# A Probabilistic Forecast Algorithm of Nonconvective Turbulence over the Tibetan Plateau
###### Abstract  
The development of upper-level turbulence forecast algorithms is important for enhancing flight safety. Seventeen nonconvextive turbulence indices were calculated over the Tibetan Plateau for August from 2012-2021 with ERA5 reanalysis data. The thresholds for these turbulence indices were reclassified by using the percentile method based on the Richardson number. The reclassified thresholds were found to be more reasonable than the empirical thresholds. A turbulence probability index was used to aggregate the 17 turbulence indices without requiring observational data. The validity of the turbulence probability index was preliminarily confirmed by comparing it with turbulence events. Further research revealed that the turbulence probability index exhibited interannual fluctuation.  
upper-level turbulence; probabilistic forecast algorithm; turbulence index; Tibetan Plateau +
Footnote †: journal: Medical  
requires a uniform measure of the turbulence indices. An early algorithm by GTG normalized the turbulence indices [16]. With the development of airborne equipment, [PERSON] and [PERSON] [12] improved the algorithm with statistical methods to map the turbulence indices to the energy dissipation rate (EDR). [PERSON] et al. [17] developed the global Graphical Turbulence Guidance (G-GTG), which is based on the GTG over the United States. The system uses a multidiagnostic-based en route nonconvective turbulence forecasting algorithm, which captures multiple turbulence sources of CAT and MWT. In terms of the threshold classification of turbulence indices, [PERSON] et al. [16] normalized the indices and defined five classes of 0, 0.25, 0.5, 0.75, and 1 as none, light, moderate, strong, and very strong, respectively. Based on the empirical threshold of the EDR, [PERSON] [18] classified the percentile of turbulence intensity and then classified 21 other clear-air turbulence indices.  
The Tibetan Plateau has unique atmospheric dynamics and thermal structures owing to its prominent and complicated terrain [19]. [PERSON] et al. [20] found significant differences in the atmospheric turbulent vertical structure in different regions of the Tibetan Plateau. The route over the plateau is affected by turbulence year-round [20; 21; 22], posing a serious threat to the flight. Thus, we focus on the forecast method for the upper-level turbulence in this region. [PERSON] [23] and [PERSON] et al. [24] applied [PERSON]'s [16] method for calculating the turbulence index thresholds for several indices in Beijing and China using the WRF model and T213 model, respectively. However, their results differed from those reported by [PERSON]. These discrepancies highlight the fact that the threshold values for the turbulence indices can vary based on the geographical region of study and the data sources utilized. Therefore, the thresholds of the turbulence indices should be redefined when applied to the Tibetan Plateau. The uncertainty of the turbulence index thresholds and the lack of availability and resolution of the observations over the Tibetan Plateau are the main issues in the turbulence forecast practice.  
In this study, we reclassify the turbulence index thresholds for the Tibetan Plateau based on the negative Richardson number. The ERA5 reanalysis data were used to calculate the indices, with a horizontal resolution of 0.25\({}^{\circ}\). Furthermore, a probabilistic turbulence forecast algorithm was used to aggregate the turbulence indices. The probability index (PI) successfully identified several real turbulence events, and its statistical results were consistent with those found in previous studies, providing evidence for its practicality and validity. As there are currently few available CIT indices and due to the difficulty in verifying their performance after incorporating them into the probabilistic algorithm, this study focused on nonconvective turbulence and calculated only the CAT and MWT indices.