[논문]안전한 항공기 운항을 위한 현업 전지구예보모델 기반 깊은 대류 예측 지수: Part 2. 계절별 최적화 및 사례 분석

박이준; 김정훈

doi:10.14191/atmos.2023.33.5.531

안전한 항공기 운항을 위한 현업 전지구예보모델 기반 깊은 대류 예측 지수: Part 2. 계절별 최적화 및 사례 분석
Aviation Convective Index for Deep Convective Area using the Global Unified Model of the Korean Meteorological Administration, Korea: Part 2. Seasonal Optimization and Case Studies 원문보기

대기 = Atmosphere, v.33 no.5, 2023년, pp.531 - 548

박이준 (서울대학교 지구환경과학부) , 김정훈 (서울대학교 지구환경과학부)

Abstract ▼ AI-Helper

We developed the Aviation Convective Index (ACI) for predicting deep convective area using the operational global Numerical Weather Prediction model of the Korea Meteorological Administration. Seasonally optimized ACI (ACISnOpt) was developed to consider seasonal variabilities on deep convections in Korea. Yearly optimized ACI (ACIYrOpt) in Part 1 showed that 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 in winter season. In Part 2, we developed new membership function (MF) and weight combination of input variables in the ACI algorithm, which were optimized in each season. Finally, the seasonally optimized ACI (ACISnOpt) showed better performance skills with the significant improvements in AUC and TSS by 0.983% and 25.641% respectively, compared with those from the ACIYrOpt. To confirm the improvements in new algorithm, we also conducted two case studies in winter and spring with observed Convectively-Induced Turbulence (CIT) events from the aircraft data. In these cases, the ACISnOpt predicted a better spatial distribution and intensity of deep convection. Enhancements in the forecast fields from the ACIYrOpt to ACISnOpt in the selected cases explained well the changes in overall performance skills of the probability of detection for both "yes" and "no" occurrences of deep convection during 1-yr period of the data. These results imply that the ACI forecast should be optimized seasonally to take into account the variabilities in the background conditions for deep convections in Korea.

주제어

표/그림 (19)

그림 Fig. 1. ROC curves of ACI_NoOpt (da shed l ine) and ACI_YrOpt (solid line) of 6-hour (black), 24-hour (red), and 42-hour (blue) forecast in (a) DJF, (b) MAM, (c) JJA, and (d) SON. The circle, triangle, and square marker indicate the ACI of 0.25, 0.50, and 0.75, respectively.
표 Table 1. Total number of grids where convection with reflectivity over 15 dBZ is observed in radar.
표 Table 2. Forecast period averaged AUC and TSS of ACI_NoOpt and ACI_YrOpt with rate of change in each season.
그림 Fig. 2. Variability on seasonal MF (CAPE, APCP; 1σ ~ 99%, OLR; 1% ~ -1σ).
표 Table 3. Coefficients of MF coefficients in each season.
그림 Fig. 3. Cumulative distribution of Accumulated Precipitation (APCP) between +1 standard deviation and 99^th percentile value in DJF. The blue and black line indicate the 7^th and 6^th- order polynomial function with formula below. The Root Mean Square Error (RMSE) of each polynomial function is also noted.
표 Table 4. Optimized weighting coefficients in each season.
그림 Fig. 4. ROC curves of ACI_YrOpt (dashed line) and ACI_SnOpt (solid line) of 6-hour (black), 24-hour (red), and 42-hour (blue) forecast in (a) DJF, (b) MAM, (c) JJA, and (d) SON. 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_YrOpt (dashed line) and ACI_SnOpt (solid line) for each forecast time in (a) DJF, (b) MAM, (c) JJA, and (d) SON.
표 Table 5. Seasonal averaged AUC and TSS of ACI_NoOpt, ACI_YrOpt, and ACI_SnOpt.
그림 Fig. 6. Scatter plot of change of PODY and POFD between ACI_YrOpt and ACI_SnOpt (ACI_SnOpt - ACI_YrOpt) for 06-hour (black), 24- hour (red), 42-hour (blue) forecast time in (a) DJF, (b) MAM, (c) JJA, and (d) SON. And circle, triangle, square marker indicates the ACI of 0.25, 0.50, and 0.75 for each forecast time.
그림 Fig. 7. (a) Analysis synoptic map at the surface, (b) Hourly precipitation of radar Plan Position Indicator (PPI) image, KIMGDAPS Auxiliary analysis chart of (c) 850 hPa Moisture flux, and (d) 850 hPa Convergence at 0000 UTC 16 December 2021 from the Korea Meteorological Administration.
그림 Fig. 8. (a) 15 dBZ ETH, 24-hour forecast of (b) ACI_YrOpt and (c) ACI_SnOpt at 0000 UTC 16 December 2021. Grey and black line indicate the area of 15 dBZ ETH over FL0 and the deep convective area (15 dBZ ETH ≥ FL250) in (a), that is also expressed in (b) and (c). Purple line indicates the deep convective area (≥ FL250) in forecast field of (b) and (c).
그림 Fig. 9. (a) αM_CAPE, (b) βM_APCP, (c) γM_OLR of ACI_YrOpt, and (d) αM_CAPE, (e) βMAPCP, (f) γMOLR of ACISnOpt. Bracket refers to the range of MFs.
그림 Fig. 10. (a) Analysis synoptic map at the surface, (b) GK2A RGB image, KIM-GDAPS Auxiliary analysis chart of (c) 300 hPa Divergence and wind, and (d) 850-500 hPa Showalter Stability Index at 0000 UTC 28 May 2021 from the Korea Meteorological Administration.
그림 Fig. 11. (a) 15 dBZ ETH, 21-hour forecast of (b) ACI_YrOpt and (c) ACI_SnOpt at 0300 UTC 28 May 2021. Grey and black line indicate the area of 15 dBZ ETH over FL0 and the deep convective area (15 dBZ ETH ≥ FL250) in (a), that is also expressed in (b) and (c). Purple line indicates the deep convective area (≥ FL250) in forecast field of (b) and (c). Here, the intensity of Convectively-Induced Turbulence is indicated by the dots with colors in (a). And the location of CIT encounter is indicated by the blue circle in (b) and (c).
그림 Fig.12. (a) αM_CAPE, (b) βM_APCP, (c) γM_OLR of ACI_YrOpt, and (d) αM_CAPE, (e) βM_APCP, (f) γM_OLR of ACI_SnOpt. Bracket refers to the range of MFs.
그림 Fig. 13. (a) 15 dBZ ETH, 18-hour Time-lagged ensemble-based (b) deterministic and (c) probabilistic forecast of ACI_SnOpt at 0000 UTC 16 August 2021.
표 Table 6. Time-lagged ensemble members of each forecast time.

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표제어: PCR

동의어: Packet Collision Rate

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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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