Using Severe Convective Weather Information for Flight Planning

Abstract

Aircraft fly in an environment that is subject to constant weather changes, which considerably influences the decision-making process in Air Traffic Management (ATM). The stakeholders in ATM track weather conditions to appropriately respond against new environmental settings. The proposed work builds an intelligent system to constantly monitor the impact of severe weather on airways, which are corridors with specific width and height connecting two locations in the airspace. The proposed approach integrates weather information on convection cells obtained from ground-based weather radars, and flight tracking information detailing flight positions in real time. To delimit the boundaries of airways, the set of flight positions is transformed to a more convenient one using linear interpolation. Then, a cluster analysis via Density-Based Spatial Clustering of Applications with Noise (DBSCAN) is performed into this new set. The algorithm compares the positions of the clusters found with the positions of convection cells to identify possible intersections between them. A case study was set up consisting of two phases: (1) flight positions were tracked, and the boundaries of the airways were identified; and (2) convection cell locations were monitored, and compared against the airways in order to identify possible intersections. The solution showed the clusters representing the boundaries of the underlying airways, and some intersections were found during the case study.