# Color Coding of Large Value Ranges Applied to Meteorological Data

arXiv: https://arxiv.org/abs/2207.12399 · HTML: https://mlsanity.com/paper/2207.12399/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 24 references that are in the map.
- First posted on arXiv 13 Jul 2022.

## Builds on (most cited first, 2 of 2)

- [Color Crafting: Automating the Construction of Designer Quality Color Ramps](https://mlsanity.com/paper/1908.00629/) (2019)
- [A Survey of Perception-Based Visualization Studies by Task](https://mlsanity.com/paper/2107.07477/) (2021)

## Built on by (most cited first, 1 of 1)

- [Reclaiming the Horizon: Novel Visualization Designs for Time-Series Data with Large Value Ranges](https://mlsanity.com/paper/2307.10278/) (2023)

## Abstract (arXiv)

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.