• Proceedings of the Korea Water Resources Association Conference
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• 2008.05a
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• pp.1013-1017
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• 2008

이 연구는 기상수치예보 모델 중 지역수치예보모델인 RDAPS 모델을 이용하여 강우자료를 예측한 값과 실제 강우관측지점에서의 강우량을 비교해 보고 RDAPS 예측량의 정확도를 높이기 위한 연구이다. RDAPS 모델의 자료는 00UTC와 12UTC에 3시간 누적 자료를 48시간에 대해서 생성하고, 30km 격자망에 대한 정보를 담고 있기 때문에 1시간 간격으로 측정된 지점 강우량과의 비교를 위해서는 관측지점과 근거리 정보를 찾고 1시간 간격의 관측 자료를 3시간 누적강우량으로 바꾸는 전처리 과정이 필요하다. 실제 강우예측이 어려움을 겪는 것처럼 RDAPS의 예측 강우량과 관측 강우량은 큰 차이를 보이는 것으로 나타났다. 예측 강우량의 정확도를 높이고자 인공신경망을 적용하였다. 인공신경망이란 뇌기능의 특성 몇가지를 컴퓨터 시뮬레이션으로 표현하는 것을 목표로 하는 수학 모델이다. 강우수치예측 자료 외에도 RDAPS 모델에서 얻을 수 있는 풍향, 풍속, 상대습도, 기압, 온도 등의 다른 수치자료들을 이용하여 인공신경망을 이용하여 자료들의 패턴을 시뮬레이션 하여 정확도가 높은 예측값을 얻을 수 있었다.

• Proceedings of the Korea Water Resources Association Conference
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• 2008.05a
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• pp.269-273
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• 2008

기상청(KMA, Korea Meteorological Administration)에서는 기상수치예보모델을 적용하여 수치예보를 하고 있으며 전지구 모델로는 GDAPS(Global Date Assimilation and Prediction System)를 지역모델은 RDAPS(Regional Date Assimilation and Prediction System)를 사용하고 있다. 수치예보결과를 이용하여 유출량을 예측할 경우 일반적으로 해상도가 높은 지역모델인 RDAPS의 수치예보 결과값을 사용한다. RDAPS는 00UTC와 12UTC에 3시간으로 누적된 자료를 30km 격자에 대하여 예측시간으로부터 48시간에 대하여 자료를 생성한다. 일강우자료를 입력자료로 사용하는 강우-유출 모형의 경우 3시간 누적 자료를 나타나는 RDAPS 수치예보 결과를 이용 시 3시간 scale에서 일(day)시간 scale로 변환시켜주어야 한다. 본 연구에서는 RDAPS의 수치예보 결과의 일(day)시간 scale 변환 방법에 따른 정확도를 비교하여 RDAPS 수치예보 결과의 일(day)시간 scale 변환에 대한 정확도를 비교하여 일(day)시간 scale 변환에 대한 지침을 제공하고자 한다. RDAPS 수치예보 결과값의 특징을 이용하여 RDAPS 결과값을 일(day)시간 scale로 변환하는 방법으로 총 9개방법을 적용하였으며, 참 값으로는 기상청 강수자료를 사용하였으며, 금강유역을 대상으로 유역평균강수량을 계산하여 각 변환 방법에 따른 정확도를 비교하였다.

• Proceedings of the Korea Water Resources Association Conference
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• 2004.05b
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• pp.308-312
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• 2004

본 연구에서는 기상 수치모델의 예측강우량을 활용하여 단시간 하천유출량을 계산할 수 있는 기상-수자원 연계기법을 개발하였다. 이를 위해 기상청의 RDAPS 강수자료와 수자원공사의 치수모델인 KOWACO 모델을 통해 소양강댐 상류유역의 댐유입량을 계산하고 그 정확도를 분석하려다. 대상 사례기간인 2003년 7월 18일부터 2003년 7월 24일까지 RDAPS 강우예측자료의 정확도를 평가한 결과, RDAPS 및 AWS MAP 사이의 정성적 평가에서 매우 우수한 정확도를 보이고, 수자원 측면에서 필요한 정량적 성격을 어느 정도 충족시키는 것으로 나타났다. RDAPS-KOWACO 연계 모형의 하천유출량 계산에서도 그 정확도가 비교적 높은 것으로 검토되어 현재의 하천 유출량 예측에서 기상 수치예보자료의 활용성은 매우 놀은 것으로 사료된다.

• Journal of Korea Water Resources Association
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• v.44 no.3
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• pp.221-230
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• 2011

The climate change has been observed in Korea as well as in the entire world recently. The rainstorm has been gradually increased and then the damage has been grown. It is getting important to predict short-term rainfall. The Korea Meteorological Administration (KMA) generates numerical model outputs which are computed by Global Data Assimilation and Prediction System (GDAPS) and Regional Data Assimilation and Prediction System (RDAPS). The KMA predicts rainfall using RDAPS results. RDAPS model generates 48 hours data which is organized 3 hours data accumulated at 00UTC and 12UTC. RDAPS results which are organized 3 hours time scale are converted into daily rainfall to compare observed daily rainfall. In this study, 9 cases are applied to convert RDAPS results to daily rainfall data. The MAP (mean areal precipitation) in Geum river basin are computed by using KMA which are 2005 are used. Finally, the best case which gives the close value to the observed rainfall data is obtained using the average absolute relative error (AARE) especially for the Geum River basin.

• Journal of the Korean Society for Marine Environment & Energy
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• v.13 no.3
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• pp.127-134
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• 2010

This study investigated the accuracy of mean sea level pressure(MSLP) predicted by weather models around Korean Peninsula during typhoon Manyi and Usagi period in 2007. The mesoscale regional model, RDAPS, KWRF with 30 and 10 km horizontal resolution and developed high-resolution WRF models with 9 and 3 km horizontal resolutions are used to predict the features of MSLP. The predicted MSLP aspects were verified using observed results from total 35 coastal stations including AWS and ocean buoy. Although 4 models showed the reasonable MLSP results during typhoon periods, the highest resolution, 3km WRF model show the most accurate MSLP results with maximum 69% and 60% improvement with comparisons of RDAPS and KWRF, respectively.

• Journal of Korea Water Resources Association
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• v.38 no.1
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• pp.11-23
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• 2005

The objective of this study is to develop a short-term precipitation-streamflow coupling method for real-time river flow forecast. The coupled method is based on the RDAPS model for precipitation and atmospheric simulation and the SFM model for streamflow simulation. The selected study area is the 2,703-km$^2$ Soyang River basin with outlet at Soyang dam site. The rainfall-runoff event from 18 to 24 July 2003 is selected for the performance test of predicted precipitation and streamflow. It can be seen that the simulated basin-scale precipitation from the RDAPS can be useable as an input for SFM hydrologic model. Short-term hydrometeorological simulations using the RDAPS and SFM model were well captured important hydrometeorological characteristics in this study area. It is concluded that atmospheric precipitation forecast would be useful for streamflow forecast.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.19 no.5
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• pp.467-475
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• 2007

This study compared the sea surface wind pattern between model results from KMA operational model (RDAPS) and observational results from QuikSCAT in the 2005-2006 year. The mean spatial distributions of sea surface wind show the prominent seasonal patterns of summer and winter season adjacent to Korean Peninsular. The statistical analysis also shows well seasonal variation of sea surface wind patterns between model and observation results. The BIAS value represents less than -0.5 m/s and -1 m/s in summer and winter seasons, respectively. The spatially averaged correlation coefficient shows larger than 0.7 and 0.8 in summer and winter seasons, respectively. The correlation coefficient of winter season shows higher value than that of summer season in the comparison between model and observation. This results show that the RDAPS model simulate well strong sea surface wind in winter season rather than weak sea surface wind in summer season.

• 한국전산유체공학회:학술대회논문집
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• 2011.05a
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• pp.286-290
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• 2011

This paper introduces the optimum macro-siting of a potential site for an offshore wind farm around Jeju Island using the RDAPS sea wind model. The statistical model was developed by analyzing the sea wind data from RDAPS model, and the meso-scale digital wind map was prepared. To develop the high resolution spatial calibration model, Artificial Neural Network(ANN) models were used to construct the wind and bathymetric maps. Accuracy and consistency of wind/bathymetric spatial calibration models were obtained using analysis of variance. The optimization problem was defined to maximize the energy density satisfying the criteria of maximum water depth and maximum distance from the coastline. The candidate site was selected through Genetic Algorithm(GA). From the results, it is possible to predict roughly a candidate site location for the installation of the offshore wind jam, and to evaluate the wind resources of the proposed site.

• Atmosphere
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• v.21 no.4
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• pp.497-506
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• 2011

Korean mid- and upper-level aviation turbulence guidance (KTG) system is developed using the unified model (UM)-based regional data assimilation and prediction system (RDAPS) of the Korea Meteorological Administration. The KTG system includes three steps. First, the KTG system calculates a suite of diagnostics in the UM-RDAPS domain. Second, component diagnostics that have different units and numerical magnitudes are normalized into the values between 0 and 1, according to their own thresholds in the KTG system. Finally, normalized diagnostics are combined into one KTG predictor by measuring the weighting scores based on the probability of detection, which is calculated using the observed pilot reports (PIREPs) exclusively of moderate-or-greater (MOG) and null (NIL) intensities. To investigate the optimal performance of the KTG system, two types (RD-KTG and UM-KTG) of the KTG systems are developed and evaluated using the PIREPs over Korea and East Asia. Component diagnostics and their thresholds in the RD-KTG are founded on the 8-yrs (2002.12-2010.11) MM5-based RDAPS (previous version of the RDAPS; ${\Delta}x$ = 30 km) and PIREPs data, while those in the UM-KTG are based on the 6 months (2010.12-2011.5) UM-based RDAPS (${\Delta}x$ = 12 km) and PIREPs data. In comparison between the RD-KTG and UM-KTG, overall performance of the UM-KTG (0.815) is better than that of the RD-KTG (0.79) during the recent 6 months, because forecasting skill for the upper-level wind is higher in the UM-RDAPS than in the MM5-RDAPS. It is also found that the UM-KTG is more efficient than the RD-KTG according to the statistical evaluations and sensitivity tests to the number of component diagnostics.

• Proceedings of the Korean Environmental Sciences Society Conference
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• 2019.10a
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• pp.62-62
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• 2019