Compressive Representations of Weather Scenes for Strategic Air Traffic Flow Management

TL;DR

This paper investigates the compressibility of weather scene data from METAR reports for strategic air traffic flow management, demonstrating high compression ratios and the potential for improved traffic control strategies.

Contribution

It introduces a method to represent weather scenes sparsely in a graph-spectral basis, enabling efficient data compression and analysis for aviation safety and efficiency.

Findings

Weather scenes are highly compressible, capturing 75-95% of content with less than 4% of basis vectors.

Dominant basis vectors reveal time-varying spatial weather characteristics.

Reconstruction from compressed data is feasible, supporting strategic traffic management.

Abstract

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.