• 대기
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• 제28권2호
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• pp.223-232
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• 2018

The Global-Korean aviation Turbulence Guidance (G-KTG) system is developed using the operational Global Data Assimilation and Prediction System of Korea Meteorological Administration with 17-km horizontal grid spacing. The G-KTG system provides an integrated solution of various clear-air turbulence (CAT) diagnostics and mountain-wave induced turbulence (MWT) diagnostics for low [below 10 kft (3.05 km)], middle [10 kft (3.05 km) - 20 kft (6.10 km)], and upper [20 kft (6.10 km) - 50 kft (15.24 km)] levels. Individual CAT and MWT diagnostics in the G-KTG are converted to a 1/3 power of energy dissipation rate (EDR). 12-h forecast of the G-KTG is evaluated using 6-month period (2016.06~2016.11) of in-situ EDR observation data. The forecast skill is calculated by area under curve (AUC) where the curve is drawn by pairs of probabilities of detection of "yes" for moderate-or-greater-level turbulence events and "no" for null-level turbulence events. The AUCs of G-KTG for the upper, middle, and lower levels are 0.79, 0.69, and 0.63, respectively. Comparison of the upper-level G-KTG with the regional-KTG in East Asia reveals that the forecast skill of the G-KTG (AUC = 0.77) is similar to that of the regional-KTG (AUC = 0.79) using the Regional Data Assimilation and Prediction System with 12-km horizontal grid spacing.

• 한국항공운항학회지
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• 제31권3호
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• pp.30-41
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• 2023

Aviation turbulence, caused by atmospheric eddies, is a disruptive phenomenon that leads to abrupt aircraft movements during flight. To minimize the damages caused by such aviation turbulence, the Aviation Meteorological Office provides turbulence information through the Korea aviation Turbulence Guidance (KTG) and the Global-Korean aviation Turbulence Guidance (GKTG). In this study, we evaluated the performance of the KTG and GKTG models by comparing the in-situ EDR observation data and the generated aviation turbulence prediction data collected from the mid-level Korean Peninsula region from January 2019 to December 2021. Through objective validation, we confirmed the level of prediction performance and proposed improvement measures based on it. As a result of the improvements, the KTG model showed minimal difference in performance before and after the changes, while the GKTG model exhibited an increase of TSS after the improvements.