• 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.

• 대한원격탐사학회지
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• 제27권2호
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• pp.191-201
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• 2011

본 연구에서는 다편파 레이더 산란계 자동 측정시스템 이용하여 콩 생육변화를 관측하고 레이더 시스템에서 얻어진 후방산란계수과 콩 생육인자들과의 관계분석을 통하여 콩 생육추정 가능성을 모색하고자 하였다. 2010년도 농촌진흥청 국립식량과학원 연구지역에 다편파 레이더 산란계 관측시스템 (L, C, X-밴드 안테나, 네트워크분석기, RF switch, 입사각 $40^{\circ}$)을 구축하고 콩 파종시기에서 수확기까지 10분단위로 콩 생육변화를 자동 측정하였다. 모든 안테나 밴드, 편파에서 콩 생육초기 (6월초~7월 중순)에는 VV-편파가 HH-, HV-편파보다 후방산란계수가 높게 나타났고, 그 이후 HH-편파와 다른 편파들 간의 cross-over 현상이 일어났는데 그 시기는 L-밴드가 7월 20일 (DOY 200), C-, X-밴드의 경우에는 7월 30일 (DOY 210)로 밴드에 따라 차이를 보였다. 모든 밴드 및 편파에서 9월 29일 (DOY 271)까지 후방산란계수가 증가하다가 그 이후 감소하였고 특히 종실비대기 (DOY 277, R6) 이후 감소폭이 크게 나타났는데 이 현상은 콩 생육인자 (초장, 엽면적지수, 건물중 등)변화와 일치하였다. 밴드에 따른 후방산란계수와 콩 생육인자들과의 관계를 분석한 결과 L-밴드 HH-편파에서 LAI ($r=0.93^{***}$), 초장 ($r=0.95^{***}$), 건물중 ($r=0.94^{***}$), 꼬투리중 ($r=0.92^{***}$)등 콩 생육인자들과의 상관계수가 가장 높게 나타났고 이에 비해 X-밴드 편파에서는 콩 생육인자들과의 상관계수가 상대적으로 낮게 나타났다. 후방산란계수 (L-밴드 HH-편파)를 이용하여 콩 생육인자 추정을 위한 회귀식을 작성하였다.