Statistics and Possible Sources of Aviation Turbulence over South Korea

Abstract

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.

Increased light, moderate, and severe clear-air turbulence in response to climate change

“…Note that, by construction, the probability of light-or-greater turbulence is 3.0% and the probability of moderate-or-greater turbulence is 0.4%. The probabilities for each turbulence strength category agree reasonably well with the relative frequencies at which the categories appear in automated in-flight measurements (Williams, 2014) and in PIREPs in the United States (Schwartz, 1996) and South Korea (Kim and Chun, 2011). Exact quantitative agreement cannot be expected, because of inconsistent PIREP reporting practices and because automated measurements and PIREPs contain a substantial avoidance bias, which is caused by pilots attempting to evade the strongest turbulence (Sharman et al, 2014).…”

“…As reviewed by Williams and Joshi (2016), evidence is emerging of upward trends in recent turbulence statistics (FAA, 2006;Jaeger and Sprenger, 2007;Wolff and Sharman, 2008;Kim and Chun, 2011), although the interpretation of these trends requires caution in some cases. Climate change may continue to increase the prevalence of clear-air turbulence in the coming decades.…”

Aviation turbulence encounters detected from aircraft observations: spatiotemporal characteristics and application to Korean Aviation Turbulence Guidance

“…According to the 2010 annual report of the National Transportation Safety Board (NTSB, ), turbulence was the main cause of aviation accidents related to weather from 1997 to 2006. In South Korea, turbulence has accounted for about 24% of weather‐related aviation accidents since 1957 (Kim and Chun, ). To reduce these damaging events, it is necessary to predict turbulence adequately based on an understanding of the generation mechanisms of turbulence through observational analysis and numerical modelling (Lane et al , ; Wolff and Sharman, ; Kim and Chun, ; Sharman et al , ).…”

“…Although PIREPs are used in various case studies and statistical analyses for turbulence encounters due to their relatively broad spatiotemporal distributions (e.g. Wolff and Sharman, ; Kim and Chun, , ; Lee and Chun, ), there are substantial errors in the intensities, timing and locations of these turbulence encounters (Schwartz, ; Cornman et al , ). As the use of objective and continuous data has been in demand, several studies using direct aircraft observations have been carried out recently (e.g.…”

Aviation Turbulence: Dynamics, Forecasting, and Response to Climate Change

“…Some previous studies resolved this issue using PIREPs (Kim and Chun 2011;Schwartz 1996) to identify regions of turbulence based on a semi-quantitative scale from light to extreme, but these can be unreliable (Kane et al 1998). PIREPs are subjective, in the sense that a more experienced pilot may catagorise an event as moderate, but an inexperienced pilot may record it as severe.…”