# Analysis of Wind Speed Characteristics Along a High-Speed Railway
## 1 Introduction
The goal of the hybrid model is to mix many approaches, preserve each approach's merits, and improve the system's capacity for prediction significantly. Each hybrid model, however, is created for a particular issue and needs additional time and computational power [27]. Currently, the development of hybrid models is a primary focus in wind speed forecasting research. The four key components of a hybrid model include data preprocessing, feature selection, combined forecasting, and intelligent optimization [28; 29]. The wind data used in these models often contain significant uncertainties, such as noise and outliers, which can negatively affect the training phase of prediction models due to the complex data collection environment or sensor malfunctions [30]. Techniques for denoising or outlier detection during data preparation can effectively reduce these uncertainties, thereby improving the predictability of wind data. In the study of HSR wind speed, [PERSON] et al. [31] proposed a short-term wind speed forecasting method utilizing the EMD-ARIMA model. The RARIMA algorithm forecasts each component of the empirical mode decomposition (EMD) and combines these predictions to generate the final wind speed forecast. This method's effectiveness was verified using data from two wind speed stations along the Qinghai-Tibet railway. More recently, [PERSON] et al. [2] developed Windformer, which employs a graph encoder and time series decoder to predict high-speed rail wind speed across multiple time scales. This model outperforms comparison methods. Despite expanding the alarm window and achieving an 89% success rate in predicting strong wind events, Windformer's lack of interpretability limits its practical application on railways. Existing research on HSR wind speed faces several challenges: (1) HSR systems prioritize extreme wind speed data over short time intervals, ranging from seconds to minutes. However, existing wind speed prediction methods typically operate on larger time scales, leaving dispatchers with limited time to respond, even when predictions and warnings are issued. (2) Ground-based meteorological observations and wind tower measurements are conducted in open areas, where the wind field is disrupted when a high-speed train passes, making the measured wind speed no longer representative of natural wind conditions. This discrepancy distinguishes HSR wind speed from other observed wind speeds. (3) Many previous prediction models--whether statistical, machine learning, or hybrid--rely on historical time series data and are primarily data-driven, lacking a comprehensive understanding of the unique characteristics of HSR wind speed. Although physical models incorporate various meteorological factors and some characteristics of wind farms, they are too complex to facilitate rapid wind speed predictions [32]. The goal of this work is to examine the wind speed characteristics at and around an HSR to find a reliable and beneficial method for HSR wind speed prediction. The main contributions of this paper are as follows:  
* Describe the wind field characteristics along the HSR at various time scales. The characteristics of different time interval wind synchronous observations at a railway station (RS) and the closest weather station (WS) are discussed in this work, including wind speed variation, wind speed correlation, wind speed fluctuation amplitude ratio, and wind speed distribution characteristics. * Historically, wind speed predictions were based on modeling historical wind speed data without considering the inherent predictability of the selected wind speeds. In this study, the multi-timescale predictability of wind speed along the HSR corridor is quantified using the normalized cumulative predictability (NCP) and Bartlett's tests. * This paper integrates predictability analysis with advanced wind speed prediction algorithms to forecast wind speeds for high-speed rail systems. It also proposes a method for assessing the quality of input samples, thereby enhancing the accuracy of wind speed predictions.