## 1 Introduction
Previous researchers used computational fluid dynamics (CFDs) to study the effect of fence height, porosity, bottom clearance, and inclination angle on the snow accumulation around snow fences and their ability to prevent snow, which helps optimize their design [11; 12; 13]. [PERSON] et al. [14] examined the effect of different forms of windbreaks on typical roadbed snow accumulation from aspects such as flow field, snow phase concentration, and sedimentation length. The results showed that the inclination angle of the windbreak was a key parameter affecting its effectiveness. Forward-leaning guide plates were more effective in preventing drifting snow during windy and snowy weather, and the angle between guide plates and the vertical axis should be controlled at around 45\({}^{\circ}\). Meteorological factors such as temperature, precipitation, wind speed, and wind direction have an important impact on the occurrence of drifting snow. The threshold windspeed of dry snow is generally proportional to the temperature, with lower temperatures resulting in lower starting wind speeds [15]. However, when the temperature drops below \(-\)27.27 \({}^{\circ}\)C, the snow particles start to deteriorate, leading to an increase in the required starting wind speed [16]. Temperature is also a critical parameter affecting the shape of snow particles; \(-\)5 \({}^{\circ}\)C is the critical temperature at which snow crystals transition from plate-like to columnar [17]. Snow particles of different shapes undergo distinct development processes in a snowstorm flow. The number of suspended snow particles and the single-width snow transport rate of spherical, ellipsoidal, and cylindrical snow particles are two orders of magnitude higher than those of star-shaped and hexagonal prism-shaped snow particles [18]. At a height of 10 m, a wind speed of 3-8 m/s is sufficient to cause loose snow particles to jump and move [19]. When the angle between the line and the wind direction is greater than 55\({}^{\circ}\), the most snow resistance is generated, and the closer the angle is to 90\({}^{\circ}\), the greater the probability of snow resistance and the more severe the consequences [20]. These meteorological factors are interconnected and have various impacts on drifting snow. The above research mainly focuses on the occurrence and aftermath of drifting snow. However, evaluating the susceptibility before its occurrence serves as the foundation for further research. Railway lines should avoid areas with high susceptibility or set different protection measures according to different occurrence probabilities along the way to save economic costs and improve protection efficiency. Most railways pass through high-altitude and cold regions with complex terrain and scarce meteorological information, making it difficult to judge the susceptibility of drifting snow along the railway line. The Weather Research and Forecasting Model (WRF) has been widely applied in regional simulations [21; 22; 23] and can partially address this issue. The model can decompose the simulated region into multiple grids with an accuracy of several kilometers. By providing boundary conditions, a series of atmospheric motion equations, including [PERSON]'s second law, the first law of thermodynamics, the continuity equation, and the state equation, can be solved to achieve weather forecasting. Therefore, the WRF model can provide meteorological data with a high resolution and continuous long time series at any location in the simulation area. [PERSON] [24] analyzed the wind speed and wind direction characteristics during a storm and snow disaster in the Mayitas traffic corridor using the WRF dataset. In a separate study, [PERSON] [25] employed WRF to investigate route selection schemes for the Keta Railway. Additionally, [PERSON] et al. [26] utilized WRF to analyze snowfall, wind speed, and direction along the entire Keta Railway line and derived the probability of drifting snow occurrence for the entire route.