Planetary Defense (PD) has become a critical effort of protecting our home planet by discovering potentially hazardous objects (PHOs), simulating the potential impact, and mitigating the threats. Due to the lack of structured architecture and framework, pertinent information about detecting and mitigating near earth object (NEO) threats are still dispersed throughout numerous organizations. Scattered and unorganized information can have a significant impact at the time of crisis, resulting in inefficient processes, and decisions made on incomplete data. This PD Mitigation Gateway (pd.cloud.gmu.edu) is developed and embedded within a framework to integrate the dispersed, diverse information residing at different organizations across the world. The gateway offers a home to pertinent PD-related contents and knowledge produced by the NEO mitigation team and the community through (1) a state-of-the-art smart-search discovery engine based on PD knowledge base; (2) a document archiving and understanding mechanism for managing and utilizing the results produced by the PD science community; (3) an evolving PD knowledge base accumulated from existing literature, using natural language processing and machine learning; and (4) a 4D visualization tool that allows the viewers to analyze near-Earth approaches in a three-dimensional environment using dynamic, adjustable PHO parameters to mimic point-of-impact asteroid deflections via space vehicles and particle system simulations. Along with the benefit of accessing dispersed data from a single port, this framework is built to advance discovery, collaboration, innovation, and education across the PD field-of-study, and ultimately decision support.
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Deploying an Educational JupyterHub for Exploratory Data Analysis, Visualization, and Running Idealized Weather Models on the Jetstream2 Cloud
The Unidata Program Center dedicates two software engineers to the development and maintenance of a science gateway meant to serve the members of the Earth Systems Science community. Unidata collaborated with one such community member, Dr. Greg Blumberg of the Department of Earth Sciences at Millerville University, in order to provide three undergraduate courses in atmospheric science with access to a custom JupyterHub cluster on the Jetstream2 Cloud boasting preconfigured environments, a shared network drive, and the capability to enable machine learning education and the execution of the Weather Research and Forecasting (WRF) model. The implementation of these features through the Kubernetes orchestration engine is discussed in detail, including initial failures of the Unidata Science Gateway team and the resolution of the issues that arose as a result. The performance of WRF executed at scale using JupyterHub is discussed at a surface level, with more study necessary to make further conclusions. Finally, feedback from Dr. Blumberg, both positive and constructive, is discussed along with specific use cases for the cyberinfrastructure.
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- Award ID(s):
- 2005506
- PAR ID:
- 10546271
- Publisher / Repository:
- Zenodo
- Date Published:
- Subject(s) / Keyword(s):
- Science Gateways Cyberinfrastructure Meteorology Atmospheric Science Education Jupyterhub Kubernetes Docker
- Format(s):
- Medium: X
- Right(s):
- Creative Commons Attribution 4.0 International
- Sponsoring Org:
- National Science Foundation
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