Effective Visualization of Radar Data for Users Impacted by Color Vision Deficiency

Color Vision Deficiency (CVD) is a decreased ability to discern between particular colors. About 8% of genetic males and 0.5% of genetic females have some form of CVD, with many in the radar community falling into this group. When presenting data on a two-dimensional plane, it is common to use colors to represent values with a colormap. Colormap choice in the radar community is influenced by the ability to highlight scientifically interesting data, institutional choices, and dominance of legacy colormaps in the radar community. This becomes problematic as many colormaps in the radar community that are typically used are colormaps that have uneven color gradients, and which do not project well for those with CVD. The weather radar community currently falls behind in the domain of CVD-friendliness; therefore, CVD-friendly colormaps should be accessible for weather radar imagery.

In this presentation, we will highlight how we worked with the CVD community to address this problem, while creating multiple colormaps for moments such as reflectivity and Doppler velocity for users with CVD. This was accomplished using Python tools such as colorspacious and viscm. We will also discuss the real cases we used to test these colormaps such as a mesoscale convective system, a pyrocumulonimbus storm, and a winter storm. We will discuss how we designed these colormaps to follow the latest guidance in scientific visualization literature which meet criteria such as the ability to highlight rain and frozen precipitation, perceptual uniformity, greyscale reproducibility, and visual friendliness for those with CVD. These colormaps overall were designed to be more inclusive for the community as well as to promote open software and science.