# Seamless Weather Data Integration in Trajectory-Based Operations Utilizing Geospatial Information
## 2 Materials and Methods
### Seamless Weather Integration for TBO  
#### 2.2.1 Structure of 4 DT-Wx Prototype System  
This study's 4 DT-Wx prototype system was designed to seamlessly integrate comprehensive weather data into the TBO framework. The main objective was to enhance flight planning and operations by providing real-time weather information along aircraft trajectories. The 4 DT-Wx system utilizes various NWP models to extract accurate weather parameters for given trajectories. The prototype system environment was implemented using Python programming in a Windows environment and was executed on a high-performance computing system. Python was chosen for its extensive libraries supporting data analysis, which are crucial for processing complex weather data and implementing advanced algorithms. The Windows environment was selected for its compatibility with existing aviation systems, ensuring seamless integration with the current infrastructure. The computing system was equipped with 32 physical cores and 64 logical processors (threads) to handle the parallel processing requirements of large-scale weather data. The 256 GB of memory was allocated to accommodate the substantial data volumes involved in weather modeling and trajectory calculations. The 2.9 GHz CPU was chosen to provide the necessary processing speed for real-time weather data integration and trajectory computations. This high-performance setup was essential to meet the real-time processing capabilities required in a TBO environment, where rapid data analysis and decision making are critical for efficient and safe air traffic management. The 4 DT-Wx prototype system (Figure 2) integrates NWPs and aircraft trajectories and extracts trajectory-based weather variables through queries on aircraft trajectory information (latitude, longitude, altitude, and time). Additionally, the 4D weather grid is defined using weather variables (\(Wx\)) representing weather conditions and the indices \(i,j,k,\) and \(t\), representing longitude, latitude, altitude, and time intervals, respectively. Therefore, as shown in Equation (1), the 4 DT is associated with \(Wx\) in specific grid cells defined by longitude \(i\), latitude \(j\), altitude \(k\), and time interval \(t\). The extraction of trajectory-based weather information is termed 4 DT-Wx. \[4\text{DT}=Wx\left(i,j,k,t\right) \tag{1}\]  
The central API processes various weather and aviation-related data, allowing users to perform customized parameter queries. The system's output is stored in the CoverageJSON format [31], which is used to provide user-customized weather data necessary for aviation decision making. This format offers an efficient representation of multi-dimensional grid-ded data, compact storage, enhanced interoperability, and built-in support for geospatial and temporal metadata. These features make CoverageJSON ideal for handling complex weather information in our 4 DT-Wx prototype system. This format can be utilized for various analysis and visualization tasks in the future, facilitating integration with other aviation systems and enabling sophisticated weather-based flight planning. When extracting weather data along the trajectory, weather data monitoring for take-offs and landings is performed within the LDAPS domain, while for areas outside the LDAPS domain, GDAPS data are used to monitor weather phenomena along the cruise  
Figure 2: Architecture of the 4 DT-Wx prototype system. route. This multi-model approach allows us to leverage the high spatial resolution (1.5 km) and frequent updates of LDAPS for critical takeoff and landing phases, where precise local weather information is crucial. Simultaneously, it utilizes the broader coverage of GDAPS (10 km resolution) for en route segments, ensuring continuous weather monitoring across longer flight paths. By integrating these complementary models, our system provides a comprehensive and seamless weather information service that maintains high accuracy for local operations while offering extensive coverage for long-distance flights.