## 3 Result and Discussion
### Discussion of Occurrence Probability  
As the WRF model relies on input boundary conditions, it can simulate diverse climatic scenarios by altering the input meteorological data to accommodate climate change. Climate variations, such as alterations in temperature and precipitation patterns, can exert influences on snow conditions. For instance, elevated temperatures can expedite snow melting rates, while shifts in precipitation can impact the formation and distribution of snow, ultimately affecting the occurrence of drifting snow. Given the WRF model's capacity to adjust to climate change, it can similarly accommodate shifts in drifting snow conditions due to climate variations. As a result, the derived probability of drifting snow occurrence based on the WRF model remains dependable. This study utilizes temperature, snowfall, and wind speed information derived from WRF simulations to assess the probability of drifting snow around the railway. The calculated results have been validated against measured data and data from other published papers, which enhances the credibility of the findings in the paper. However, there are two errors in this paper: one is the calculation error of the WRF model itself, and the other is the error caused by the judgment model of drifting snow. If these two aspects are improved, the calculation results will be more accurate. The accuracy of the WRF calculation results is mainly affected by boundary conditions, parameter scheme combinations, and model parameters. The initial conditions are reanalysis data, that is, the historical weather information obtained by integrating radar satellite, aircraft, ship, and station observation data and numerical forecasting products using data assimilation technology. Therefore, the accuracy of the data used for assimilation will affect the accuracy of the reanalysis data and then affect the calculation results of the WRF model. The combination of different parameter schemes of the WRF model will produce different calculation results, and one model parameter of WRF may affect multiple meteorological variables, so the selection of model parameters needs to consider multiple meteorological variables. However, WRF has many parameter schemes and model parameters. With the current computing power, it is difficult to obtain the optimal parameter scheme combination and model parameters through thousands of long-time calculations, which will also affect the calculation results of WRF. Meteorological information such as wind and snow are the main conditions that affect drifting snow occurrence, but the terrain around the railway, vegetation types, and the form of the roadbed of the railway also have a certain impact on it. Since the purpose of this paper is mainly to calculate the probability of drifting snow from the perspective of meteorological information, snow and wind data are mainly taken into account in the  
Figure 13: Probability of drifting snow along the railway. (**a**) The susceptibility of drifting snow in region 1; (**b**) the susceptibility of drifting snow in region 2.  
judgment model. However, terrain and other factors are not considered, so the occurrence probability calculated based on the drifting snow judgment model in this paper may be higher than the actual result, and the calculation result in this paper is more secure. To reduce errors of possibility caused by various factors, improvements can be made in those aspects, and the data of temperature, precipitation, and wind speed in FNL can be replaced with observation data of weather stations to improve the accuracy of boundary conditions and further improve the accuracy of WRF simulation. Sensitivity analysis of the parameter scheme and model parameters can be carried out to find the optimal scheme and parameter under the condition of saving computing resources as much as possible.