Compressive Representations of Weather Scenes for Strategic Air Traffic Flow Management
This enables more efficient data handling for aviation weather analysis, though it is incremental as it applies existing compression methods to new data.
The paper tackled the problem of compressing high-dimensional weather scene data for air traffic flow management, finding that 75-95% of scene content can be captured using only 0.5-4% of basis vectors.
Terse representation of high-dimensional weather scene data is explored, in support of strategic air traffic flow management objectives. Specifically, we consider whether aviation-relevant weather scenes are compressible, in the sense that each scene admits a possibly-different sparse representation in a basis of interest. Here, compression of weather scenes extracted from METAR data (including temperature, flight categories, and visibility profiles for the contiguous United States) is examined, for the graph-spectral basis. The scenes are found to be compressible, with 75-95% of the scene content captured using 0.5-4% of the basis vectors. Further, the dominant basis vectors for each scene are seen to identify time-varying spatial characteristics of the weather, and reconstruction from the compressed representation is demonstrated. Finally, potential uses of the compressive representations in strategic TFM design are briefly scoped.