Acquiring and managing weather data sets is time consuming and expensive. Further, a certain level of expertise is required to understand what data to collect, its source and structure, and how to interpret weather data content, as well as determine the specific weather data elements appropriate for an application. The need for readily accessible and computable weather data is growing. Researchers require faster access to weather and flight data sets than what is currently available today to explore and develop solutions for reducing weather’s impact on the NAS.
Most programs have limited weather expertise and opt to individually acquire and manger their own weather data sets, as well as build their own tools for integration and analysis. These activities tend to be repetitive with significant overlap in effort between programs. Additionally, many programs have little knowledge of how weather data is integrated and used by other systems. All of this has a negative impact to program costs and schedules.
The FAA’s Aviation Weather Branch has developed a framework called Weather Information Enterprise Services (WIES) in order to mitigate the cost and schedule impacts as well as expand programs access to expert knowledge of weather in the NAS. Built upon the emerging Big Data techniques and frameworks, WIES will accomplish this in the following ways. First, WIES will deliver an enterprise level solution for collecting, archiving and organizing weather data. Second, it will provide services to integrate and perform analysis of archived weather and NAS data sets, and visualize analytical results in a geospatial 3D animated viewer. Finally, it will provide the capability to synchronously replay archived weather data sets back to one or more NAS systems.
The vision for WIES is a shared resource (i.e., data, processes and services) accessible by many programs sharing the cost. The goal is to bring together Meteorology, Aviation, Mathematics, and Computer Science while also bringing research closer to the users of the NAS. By bringing data closer to the computation, WIES will provide the capability to show how the latest research can be more easily prototyped and studied in an operational-like setting and will benefit from more frequent input from the users.