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Cloud Computing in Ocean and Atmospheric Sciences - 1st Edition - ISBN: 9780128031926, 9780128031933

Cloud Computing in Ocean and Atmospheric Sciences

1st Edition

Editors: Tiffany Vance Nazila Merati Chaowei Yang May Yuan
eBook ISBN: 9780128031933
Paperback ISBN: 9780128031926
Imprint: Academic Press
Published Date: 24th March 2016
Page Count: 454
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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.

The book 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.

Key Features

  • 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


Primary: atmospheric scientists, climatologists, meteorologists, oceanographers.

Secondary: geoscientists

Table of 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


No. of pages:
© Academic Press 2016
24th March 2016
Academic Press
eBook ISBN:
Paperback ISBN:

About the Editors

Tiffany Vance

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.

Affiliations and Expertise

NOAA/NMFS/Alaska Fisheries Science Center, Seattle, Washington, USA

Nazila Merati

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.

Affiliations and Expertise

Principal at Merati and Associates, Seattle, Washington, USA

Chaowei Yang

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.

Affiliations and Expertise

George Mason University, Fairfax, Virginia, USA

May Yuan

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.

Affiliations and Expertise

University of Texas at Dallas, Richardson, Texas, USA

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