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Results of many atmospheric science applications are processed graphically using colors to encode certain parts of the information. Colors should (1) allow humans to process more information, (2) guide the viewer to the most important information, (3) represent the data appropriately without misleading distortion, and (4) be appealing. The second requirement necessitates tailoring the visualization and the use of color to the viewer for whom the graphics is intended. A standard way of deriving color palettes is via transitions trough a certain color space. Most of the common software packages still provide palettes derived in the RGB color model or "simple" transformations thereof as default. Confounding perceptual properties such as hue and brightness make RGB-based palettes more prone to misinterpretation. Additionally, they are often highly saturated, which makes looking at them for a longer period strenuous. Switching to a color model corresponding to the perceptual dimensions of human color vision avoids these problems. We show several practically relevant examples using such a model, the HCL color model, to explain how it works and what its advantages are. Moreover, the paper contains several tips on how to easily integrate this knowledge into software commonly used by the community, which should help readers to switch over to the new concept. The switch will result in a greatly improved quality and readability of visualized atmospheric science data for research, teaching, and communication of results to society.

Somewhere Over the Rainbow: How to Make Effective Use of Colors in Meteorological Visualizations