# Seamless Weather Data Integration in Trajectory-Based Operations Utilizing Geospatial Information  
[PERSON]  
1 Satellite Communication Research Division, Electronics and Telecommunications Research Institute (ETRI), Daejeon 34129, Republic of Korea; [EMAIL_ADDRESS] (S.-I.K. ); [EMAIL_ADDRESS] (D.J. ); [EMAIL_ADDRESS] (J.-K.); [EMAIL_ADDRESS] (D.-S.A.)1 Satellite Communication Research Division, Electronics and Telecommunications Research Institute (ETRI), Daejeon 34129, Republic of Korea; [EMAIL_ADDRESS] (S.-I.K. ); jindh@etri. information. Real-time processing will enable quick analysis and the application of large-scale weather data. Customized information delivery will cater to specific flight phases and operational needs. These advancements will support more precise and dynamic flight planning and operations in the Trajectory-Based Operations (TBO) environment, ultimately enhancing the efficiency and safety of air traffic management. At the core of this change is trajectory-based operations (TBOs) [10]. The ICAO has proposed TBOs as part of its GANP, with Europe (SESAR) and the United States (NextGen) playing leading roles in its development and implementation [11; 12; 13]. TBOs aim to increase airspace resource utilization, achieve more precise flight control, improve fuel efficiency, and reduce the environmental impact through enhanced predictability and coordination in global ATM systems. As TBO becomes increasingly popular, a highly accurate trajectory prediction becomes more crucial, not only for optimizing airspace resources, but also for improving ATM efficiency and reducing flight delays [14]. TBO is defined as \"considering the trajectory of aircraft in all flight phases and managing the interaction of that trajectory with other trajectories or hazards to achieve the optimum system outcome\" [15]. The transition from current aircraft position-tracking ATM to TBO is crucial for improving flight planning efficiency [14; 15]. The TBO approach enables the dynamic trajectory information sharing among air navigation service providers (ANSPs) within and beyond flight information regions (FIRs), allowing ATM to predict future scenarios using 3D (\(x\), \(y\), \(z\)) and 4D (\(x\), \(y\), \(z\), \(t\)) data. The effective implementation of TBO requires significant improvements in weather information systems, particularly in terms of data availability, accuracy, resolution, and service quality. Recent studies have focused on enhancing the robustness, accuracy, and efficiency of aircraft trajectory optimization and prediction under uncertain and dynamic weather conditions [16; 17; 18; 19; 20; 21; 22]. These advancements are being achieved through machine learning-based prediction models, multi-objective optimization techniques, and the utilization of ensemble weather forecasts. However, research from a meteorological perspective on TBOs and the development of weather data availability and service aspects for TBO weather integration remain insufficient. The importance of improved weather integration in TBO systems is emphasized from multiple angles. Accurate weather information contributes to enhanced flight safety through hazardous weather avoidance, increased operational efficiency through optimal route selection, minimized environmental impact through precise flight planning, and improved overall airspace utilization through flexible airspace management. These factors play a crucial role in the successful implementation of TBO and the overall improvement of air traffic management system performance. This gap highlights the critical need for improved weather integration in TBO systems. The challenge is exemplified by the vast amounts of data generated by high-resolution numerical prediction models for aviation weather. For instance, a model centered on the Incheon Airport, based on LDAPS (Local Data Assimilation and Prediction System) data, predicts a 1000 \(\times\) 1000 km area with 1 km resolution across 50 vertical layers for 36 h. This generates 3.6 billion data points for a single variable.