# Automated Aviation Wind Nowcasting: Exploring Feature-Based Machine Learning Methods
## 5 Discussion  
This research offers an extensive examination of wind nowcasting, focusing on the role of nowcast duration and feature selection in predictive model performance. Our findings underscore the complexity of wind prediction as the nowcast horizon extends, as evidenced by the rising error values between 2 min and 20 min prediction window. This complexity stems from the inherent variability and nonlinearity of wind speed and direction, which increase as the prediction window expands. Furthermore, the higher prediction errors for the mean wind direction highlight the intricate atmospheric dynamics that influence wind direction and the associated challenge for machine learning models in predicting this variable. Based on Tables 2-4, when forecasting wind speeds and directions, distinct patterns become evident over the different time horizons. For the mean wind speed, the 2 min forecast presents the narrowest discrepancy between methods, with MAE values oscillating between 1.00 (All methods) and 1.05 (XGBoost). Extending the prediction horizon to the 10 min forecast reveals that mRMR exhibits the most substantial deviation, with an MAE of 1.36, while RFECV registers the lowest error at 1.31. At the 20 min mark, the variations are more pronounced, with values spanning from 1.46 (PCA) to 1.52 (mRMR). For the wind direction mean, the 2 min forecast sees MAE values ranging from 63.53 (RFECV) to 68.26 (PCA). This discrepancy broadens at the 10 min forecast, with RFECV at the lower end registering an MAE of 81.30, and mRMR at the top with a significant 114.68. This trend persists into the 20 min forecast, where MAE values fluctuate between 89.58 (control) and 117.10 (mRMR). Lastly, for the wind gust, the 2 min predictions range between 1.33 (all method) and 1.42 (XGBoost). For the 10 min forecast, RFECV shows the least MSE at 1.70, while mRMR records an error of 1.75. For the 20 min predictions, the error for mRMR peaks at 1.95, contrasting with the all method's more modest 1.89. Overall, predictions for wind speed are relatively consistent across the methods implemented, but predictions for wind direction exhibit more variability as the forecasting window extends. Therefore, the impact of feature creation and selection on model performance emerged as a significant aspect of this study. Our results suggest that generating wind features mathematically may enhance predictive accuracy and reduce computational requirements. Indeed, our research revealed that for wind speed nowcast, the fewer features selected through RFECV and PCA not only maintained but improved prediction accuracy, hinting at opportunities for computational optimization. However, when examining the wind direction nowcast, it is clear that the errors are immense due to two factors, specifically, the large range of the values, varying from 1\({}^{\circ}\) to 360\({}^{\circ}\), and the variations at the border between 360\({}^{\circ}\) and 1\({}^{\circ}\). (For example, when the model forecasted the direction to be 1\({}^{\circ}\), but the ground truth label was 360\({}^{\circ}\), it leads to an error of 359\({}^{\circ}\), enlarging the error metrics, but in reality, the error is just 1\({}^{\circ}\).) It is also crucial to consider the complex wind patterns in the studied location, where the wind can be considerably turbulent with fast direction variation. Lastly, the gust nowcast error was lower in the methods that used more features, stressing the importance of the additional information provided by the features. The marginal improvements in nowcast accuracy achieved through advanced feature creation and selection techniques require a careful analysis of the trade-off between the computational cost and the predictive performance. Upon further examination of the results, it was observed that the 14 engineered features per sensor did not yield a notable enhancement in model accuracy.