An Informal Introduction to Numerical Weather Models with Low-Cost Hardware

Weather, climate, and other earth system models are growing in complexity as computing resources and technologies continue to evolve with time. Thus, models are and will remain a vital tool for scientific research. Studies have suggested that early exposure to such models has the ability to affect the career trajectory of students. However, there are barriers that limit students' exposure to these models outside of internships and research experiences. Some of these barriers can include hardware and computing costs, curriculum structure, and available training materials for scientific models in high performance computing environments.

As a means of addressing these barriers, the goal of this work is to utilize low-cost hardware and abstract away some of the complexities of running a numerical weather model without sacrificing fidelity. The approach is to create a graphical user interface where students can quickly configure the model, run it, and analyze the output in a Unix-like environment without knowledge of model configuration, system architecture, or navigation via a command line interface.

In this session, the concept will be demonstrated by running the Weather Research and Forecasting Model (WRF) on a cluster of low-cost Raspberry Pi computers. The demonstration will show how students and members of the general public can learn via a hands-on experience of how numerical models are configured and used operationally. Beyond atmospheric science and modeling concepts, users of this cluster will also learn basic high performance computing (HPC) concepts like parallelization, job submission and scheduling, and components of HPC systems.