AMERICAN METEOROLOGICAL SOCIETY Bulletin of the American Meteorological Society EARLY ONLINE RELEASE This is a preliminary PDF of the author-produced manuscript that has been peer-reviewed and accepted for publication. Since it is being posted so soon after acceptance, it has not yet been copyedited, formatted, or processed by AMS Publications. This preliminary version of the manuscript may be downloaded, distributed, and cited, but please be aware that there will be visual differences and possibly some content differences between this version and the final published version. The DOI for this manuscript is doi: 10.1175/BAMS-D-13-00155.1 The final published version of this manuscript will replace the preliminary version at the above DOI once it is available. © 2014 American Meteorological Society Generated using version 3.2 of the official AMS LATEX template 1 Somewhere over the rainbow: How to make effective use of colors 2 in meteorological visualizations ∗ 3 Reto Stauffer, Georg J. Mayr and Markus Dabernig Institute of Meteorology and Geophysics, University of Innsbruck, Innsbruck, Austria 4 Achim Zeileis Department of Statistics, Faculty of Economics and Statistics, University of Innsbruck, Innsbruck, Austria ∗Reto Stauffer, Institute of Meteorology and Geophysics, University of Innsbruck, Innrain 52, A{6020 Innsbruck E-mail: reto.stauff[email protected] 1 5 CAPSULE 6 Effective visualizations have a wide scope of challenges. The paper offers guidelines, a 7 perception-based color space alternative to the famous RGB color space and several tools to 8 more effectively convey graphical information to viewers. 9 ABSTRACT 10 Results of many atmospheric science applications are processed graphically. Visualizations 11 are a powerful tool to display and communicate (complex) data. But to create effective 12 figures a wide scope of challenges have to be considered. Therefore, this paper offers several 13 guidelines with a focus on colors. Colors are often used to add additional information or to 14 code information. Colors should (i) allow humans to process the information rapidly, (ii) 15 guide the reader to the most important information, and (iii) represent the data appropriately 16 without misleading distortion. The second and third requirements necessitate tailoring the 17 visualization, and the use of colors, to the specific purpose of the graphic. A standard way of 18 deriving color palettes is via transitions through a particular color space. Most of the common 19 software packages still provide default palettes derived in the Red-Green-Blue (RGB) color 20 model or \simple" transformations thereof. Confounding perceptual properties such as hue 21 and brightness make RGB-based palettes more prone to misinterpretation. Switching to a 22 color model corresponding to the perceptual dimensions of human color vision avoids these 23 problems. We show several practically relevant examples using one such model, the Hue- 24 Chroma-Luminance (HCL) color model, to explain how it works and what its advantages 25 are. Moreover, the paper contains several tips on how to easily integrate this knowledge into 26 software commonly used by the community. The guidelines and examples should help readers 27 to switch over to the alternative HCL color model, which will result in a greatly improved 28 quality and readability of visualized atmospheric science data for research, teaching, and 29 communication of results to society. 1 30 1. Introduction 31 One of the many challenges associated with atmospheric sciences is the analysis and 32 utilization of large, usually very complex data sets. One way to gather the information 33 and better understand it, is to visualize it graphically. Visualizations may be as simple 34 as one-dimensional plots (e.g., time series plots) or as complex as multidimensional charts 35 (e.g., from numerical weather prediction model output). As a scientist, an important part of 36 daily work is to create plots and graphs that visualize results and outcomes earned through 37 weeks and possibly months of work. The key feature of visualization is helping the reader to 38 capture the information as simply and quickly as possible. This reader can be a colleague, 39 a customer, or even you. 40 The term \visualization" encompasses many aspects. Much work has been carried out 41 during the last century to investigate the human perception and the influence of different 42 aspects on how to best convey information. Initially, fundamental research was done in 43 physics, biology, and medicine (see Miles 1943; Stevens 1966; Carswell and Wickens 1990), 44 but with the advent of the computer industry this focus expanded into how to deal with the 45 new technological achievements in different disciplines, including 3D graphics, interactive 46 visualization, and animation (Smith 1978; Ware 1988; BAMS 1993; Rogowitz and Treinish 47 1996; Light and Bartlein 2004; Hagh-Shenas et al. 2007). Today, the ubiquitous availability 48 of computers and software enables everyone to create graphics for all different devices. We 49 will focus on only one aspect: how to make effective use of color for visualization. Therefore 50 we are using relatively \simple" spatial plots to illustrate the guidelines and typical user 51 tasks in atmospheric science. 52 Color is a good instrument to improve graphics, but carelessly applied color schemes can 53 result in figures that are less effective than gray-scale ones (Light and Bartlein 2004). For 54 large parts of our vision, hue is irrelevant in comparison to shading. Color does not help 55 us measure distances, discern shapes, detect motion, or to identify small objects over long 56 distances. Hue is useful for labeling and categorization, but less effective for representing 2 57 (fine) spatial data or shape. However, if used effectively, colors are a powerful tool to improve 58 (highly) complex visualizations. Therefore it is important to know how color perception 59 works and how we can make use of it to improve visualization (Ware 2004). Most common 60 software packages supply methods to create different types of plots with different color maps 61 (or color palettes). Nevertheless, because most plotting functions are rather generic it is 62 impossible for the software developers to provide adequate color schemes for all applications. 63 The default color map is often an RGB rainbow palette. This is probably the most 64 known color map and consequently many people use it uncritically as the default for their 65 visualization, even it has been shown to be difficult or even harmful (Brewer 1997; Borland 66 and Taylor 2007). In addition to the rainbow scheme, other color maps defined in the RGB 67 color space also exhibit similar problems and have to be handled with care, because the 68 RGB color space has some critical disadvantages (Rogowitz and Treinish 1998; Light and 69 Bartlein 2004). Vivid colors along the spectrum of the RGB color space strongly differ in their 70 luminance, which can lead to artificial dark or bright bands that can obscure the information 71 shown. Furthermore, the RGB color space is not a uniform color space, meaning that 72 color pairs with the same distance within the color space do not show the same perceptual 73 difference. Other prevalent color spaces, such as Hue-Saturation-Value (HSV) and Hue- 74 Saturation-Luminance (HSL), suffer from the same problems as the RGB color space (Smith 75 1978). 76 To avoid these disadvantages several transformations of the RGB color space have been 77 developed. These transformations are approximations of the human color perception sys- 78 tem, in that they, for example, allow for the fact that we have a logarithmic perception 79 of luminance (Stevens 1966). The most profound work has been done by the Comission 80 Internationale de l'Eclairage´ (CIE) in creating color spaces like the CIELUV or CIELAB 81 color space, based on a standard observer (Ware 2004). Keep in mind that there is not \one 82 omnipotent best" color model or color scheme. Depending on the user task or the medium 83 the most effective color models and palettes can differ. Even with extensive user testing 3 84 there is always a number of different effective color maps for a given purpose. The work of 85 Mahy et al. (1994) contains a good experiment-based comparison of many of the available 86 color spaces. In this paper we will focus on a perception based color concept called HCL, 87 which is the CIELUV gamut in polar coordinates. Thus, the HCL color space is based on 88 how humans perceive color, in contrast to the RGB color space, which is based on technical 89 demands of TV and computer screens. 90 In this article we will demonstrate the benefits of the HCL alternative, which is already 91 becoming better known and more frequently used in other scientific fields (Zeileis et al. 2009; 92 Silva et al. 2011). We argue that the use of misleading and distorting RGB color maps is 93 not necessary as alternative models are available, and that changing to a perception-based 94 color model can strongly improve the visual reception on graphical information with very 95 little additional effort. 96 2. RGB versus HCL color space 97 Hue-Chroma-Luminance (HCL) model: The HCL color space is the polar transformation 98 of the uniform CIELUV color space and forms a distorted double cone where each of the three 99 dimensions directly controls one of the three major perceptual dimensions directly (additional 100 information in the sidebars). The first one is hue, the dominant wavelength (defining the 101 color), the second dimension is chroma, capturing colorfulness (color intensity compared to 102 gray), and the third is luminance, pertaining the brightness (\amount" of gray). Figure 1 103 shows the three perceptual HCL dimensions. In each of the subfigures one dimension changes 104 linearly across the corresponding axis while the others are held constant.