Module #1 Introduction to Cloud Computing Overview of cloud computing, its benefits, and relevance to climate research
Module #2 Climate Research and Big Data Challenges and opportunities of big data in climate research, and how cloud computing can help
Module #3 Cloud Service Models (IaaS, PaaS, SaaS) Understanding the different service models of cloud computing and their applications in climate research
Module #4 Cloud Deployment Models (Public, Private, Hybrid) Understanding the different deployment models of cloud computing and their trade-offs in climate research
Module #5 Cloud Providers for Climate Research Overview of popular cloud providers (AWS, Azure, GCP) and their offerings for climate research
Module #6 Cloud Storage for Climate Data Storage solutions for large climate datasets, including object storage and file systems
Module #7 Cloud Compute for Climate Modeling Scaling climate models on cloud infrastructure, including high-performance computing and containerization
Module #8 Cloud-based Climate Data Processing Using cloud-based tools and services for processing and analyzing climate data, including data pipelines and workflows
Module #9 Cloud-based Climate Data Visualization Visualizing climate data on the cloud, including interactive dashboards and geospatial visualization
Module #10 Cloud Security and Compliance for Climate Research Ensuring the security and integrity of climate data in the cloud, including access control and encryption
Module #11 Cloud Cost Estimation and Optimization for Climate Research Estimating and optimizing cloud costs for climate research projects, including cost models and reservations
Module #12 Cloud-based Collaboration for Climate Research Collaborative tools and platforms for climate research teams, including collaboration software and version control systems
Module #13 Introduction to Cloud Native Applications for Climate Research Designing and deploying cloud-native applications for climate research, including microservices and serverless architectures
Module #14 Cloud-based Machine Learning for Climate Research Applying machine learning to climate research using cloud-based frameworks and services, including TensorFlow and PyTorch
Module #15 Cloud-based Climate Data Integration and Interoperability Integrating and interoperating climate data from different sources and formats, including data federation and APIs
Module #16 Cloud-based Climate Model Validation and Verification Validating and verifying climate models using cloud-based tools and services, including ensemble simulations and uncertainty quantification
Module #17 Cloud-based Climate Data Dissemination and Sharing Disseminating and sharing climate data through cloud-based platforms, including data catalogs and data sharing agreements
Module #18 Cloud-based Climate Research Workflow Management Managing climate research workflows using cloud-based tools and services, including workflow automation and reproducibility
Module #19 Cloud-based Climate Research Reproducibility and Transparency Ensuring reproducibility and transparency in climate research using cloud-based tools and services, including version control and provenance tracking
Module #20 Cloud-based Climate Research Ethics and Governance Addressing ethical and governance challenges in climate research using cloud computing, including data privacy and ownership
Module #21 Case Studies in Cloud Computing for Climate Research Real-world examples and case studies of cloud computing in climate research, including success stories and lessons learned
Module #22 Future Directions in Cloud Computing for Climate Research Emerging trends and future directions in cloud computing for climate research, including edge computing and quantum computing
Module #23 Hands-on Exercise:Deploying a Climate Model on the Cloud Practical exercise in deploying a climate model on a cloud platform, including hands-on experience with cloud infrastructure and services
Module #24 Course Wrap-Up & Conclusion Planning next steps in Cloud Computing in Climate Research career
