Abstract. Climate scientists and meteorologists are working towards a better understanding of atmospheric conditions and global climate change. To explore the relationships present in numerical predictions of the atmosphere, ensemble datasets are produced that combine time-and spatially-varying simulations generated using multiple numeric models, sampled input conditions, and perturbed parameters. These data sets mitigate as well as describe the uncertainty present in the data by providing insight into the effects of parameter perturbation, sensitivity to initial conditions, and inconsistencies in model outcomes. As such, massive amounts of data are produced, creating challenges both in data analysis and in visualization. This work presents an approach to understanding ensembles by using a collection of statistical descriptors to summarize the data, and displaying these descriptors using variety of visualization techniques which are familiar to domain experts. The resulting techniques are integrated into the ViSUS/Climate Data and Analysis Tools (CDAT) system designed to provide a directly accessible, complex visualization framework to atmospheric researchers.
In an approach presented by REF , a collection of statistical descriptors is used for analyzing ensemble data sets.
Visualization of uncertainty and ensemble data: Exploration of climate modeling and weather forecast data with integrated ViSUS-CDAT systems
Journal of Physics: Conference Series
Journal of Physics: Conference Series
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