Using Visualization Science to Improve Expert and Public Understanding of Probabilistic Temperature and Precipitation Outlooks

Visually communicating temperature and precipitation climate outlook graphics is challenging because it requires the viewer to be familiar with probabilities as well as to have the visual literacy to interpret geospatial forecast uncertainty. In addition, the visualization scientific literature has open questions on which visual design choices are the most effective at expressing the multidimensionality of uncertain forecasts, leaving designers with a lack of concrete guidance. Using a two-phase experimental setup, this study shows how recently developed visualization diagnostic guidelines can be used to iteratively diagnose, redesign, and test the understandability the U.S. National Oceanic and Atmospheric Administration (NOAA) Climate Prediction Center (CPC) climate outlooks. In the first phase, visualization diagnostic guidelines were used in conjunction with interviews and focus groups to identify understandability challenges of existing visual conventions in temperature and precipitation outlooks. Next, in a randomized control versus experimental treatment setup, several graphic modifications were produced and tested via an online survey of end users and the general public. Results show that, overall, end users exhibit a better understanding of outlooks, but some types of probabilistic color mapping are misunderstood by both end users and the general public, which was predicted by the diagnostic guidelines. Modifications lead to significant gains in end-user and general public understanding of climate outlooks, providing additional evidence for the utility of using control versus treatment testing informed by visualization diagnostics.

Initial Antinoise Performance Analysis of Pupil Phase Diversity Based on Genetic Algorithm

Pupil phase diversity (PPD) wavefront sensor is a new kind of phase-visualization methods, and the output signal of PPD represents the input pupil phase and shows a 1-1 mapping between the position of the wavefront error in the pupil and its position in the output signal. High-precisely wavefront measuring can be obtained under no noise by using appropriate phase restoration algorithm while performance of PPD under noise is unknown. We analyzed antinoise performance of PPD based on genetic algorithm (GA) through measuring the distorted wavefront under different noise level. Simulation results show that wavefront measuring is almost not affected by the existence of noise, which indicates that PPD based on GA can be used in applications with noise.