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In the
field similarity plot after adding all the discussed derived
fields (Fig.
1
)
one can observe a wide spread of the original
fields. However, we can also see a
very dense group of points including all Hessian determinant
fields. A few gradient
magnitude
fields of cloud water xl and cloud
ice xi (occluded by xl in Fig.
1
) are placed very closely to this group. The plot
indicates high similarity between the aforementioned
field points as well as the two original
fields with respect to
Euclidean distance. By looking at the linked views of slice-based volume visual-
izations and 1D histograms for a few
fields from this area (Fig.
2
), we see that,
while the
fields exhibit different patterns, the distribution of the data values are very
much in the lower range. Using the Euclidean distance for the projection, the
elds
are closer to each other than to other
fields with values in the upper range.
When observing outliers among the original
fields, we can see that each of them
has strong unique features (Fig.
3
). The overall distribution of the values of the
temperature
field t and the relative humidity
field rhumidity are more similar than of
the values of the speci
c humidity
field q. The latter
field is more similar to the
group described above.
5.2 Time-Varying Field
In the second scenario, we investigate the change of
the temperature
eld
throughout the
first year by treating each month as a separate, independent
field.
fields arranges the points in a loop (Fig.
4
)
that corresponds to the annual cycle, which documents, again, the feasibility of the
overall approach.
The loop has a pendular behavior with the winter months on one side and the
summer months on the other. The spring and fall months are close together (there is
even a crossing) and form transitional phases. Moreover, we can conclude that
changes over months are gradual.
The full projection of original and derived
The projection of only the original
fields (Fig.
5
) clearly separates the
five types of derived
fields as well as the original dataset
fields. Thus, we can
conclude that each of the chosen types of derived
fields conveys distinctly different
information from the original data and from each other, i.e. differences within a
group are much smaller than between the groups. It can easily be con
rmed by
looking at the individual views (Fig.
6
).
6 Discussion
The presented approach describes a conceptual work
ow, where a design choice
among many alternatives is made at each step. In this paper, we followed some of
the common decisions. In the following, we discuss other possibilities. A thorough
fl
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