Using MPAS model to forecast the Convectively Induced Turbulence

Convectively induced turbulence (CIT) is a serious aviation hazard and it is challenging to forecast the CIT in the region near convection. Previous studies used reginal model with high resolution or global model with low resolution and selected empirical indices to diagnose the turbulence. In this study, we used The Model for Prediction Across Scales (MPAS) to simulate some cases of CIT reported near Hong Kong. MPAS allows us to use convection-permitting resolution in the interested area while including the global-large scale circulation with coarser resolutions in other regions. The eddy dissipation rate (EDR) is computed to diagnose the potential occurrence of CIT. We compared three methods for calculating EDR from the resolved flow in the MPAS, the first one based on second order structure function, the second one based on Scale-Similarity in Large Eddy Simulation (LES) and the third is Near Cloud Turbulence (NCT) diagnostics by using Convective Gravity Wave Drag. Comparing with the NOAA Graphical Turbulence Guidance (GTG) product and flight data suggests that computing EDR with Scale-Similarity is more effective and accurate than second order structure function and NCT diagnostics. Resolution is also an important factor in forecast, we tested the method in mesh with different resolutions but similar distributions, the results from low resolution simulations can generate a useful turbulence pattern forecast, but the intensity is weak, highlighting the value of high resolution simulations that can resolve convection. We evaluated the sensitivity to several model physics and numeric options in simulations. Those variations can change the EDR prediction by influencing the intensity and the life cycle of the convection. No particular scheme produces systematically more intense turbulence than others, suggesting varying model physics captures some stochasticity of convection. Compared with flight records of EDR along the flight routes, MPAS could produce in three out of five cases showing maximum EDR is close to the observed intensity of turbulence (EDR>0.4). However, in the other two cases, the results are not satisfactory mainly because of significant location biases of the predicted convection. We also add initial condition perturbation-based large ensemble in one case and find it possible to improve the prediction of the failed cases by influencing the position of the convection. Further work should be conducted to prioritize the ensemble members since only a few members can capture the turbulence and doing the average will erase them easily.