The goal of the Earth Prediction Innovation Center (EPIC) is to enable the most accurate and reliable operational numerical weather prediction (NWP) forecast model in the world. EPIC will achieve this goal through community engagement where students, researchers, professors, and other community members can collaborate to develop open-source code for the Unified Forecast System (UFS). One way to spark the interest of new community members is through Graduate Student Tests (GST), which are usability tests that entail running, modifying, rerunning, and comparing outputs of the UFS code and its applications.
In order to increase accessibility of the UFS GSTs, cloud versions were developed as part of a William M. Lapenta internship project at NOAA. The cloud-based GSTs include documentation with instructions to run containerized versions of the UFS usability tests on the Amazon Web Services (AWS) platform that utilize new plotting python scripts to visualize results, as well as a FAQ document. Running the GSTs on the cloud is important for increasing accessibility because community members without access to HPCs will now be able to run the GST quickly and cheaply using any device that can connect to the internet. Cloud-based GSTs are expected to increase the number of people running the UFS. This will in turn increase the pool of people contributing to the UFS, which will help NOAA develop the most accurate and reliable operational numerical forecast model in the world.
This talk will discuss the GSTs in depth and explain some of the challenges of deploying the GSTs in the AWS cloud. Performance metrics from running the GSTs in the cloud along with opportunities of future engagement with the UFS will also be discussed.
