Envisioning a Future of Computational Geoscience in a Data Rich Semantic World

Abstract:

Advances in observational systems and reduction in their cost are allowing us to explore, monitor, and digitally represent our environment in unprecedented details and over large areas. Low cost in situ sensors, unmanned autonomous vehicles, imaging technologies, and other new observational approaches along with airborne and space borne systems are allowing us to measure nearly everything, almost everywhere, and at almost all the time. Under the aegis of observatories they are enabling an integrated view across space and time scales ranging from storms to seasons to years and, in some cases, decades. Rapid increase in the convergence of computational, communication and information systems and their inter-operability through advances in technologies such as semantic web can provide opportunities to further facilitate fusion and synthesis of heterogeneous measurements with knowledge systems. This integration can enable us to break disciplinary boundaries and bring sensor data directly to desktop or handheld devices. We describe CyberInfrastructure effort that is being developed through projects such as Earthcube Geosemantics (http://geosemantics.hydrocomplexity.net), (SEAD (http://sead-data.net/), and Browndog (http://browndog.ncsa.illinois.edu/)s o that data across all of earth science can be easily shared and integrated with models. This also includes efforts to enable models to become interoperable among themselves and with data using technologies that enable human-out-of-the-loop integration. Through such technologies our ability to use real time information for decision-making and scientific investigations will increase multifold. The data goes through a sequence of steps, often iterative, from collection to long-term preservation. Similarly the scientific investigation and associated outcomes are composed of a number of iterative steps from problem identification to solutions. However, the integration between these two pathways is rather limited. We describe characteristics of new technologies that are needed to bring these processes together in the near future to significantly reduce the latency between data, science, and agile and informed actions that support sustainability.