Application of a Convective Gravity Wave Drag Parameterization to Aviation Turbulence Forecasting

Near-cloud turbulence (NCT) may occur in the clear air above deep convection due to the breakdown of convective gravity waves (CGWs). Current operational forecasting systems of aviation turbulence do not include diagnostics related to NCT. Accordingly, we developed eight NCT diagnostics utilizing the parameterization scheme for CGW drag (CGWD) proposed by Chun and Baik, and examined the feasibility of the diagnostics by comparing to observed moderate-or-greater-level turbulence encounters related to NCT events through multi-scale numerical simulations. It was found that the proposed eight NCT diagnostics well represented the observed NCT events. In the present study, we further evaluate the forecasting performance of these eight NCT diagnostics for general use in aviation turbulence forecasting, using the global in situ flight data and outputs from the operational global weather forecasting model of the Korea Meteorological Administration, Global Data Assimilation and Prediction System (GDAPS), for one year (from December 2016 to November 2017). The convective heating rate, which is required for calculation of NCT diagnostics but is not provided from GDAPS, is estimated from two approaches, (i) application of the Ricciardulli and Garcia formulation which uses the convective precipitation rate and convective cloud top and bottom heights and (ii) through the post-processing WRF-RIP program which uses water vapor mixing ratio and vertical velocity. The skill score of the individual NCT diagnostics, and the effects of including the NCT diagnostics in a current global operational turbulence forecasting system are examined. The results will be presented in the conference.