Color Vision Deficiency-Accessible Colormaps for Cloud and Aerosol Atmospheric Lidar Visualization
Déficience de la vision des couleurs - Cartes de couleurs accessibles pour la visualisation lidar atmosphérique des nuages ??et des aérosols
Guélis, Thibault Vaillant de ; Tackett, Jason L. ; Garnier, Anne ; Ryan, Robert A. ; Burton, Sharon P. ; Powell, Kathleen A.
Année de publication
2026
Choosing a colormap to display atmospheric lidar data is challenging in several aspects. First, light backscattered by aerosol and cloud layers generates signals that spread over many orders of magnitude. Atmospheric scientists require images of this data to have high contrast in many portions of this extensive scale. Second, some lidar images can be very noisy, such as daytime images that are contaminated by solar background or spaceborne lidar for which the signal-to-noise ratio can be quite low due to power limitations and the great distance to the targets. Finally, the images need to be accessible to people with color vision deficiency, which represents a sizable portion of the population?approximately 4%-8% of men and 0.4%-1.7% of women, depending on ethnicity. We define a set of colormaps that address these challenges that were developed using lidar measurements from the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) space lidar and the high-spectral-resolution lidar (HSRL) airborne lidar (backscatter, depolarization, and color ratio). They are distributed in a package called cmlidar and released for Python. Significance Statement Choosing the right colormap for atmospheric lidar data is crucial for clear and accurate visual interpretation. Lidar signals vary over a wide intensity range and can be noisy, making it difficult to display fine details effectively. Additionally, colormaps must be accessible to those with color vision deficiencies. This study presents a set of colormaps designed to enhance contrast across different signal levels, reduce noise impact, and improve accessibility. Developed using spaceborne and airborne lidar measurements, these colormaps are available in the Python package cmlidar. This work enables more effective data visualization in atmospheric science, helping researchers analyze and communicate complex lidar data more clearly.</div>
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