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Cloud Computing in Ocean and Atmospheric Sciences

  • Provides real examples that help new users quickly understand the cloud and provide guidance for new projects
  • Presents proof of the usability of the techniques and a clear path to adoption of the techniques by other researchers
  • Includes real research and development examples
  • that are ideal for cloud computing adopters in ocean and atmospheric domains

By: Tiffany C Vance(Editor), Nazila Merati(Editor), Chaowei Phil Yang(Editor), May Yuan(Editor)

Elsevier

Paperback | Mar 2016 | #233833 | ISBN-13: 9780128031926
Availability: Usually dispatched within 1-2 weeks Details
NHBS Price: £82.99 $112/€95 approx

About this book

Cloud Computing in Ocean and Atmospheric Sciences provides the latest information on this relatively new platform for scientific computing, which has great possibilities and challenges, including pricing and deployments costs and applications that are often presented as primarily business oriented. In addition, scientific users may be very familiar with these types of models and applications, but relatively unfamiliar with the intricacies of the hardware platforms they use.

Cloud Computing in Ocean and Atmospheric Sciences provides a range of practical examples of cloud applications that are written to be accessible to practitioners, researchers, and students in affiliated fields. By providing general information on the use of the cloud for oceanographic and atmospheric computing, as well as examples of specific applications, this book encourages and educates potential users of the cloud. The chapters provide an introduction to the practical aspects of deploying in the cloud, also providing examples of workflows and techniques that can be reused in new projects.


Contents

    Dedication
    List of Contributors
    Author Biographies
    Foreword
    Acknowledgments
    Introduction
    Chapter 1. A Primer on Cloud Computing
        The Characteristics of Cloud Computing
        Service Models for Cloud Computing
        Types of Clouds
        Science in the Cloud
    Chapter 2. Analysis Patterns for Cloud-Centric Atmospheric and Ocean Research
        Introduction
        What is e-Science?
        e-Science and Cloud Computing
        Pattern Language and Analysis Patterns
        e-Science Analysis Patterns for the Cloud
        Conclusion
    Chapter 3. Forces and Patterns in the Scientific Cloud: Recent History and Beyond
        2005 to 2015: A Period of Fit and Retrofit
        Forces and Challenges in Scientific Cloud Adoption
        Looking Beyond Fit and Retrofit
        Collaboration and Visualization as Underserved Challenges
        Conclusion
    Chapter 4. Data-Driven Atmospheric Sciences Using Cloud-Based Cyberinfrastructure: Plans, Opportunities, and Challenges for a Real-Time Weather Data Facility
        Science
        Education
        Data
        Campus Information Technology Infrastructure
        Vision for the Future: Moving Unidata’s Services and Software to “the Cloud”
        Categories of Services
        Community Collaboration
        Managing Change for Our Community
        Current Unidata Cloud-related Activities
        Integrated Data Viewer Application-streaming Cloud Servers
        Community Engagement, Education, and Leadership
        Closing Remarks
    Chapter 5. Supporting Marine Sciences With Cloud Services: Technical Feasibility and Challenges
        Introduction
        Bridging Technical Gaps Between Scientific Communities
        Climate Model Output Processing
        Scalable Data Processing: Nuts and Bolts
        Building a Sharable Data-processing Chain
        Conclusion
    Chapter 6. How We Used Cloud Services to Develop a 4D Browser Visualization of Environmental Data at the Met Office Informatics Lab
        Introduction
        The Generic Lab Approach
        The Project: Interactive 4D Browser Visualization of High-Resolution Numerical Weather Prediction Data
        Collaboration and Outreach
        Conclusions and Final Remarks
    Chapter 7. Cloud Computing in Education
        Introduction
        Cloud-Computing Benefits for Education
        Cloud-Computing Challenges for Education
        Sample Cloud Instance
    Chapter 8. Cloud Computing for the Distribution of Numerical Weather Prediction Outputs
        Introduction
        Pushing Large Quantities of Data to the Cloud Under Time Constraints
        Making a Multi-PB Dataset Available in the Cloud
        Private Cloud
        Conclusion
    Chapter 9. A2CI: A Cloud-Based, Service-Oriented Geospatial Cyberinfrastructure to Support Atmospheric Research
        Introduction
        Literature Review
        Cloud-Based CI Framework for Atmospheric Research
        Components
        2D Visualization Service
        3D Visualization Service
        Graphical User Interface of A2CI
        Conclusion and Discussion
    Chapter 10. Polar CI Portal: A Cloud-Based Polar Resource Discovery Engine
        Background and Challenges
        System Architecture
        Implementation and Methodology
        Status
        Conclusions and Discussion
    Chapter 11. Climate Analytics as a Service
        Introduction
        An Architectural Framework for Climate Analytics as a Service
        Climate Analytics as a Service Reduced to Practice: The MERRA Analytic Service and the MERRA Persistence Service
        The Climate Data Services Application Programming Interface
        Implications and Vision for the Future
        Conclusions
    Chapter 12. Using Cloud-Based Analytics to Save Lives
        Introduction
        Background
        Cloud Computing: Enabling Public, Private, and Academic Partnerships
        Cloud Computing-Enabled Partnerships Example: The National Flood Interoperability Experiment
        Cloud Computing and Big Data: Made for Each Other
        Cloud Computing, Big Data, and High Processing: Meaningful Insight
        Cloud Computing, Big Data, and Machine Learning
        NFIE Analytics With Microsoft Azure
        Benefits and Summary
        Conclusions
    Chapter 13. Hadoop in the Cloud to Analyze Climate Datasets
        Introduction
        Challenges
        Hadoop for Large-scale Datasets
        Analysis of Climate Datasets
        Distributed Processing of Gridded Data
        Distributed Processing of Satellite Imagery
        Discussion
        Conclusion
    Chapter 14. LiveOcean
        Introduction
        LiveOcean Project Motivation
        Past Work: ROMS Validation
        LiveOcean Technical Components
        Further Scenarios for LiveOcean Use
        Conclusions
    Chapter 15. Usage of Social Media and Cloud Computing During Natural Hazards
        Introduction
        Social Media for Disaster Management
        Cloud Computing to Facilitate Disaster Management
        Case Studies
        Conclusions
    Chapter 16. Dubai Operational Forecasting System in Amazon Cloud
        Introduction
        Operational Forecasting System Overview
        System Architecture
        Cloud Implementation
        Results of the Cloud Implementation
        Ongoing and Future System Development
        Conclusion
    Chapter 17. Utilizing Cloud Computing to Support Scalable Atmospheric Modeling: A Case Study of Cloud-Enabled ModelE
        Atmospheric Modeling: An Overview
        Computing Solutions for Atmospheric Modeling
        Building Cloud Infrastructure for Scenario-Based Atmospheric Modeling
        Case Study: ModelE
        Discussion and Conclusion
    Chapter 18. ERMA® to the Cloud
        Introduction
        The Process of Moving to the Cloud
        Security Considerations
        Contracting, Procurement, and Planning
        System Design
        Project Management
        Lessons Learned
    Chapter 19. A Distributed, RESTful Data Service in the Cloud in a Federal Environment—A Cautionary Tale
        Introduction
        Environmental Research Division’s Data Access Program
        Why a Federal (or Other Governmental) Setting Matters
        Conclusion
    Chapter 20. Conclusion and the Road Ahead
    Index


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Biography

Tiffany C. Vance is a geographer working for the National Oceanic and Atmospheric Administration (NOAA). She received her Ph.D. in geography and ecosystem informatics from Oregon State University and her Masters in marine geology and geophysics from the University of Washington. Her research addresses the application of multidimensional GIS to both scientific and historical research, with an emphasis on the use and diffusion of techniques for representing three- and four-dimensional data. Ongoing projects include developing cloud-based applications for particle tracking and data discovery, supporting enterprise GIS adoption at NOAA, developing histories of environmental variables affecting larval pollock recruitment and survival in Shelikof Strait, Alaska, and the use of GIS and visualizations in the history of recent arctic science. She was a participant in the first USGS-initiated GeoCloud Sandbox to explore the use of the cloud for geospatial applications.

Nazila Merati is an innovator successful at marketing and executing uses of technology in science. She focuses on peer data sharing for scientific data, integrating social media information for science research and model validation. Nazila has more than 20 years of experience in marine data discovery and integration, geospatial data modeling and visualization, data stewardship including metadata development and curation, cloud computing and social media analytics and strategy. She is the past chair of the Environmental Information Processing Technologies Conference of the American Meteorological Society where she organized sessions on cloud computing, crowdsourcing and social media for atmospheric research and GIS applications. She has received research funding for the rescue of oceanographic data and application of advanced technologies to oceanographic research. She received Masters in both fisheries oceanography and landscape architecture from the University of Washington.

Chaowei Phil Yang is Professor of Geographic Information Science at George Mason University, where he founded the joint Center for Intelligent Spatial Computing and led the establishment of the NSF Spatiotemporal Innovation Center. His research focuses on utilizing spatiotemporal principles to optimize computing infrastructure to support science discoveries and engineering development. He acted as the NASA Goddard cloud computing chief architect. He is a leader of GIScience computing by proposing several research frontiers including distributed geographic information processing, geospatial cyberinfrastructure, and spatial computing. These research directions are further consolidated through his research, publications, and workforce training activities. He has received over $10M research funding for advancing these directions. He has published over 100 papers, edited three books and eight special issues for international journals. His spatial cloud computing paper published with International Journal of Digital Earth was one of the most cited articles and his book Spatial Cloud Computing: A Practical Approach is used as text for graduate students in geography and computer science departments. He has placed six faculty members in the U.S. and over ten in other countries.

May Yuan received all her degrees in Geography: B.S. 1987 from National Taiwan University and M.S. 1992 and Ph.D. 1994 from State University of New York at Buffalo. She is Ashbel Smith Professor of Geospatial Information Sciences in the School of Economic, Political, and Policy Sciences at the University of Texas at Dallas. Before she joined UT-Dallas in August 2014, she was Brandt Professor and Edith Kinney Gaylord Presidential Professor and Director of Center for Spatial Analysis at the University of Oklahoma (1994-2014). Her research interest expands upon temporal GIS and its applications to understanding geographic dynamics, including weather and climate. Over the years, she has been working to develop new approaches to represent geographic processes and events in GIS databases to support space-time query, analytics and knowledge discovery and promote cyber- and cloud-based GIS solutions for environmental, ecological, and social applications.

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