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With the continuous increase in the computational power and resources ofmodern high-performance computing (HPC) systems, large-scale ensemblesimulations have become widely used in various fields of science andengineering, and especially in meteorological and climate science. It is widelyknown that the simulation outputs are large time-varying, multivariate, andmultivalued datasets which pose a particular challenge to the visualization andanalysis tasks. In this work, we focused on the widely used ParallelCoordinates Plot (PCP) to analyze the interrelations between differentparameters, such as variables, among the members. However, PCP may suffer fromvisual cluttering and drawing performance with the increase on the data size tobe analyzed, that is, the number of polylines. To overcome this problem, wepresent an extension to the PCP by adding B\'{e}zier curves connecting theangular distribution plots representing the mean and variance of theinclination of the line segments between parallel axes. The proposedAngular-based Parallel Coordinates Plot (APCP) is capable of presenting asimplified overview of the entire ensemble data set while maintaining thecorrelation information between the adjacent variables. To verify itseffectiveness, we developed a visual analytics prototype system and evaluatedby using a meteorological ensemble simulation output from the supercomputerFugaku.

Angular-based Edge Bundled Parallel Coordinates Plot for the Visual Analysis of Large Ensemble Simulation Data