# Seamless Weather Data Integration in Trajectory-Based Operations Utilizing Geospatial Information
## 3 Performance Evaluation of the 4 DT-Wx Prototype System  
### Evaluation of MET Data Processing Accuracy  
The \(T\), _WD_, and _WS_ values extracted from the ADS-B data collected by the AMO, as presented in Section 2.1.1, were verified. The verification results are shown in Table 1. To analyze the extraction performance according to the interpolation methods, the accuracies of three interpolation methods--spline, IDW, and linear--were compared and analyzed.  
The weather variables of ADS-B underwent a quality control process, extracting more than 900,000 valid data points from about 1 million initial data points for accuracy analysis. For \(T\), all three interpolation methods showed high accuracy and a high correlation, with \(R^{2}\) values above 0.93 and a root mean square error (RMSE) of less than 3.3 \({}^{\circ}\)C. For _WD_, all three methods showed relatively low accuracy, with an \(R^{2}\) value of 0.58, showing a lower correlation compared with \(T\). WS showed higher accuracy than that of _WD_ but lower than that of \(T\) with an \(R^{2}\) value greater than 0.92, showing a high correlation. However, WS's RMSE was relatively large at over 12.6 knots. These results are similar to those found in previous studies [24; 50; 51]. In this study, the estimation of _WD_ and _WS_ using ADS-B data was found to have a lower accuracy compared with the T estimations. This is interpreted as being due to the characteristics of the ADS-B system and the impact of the aircraft's motion state on the measurements of WD and WS.  
The Taylor diagram in Figure 4 allows for a comprehensive comparison of the performance of each variable and interpolation method. In this diagram, \(T\) is closest to the reference point, indicating the highest accuracy, while _WD_ is farthest away, indicating the lowest accuracy. _WS_ shows a moderate performance. Although the performance differences among the interpolation methods were minimal, there were differences in the processing speed. According to Table 2, linear interpolation demonstrated the fastest processing speed.  
Therefore, in this study, the linear interpolation method was adopted for the performance analysis because of the negligible differences in accuracy but superior processing performance.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Variables**} & \multicolumn{3}{c}{**Spline Interpolation**} & \multicolumn{3}{c}{**IDW Interpolation**} & \multicolumn{3}{c}{**Linear Interpolation**} \\ \cline{2-10}  & \(\mathbf{R^{2}}\) & **RMSE** & **Bias** & \(\mathbf{R^{2}}\) & **RMSE** & **Bias** & \(\mathbf{R^{2}}\) & **RMSE** & **Bias** \\ \hline Temperature \(T\) (\({}^{\circ}\)C) & 0.93 & 3.24 & \(-\)0.7 & 0.93 & 3.28 & \(-\)0.7 & 0.93 & 3.24 & \(-\)0.7 \\ Wind Direction _WD_ (\({}^{\circ}\)) & 0.58 & 6.91 & \(-\)0.58 & 0.58 & 6.99 & \(-\)0.58 & 0.59 & 6.91 & \(-\)0.58 \\ Wind Speed _WS_ (knots) & 0.92 & 12.68 & 2.02 & 0.92 & 12.73 & 1.86 & 0.92 & 12.68 & 2.02 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Accuracy validation of 4 DT-Wx data using different interpolation methods.