PNNL-13501 - Pacific Northwest National Laboratory

We demonstrate our design using a climate modeling data
set provided by the Laboratory’s Global Change Program.
The basic multivariate time-dependent data set has all the
characteristics and features for experiments. The data set
has five data variables (pressure, temperature, water vapor
mixing ratio, and two wind-velocity components) of
different types (scalars and vectors) and dimensions (two-
and three-dimensional) recorded daily.
Figure 1 shows a cluster analysis of 92 scalar fields (water
vapor flux) encoded into grayscale images with the
highest moisture value mapped to the white end. Each of
the individual three-dimensional scalar fields, shown in
Figure 1 as two-dimensional images for simplicity, has
over 120,000 real numbers. Each of the data signatures
that represent individual scalar fields in the cluster
analysis has only 200 real numbers. The analysis process
clearly separates individual images into clusters according
to their patterns and characteristics.
Figure 1. A cluster analysis of 92 scalar fields of a time
varying climate simulation
Our data signature approach is powerful and flexible
enough to merge multiple data dimensions of different
data types into a signature vector for computation. In this
example, we merge a scalar field (water vapor flux) and a
vector field (wind velocity) for each of the 92 simulation
steps into a single signature for cluster analysis. Figure 2
shows 92 icons (one for each signature) that are colorcoded
according to the sequential order of the simulation.
The simulation starts from the left (red and orange),
across the middle (yellow, green, and cyan), and stops at
the right (blue and purple). The three-dimensional
scatterplot based on the combined signatures correctly
separates the seven periods that are characterized by three
heavy rainfall episodes (orange, green, and blue) in the
simulation.
170 FY 2000 Laboratory Directed Research and Development Annual Report
Figure 2. A cluster analysis of 92 scalar and 92 vector fields
of a time varying climate simulation
We also explored the idea of using data signature in
scientific data mining and developed a feature decimation
technique to simplify a vector flow field in visualization.
Figure 3 shows a three-dimensional atmospheric space
crowded with flow field information such as critical
points, flow field lines, and boundary regions. Figure 4
shows a filtered version of Figure 3 with threshold
relative vorticity magnitudes of 6. In both Figures 3 and
4, critical points are grouped together into saddle (cross)
and non-saddle (cube) classes to simplify the
visualization.
Figure 3. An atmospheric data volume with all critical
points and field flow lines
Summary and Conclusions
Data signatures allow scientists with limited resources to
conduct analysis on very large data sets at a higher level
of abstraction. Its novel design is flexible enough to
merge different data types into a quantitative data vector