## 2 Materials and Methods
### Data  
This article uses wind speed data collected from 15 different height levels ranging from 8 m to 320 m at the Beijing 325 m meteorological tower. Each platform of the tower is equipped with anemometers in a dual arm configuration in the two prevailing wind  
Figure 1: Comparative satellite imagery of the 325 m meteorological tower area in March 1973 (USGS Landsat, yellow box marking the proposed site of the meteorological tower) and June 2023 (accessed from [[https://earth.google.com/](https://earth.google.com/)]([https://earth.google.com/](https://earth.google.com/)) on 20 February 2024, blue box marking the location of the meteorological tower).  
directions (southeast and northwest), with simultaneous data collection at each height and a sampling frequency of 20 s. This design effectively reduces the interference of the tower structure on the wind speed measurements. The wind speed data are averaged every ten minutes, aggregated into an hourly set, and there are 24 sets of data at each height level every day. To systematically analyze the trend of wind speed changes in the urban area of Beijing over the past thirty years, data from specific years were selected, namely 1991, 1995, 2000, 2005, 2010, 2015, and 2020 (1990 was replaced by 1991 due to significant data loss caused by equipment maintenance). Figure 2 shows the Beijing 325 m meteorological tower.  
High-frequency sampling captures environmental noise, such as equipment vibration and nearby traffic flow, which can distort the true values in the data. Additionally, the long-term use of instruments and certain sudden extreme weather events can also cause data anomalies. For the accuracy of the analysis, the first step was quality control of the data, with the following process:  
1. Stagnation value removal: In the time series data, if the measurement value remains unchanged over a period, it often indicates a failure in the sensor, signal transmission, or instrument operation. This continuous unchanging measurement value is defined as a stagnation value. Identify data points in the series where the difference from the stagnation value is greater than 0.5 and remove the data segment from the stagnation value starting point to the point before it.
2. Outlier removal: Based on the historical climatic statistical characteristics of the Beijing area, wind speed values greater than 50 m/s are defined as outliers and are directly removed.
3. Temporal continuity control: For a series of consecutive data points, calculate the difference between a point and the weighted average of its four contiguous points. If this difference exceeds the set threshold (5 m/s for wind speed), the point is considered discontinuous, and a central four-point interpolation is used to replace the value of that point with the calculated weighted average.
4. Spatial continuity control: When the location of an outlier does not allow for central four-point interpolation, and if data at other heights at the current location are complete, use the spatial continuity and surrounding height layer valid measurement data to estimate and fill missing data points through spline interpolation.  
Figure 3 shows a comparison of the effects before and after the correction of the measured wind speed data (taking the wind speed data at 100 m height from 1991 to 2000 as an example). After quality control, the wind speed dataset is now representative enough  
Figure 2: The Beijing 325 m meteorological tower.  
to support reliable statistical analysis (with a usable data ratio greater than 95%). The total remaining missing values and the total data count after processing are shown in Table 1.