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NOTICE: This version of the NSF Unidata web site (archive.unidata.ucar.edu) is no longer being updated.
Current content can be found at unidata.ucar.edu.
To learn about what's going on, see About the Archive Site.
In case you have not heard, we are chairing a session on Frameworks for Environmental Data Integration, Modeling and Analysis at the upcoming AGU Joint Assembly in May in Fort Lauderdale. In particular, we are soliciting contributions from diverse, multi-disciplinary developer and user groups within the AGU community, particularly with experience in both the scientific and technological issues in data model, management and archive systems, model development, observing systems, data analysis and assimilation, visualization, standards activities, and community software and data set development and support. As background, consider that frameworks to support environmental data exchange and integration associated with modeling and analysis have recently been proposed in the Earth and Space Science community. This notion should be extended beyond the enabling computing technology and infrastructure to address any data generator. Hence, output from sensors (in situ and remote), simulations, analyses (e.g., data assimilation), visualization, etc. need to be treated in an uniform fashion, for which data exchange and coupling need to be addressed. This session is intended to provide a forum for sharing algorithms, techniques, experience and best practices across such diverse considerations for which there is underlying commonality but not necessarily collaboration to date. As you know, many research activities and operational decisions in environmental sciences require extensive computational modeling and data analysis. Hence, questions of critical importance often require multiple models to be coupled, running either sequentially or in parallel, and the results compared to or integrated with observational data sets. But the approach needs to focus on the rationale behind such questions and recognize the technology as an enabler. For example, predicting the impact of certain land use decisions in a river basin might involve the coupling of water balance, water quality, carbon storage, crop production, and biodiversity models. Prediction of impacts of global climate change on flooding patterns might involve perturbing numerical weather predictions, and coupling them to hydrological calculations based upon results of ensembles of climate models. While the experience behind extant frameworks and data models is critical to this session, an important goal is to identify methodologies that can be more generalized and avoid a tight coupling between data representation and the architectures behind computation or observation (e.g., being able to capture the semantics of the data to enable proper utilization). A further result will be recommendations for both research and development activities as well as case studies to help evaluate the methodologies that are identified. Additional information is available at http://www.agu.org/meetings/ja08/?content=program. We hope that you will consider a contribution to this session. Thank you, Barbara Eckman and Lloyd Treinish IBM Big Green Innovations -------------------------- Lloyd A. Treinish Project Scientist, Big Green Innovations IBM Systems & Technology Group 1101 Kitchawan Road, Yorktown Heights, NY 10598 914-945-2770, lloydt@xxxxxxxxxx http://www.research.ibm.com/people/l/lloydt/ http://www.research.ibm.com/weather/DT.html http://www.ibm.com/technology/greeninnovations
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