## 4 Experiments and Results
### Results and Analysis  
#### 4.2.1 Weather-Influence Coefficient  
According to the meteorological data extracted from the METAR messages, it was determined that the types of bad weather affecting flight operations at Yinchuan Hedong International Airport mainly include thunderstorms, rain, high-blowing dust, and clouds. According to the meteorological type, the weather can be divided into three types of bad weather: cloudy, sand dust (including high-blowing dust weather), and heavy precipitation (including rain and thunderstorms). According to the weather radar echo information, the echo colors of weather radar echo intensities can be divided into a yellow/green system (\(\geq\)31 and \(<\)45 dBZ), red system (\(\geq\)45 and \(<\)55 dBZ), and purple system (\(\geq\)55 dBZ). Most of the echo intensity data for bad weather exist between yellow/green and red systems, with yellow/green data accounting for 63.26% and red data accounting for 35.87%. The echo intensities were therefore divided into three grades (Table 1): low, moderate, and high. The weather-influence coefficient in the WITI model was optimized mainly by refining the bad-weather types, and the severity of weather was expressed in the form of echo intensity. However, there was no suitable measurement relationship between echo intensity and bad-weather types to classify them. The relationship between the three bad-weather types and radar echo intensities was investigated using a Gaussian mixture clustering model. In cloudy weather, the severity of weather can be expressed by the cloud base height, and so the clustering model used two indexes (cloud base height and echo intensity) in the clustering analysis; the results are shown in Figure 5.  
According to the clustering results, rainy weather could be divided into four types: high-cloud-base echo intensity, high-cloud-base echo intensity, middle-cloud-base echo \begin{table}
\begin{tabular}{c c c} \hline \hline
**Echo Intensity** & **Echo Color** & **Echo Color** \\ \hline Low & Light-yellow/green & \(>\)30 and \(\leq\)35 dBZ \\ Moderate & Deep-yellow/green & \(>\)35 and \(\leq\)45 dBZ \\ High & Red & \(>\)45 dBZ \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Echo intensity grades. Figure 5: Clustering results for rainy weather. intensity, and low-cloud base echo intensity; these types of weather accounted for 21.76%, 53.98%, 13.67%, and 9.59% of cases, respectively. The visibility can be used to indicate the severity of sand-dust weather, and so the clustering model used two indicators (visibility and echo intensity) in the clustering analysis; the results are shown in Figure 6.  
According to the clustering results, dust weather could be divided into five types: high visibility and low echo intensity, high visibility and moderate echo intensity, moderate visibility and high echo intensity, and low visibility and high echo intensity; these types of weather accounted for 7.32%, 37.62%, 33.45%, 12.60%, and 9.01% of cases, respectively. The severity of bad weather can be expressed by the precipitation in heavy-precipitation weather, and so the clustering analysis was performed using precipitation and echo intensity; the results are shown in Figure 7.  
According to the clustering results, heavy-precipitation weather could be divided into three types: low precipitation and low echo intensity, moderate precipitation and moderate echo intensity, and high precipitation and high echo intensity; these types of weather with low precipitation and echo intensity accounted for 29.72%, 49.67%, and 20.61% of cases, respectively. Therefore, bad weather could be divided into high cloud base and low echo intensity, high cloud base and low-high echo intensity, middle cloud base and high echo intensity,  
Figure 6: Schematic diagram of dust weather clustering results. Figure 7: Schematic diagram of clustering results for heavy-precipitation weather.