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Examples of different HCL color maps: (a) isoluminant qualitative schemes (e.g., for classification); (b),(c) sequential color maps (e.g., for continuous data: for either increasing or decreasing data with only one extreme); and (d) diverging schemes (e.g., for data with two extremes centered around a neutral value). Sequential schemes can contain one single hue or by passing from one to another trough the HCL color space along the hue dimension. The single-hue sequential color maps shown in (b) are all based on the identical luminance function. Even if they do have different/no hue, the grayscale representations of those three examples are exactly equal. Beside those main types, mixed/hybrid color maps are also possible.
Juxtaposition of the RGB rainbow color map and an HCL-based rainbow. Below the color wheel, the same palette is shown as a color bar in the colorized version and the corresponding desaturated version, respectively. The RGB rainbow creates unwanted variations in luminance, while the HCL rainbow is fully isoluminant.
ECMWF analysis of the equivalent potential temperature (C) at 700 hPa over the Atlantic/Europe. (a) The old product based on highly saturated RGB rainbow colors. (b) The revamped product including an HCL-based color map.
Color is a good instrument to improve graphics, but carelessly applied color schemes can result in figures that are less effective than grayscale ones (Light and Bartlein 2004). For large parts of our vision, hue is irrelevant in comparison to shading. Color does not help us measure distances, discern shapes, detect motion, or identify small objects over long distances. Hue is useful for labeling and categorization but less effective for representing (fine) spatial data or shape. However, if used effectively, colors are a powerful tool to improve (highly) complex visualizations. Therefore, it is important to know how color perception works and how we can make use of it to improve visualization (Ware 2004). Most common software packages supply methods to create different types of plots with different color maps (or color palettes). Nevertheless, because most plotting functions are rather generic it is impossible for the software developers to provide adequate color schemes for all applications.
In this article, we will demonstrate the benefits of the HCL alternative, which is already becoming better known and more frequently used in other scientific fields (Zeileis et al. 2009; Silva et al. 2011). We argue that the use of misleading and distorting RGB color maps is not necessary, as alternative models are available and that changing to a perception-based color model can strongly improve the visual reception on graphical information with very little additional effort.
The coordinates for the HCL color maps used to create Figs. 1, 4, 5, and 6. The second column indicates the color followed by the coordinates for hue, chroma, and luminance. The first color indicates either the most left color (for horizontal color bars) or the bottom color (for vertical color bars). For the diverging scheme (Fig. 6b), the hue of the center value does not matter. It is exactly at the border for the two opposite hues, but its chroma is zero (gray).
Historically, the RGB model is based on how screens work. Cathode ray tube (CRT), light-emitting diode (LED), and plasma screens attached to TVs, computer monitors, and projectors all use the same technique: Images are created by additive color mixing. Each image consists of hundreds to thousands of pixels where each pixel emits a mixture of red, green, and blue light. Each single RGB color is defined by a triplet of intensities for those three primary colors. Appropriate mixing produces a wide range of colors. Three zero intensities result in black, while maximum intensities for all three primary colors yield white and, in between, all other colors can be defined. Two widespread simple transformations of the RGB color space are the HSV (hexcone model) and the HSL (triangle model). Although they have a slightly better behavior, the basic problems of the RGB color space cannot be solved.
To focus on the luminance dimension of a color palette, it can be desaturated, for example, by transforming to HCL space, removing all chroma (so that hue does not matter), and transforming back to the original color space. This just removes hue/chroma information but keeps luminance fixed. In HCL dimensions, changes in hue or chroma do not influence the underlying luminance information.
In recent years, several publications created guidelines for how to use colors effectively. Although those guidelines differ slightly, there are some cornerstones on how to create effective visualization. Before showing some real-world examples, it is worth introducing these rules (see Ware 2004; Rogowitz and Treinish 1996; Brewer 1997; Rogowitz and Treinish 1998; Treinish 1998; Light and Bartlein 2004; Hagh-Shenas et al. 2007):
Number of colors: For classification tasks (search and distinguishing), only a small number of different hues can be processed with a low error rate. Healey (1996) showed only five to seven different hues can be found accurately and rapidly on a map. Furthermore, MacEachren (1995) wrote that, if the task is to precisely identify a certain color in a plot, the detection rate can plummet when the number of colors increases (detection rate for 10 colors: 98%; for 17 colors: 72%).
Data: Color should be seen more as an attribute of an object than as its primary feature. The human brain is more effective in capturing shape, form, position, lengths, or orientation than in gathering different colors. Therefore, plain plot types should be used if possible (e.g., line, bar, or box plots; see Carswell and Wickens 1990). Additional color can support the reader/analyst if the color matches the data. For continuous variables (e.g., temperature, total number of people in a region), sequential schemes are very effective (Figs. 2b,c). Isoluminant qualitative schemes (Fig. 2a) work best for classification because they do not add perceptional distortion to the data. For data with a well-defined neutral value (e.g., precipitation anomalies, balance data), a diverging color scheme with a neutral color around this center point works well (Fig. 2d). 1e1e36bf2d