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
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초록
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.
Abstract
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.
참고문헌 (22)
- "Operational system for predicting thunderstorms two to six hours in advance" (1977)[URL]
- Chuda, T., "Climatology of environmental parameters for mesoscale convections in Japan", J. Meteor. Soc. Japan. Ser. II, 83, pp.391~408 (2005)[DOI]
- Doswell III, C. A., "The distinction between largescale and mesoscale contribution to severe convection : A case study example", Wea. Forecasting, 2, pp.3~16 (1987)[DOI]
- Galway, J. G., "The lifted index as a predictor of latent instability", Bull. Amer. Meteor. Soc, 37, pp.528~529 (1956)[DOI]
- George, J. J., "Weather forecasting for aeronautics", pp.684~ (1960), Academic press
- Haklander, A. J., "Thunderstorm predictors and their forecast skill for the Netherlands", Atmos. Res, 67, pp.273~299 (2003)[DOI]
- Hu, S., "Spatial and seasonal variations of sea surface temperature threshold for tropical convection", J. Climate, 36, pp.4899~4912 (2023)[DOI]
- "Seventh Meeting of the World Area Forecast System Operations Group (WAFSOPSG)" (2012)[URL]
- "Ensemble Prediction of Oceanic Convective Hazards (EPOCH) Assessment: Part II" (2019)[URL]
- Notes on analysis and severe-storm forecasting procedures of the Military Weather Warning Center, "Miller, R. C.", Air Weather Service, 2~ (1967)
- Notes on analysis and severe-storm forecasting procedures of the Air Force Global Weather Central, "Miller, R. C.", AWS, 200~ (1975)
- Poręba, S., "Diurnal and seasonal variability of ERA5 convective parameters in relation to lightning flash rates in Poland", Wea. Forecasting, 37, pp.1447~1470 (2022)[DOI]
- Reap, R. M., "Automated 12–36hour probability forecasts of thunderstorms and severe local storms", J. Appl. Meteor. Climatol, 18, pp.1304~1315 (1979)[DOI]
- Riemann-Campe, K., "Global climatology of convective available potential energy(CAPE)and convective inhibition(CIN)in ERA-40 reanalysis", Atmos. Res, 93, pp.534~545 (2009)[DOI]
- Schmeits, M. J., "Probabilistic forecasts of(severe)thunderstorms for the purpose of issuing a weather alarm in the Netherlands", Wea. Forecasting, 23, pp.1253~1267 (2008)[DOI]
- Schultz, D. M., "The use and misuse of conditional symmetric instability", Mon. Wea. Rev, 127, pp.2709~2732 (1999)[DOI]
- Showalter, A. K., "A stability index for thunderstorm forecasting", Bull. Amer. Meteor. Soc, 34, pp.250~252 (1953)[DOI]
- Simon, T., "Probabilistic forecasting of thunderstorms in the Eastern Alps", Mon. Wea. Rev, 146, pp.2999~3009 (2018)[DOI]
- Slangen, A. B. A., "Probabilistic forecasts of winter thunderstorms around Amsterdam Airport Schiphol", Adv. Sci. Res, 3, pp.39~43 (2009)[DOI]
- Sobash, R. A., "Probabilistic forecast guidance for severe thunderstorms based on the identification of extreme phenomena in convectionallowing model forecasts", Wea. Forecasting, 26, pp.714~728 (2011)[DOI]
- Sobash, R. A., "Explicit forecasts of low-level rotation from convection-allowing models for nextday tornado prediction", Wea. Forecasting, 31, pp.1591~1614 (2016)[DOI]
- Wendt, N. A., "Verification of severe weather proxies from the NSSL-WRF for hail forecasting", 28th Conf. on Severe Local Storms, pp.15~ (2016)