# A Probabilistic Forecast Algorithm of Nonconvective Turbulence over the Tibetan Plateau
## 5 Conclusions  
In this study, 17 turbulence indices for nonconvective turbulence at 200 hPa over the Tibetan Plateau from 2012 to August 2021 were calculated by using ERA5 reanalysis data. These turbulence indices with different intensities were reclassified based on the percentile threshold of -Ri. Compared with some empirical turbulence index thresholds, the new threshold delineation is more reasonable. It also suggests that the thresholds for turbulence intensity should be adjusted according to regions and altitude.  
Due to indicating the different physical processes, these turbulence indices show inconsistency with each other in their depiction of areas with a high turbulence occurrence. This means that the statistical results of a single index are inferior. Drawing on the principles of ensemble and probabilistic forecasting, a turbulence probability index is defined, which aggregates the various turbulence indices. The statistical results show that the edges of the Tibetan Plateau, including southern and northern Tibet and parts of Qinghai, Gansu, and Sichuan, are turbulence-prone areas. The validity of the turbulence probability index is preliminarily confirmed by comparing them with three turbulence events. Our results indicate that the impact of the complex terrain of the Tibetan Plateau cannot be ignored, and the inclusion of the MWT indices is necessary. Furthermore, the interannual variation analysis reveals that the probability and distribution of turbulence occurrence vary from year to year, especially in eastern Tibet and central-western Sichuan.  
In previous studies, the normalization method has been chosen to map the turbulence indices onto EDR [12, 18, 26]. In this study, the percentile method is used to map the turbulence indices onto -Ri, taking advantage of the simplicity and accuracy of the calculation. This study is a preliminary attempt to diagnose turbulence over the Tibetan Plateau using the probabilistic method. We further aim to apply this algorithm to model data and improve turbulence forecasting in this area. Statistical verification could be more precise if there were more observational data, such as PIREPs, and, thus, future collaboration with the civil aviation department will be necessary. An evaluation of the types of turbulence resources needs to be performed in future work. Furthermore, due to the limited availability of CIT indices, future research should expand the number of CIT indices and validate their effectiveness in probabilistic diagnostic methods.  
Methodology, investigation, writing-original draft, visualization and writing-review & editing, D.S.; methodology, writing-review & editing, and funding acquisition, L.R. and L.T.; software and resources, K.Z. and J.T. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the Strategic Priority Research Program of the Chinese Academy of Sciences (Grant XDA17010105), the Key Research and Development Program of Hebei Province (Grant 22375404D) and the National Natural Science Foundation of China (Grant U2233218, 42275010).  
Not applicable.  
The authors declare no conflict of interest.