• 대기
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• 제25권4호
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• pp.623-637
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• 2015

The accurate analysis of water vapor in initial of numerical weather prediction (NWP) model is required as one of the necessary conditions for the improvement of heavy rainfall prediction and reduction of spin-up time on a very-short-range forecast. To study this effect, the impact of a ground-based Global Positioning System (GPS)-Precipitable Water Vapor (PWV) on very-short-range forecast are examined. Data assimilation experiments of GPS-PWV data from 19 sites over the Korean Peninsula were conducted with Advanced Storm-scale Analysis and Prediction System (ASAPS) based on the Korea Meteorological Administration's Korea Local Analysis and Prediction System (KLAPS) included "Hot Start" as very-short-range forecast system. The GPS total water vapor was used as constraint for integrated water vapor in a variational humidity analysis in KLAPS. Two simulations of heavy rainfall events show that the precipitation forecast have improved in terms of ETS score compared to the simulation without GPS-PWV data. In the first case, the ETS for 0.5 mm of rainfall accumulated during 3 hrs over the Seoul-Gyeonggi area shows an improvement of 0.059 for initial forecast time. In other cases, the ETS improved 0.082 for late forecast time. According to a qualitative analysis, the assimilation of GPS-PWV improved on the intensity of precipitation in the strong rain band, and reduced overestimated small amounts of precipitation on the out of rain band. In the case of heavy rainfall during the rainy season in Gyeonggi province, 8 mm accompanied by the typhoon in the case was shown to increase to 15 mm of precipitation in the southern metropolitan area. The GPS-PWV assimilation was extremely beneficial to improving the initial moisture analysis and heavy rainfall forecast within 3 hrs. The GPS-PWV data on variational data assimilation have provided more useful information to improve the predictability of precipitation for very short range forecasts.

• 한국수자원학회논문집
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• 제45권7호
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• pp.631-641
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• 2012

본 연구에서는 지상의 관측 자료와 광역의 정보를 제공하는 수치 예보 모형 자료 및 인공위성 자료를 이용하고 자료와 강수예측치의 물리적 상관 특성을 나타내기 위하여 자료 사이의 비선형 거동을 잘 나타내는 신경망 모형에 적용시켜 단시간 강수 예측을 수행하였다. 이를 위하여 서울지점에 대하여 현재로부터 3시간, 6시간, 9시간, 12시간의 선행시간을 가지는 인공위성자료(MTSAT-1R) 및 수치 예보 모형 자료(RDAPS, Regional Data Assimilation and Prediction System)와 실시간 전송되는 자동 기상 관측 시스템(AWS, Automatic Weather System)의 관측치를 신경망 모형의 입력 자료로 하여 3시간, 6시간, 9시간, 12시간의 선행시간을 가지는 자료로 강수를 예측 할 수 있는 강수 예측 모형을 개발하였다. 장마와 태풍과 같이 전선형강수와 선풍형강수 등 강수 양상의 차이를 고려하기 위하여 6월, 7월과 8월, 9월 자료를 구분하여 신경망을 구축하였으며, 자료가용성에 기초하여 2006년에서 2008년 기간 동안에 대하여 모형을 학습하고 2009년에 대하여 모형의 적용성을 검증한 결과, 단시간 강수예측에 대한 모형의 적용 가능성을 보여주었으나 다양한 광역 자료와 인공신경망을 사용함에도 불구하고 단시간 강수예측의 정량적 정도향상을 위한 여지가 많음을 보여준다.

• 한국해안·해양공학회논문집
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• 제20권2호
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• pp.194-202
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• 2008

일반적으로 지진해일 경보 발령 시 예상해일고를 산정하기 위해 큰 격자의 선형모형을 사용하게 되므로 범람역이 과소 산정된다. 그러므로 비상대피 계획을 위해 예보해일고에 상응하는 정도 높은 범람도를 필요로 한다. 본 연구에서는 범람역의 정량예보를 위해 상대적으로 정도 높은 지진해일 범람도를 작성하는 실용적인 방법을 제안하였다. 이 방법은 다음과 같다. 선형 지진해일 수치모형을 사용하여 대상지역 주변에 특정 지진해일고를 유발시키는 잠재 지진해일 발생원의 단층 변위를 산정한다. 이렇게 구해진 단층 변위에 대해 비선형 범람 수치모형을 이용하여 대상지역의 최대 침수포락선을 계산하고 범람도를 작성한다. 본 연구에서는 임원항을 대상지역으로 11개의 잠재 지진해일 발생원에 대해 예상범람도를 작성하여 제안된 기법의 타당성을 검토하였다.

• 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.

• 대기
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• 제31권1호
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• pp.73-83
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• 2021

Numerical weather prediction (NWP) models play an essential role in predicting weather factors, but using them is challenging due to various factors. To overcome the difficulties of NWP models, deep learning models have been deployed in weather forecasting by several recent studies. This study adapts long short-term memory (LSTM), which demonstrates remarkable performance in time-series prediction. The combination of LSTM model input of meteorological features and activation functions have a significant impact on the performance therefore, the results from 5 combinations of input features and 4 activation functions are analyzed in 9 Automated Surface Observing System (ASOS) stations corresponding to cities/islands/mountains. The optimized LSTM model produces better performance within eight forecast hours than Local Data Assimilation and Prediction System (LDAPS) operated by Korean meteorological administration. Therefore, this study illustrates that this LSTM model can be usefully applied to very short-term weather forecasting, and further studies about CNN-LSTM model with 2-D spatial convolution neural network (CNN) coupled in LSTM are required for improvement.

• Journal of the Korean Data and Information Science Society
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• 제26권2호
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• pp.289-299
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• 2015

본 연구는 PM10 농도에 대한 계량치 예측모형 개발을 목적으로 한다. 세 종류의 자료 (기상관측 자료, 세계기상통신망 중국 관측자료, 대기질 화학수치모델자료)를 예측인자로 사용하였으며, 일일 단기예보 시스템에 쉽게 적용할 수 있도록 시간자료를 일자료로 변환하였고 시차변환을 수행하였다. 상관분석과 다중공선성 진단을 통하여 예측인자를 선택하고 두 종류의 모형 (중회귀모형, 문턱치 회귀모형)을 각각 적합하였다. 모형 안정성 검사를 위하여 모형검증을 수행하였으며, 전체자료를 사용하여 모형을 재추정한 후 예측치와 관측치 사이의 산점도와 시계열그림, RMSE, 예측성 평가측도를 작성 및 산출하여 두 모형을 비교하였다. 문턱치 회귀모형의 예측력이 고농도 PM10예측에서 다소 우수한 결과를 보였다.

• 한국수자원학회논문집
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• 제50권8호
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• pp.537-549
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• 2017

본 연구에서는 기상청에서 제공하는 국지예보모델(LDAPS)과 일본 기상청의 중규모모델(Meso-Scale Model, MSM)을 이용하여 태풍 및 정체전선 등 3개의 강우사상과 남강댐 유역 내 산청 유역에 대해 강우 및 홍수 예측 정확도를 평가하고 비교 검토하였다. 강우예측 정확도 평가 결과, LDAPS와 MSM 모두 태풍 사상과 같은 광역적인 예측에 대해서는 예측 정확도가 높은 것으로 나타났으나, 정체전선과 같이 국지적으로 발생하는 강우사상의 경우 예측 오차가 많이 발생하는 것으로 나타났다. 홍수예측 정확도 평가 결과, 선행시간이 증가함에 따라 점점 예측 정확도가 향상되는 것을 확인할 수 있었으며, LDAPS와 MSM 모두 기상 및 수자원간의 연계를 통하여 강우 및 홍수 예측 분야에서의 활용 가능성을 확인할 수 있었다.

• Structural Engineering and Mechanics
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• 제75권4호
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• pp.477-485
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• 2020

Finite element (FE) model based structural damage detection (SDD) methods play vital roles in effectively locating and quantifying structural damages. Among these methods, structural model updating should be conducted before SDD to obtain benchmark models of real structures. However, the characteristics of updating parameters are not reasonably considered in existing studies. Inspired by the l_∞ norm regularization, a novel anti-sparse representation method is proposed for structural model updating in this study. Based on sensitivity analysis, both frequencies and mode shapes are used to define an objective function at first. Then, by adding l_∞ norm penalty, an optimization problem is established for structural model updating. As a result, the optimization problem can be solved by the fast iterative shrinkage thresholding algorithm (FISTA). Moreover, comparative studies with classical regularization strategy, i.e. the l₂ norm regularization method, are conducted as well. To intuitively illustrate the effectiveness of the proposed method, a 2-DOF spring-mass model is taken as an example in numerical simulations. The updating results show that the proposed method has a good robustness to measurement noises. Finally, to further verify the applicability of the proposed method, a six-storey aluminum alloy frame is designed and fabricated in laboratory. The added mass on each storey is taken as updating parameter. The updating results provide a good agreement with the true values, which indicates that the proposed method can effectively update the model parameters with a high accuracy.

• 대기
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• 제21권1호
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• pp.85-93
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• 2011

UK Met Office Unified Model (UM) is a grid model applicable for both global and regional model configurations. The Met Office has developed a 4D-Var data assimilation system, which was implemented in the global forecast system on 5 October 2004. In an effort to improve its Numerical Weather Prediction (NWP) system, Korea Meteorological Administration (KMA) has adopted the UM system since 2008. The aim of this study is to provide the basic information on the effects of satellite data assimilation on UM performance by conducting global satellite data denial experiments. Advanced Tiros Operational Vertical Sounder (ATOVS), Infrared Atmospheric Sounding Interferometer (IASI), Special Sensor Microwave Imager Sounder (SSMIS) data, Global Positioning System Radio Occultation (GPSRO) data, Air Craft (CRAFT) data, Atmospheric Infrared Sounder (AIRS) data were assimilated in the UM global system. The contributions of assimilation of each kind of satellite data to improvements in UM performance were evaluated using analysis data of basic variables; geopotential height at 500 hPa, wind speed and temperature at 850 hPa and mean sea level pressure. The statistical verification using Root Mean Square Error (RMSE) showed that most of the satellite data have positive impacts on UM global analysis and forecasts.

• 대기
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• 제18권4호
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• pp.299-316
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• 2008

The intensive upper-air observation network was organized over southwestern region of the Korean Peninsula during the Korea Enhanced Observing Program in 2005 (KEOP-2005). In order to examine the effect of additional upper-air observation on the numerical weather forecasting, three Observing System Experiments (OSEs) using Korea Local Analysis and Prediction System (KLAPS) and Weather Research and Forecasting (WRF) model with KEOP-2005 data are conducted. Cold start case with KEOP-2005 data presents a remarkable predictability difference with only conventional observation data in the downstream and along the Changma front area. The sensitivity of the predictability tends to decrease under the stable atmosphere. Our results indicates that the effect of intensive observation plays a role in the forecasting of the sensitive area in the numerical model, especially under the unstable atmospheric conditions. When the intensive upper-air observation data (KEOP-2005 data) are included in the OSEs, the predictability of precipitation is partially improved. Especially, when KEOP-2005 data are assimilated at 6-hour interval, the predictability on the heavy rainfall showing higher Critical Success Index (CSI) is highly improved. Therefore it is found that KEOP-2005 data play an important role in improving the position and intensity of the simulated precipitation system.