• Atmosphere
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• v.22 no.3
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• pp.321-330
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• 2012

Generation mechanisms of the three moderate-or-greater (MOG)-level clear-air turbulence (CAT) encounters over South Korea are investigated using the Weather Research and Forecasting (WRF) model. The cases are selected among the MOG-level CAT events occurred in Korea during 2002-2008 that are categorized into three different generation mechanisms (upper-level front and jet stream, anticyclonic flow, and mountain waves) in the previous study by Min et al. For the case at 0127 UTC 18 Jun 2003, strong vertical wind shear (0.025 $s^{-1}$) generates shearing instabilities below the enhanced upper-level jet core of the maximum wind speed exceeding 50 m $s^{-1}$, and it induces turbulence near the observed CAT event over mid Korea. For the case at 2330 UTC 22 Nov 2006, areas of the inertia instability represented by the negative absolute vorticity are formed in the anticyclonically sheared side of the jet stream, and turbulence is activated near the observed CAT event over southwest of Korea. For the case at 0450 UTC 16 Feb 2003, vertically propagating mountain waves locally trigger shearing instability (Ri < 0.25) near the area where the background Richardson number is sufficiently small (0.25 < Ri < 1), and it induces turbulence near the observed CAT over the Eastern mountainous region of South Korea.

• Atmosphere
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• v.21 no.4
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• pp.497-506
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• 2011

Korean mid- and upper-level aviation turbulence guidance (KTG) system is developed using the unified model (UM)-based regional data assimilation and prediction system (RDAPS) of the Korea Meteorological Administration. The KTG system includes three steps. First, the KTG system calculates a suite of diagnostics in the UM-RDAPS domain. Second, component diagnostics that have different units and numerical magnitudes are normalized into the values between 0 and 1, according to their own thresholds in the KTG system. Finally, normalized diagnostics are combined into one KTG predictor by measuring the weighting scores based on the probability of detection, which is calculated using the observed pilot reports (PIREPs) exclusively of moderate-or-greater (MOG) and null (NIL) intensities. To investigate the optimal performance of the KTG system, two types (RD-KTG and UM-KTG) of the KTG systems are developed and evaluated using the PIREPs over Korea and East Asia. Component diagnostics and their thresholds in the RD-KTG are founded on the 8-yrs (2002.12-2010.11) MM5-based RDAPS (previous version of the RDAPS; ${\Delta}x$ = 30 km) and PIREPs data, while those in the UM-KTG are based on the 6 months (2010.12-2011.5) UM-based RDAPS (${\Delta}x$ = 12 km) and PIREPs data. In comparison between the RD-KTG and UM-KTG, overall performance of the UM-KTG (0.815) is better than that of the RD-KTG (0.79) during the recent 6 months, because forecasting skill for the upper-level wind is higher in the UM-RDAPS than in the MM5-RDAPS. It is also found that the UM-KTG is more efficient than the RD-KTG according to the statistical evaluations and sensitivity tests to the number of component diagnostics.

• Journal of the Korean Society for Aviation and Aeronautics
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• v.31 no.3
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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.