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Colormapping resources and strategies for organized intuitive environmental visualization

  • Francesca SamselEmail author
  • Phillip Wolfram
  • Annie Bares
  • Terece L. Turton
  • Roxana Bujack
Thematic Issue
  • 95 Downloads
Part of the following topical collections:
  1. Visual Data Exploration

Abstract

Visualizations benefit from the use of intuitive organized color application, enabling a clearer understanding and communication. In this paper, we apply the concept of semantic color association to the generation of thematic colormaps for the environmental sciences in combination with principals of artistic color theory to expand feature resolution and create visual hierarchies within a visualization. In particular, we provide sets of color scales, colormaps and color organization guidance for semantically aligned water, atmosphere, land, and vegetation visualization. Strategies for directing attention via saturation levels and saturation sets of colormaps enable deployment of these techniques. All are publicly available online and accompanied by tools and strategy guidance.

Keywords

Colormaps Visualization Semantic color and environmental data 

Notes

Acknowledgements

We would like to acknowledge: Lucy Nowell and Laura Biven, Program Managers at ASCR, DOE for research funding, the Data Science at Scale team at Los Alamos National Laboratory, specifically James Ahrens and David Rogers for their guidance and support as well as Ethan Stam for developing the website materials enabling easy use of the tools and systems developed for this paper; Mark Petersen and Matthew Maltrud, COSIM, LANL, for data usage and evaluation; Sebastian Klaassen, University of Vienna, and Gregory Abram, TACC, University of Texas at Austin, for their technical contributions. Data sources contained in this paper use the Model for Prediction Across Scales Ocean (MPAS-O) and the Exascale Earth System Model (E3SM), funded by the U.S. Department of Energy, Office of Science, Office of Biological and Environmental Research; a bathymetry dataset from the Los Alamos Laboratory Directed Research and Development Directed Research Adaptation Science for Complex Natural-Engineered Systems project; and biogeochemistry analysis for the Advanced Research Projects Agency - Energy Macroalgae Research Inspiring Novel Energy Resources Multi-Resolution, Multi-Scale Modeling for Scalable Macroalgae Production project. LANL, LA-UR-19-20060.

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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.University of Texas at AustinAustinUSA
  2. 2.Los Alamos National LaboratoryLos AlamosUSA

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