Geoscience Reference
In-Depth Information
• User-friendly interface
• Transparency (format, protocol, etc.)
• Customization and personalization of services
• Capability for server-side operations (e.g., subsetting, sub-sampling, etc)
• Aggregation of data and products
• Provision of rich metadata
• Integration across data types, formats, and protocols
• Intelligent client-server approaches to data access and analysis
• Interoperability across components and services
• Flexibility, extensibility, and scalability
• Ability to chain services via workflows
• Support an array of tools for access, processing, management, and visualization
As a result of the aforementioned trends, the last decade has seen an evolution of
data systems like Earth Observing System Data and Information System (EOSDIS)
towards a more layered and open architecture, while new data systems have been
built and deployed using many open source and standards-based technologies (e.g.,
the NOAA National Operational Model Archive and Distribution System (NOMADS;
Rutledge et al., 2002), Community Data Portal (Middleton, 2001) and Earth System
Grid (Foster et al., 2002); and data system at the British Atmospheric Data Centre
(Lawrence, 2003) However, the transition has not been without challenges for a num-
ber of reasons, including:
• Heterogeneity and complexity of distributed observing, modeling, data, and
communication systems
• Nature of data coverage: diversity and multiple spatial and temporal scales
• Data systems using both legacy components alongside contemporary applica-
tions, creating integration challenges
• A lack of standards and interoperability
• Non-monolithic user community
• Political, technological, and cultural and regulatory barriers, especially in global
sharing of and access to data
Given the very high data rates from current and future generation observing sys-
tems such as GOES-R and NPOESS satellites, the user community will need a hybrid
solution that couples a satellite-based data reception system with a terrestrial, Internet-
based data access system. Both local and remote data access mechanisms will be re-
quired to deal with the large volumes of data. Both push systems for distributing data
(e.g., Unidata Local Data Manager) or just notifi cations (using RSS feeds) and pull
systems for remote access (e.g., THREDDS and OPeNDAP) will be required.
Broad Data Categories
While far too many data categories exist to describe in detail, typical data systems
in atmospheric sciences must provide a seamless, end-to-end services for accessing,
utilizing, and integrating data across the following data types:
