[논문]안전한 항공기 운항을 위한 현업 전지구예보모델 기반 깊은 대류 예측 지수: Part 1. 개발 및 통계적 검증

박이준; 김정훈

doi:10.14191/atmos.2023.33.5.519

안전한 항공기 운항을 위한 현업 전지구예보모델 기반 깊은 대류 예측 지수: Part 1. 개발 및 통계적 검증
Aviation Convective Index for Deep Convective Area using the Global Unified Model of the Korean Meteorological Administration, Korea: Part 1. Development and Statistical Evaluation 원문보기

대기 = Atmosphere, v.33 no.5, 2023년, pp.519 - 530

박이준 (서울대학교 지구환경과학부) , 김정훈 (서울대학교 지구환경과학부)

Abstract ▼ AI-Helper

Deep convection can make adverse effects on safe and efficient aviation operations by causing various weather hazards such as convectively-induced turbulence, icing, lightning, and downburst. To prevent such damage, it is necessary to accurately predict spatiotemporal distribution of deep convective area near the airport and airspace. This study developed a new index, the Aviation Convective Index (ACI), for deep convection, using the operational global Unified Model of the Korea Meteorological Administration. The ACI was computed from combination of three different variables: 3-hour maximum of Convective Available Potential Energy, averaged Outgoing Longwave Radiation, and accumulative precipitation using the fuzzy logic algorithm. In this algorithm, the individual membership function was newly developed following the cumulative distribution function for each variable in Korean Peninsula. This index was validated and optimized by using the 1-yr period of radar mosaic data. According to the Receiver Operating Characteristics curve (AUC) and True Skill Score (TSS), the yearly optimized ACI (ACIYrOpt) based on the optimal weighting coefficients for 1-yr period shows a better skill 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, showing the improvement of averaged value of AUC and TSS by 1.67% and 4.20%, respectively.

주제어

표/그림 (13)

표 Table 1. Model Information of Global Data Assimilation and Prediction System (GDAPS) based on Unified Model (UM).
그림 Fig. 1. Samples of Vertically-Integrated Liquid water content (VIL) (left), and 15 dBZ Echo Top Height (right) with radar location (purple marker). Grids only located within the observation boundary are masked.
그림 Fig. 2. A flow chart on computing ACI based on fuzzy-logic algorithm. Here, CAPE, APCP, and OLR indicates the convective available potential energy, the accumulated precipitation, and the outgoing longwave radiation. M and MF means the membership value and the membership function. And, α, β, and γ are the weight coefficients for CAPE, APCP, and OLR.
그림 Fig. 3. Cumulative Distribution Function of CAPE (left), APCP (middle), and OLR (right). The Membership Function is indicated by the blue line in each plot.
표 Table 2. Percentile of CAPE, APCP, and OLR in 1-yr period of 2021. Bracket correspond to the percentile of OLR. Bold lettersrepresent the severe range.
표 Table 3. Coefficients of the membership function of CAPE, APCP, and OLR.
표 Table 4. Contingency table between observation and forecast.
표 Table 5. Top 10 weighting coefficients by AUC value among all possible weight combinations. Bold letter represents the optimized weighting set.
그림 Fig. 4. ROC curves of ACI_NoOpt for 1-yr with each forecast time. The numbers are the AUC values. And, the circle, triangle, and square marker indicate the ACI of 0.25, 0.50, and 0.75, respectively
그림 Fig. 5. AUC (black) and TSS (grey) values of ACI_NoOpt for each forecast time.
그림 Fig. 6. ROC curves of ACI_YrOpt (solid line) and ACI_NoOpt (dashed line) of 6-hour (black), 24-hour (red), and 42-hour (blue) forecast. The circle, triangle, and square marker indicate the ACI of 0.25, 0.50, and 0.75, respectively.
그림 Fig. 7. AUC (black) and TSS (grey) values of ACI_NoOpt (dashed line) and ACI_YrOpt (solid line) for each forecast time. The dashed lines are same as the solid lines in Fig. 5.
그림 Fig. 8. Diagram (left) for interpreting the plot of change of PODY and POFD and Scatter plot (right) of change of PODY and POFD between ACI_YrOpt and ACI_NoOpt (ACI_YrOpt-ACI_NoOpt) for 06-hour (black), 24-hour (red), 42-hour (blue) forecast time. And circle, triangle, and square marker indicates the ACI of 0.25, 0.50, and 0.75 for 06-hour forecast and these are associated with the change of PODY and POFD between ACI_YrOpt and ACI_NoOpt of the black markers in Fig. 6.

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표제어: PCR

동의어: Packet Collision Rate

용어 설명 출처 목록 (6)

용어 설명: PCR은 세균 특이성이 있는 primer를 이용하여 적은 수의 세균이 있을지라도 쉽게 검출할 수 있는 유용한 방법이며, 이를 이용하여 구강 내 치면세균막이나 타액에서 직접 세균을 검출할 수 있게 되었다[8].

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