# Automated Aviation Wind Nowcasting: Exploring Feature-Based Machine Learning Methods
## 6 Conclusions  
This work explores the intricacies of wind nowcasting, contributing to meteorological studies and the domain of computational modeling. The investigation proved the escalated challenges when extending nowcast horizons, evidenced by the surge in error values from a 2 min to a 20 min prediction window. This trend results from the inherent variability and nonlinearity of wind speed and direction, which become increasingly conspicuous over extended durations. Furthermore, the discrepancy in error rates between mean wind direction and other parameters underlines the intricate, atmospheric dynamics influencing wind direction, showing ML models' increased difficulty in predicting this variable. A key facet of this study was the emphasis on feature creation and selection and determining the efficacy of the resulting prediction models. The data suggest that the mathematical generation of wind features could slightly enhance predictive accuracy. Notably, the research demonstrated that a reduction in features, through feature selection methods, in certain instances, did not compromise accuracy but improved it, implying potential avenues for optimizing computational efficiency. Nonetheless, the marginal enhancement in nowcast accuracy through advanced feature creation and selection mechanisms necessitates a thorough examination of the trade-off between computational cost and predictive performance for each application. The limited variation in metrics across distinct feature selection methodologies indicates that while certain methods may render marginal improvements for specific evaluation metrics, they only sometimes assure superior performance across all metrics and time intervals. One of the most significant revelations from this research was the robust spatial correlation detected among sensors positioned at varying distances from the target prediction point. This correlation, coupled with the observation of shifting feature importance about varying nowcast durations, suggests a valuable spatial-temporal context that can enhance prediction accuracy. This insight enables the recognition of complex spatial patterns in wind behavior, potentially improving the performance of wind nowcast models over various nowcast periods. These findings underline the importance of careful consideration of nowcast duration, feature selection, computational cost-benefit trade-offs, and spatial-temporal interrelationships in wind nowcasting. While advanced mathematical features and machine learning models exhibit potential, their implementation should be cautiously assessed on a case-by-case basis due to the associated computational expense. Notably, the acknowledgment of spatial correlations propounds a promising direction for augmenting the accuracy and reliability of wind nowcasting, a critical aspect for the operational efficiency of wind-dependent systems and sectors. By addressing the difficulties of wind predictions, this research aims to enhance wind prediction accuracy while also identifying methods to boost computational efficiency, using features engineering methods. These advancements seek to add knowledge, particularly in the aeronautics sector, by improving the speed and accuracy of wind information. A general conclusion of this study is that employing feature engineering to wind speed and direction using mathematical approaches can result in significant computational