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Somewhere over the rainbow: How to make effective use of colors in meteorological visualizations

机译:在彩虹的某个地方:如何在气象可视化中有效利用颜色

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摘要

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.
机译:许多大气科学应用的结果都使用颜色以图形方式处理,以对信息的某些部分进行编码。颜色应(1)允许人类处理更多信息,(2)引导观看者获得最重要的信息,(3)适当地表示数据而不会引起误解,并且(4)具有吸引力。第二个要求必须针对要使用图形的查看者来定制可视化和颜色的使用。导出调色板的标准方法是通过特定颜色空间的过渡。大多数通用软件包仍默认提供以RGB颜色模型或其“简单”转换衍生的调色板。诸如色相和亮度之类的令人困惑的感知属性使基于RGB的调色板更容易产生误解。此外,它们通常高度饱和,这使得长时间查看它们很费劲。切换到与人类色彩视觉的感知维度相对应的色彩模型可以避免这些问题。我们将使用这种模型(HCL颜色模型)显示几个与实际相关的示例,以说明其工作方式及其优点。此外,本文还包含有关如何轻松地将这些知识集成到社区常用软件的一些技巧,这应该有助于读者切换到新概念。这种转换将大大提高可视化大气科学数据的质量和可读性,以用于研究,教学以及将结果传达给社会。

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