Geoscience Reference
In-Depth Information
evacuation routes, medical facilities, and so on to predict the consequences of various
release scenarios (e.g., number of people may be exposed to or will be injured by po-
tentially dangerous concentrations of those materials).
In addition to identifying national priorities and computational grand challenges in
the sciences, many of the NSF and NAS reports cited above have also documented in-
frastructure needs, including comprehensive data collection, management and archival
systems, and new methods of data mining and knowledge extraction. For example, the
NSF ERE Advisory Committee calls for building infrastructure and technical capacity
with a new generation of cyberinfrastructure “to support local and global research and
to disseminate information to a diverse set of users including environmental profes-
sionals, the public, and decision makers at all levels.” Toward building the cyberinfra-
structure, the ERE agenda foresees the need for a comprehensive suite of data services
that will facilitate synthesis of datasets from diverse fi elds and sources, information in
digital libraries, data networks, and web-based materials so that they can serve as
essential tools for educators, students, scientists, policy-makers, and the general pub-
lic. Similar needs for web-based real-time and archived data services, including digital
library integration and fusion of scientifi c information systems (SISs) with geograph-
ic information systems (GISs), were expressed at the NSF-sponsored Workshop on
Cyberinfrastructure for ERE (CIERE, 2003).
Growing numbers of universities are engaged in real-time modeling activities, and
this number is expected to increase as advances in computing and communication
technologies facilitate local atmospheric modeling. A new generation of models (e.g.,
the weather research and forecasting model, (Michalakes et al., 2001)) can predict
weather on the sub 1 km scale, with the potential to address community-scale con-
cerns. Providing initial and boundary condition data along with analysis and visualiza-
tion tools for these efforts requires an extensive cyberinfrastructure.
The recent decades have also been marked by a revolution in our ability to survey,
probe, map, and profi le our global environment. For instance, a plethora of instruments
and digital sensors mounted on geostationary and polar orbiting satellites scan vast areas
of the Earth's surface round the clock. With their powerful ability to continuously
and remotely monitor the global environment, observations from satellite platforms
are increasingly replacing
in-situ
surface and upper air observations. Today, dozens
of satellites are rapidly scanning and measuring the global environment and in the
process generating an ever-expanding range of geoscience data to help us manage and
solve some of the most vexing and complex multidisciplinary problems of the society.
This revolution in remote sensing technology and techniques and their many geo-
science applications have had a profound impact on geoscience operations. At the
same time, the complexity and explosive growth in the volume of remotely sensed has
also transformed the provision and use of data from remote sensing platforms such as
satellites, radars, and lidars.
Modern environmental studies rely on diverse datasets, requiring tools to fi nd and
use the data. The data discovery process has become an important dimension of the
scientifi c method, complementing theory, experimentation, and simulation as the tools
of the trade.
