 층으로(0-1, 0-3, 0-6, 0-10 km) 세분화하였다. 강수 유무에 따른 분석은 강수가 없는 경우, 강수발생 전 12시간, 강수 발생 시로 구분하여 수행하였다. 그 결과, 2019년도 보성에서 발생한 여름철 강수 예측에는 CAPE 보다 SRH가 더 적합하며 0-6 km SRH가 약한 토네이도가 발생가능한 기준과 같은 150 m² s^-2 이상일 경우 강수가 발생한 것으로 분석 된다. 또한, 장마와 태풍 기간의 대기 안정도를 분석한 결과를 보면, 일반적으로 SRH는 대기 깊이가 두꺼워 질수록 값이 커지는 데 반해서 0-10 km SRH 평균값 보다 0-6 km 의 SRH 값이 더 크게 나타났다. 따라서, 2019년도 보성에서 발생한 태풍에 의한 강수를 판별하는 데는 0-6 km 의 SRH 값이 더 효과적이라고 할 수 있다.

• 한국지구과학회지
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• 제33권7호
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• pp.569-582
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• 2012

2011년 7월 26일 서울은 장마에 동반된 기록적인 대류성 집중호우로 인해 약 2천5백억 원 이상의 재산피해와 57명(사망자)의 인명손실이 발생되었고, 2012년 8월 27일 15호 태풍 볼라벤에 동반된 집중호우로 광주광역시에는 보다 약한 집중호우와 강풍을 동반하여 피해는 상대적으로 적게 발생시켰다. 위의 사례에 대해 KLAPS(기상청 국지분석 및 예측시스템)을 사용하여 집중호우 시 다른 물리적 요소들에 의한 중규모 과정들의 조사 및 분석을 수행하였다. 이것은 레이더관측과 천리안 위성관측 자료로부터 강우강도를 도출하는데 호조건의 전형적인 중규모 시스템이기 때문에 선택되었으며, 두 사례는 모두 집중호우 발생에 좋은 환경임을 보였다. 2011년 장마에 동반되어 서울에 나타난 사례에서 레이더와 천리안의 정량적인 강우강도를 지상강우계 관측과 비교했을 때, 최대 관측값이 85 mm/hr 이상이 나타난 시점에 비해 약 50 mm/hr 이상이 과소 추정되는 차이가 나타났으나, 레이더 강우강도는 35 mm/hr의 차이와 천리안 강우강도는 60 mm/hr의 차이를 보였다. 그러나 2012년 8월 27일 15호 태풍 볼라벤에 동반되어 광주광역시에 나타난 강우강도와 지상강우강도의 경향은 위의 사례와 유사하게 나타났으며, 정량적인 강우강도 차이는 최대 관측값이 17 mm/hr 이상이 나타난 시점에 비해 약 10 mm/hr 이상이 과소 추정되는 차이가 나타났으나, 레이더 강우강도는 5 mm/hr의 차이와 천리안 강우강도는 10 mm/hr의 차이를 보였다. 이것은 태풍 볼라벤에 의한 집중호우가 상대적으로 약했기 때문이었다. 두 사례에 대해 레이더 강우강도와 천리안 강우강도는 지상강우강도와 시계열적으로 비교했을 때, 모두 유사한 경향을 보였다.