A Scalable Grid-Enabled Data Framework for EastFIRE Decision Support
Wildfire management in the eastern U.S. is more complex than in the west because of higher population density, increased closeness of housing and people with wildlands, and large spatial and temporal variability of topography, climate, ecosystems, and development patterns. The diversity requires a large group of data sets from heterogeneous sources, such as fuel property and fire characteristics data from in situ or remote sensing observations, weather data from observations and model prediction, topography data over long temporal scales but high spatial resolution, socio-economic data, etc. Those data sets are different in spatial and temporal resolutions, data types (data models) and data formats, and are in different distributed sites, accessible in a variety of ways. An integrated data access is needed to support decision making process. Current existing technology in earth science community for data interoperability, and in particular, Grid technology will be valuable for building such an integrated data access system. In this paper, we will identify the data needs and describe a scalable Grid-enabled data framework for wildfire decision support. We will also give overall system architecture and outline the major components.
Yang, R., Qu, J., Chi, Y., Kafatos, M., (2003) A scalable grid-enabled data framework for EastFIRE decision support. Center for Earth Observing and Space Research, George Mason University.
University Consortium of Atmospheric Research, George Mason University