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안전한 항공기 운항을 위한 현업 전지구예보모델 기반 깊은 대류 예측 지수: Part 2. 계절별 최적화 및 사례 분석
박이준,김정훈 한국기상학회 2023 대기 Vol.33 No.5
We developed the Aviation Convective Index (ACI) for predicting deep convectivearea using the operational global Numerical Weather Prediction model of the Korea MeteorologicalAdministration. Seasonally optimized ACI (ACISnOpt) was developed to consider seasonalvariabilities on deep convections in Korea. Yearly optimized ACI (ACIYrOpt) in Part 1 showedthat seasonally averaged values of Area Under the ROC Curve (AUC) and True Skill Statistics(TSS) were decreased by 0.420% and 5.797%, respectively, due to the significant degradation inwinter season. In Part 2, we developed new membership function (MF) and weight combinationof input variables in the ACI algorithm, which were optimized in each season. Finally, the seasonallyoptimized ACI (ACISnOpt) showed better performance skills with the significant improvementsin AUC and TSS by 0.983% and 25.641% respectively, compared with those from theACIYrOpt. To confirm the improvements in new algorithm, we also conducted two case studies inwinter and spring with observed Convectively-Induced Turbulence (CIT) events from the aircraftdata. In these cases, the ACISnOpt predicted a better spatial distribution and intensity of deepconvection. Enhancements in the forecast fields from the ACIYrOpt to ACISnOpt in the selectedcases explained well the changes in overall performance skills of the probability of detection forboth “yes” and “no” occurrences of deep convection during 1-yr period of the data. Theseresults imply that the ACI forecast should be optimized seasonally to take into account the variabilitiesin the background conditions for deep convections in Korea.
안전한 항공기 운항을 위한 현업 전지구예보모델 기반 깊은 대류 예측 지수: Part 1. 개발 및 통계적 검증
박이준,김정훈 한국기상학회 2023 대기 Vol.33 No.5
Deep convection can make adverse effects on safe and efficient aviation operationsby causing various weather hazards such as convectively-induced turbulence, icing, lightning,and downburst. To prevent such damage, it is necessary to accurately predict spatiotemporal distributionof deep convective area near the airport and airspace. This study developed a newindex, the Aviation Convective Index (ACI), for deep convection, using the operational globalUnified Model of the Korea Meteorological Administration. The ACI was computed from combinationof three different variables: 3-hour maximum of Convective Available Potential Energy,averaged Outgoing Longwave Radiation, and accumulative precipitation using the fuzzy logicalgorithm. In this algorithm, the individual membership function was newly developed followingthe cumulative distribution function for each variable in Korean Peninsula. This index wasvalidated and optimized by using the 1-yr period of radar mosaic data. According to theReceiver Operating Characteristics curve (AUC) and True Skill Score (TSS), the yearly optimizedACI (ACIYrOpt) based on the optimal weighting coefficients for 1-yr period shows a betterskill than the no optimized one (ACINoOpt) with the uniform weights. In all forecast time from 6-hour to 48-hour, the AUC and TSS value of ACIYrOpt were higher than those of ACINoOpt, showingthe improvement of averaged value of AUC and TSS by 1.67% and 4.20%, respectively.