In this work, the interactive multidimensional data exploration tool Glue is extended for use in atmospheric science. This is done by introducing data handlers for geo-referenced and arbitrary multi-dimensional data stored in netCDF format (using Iris and xray, respectively), and by providing mapping capabilities for visualizing geospatial data (using cartopy).
Glue is both a library and an interactive multidimensional data exploration tool, written in Python. Users can create scatter plots, histograms, and other viszalizations of their data, and selections in any graph propagate to all others. Glue uses the logical links that exist between different data sets to combine visualizations of different data. These links are arbitrarily flexible and can be specified by the user.
Originally, Glue comes from the field of astronomy, and therefore, a lot of the built-in functionality of Glue is geared towards applications in this field. Here, I present extensions of Glue to the field of atmospheric science. In particular, I present the implementation of data handlers for arbitrary multi-dimensional data stored in netCDF format (using the xray library), of mapping capabilities (using cartopy), and metadata-aware handling of geo-referenced data in the form of Iris cubes.