Correlative Visualization Techniques for Disparate Data in the Earth,
Space and Environmental Sciences
Lloyd A. Treinish
lloydt@watson.ibm.com
IBM Thomas J. Watson Research Center
Yorktown Heights, NY
Introduction
The visualization and analysis of large scientific data represents a very
challenging task, especially in the earth, space and environmental
sciences. The myriad of earth science data, for example, from observation (in
situ and remotely sensed) and computation (simulations and empirical models)
are complex and very large in volume. These data are multidimensional
(typically two or three spatial dimensions, perhaps one or more non-spatial
dimensions, e.g., energy), dynamic (time-varying in data and dimensionality)
and consist of many parameters. There is enormous variation in the
instrumentation used to observe the Earth that have consequences in the data
geometries, sampling and error characteristics. Such variation is often
compounded by inconsistencies in the data gathering process, especially for
instruments that perform long-term monitoring of the Earth. These
measurements each relate some aspect of the physical phenomena under
observation. Typically they must be combined in order to glean some knowledge
of the data. Furthermore, they are often used in conjunction with simulations
to verify theory or as initial or boundary conditions for empiricial
models. A long term goal of such on-going activities as well as planned
data acquisition and computational efforts is to view the Earth as an
integrated system. This would merge and define the interactions between the
near-space environment, the atmosphere, the oceans, the land (both surface and
subsurface), etc. An additional aspect of such work is the evaluation of
the environmental effects of anthropomorphic activities. Greater cognizance
of data characteristics and handling of the diversity of earth, space and
environmental data can lead to effective solutions to their visualization
and analysis. In addition, traditional visualization techniques have been
quite limited in their utility in these disciplines. To illustrate the
complexity of this challenge and viable solutions, consider some example
problems using real data and real science. The first example concerns the
analysis of spacecraft observations of global ozone, which are useful in
understanding ozone depletion. An approach called correlative
visualization, which is a set of methods to examine disparate data
simultaneously, is applied to these observations as well as atmospheric
dynamics data. For earth, space and environmental sciences applications,
cartography must be introduced, which are methods of creating maps of the
Earth.
Cartography is an ancient art and science of methods to project --
mathematically transform all or part of the surface of a sphere (e.g., the
earth) onto a two-dimensional, flat surface or plane. The process of map
projection introduces distortions of the data and/or its geometry. The choice
of a specific projection method in visualization is very important for the
proper communications of information. It is very much dependent on the
visualization task (e.g., exploration, analysis, presentation, decision
support, etc.). Too often, a very popular projection, such as Mercator, or
a simple rectilinear projection is employed without knowledge of the resultant
distortion of the visualized data. A brief survey of
common projection methods is provided.
Data Explorer Cartography Tutorial for the Earth, Space and
Environmental Sciences
Observations made by the Total Ozone Mapping Spectrometer (TOMS) aboard NASA's
Nimbus-7 spacecraft have been critical to the study of stratospheric ozone.
Direct analysis of the TOMS data yields important information on the
morphology of the annual austral depletion region. However, when these data
are visually correlated with other relevant atmospheric data (e.g.,
objective analyses of temperature, geopotential heights, winds), information
about the underlying diurnal atmospheric dynamics of the stratospheric polar
vortex and potential contributions from the upper troposphere can be
gleaned. This includes the formation and breakup of the depletion region each
Antarctic spring. These data require care in their presentation so that
artifacts due to the visualization process are not introduced and
erroneously interpreted as features in the data. The provided form of these
data is ill-suited for the study of such phenomena that occur continuously
over a nominally spherical surface (i.e., it tears the data). In addition,
they are not uniformly available for the entire earth or at least spatial
regimes being examined. Each of the data sets being examined are generally
not geographically coregistered and are defined on differing geometric
structures. These characteristics require non-traditional techniques for
visual correlation based upon registration of multiple data sets of
disparate structure with cartographic warping of regular and irregular
geometries. Such an approach does not introduce interpolation and its
artifacts into the registration or realization process. It is also
independent of the choice of realization technique, and hence, provides a
framework for experimenting with different visualization strategies. As a
result, the fidelity of the original data sets is preserved in a coordinate
system suitable for three-dimensional, dynamic presentation and examination of
upper atmospheric phenomena.