NSF Award
2324737
The adoption of cloud computing has increased tremendously over the years, especially in business. However, the use of this technology in computational geosciences—and particularly, in polar sciences and glaciology—has lagged behind. For scientists working in these areas, cloud computing can greatly simplify how users access and leverage high-performance computing resources, as well as open source multiphysics simulation software. To realize these benefits, it is necessary to create open, inclusive cyberinfrastructure that bridges existing knowledge gaps and to conduct outreach to improve adoption of the cloud and existing geosciences software platforms. This project achieves these goals by: (1) building an open cloud computing platform, Advanced cloud-based Data and Visualization Integrated Simulation EnviRonment (ADVISER), that can be used by computational geoscientists; and (2) by extending existing geosciences software (Icepack) to run easily and efficiently on ADVISER. Extending ADVISER for use in computational glaciology research and education democratizes the power of cloud computing and gives its benefits to those with little or no expertise in the cloud. By its design, no special hardware or knowledge is required to use ADVISER, so students in classroom environments will be able to participate in new kinds of lesson plans, such as running glacier flow simulations on ADVISER during class sessions. Through a planned workshop/hackathon, this project will involve students across several universities and colleges, educating them to better harness computational power (via the cloud), a skillset not traditionally available through course curricula in geosciences or engineering.<br/><br/>The project comprises four specific tasks: (1) extending a cloud-native platform, ADVISER, for running and managing scientific software and data in the cloud; (2) adapting the Icepack software for modeling of glacier flow to run on ADVISER; (3) creating data assimilation pipelines that enable importing various existing and novel data sets into the ADVISER platform in formats amenable to usage in software like Icepack; and (4) hosting a workshop and a hackathon that spur sustainable adoption and extensions of ADVISER and Icepack for the broader computational geosciences community. The investigators' vision is that their ADVISER platform will allow for the seamless incorporation of emerging remote sensing data, machine learning algorithms, and physics-based computational models for forward and inverse problems. The investigators will particularly focus on real-world data assimilation from Antarctic ice shelves.<br/><br/>This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
