Optimizing Geospatial Cyberinfrastructure to Improve the Computing Capability for Climate Studies

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dc.contributor.advisorYang, Chaowei
dc.contributor.authorLi, Zhenlong
dc.creatorLi, Zhenlong
dc.date.accessioned2015-07-29T18:35:18Z
dc.date.available2015-07-29T18:35:18Z
dc.date.issued2015
dc.description.abstractClimate simulation has significant uncertainties due to our current limited understanding of the processes and interactions between different components of the Earth. Model sensitivity analysis, which tests the sensitivity of model output to the input parameter values, is a standard practice for determining the model uncertainties and improving model accuracy. A common approach for climate model sensitivity analysis is to run a model many times by sweeping a large number of adjustable parameters. However, this approach is hampered by three computational challenges: computing intensity, data intensity, and procedure complexity. This dissertation proposes three optimization methodologies to address these challenges respectively, including 1) tackling the computing intensity challenge posed by climate simulation using Model as a Service, a new service model in the context of cloud computing; 2) managing and processing the big model output – “data intensity” – using a scalable big spatiotemporal data analytics framework; 3) solving the procedure complexity issue using a service-oriented cloud-based scientific workflow framework.
dc.format.extent123 pages
dc.identifier.urihttps://hdl.handle.net/1920/9630
dc.language.isoen
dc.rightsCopyright 2015 Zhenlong Li
dc.subjectGeographic information science and geodesy
dc.subjectClimate
dc.subjectCloud computing
dc.subjectGeospatial cyberinfrastructure
dc.subjectModel sensitivity analysis
dc.subjectOptimization
dc.subjectParallel computing
dc.titleOptimizing Geospatial Cyberinfrastructure to Improve the Computing Capability for Climate Studies
dc.typeDissertation
thesis.degree.disciplineEarth Systems and Geoinformation Sciences
thesis.degree.grantorGeorge Mason University
thesis.degree.levelDoctoral

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