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.
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
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.