  This article proposes a visualization method for multidimensional data based
on: (i) Animated functional Hypothetical Outcome Plots (f-HOPs); (ii)
3-dimensional Kiviat plot; and (iii) data sonification. In an Uncertainty
Quantification (UQ) framework, such analysis coupled with standard statistical
analysis tools such as Probability Density Functions (PDF) can be used to
augment the understanding of how the uncertainties in the numerical code inputs
translate into uncertainties in the quantity of interest (QoI).
  In contrast with static representation of most advanced techniques such as
functional Highest Density Region (HDR) boxplot or functional boxplot, f-HOPs
is a dynamic visualization that enables the practitioners to infer the dynamics
of the physics and enables to see functional correlations that may exist. While
this technique only allows to represent the QoI, we propose a 3-dimensional
version of the Kiviat plot to encode all input parameters. This new
visualization takes advantage of information from f-HOPs through data
sonification. All in all, this allows to analyse large datasets within a
high-dimensional parameter space and a functional QoI in the same canvas. The
proposed method is assessed and showed its benefits on two related
environmental datasets.
