A Containerized Mesoscale Model and Analysis Toolkit to Accelerate Classroom Learning, Collaborative Research, and Uncertainty Quantification

Abstract

Numerical weather prediction (NWP) experiments can be complex and time consuming; results depend on computational environments and numerous input parameters. Delays in learning and obtaining research results are inevitable. Students face disproportionate effort in the classroom or beginning graduate-level NWP research. Published NWP research is generally not reproducible, introducing uncertainty and slowing efforts that build on past results. This work exploits the rapid emergence of software container technology to produce a transformative research and education environment. The Weather Research and Forecasting (WRF) Model anchors a set of linked Linux-based containers, which include software to initialize and run the model, to analyze results, and to serve output to collaborators. The containers are demonstrated with a WRF simulation of Hurricane Sandy. The demonstration illustrates the following: 1) how the often-difficult exercise in compiling the WRF and its many dependencies is eliminated, 2) how sharing containers provides identical environments for conducting research, 3) that numerically reproducible results are easily obtainable, and 4) how uncertainty in the results can be isolated from uncertainty arising from computing system differences. Numerical experiments designed to simultaneously measure numerical reproducibility and sensitivity to compiler optimization provide guidance for interpreting NWP research. Reproducibility is independent from the operating system and hardware. Results here show numerically identical output on all computing platforms tested. Performance reproducibility is also demonstrated. The result is an infrastructure capable of accelerating classroom learning, graduate research, and collaborative science.

CLASSIC v1.0: the open-source community successor to the Canadian Land Surface Scheme (CLASS) and the Canadian Terrestrial Ecosystem Model (CTEM) – Part 1: Model framework and site-level performance

“…Peng et al, 2014;Arora, 2014, 2016;Melton et al, 2015). The fire subroutine has been extensively eval-uated as part of the Fire Model Intercomparison Project (FireMIP; Hantson et al, 2016;Forkel et al, 2019) in addition to being used to estimate carbon cycle implications of the reduction in global wildfire since the 1930s . The parameterization for competition between PFTs has been evaluated at the site level (Shrestha et al, 2016) as well as at the global scale (Melton and Arora, 2016).…”

Open Science Expectations for Simulation-Based Research

“…Particularly for highly non-linear case studies, the goal is not exact reproducibility, but rather enough output to understand the environmental state that forced, and the impacts of, the features being investigated. There may be unique projects in which bitwise reproducibility is deemed necessary; in those cases, containerization can be useful (Hacker et al, 2016). However, to build upon prior research, most knowledge production research does not require bitwise reproducibility.…”

Enhancing the Accessibility of Unified Modeling Systems: GFDL SHiELD v2021b in a Container

“…Advantages of the containerization of geoscience models are discussed in many papers (e.g., Hacker et al, 2017 andMelton et al, 2020) and will be shown throughout this paper, including easy installation, high portability, and perfect reproducibility. We want to add one additional advantage that motivates innovation and spurs model development.…”

“…Assuring easy access to the infrastructure will help achieve cooperation. In an educational setting, Hacker et al (2017) has shown that better access to a mesoscale model benefits classroom learning. Better access to unified modeling systems can greatly improve learning experience.…”