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

• DOI: 10.1175/bams-d-23-0056.1
• Publication Date: 2024-06-11T10:51:41Z
• Authors: Zachary Sherman; Max Grover; Robert Jackson; Scott Collis; Joseph O’Brien; Cameron R. Homeyer; Randy J. Chase; Timothy J. Lang; Daniel M. Stechman; Alyssa Sockol; Kai Muehlbauer; Jonathan Thielen; Adam Theisen; Sam Gardner; Daniel Michelson
• Keywords: Weather radar; Interpretability

Abstract Color vision deficiency (CVD) is a decreased ability to discern between particular colors. Eight percent of genetic males and half females have some form CVD, with many in the radar community falling into this group. When presenting data on two-dimensional plane, it common use colors represent values via colormap. Colormap choice influenced by highlight scientifically interesting features data, institutional choices, domain dominance legacy colormaps. The problem these current colormaps that do not project well for those CVD (i.e., green next red). In working address problem, multiple moments such as equivalent reflectivity factor Doppler velocity were created users forms deuteranomaly, protanomaly, protanopia, deuteranopia using Python tools colorspacious viscm. We show how can improve interpretability four cases: mesoscale convective system, pyrocumulonimbus storm, wintertime midlatitude cyclone, widespread storms large bird migration. These new factor, velocity, polarization are designed rain, frozen precipitation, nonmeteorological targets, velocity-based items, perceptually uniform, visually friendly CVD.