  A wide range of data that appear in scientific experiments and simulations
are multivariate or multifield in nature, consisting of multiple scalar fields.
Topological feature search of such data aims to reveal important properties
useful to the domain scientists. It has been shown in recent works that a
single scalar field is insufficient to capture many important topological
features in the data, instead one needs to consider topological relationships
between multiple scalar fields. In the current paper, we propose a novel method
of finding similarity between two multifield data by comparing their respective
fiber component distributions. Given a time-varying multifield data, the method
computes a metric plot for each pair of histograms at consecutive time stamps
to understand the topological changes in the data over time. We validate the
method using real and synthetic data. The effectiveness of the proposed method
is shown by its ability to capture important topological features that are not
always possible to detect using the individual component scalar fields.
