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Color Coding of Large Value Ranges Applied to Meteorological Data

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Daniel Braun, Kerstin Ebell, Vera Schemann, Laura Pelchmann, Susanne Crewell, Rita Borgo, Tatiana von Landesberger

This paper presents a novel color scheme designed to address the challenge of visualizing data series with large value ranges, where scale transformation provides limited support. We focus on meteorological data, where the presence of large value ranges is common. We apply our approach to meteorological scatterplots, as one of the most common plots used in this domain area. Our approach leverages the numerical representation of mantissa and exponent of the values to guide the design of novel “nested” color schemes, able to emphasize differences between magnitudes. Our user study evaluates the new designs, the state of the art color scales and representative color schemes used in the analysis of meteorological data: ColorCrafter, Viridis, and Rainbow. We assess accuracy, time and confidence in the context of discrimination (comparison) and interpretation (reading) tasks. Our proposed color scheme significantly outperforms the others in interpretation tasks, while showing comparable performances in discrimination tasks.

Dieses Paper zitieren

D. Braun et al., “Color Coding of Large Value Ranges Applied to Meteorological Data,” in 2022 IEEE Visualization and Visual Analytics (VIS), 2022-10. doi: 10.1109/vis54862.2022.00034.


  author           = {Daniel Braun and Kerstin Ebell and Vera Schemann and Laura Pelchmann and Susanne Crewell and Rita Borgo and Tatiana von Landesberger},
  booktitle        = {2022 {IEEE} Visualization and Visual Analytics ({VIS})},
  date             = {2022-10},
  title            = {Color Coding of Large Value Ranges Applied to Meteorological Data},
  doi              = {10.1109/vis54862.2022.00034},
  publisher        = {{IEEE}},