E ach year across the US, mesoscale weather events-flash floods, tornadoes, hail, strong winds, lightning, and localized winter storms-cause hundreds of deaths, routinely disrupt transportation and commerce, and lead to economic losses averaging more than US$13 billion.1 Although mitigating the impacts of such events would yield enormous economic and societal benefits, research leading to that goal is hindered by rigid IT frameworks that can't accommodate the real-time, on-demand, dynamically adaptive needs of mesoscale weather research; its disparate, high-volume data sets and streams; or the tremendous computational demands of its numerical models and data-assimilation systems.In response to the increasingly urgent need for a comprehensive national cyberinfrastructure in mesoscale meteorology-particularly one that can interoperate with those being developed in other relevant disciplines-the US National Science Foundation (NSF) funded a large information technology research (ITR) grant in 2003, known as Linked Environments for Atmospheric Discovery (LEAD). A multidisciplinary effort involving nine institutions and more than 100 scientists, students, and technical staff in meteorology, computer science, social science, and education, LEAD addresses the fundamental research challenges needed to create an integrated, scalable framework for adaptively analyzing and predicting the atmosphere.LEAD's foundation is dynamic workflow orchestration and data management in a Web services framework. These capabilities provide for the use of analysis tools, forecast models, and data repositories,

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A multi-institution collaboration demonstrated real time compression and Internet-based transmission technology to make possible an affordable nationwide operational capture, distribution, and archiving of Level II WSR-88D data.

Access to real-time distributed Earth and Space Science (ESS) information is essential for enabling critical Decision Support Systems (DSS). Thus, data model interoperability between the ESS and DSS communities is a decisive achievement for enabling cyber-infrastructure which aims to serve important societal benefit areas. The ESS community is characterized by a certain heterogeneity, as far as data models are concerned. Recent spatial data infrastructures implement international standards for the data model in order to achieve interoperability and extensibility. This paper presents well-accepted ESS data models, introducing a unified data model called the Common Data Model (CDM). CDM mapping into the corresponding elements of the international standard coverage data model of ISO 19123 is presented and discussed at the abstract level. The mapping of CDM scientific data types to the ISO coverage model is a first step toward interoperability of data systems. This mapping will provide the abstract framework that can be used to unify subsequent efforts to define appropriate conventions along with explicit agreed-upon encoding forms for each data type. As a valuable case in point, the content mapping rules for CDM grid data are discussed addressing a significant example.

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