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. Eight percent of genetic males and half a percent of genetic females have some form of CVD, with many in the science community falling into this group. When presenting data on a two-dimensional plane, it is common to use colors to represent values via a colormap. Individuals that have CVD can have issues with reading these data depending on the colormap that is used. This is especially true in the weather radar community. Colormap choice in the radar community is influenced by the ability to highlight scientifically interesting features in data, institutional choices, and domain dominance of legacy colormaps. The problem with these current colormaps is that many do not project well for those with CVD (i.e., green next to red). This excludes those with CVD while also causing difficulty when trying to discern between features in different images. In working with the CVD community to address this problem, multiple colormaps for moments such as equivalent reflectivity factor and Doppler velocity were created for users with forms of CVD such as deuteranomaly, protanomaly, protanopia, and deuteranopia using Python tools such as colorspacious and viscm that allow for visualization of images through a CVD lens. We show how these colormaps can improve interpretability for four cases: a mesoscale convective system, a pyrocumulonimbus storm, a wintertime mid-latitude cyclone, and widespread storms with a large bird migration. These new radar equivalent reflectivity factor, Doppler velocity, and polarization colormaps are designed to highlight rain, frozen precipitation, non-meteorological targets and velocity-based items, be perceptually uniform, and are accessible and inclusive to those with CVD. These colormaps also create a broader discussion for more CVD friendly colormaps while working with the community to do so.