The characteristics of aviation turbulence over South Korea during the recent five years (2003–08, excluding 2005) are investigated using pilot reports (PIREPs) accumulated by the Korea Aviation Meteorological Agency (KAMA). Among the total of 8449 PIREPs, 4607 (54.53%), 1646 (19.48%), 248 (2.94%), 7 (0.08%), and 1941 (22.97%) correspond to the turbulence categories of null, light, moderate, severe, and missing, respectively. In terms of temporal variations, the annual total number of turbulence events increased from 2003 to 2008, and the seasonal frequency is the highest in the spring. With regard to spatial distributions, reported turbulence encounters are dominant along the prevailing flight routes, but are locally higher over the west coast, Jeju Island, and the Sobaek and Taebaek mountains. The turbulence events in these regions vary by season. To examine the regional differences and possible sources of the observed turbulence, lightning flash data, Regional Data Assimilation and Prediction System (RDAPS) analysis data with a 30-km horizontal grid spacing provided by the Korean Meteorological Administration (KMA), and a digital elevation model (DEM) dataset with a 30-s resolution, are additionally used. Convectively induced turbulence (CIT) and clear-air turbulence (CAT) events comprised 11% and 89% of the total 255 moderate or greater (MOG)-level turbulence events, respectively. CAT events are classified as tropopause/jet stream–induced CAT (TJCAT) and mountain-wave-induced CAT (MWCAT) events. The MOG-level TJCAT and MWCAT events are responsible for 41.2% and 19.6% of the total MOG-level turbulence events, respectively. The CIT events in summer and the TRCAT and MWCAT events in spring occur most frequently over the previously mentioned regions of South Korea, associated with specific generation mechanisms.

On 2 April 2007, nine cases of moderate-or-greater-level clear-air turbulence (CAT) were observed from pilot reports over South Korea during the 6.5 h from 0200 to 0830 UTC. Those CAT events occurred in three different regions of South Korea: the west coast, Jeju Island, and the eastern mountain areas. The characteristics and possible mechanisms of the CAT events in the different regions are investigated using the Weather Research and Forecasting model. The simulation consists of six nested domains focused on the Korean Peninsula, with the finest horizontal grid spacing of 0.37 km. The simulated wind and temperature fields in a 30-km coarse domain are in good agreement with those of the Regional Data Assimilation and Prediction System (RDAPS) analysis data of the Korean Meteorological Administration and observed soundings of operational radiosondes over South Korea. In synoptic features, an upper-level front associated with strong meridional temperature gradients is intensified, and the jet stream passing through the central part of the Korean Peninsula exceeds 70 m s−1. Location and timing of the observed CAT events are reproduced in the finest domains of the simulated results in three different regions. Generation mechanisms of the CAT events revealed in the model results are somewhat different in the three regions. In the west coast area, the tropopause is deeply folded down to about z = 4 km because of the strengthening of an upper-level front, and the maximized vertical wind shear below the jet core produces localized turbulence. In the Jeju Island area, localized mixing and turbulence are generated on the anticyclonic shear side of the enhanced jet, where inertial instability and ageostrophic flow are intensified in the lee side of the convective system. In the eastern mountain area, large-amplitude gravity waves induced by complex terrain propagate vertically and subsequently break down over the lee side of topography, causing localized turbulence. For most of the CAT processes considered, except for the mountain-wave breaking, standard NWP resolutions of tens of kilometers are adequate to capture the CAT events.

For the next generation of the World Area Forecast System (WAFS), the global Graphical Turbulence Guidance (G-GTG) has been developed using global numerical weather prediction (NWP) model outputs as an input to compute a set of turbulence diagnostics, identifying strong spatial gradients of meteorological variables associated with clear-air turbulence (CAT) and mountain-wave turbulence (MWT). The G-GTG provides an atmospheric turbulence intensity metric of energy dissipation rate (EDR) to the 1/3 power (m^2/3 s^–1), which is the International Civil Aviation Organization (ICAO) standard for aircraft reporting. Deterministic CAT and MWT EDR forecasts are derived from ensembles of calibrated multiple CAT and MWT diagnostics, respectively, with the final forecast provided by the gridpoint-by-gridpoint maximum of the CAT and MWT ensemble means. In addition, a probabilistic EDR forecast is produced by the percentage agreement of the individual CAT and MWT diagnostics that exceed a certain EDR threshold for turbulence (i.e., multidiagnostic ensemble). Objective evaluations of the G-GTG against global in situ EDR measurement data show that both deterministic and probabilistic G-GTG significantly improve the current WAFS CAT product, mainly because the G-GTG takes into account turbulence from various sources related to CAT and MWT. The probabilistic G-GTG forecast is more reliable at predicting light-or-greater (EDR > 0.15)- than moderate-or-greater (EDR > 0.22)-level turbulence, although it suffers from overforecasting. This will be improved in the future when we use this methodology with NWP ensembles and more observation data will be available for calibration. We expect that the new G-GTG forecasts will be beneficial to aviation users globally.

Spatial and temporal distributions of Clear‐Air Turbulence (CAT) in the Northern Hemisphere were investigated using 41 years (1979–2019) of the European Centre for Medium‐range Weather Forecast Reanalysis version 5 (ERA5) data. We used two groups of CAT diagnostics to determine occurrence frequencies: (a) commonly used empirical turbulence indices (TI1, TI2, and TI3) and their components [vertical wind shear (VWS), deformation, ‐divergence, and divergence tendency], and (b) theoretical instability indicators [Richardson number (Ri), potential vorticity (PV), and Brunt‐Vӓisӓlӓ frequency]. The empirical indices showed high frequencies of moderate‐or‐greater (MOG)‐level CAT potential over the East Asian, Eastern Pacific, and Northwestern Atlantic regions in winter. Over East Asia, the entrance region of strong upper‐level jets showed the highest frequencies in TI1, TI2, and TI3 due mainly to strong VWS. The Eastern Pacific and Northwestern Atlantic areas near the exit region of jets had relatively high frequencies of these indices and also Ri. PV frequency was high on the southern side of jet primarily due to negative relative vorticity. Long‐term increasing trends of MOG‐level CAT potential also appeared in those three regions mainly due to warming in lower latitudes. The most significant increasing trend was found over East Asia, due to the strengthening of the East Asian jet and increased VWS due to the strong meridional temperature gradients in the mid‐troposphere induced by warming in the tropics and cooling in eastern Eurasia. These trends over East Asia, if continued, are expected to be of importance to efficient aviation operations across the northwestern Pacific Ocean.

At 1034 UTC 2 September 2007, a commercial aircraft flying from Jeju, South Korea, to Osaka, Japan, at an altitude of approximately 11.2 km encountered severe turbulence above deep convection. To investigate the characteristics and generation mechanism of this event, the real atmosphere is simulated using the Weather Research and Forecasting model with six nested domains, the finest of which is a horizontal grid spacing of 120 m. The model reproduces well the observed large-scale flows and the location and timing of the turbulence along the evolving deep convection. Three hours before the incident, isolated deep convection with two overshooting tops develops in a warm area ahead of the cold front in the southwestern region of the turbulence. As the deep convection moves with the dominant southwesterly flow toward the incident region, its thickness shrinks significantly because of weakening of upward motions inside the convection. Twenty minutes before the incident, the dissipating convection disturbs the southwesterly flow at the incident altitude, enhancing local vertical wind shear above the dissipating convection. The leading edge of the cloud stretches toward the lee side because of shear-induced y vorticity, finally overturning. This activates turbulence and vertical mixing at the cloud boundary through convective instability in the entrainment process. While the dissipating convection, its thickness still shrinking, continues to move toward the observed turbulence region, the turbulence generated at the cloud interface is advected by the dominant southwesterly flow, emerging about 1–2 km above the dissipating convection and intersecting the aircraft’s flight route at the incident time.

A time-lagged ensemble of energy dissipation rate (EDR)-scale turbulence metrics is evaluated against in situ EDR observations from commercial aircraft over the contiguous United States and applied to air-traffic management (ATM) route planning. This method uses the Graphic Turbulence Guidance forecast methodology with three modifications. First, it uses the convection-permitting-scale (Δx = 3 km) Advanced Research version of the Weather Research and Forecasting Model (ARW) to capture cloud-resolving-scale weather phenomena. Second, turbulence metrics are computed for multiple ARW forecasts that are combined at the same forecast valid time, resulting in a time-lagged ensemble of multiple turbulence metrics. Third, probabilistic turbulence forecasts are provided on the basis of the ensemble results, which are applied to the ATM route planning. Results show that the ARW forecasts match well with observed weather patterns and the overall performance skill of the ensemble turbulence forecast when compared with the observed data is superior to any single turbulence metric. An example wind-optimal route (WOR) is computed using areas experiencing ≥10% probability of encountering severe-or-greater turbulence. Using these turbulence data, lateral turbulence avoidance routes starting from three different waypoints along the WOR from Los Angeles International Airport to John F. Kennedy International Airport are calculated. The examples illustrate the trade-off between flight time/fuel used and turbulence avoidance maneuvers.