• 대기
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• 제26권4호
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• pp.495-508
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• 2016

In order to determine the prediction possibility of heavy rainfall, a variety of analyses was conducted by using three-dimensional data obtained from Korea Local Analysis and Prediction System (KLAPS) re-analysis data. Strong moisture convergence occurring around the time of the heavy rainfall is consistent with the results of previous studies on such continuous production. Heavy rainfall occurred in the cloud system with a thick convective clouds. The moisture convergence, temperature and potential temperature advection showed increase into the heavy rainfall occurrence area. The distribution of integrated liquid water content tended to decrease as rainfall increased and was characterized by accelerated convective instability along with increased buoyant energy. In addition, changes were noted in the various characteristics of instability indices such as K-index (KI), Showalter Stability Index (SSI), and lifted index (LI). The meteorological variables used in the analysis showed clear increases or decreases according to the changes in rainfall amount. These rapid changes as well as the meteorological variables changes are attributed to the surrounding and meteorological conditions. Thus, we verified that heavy rainfall can be predicted according to such increase, decrease, or changes. This study focused on quantitative values and change characteristics of diagnostic variables calculated by using numerical models rather than by focusing on synoptic analysis at the time of the heavy rainfall occurrence, thereby utilizing them as prognostic variables in the study of the predictability of heavy rainfall. These results can contribute to the identification of production and development mechanisms of heavy rainfall and can be used in applied research for prediction of such precipitation. In the analysis of various case studies of heavy rainfall in the future, our study result can be utilized to show the development of the prediction of severe weather.

• 전기학회논문지
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• 제64권1호
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• pp.128-135
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• 2015

In this study, we develop the very short-term precipitation forecasting model as well as classifier based on polynomial radial basis function neural networks by using AWS(Automatic Weather Station) and KLAPS(Korea Local Analysis and Prediction System) meteorological data. The polynomial-based radial basis function neural networks is designed to realize precipitation forecasting model as well as classifier. The structure of the proposed RBFNNs consists of three modules such as condition, conclusion, and inference phase. The input space of the condition phase is divided by using Fuzzy C-means(FCM) and the local area of the conclusion phase is represented as four types of polynomial functions. The coefficients of connection weights are estimated by weighted least square estimation(WLSE) for modeling as well as least square estimation(LSE) method for classifier. The final output of the inference phase is obtained through fuzzy inference method. The essential parameters of the proposed model and classifier such ad input variable, polynomial order type, the number of rules, and fuzzification coefficient are optimized by means of Particle Swarm Optimization(PSO) and Differential Evolution(DE). The performance of the proposed precipitation forecasting system is evaluated by using KLAPS meteorological data.

• 대기
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• 제25권3호
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• pp.389-405
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• 2015

In this study, the occurrence circumstances of 3 cases (12 Jan 2006, 11 Jan 2008, 22 Feb 2009) when the freezing rain was observed at more than two observatories in a day with more than three times each observatory, were investigated. Following the advanced study about the same cases, we have tried to find more delicate differences in using the Korea Local Analysis and Prediction System (KLAPS; 5 km reanalysis data) that has the smallest grid scale at current situation. As results, three common characteristics are found: (1) Just before the occurrence of the freezing rain, the wind direction was consistently continuous and the wind speed was constant or gradually increased for at least 3 hr more. (2) Surface air temperature (Relative humidity) was respectively $3.08^{\circ}C$ (28.76%), $0.47^{\circ}C$ (50.07%) and $-3.60^{\circ}C$ (71.07%) 3 hr ago to break out the freezing rain. It means the freezing rain occurs in a wide range of atmospheric environments. However, the closer it got to the occurrence time of the freezing rain, the closer the surface air temperature was to $0^{\circ}C$, and the bigger the humidity of the surface air was. (3) The liquid precipitation formed in the upper atmosphere, met a cold advection bellower than 950 hPa level and suspected to be changed to the super-cooled condition.

• 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.

• 한국지능시스템학회논문지
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• 제25권5호
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• pp.477-482
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• 2015

단기풍속 예측을 위한 진화적 선형 및 비선형 회귀분석 기반의 보정 기법을 비교한다. 모델의 체계적 오류를 교정하기 위한 효율적인 MOS(Model Output Statistics)의 개발이 필요하나, 기존의 선형회귀분석 기반의 보정기법은 다양한 기상요소의 복잡한 비선형 특성을 반영하기 힘들다. 이를 개선하기 위해서 유전 프로그래밍을 사용하여 풍속 예측에 대한 비선형 보정 수식을 생성하는 기법을 제안하고 기본 다중선형회귀분석법 및 Ridge, Lasso 회귀분석법과 비교한다. 더불어, 선형회귀분석법과 진화적 비선형회귀분석 기법의 인자 선택의 차이와 유사성을 비교하고 분석한다. 2007년~2013년의 KLAPS(Korea Local Analysis and Prediction System) 재분석자료를 사용하여 제주도와 부산지역의 격자점에 대한 실험을 수행한다.

• 통합자연과학논문집
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• 제4권1호
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• pp.44-57
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• 2011

In general, heavy rainfall in Korea is mostly associated with inflow of 850hPa low-level jet. It transports abundant heat and moisture flux to the Changma front. In this study, synoptic characteristics of heavy rainfall in Korea from a case study is examined by classifying heavy rainfall cases with synoptic patterns, in particular distribution of upper- and low-level jets, western North Pacific high, and moisture flux. The surface and upper-level weather charts including auxiliary analysis chart and radar and satellite images obtained from the Korea Meteorological Administration, and 500hPa geopotential heights from NCEP/NCAR are used and then KLAPS is applied to understand the local atmospheric structure associated with heavy rainfall. Results show that maximum frequency in 60 heavy rainfall cases with more than 150mm/day appears in the Changma type of 43 cases (a proportion in relation to a whole is 52%) including the combined Changma types with typhoon and cyclone. As indicated in previous studies, most heavy rainfall cases are related to inflow of low-level jet. In addition, synoptic characteristics based on the analyses of weather charts, radar and satellite images, and KLAPS in heavy rainfall case of 12 July, 2009 reveal that the atmospheric vertical structure in particular equivalent potential temperature favorable for effective inflow of warm and moist southwesterly into the Changma front is linked to large potential instability and the strong convergence accompanied with low-level jet around Suwon contributes to atmospheric upsliding along the Changma front, producing heavy rainfall.

• 통합자연과학논문집
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• 제5권4호
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• pp.220-228
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• 2012

District of Korea affected by westerly wind and heavy rainfall is predominantly distributed in the west and south of Honam district. So, this study is becoming a necessity. In this study, it is investigated that the characteristics of heavy rainfall occurred frequently in Honam district along the border of mT airmass after the end of rainy season due to atmospheric instability, lower (850 hPa) convergence and topographic effect. Our results show that heavy rainfall occurred in Honam district along the border of mT airmass results from the appropriate mechanism of the unstable vertical structure and moisture flux in the expansion and contraction of the border of mT airmass. All things considered, the improvement of the predictability of heavy rainfall occurred in Honam district along the border of mT airmass could be possible by the generalization of the results of this study.

• 전기학회논문지
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• 제66권12호
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• pp.1799-1803
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• 2017

This paper introduces the prediction methods of windstorm advisory using GP nonlinear compensation and SVM. The existing special report prediction is not specialized for strong wind, such as windstorm, because it is based on the wide range of predicted values for wind speed from low to high. In order to improve the performance of strong wind reporting prediction, a method that can efficiently classify boundaries of strong wind is necessary. First, evolutionary nonlinear regression based compensation technique is applied to obtain more accurate values of prediction for wind speed using UM data. Based on the prediction wind speed, the windstorm advisory is determined. Second, SVM method is applied to classify directly using the data of UM predictors and windstorm advisory. Above two methods are compared to evaluate of the performances for the windstorm data in Jeju Island in South Korea. The data of 2007-2009, 2011 year is used for training, and 2012 year is used for test.

• 한국지리정보학회지
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• 제20권2호
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• pp.47-59
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• 2017

본 연구는 InVEST(Integrated Valuation of Ecosystem Services and Tradeoff) Offshore Wind 모형을 활용하여 제주도 장선주 인근 해역의 해상풍력 에너지 자원을 평가하였다. 초단기 기상분석 및 예측 시스템(KLAPS)의 재분석 자료를 이용하여 제주도 인근 해역의 풍력밀도를 계산하고 터빈 조성비용, 터빈의 운영 효율, 해저케이블 설치비용, 20년 운영시나리오, 유지관리비 등을 고려하여 168MW 해상풍력 단지를 설치하였을 때의 순현재가치를 산정하였다. 제주도 인근 해역의 풍력밀도 분포도를 통하여 제주도 서쪽해역과 동쪽해역에 높은 풍력자원이 있음을 알 수 있었으며, 대부분의 서측해역과 동측해역은 $400W/m^2$ 이상의 높은 풍력밀도를 보였다. 제주지역 해상풍력발전에 대한 순현재가치를 가시적으로 평가하기 위하여 5등급으로 구분하였으며, $400W/m^2$ 이상의 풍력자원이 존재하는 서측 해역에서 높은 순현재가치를 보였다. InVEST Offshore Wind 모형은 다양한 운영시나리오에 대하여 최적의 공간정보를 신속하게 제공해 줄 수 있으며, 해양생태계서비스 평가 결과와 혼용하여 사용한다면 보다 효율적인 해양공간을 이용할 수 있을 것으로 판단된다.

• 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.