  With the continuous increase in the computational power and resources of
modern high-performance computing (HPC) systems, large-scale ensemble
simulations have become widely used in various fields of science and
engineering, and especially in meteorological and climate science. It is widely
known that the simulation outputs are large time-varying, multivariate, and
multivalued datasets which pose a particular challenge to the visualization and
analysis tasks. In this work, we focused on the widely used Parallel
Coordinates Plot (PCP) to analyze the interrelations between different
parameters, such as variables, among the members. However, PCP may suffer from
visual cluttering and drawing performance with the increase on the data size to
be analyzed, that is, the number of polylines. To overcome this problem, we
present an extension to the PCP by adding B\'{e}zier curves connecting the
angular distribution plots representing the mean and variance of the
inclination of the line segments between parallel axes. The proposed
Angular-based Parallel Coordinates Plot (APCP) is capable of presenting a
simplified overview of the entire ensemble data set while maintaining the
correlation information between the adjacent variables. To verify its
effectiveness, we developed a visual analytics prototype system and evaluated
by using a meteorological ensemble simulation output from the supercomputer
Fugaku.
