Grant Firl
Project Scientist I at NCAR - The National Center for Atmospheric Research
Arvada, Colorado, United States
Summary
👤
Senior🎓
Top SchoolGrant Firl is a Project Scientist I with nine years of experience applying atmospheric modeling, machine learning, and large-data analysis to improve representation of sub-grid scale processes. He holds a Ph.D. in Atmospheric Science from Colorado State University and has a strong track record of innovating new parameterizations, publishing peer-reviewed work, and running large eddy simulations using Fortran and parallelized Python. At NCAR he combines research rigor with practical software contributions—recently supporting test automation for the UFS Weather Model—to help transition research into operational modeling. Known for inventing a multi-parcel entraining bubble convective parameterization and for integrating expectation-maximization clustering into LES analysis, he brings both theoretical depth and hands-on engineering to complex atmospheric problems. Based in Arvada, Colorado, he pairs a mathematician’s precision (BS with a 4.0 GPA) with a persistent focus on continuing education and real-world impact.
9 years of coding experience
8 years of employment as a software developer
Doctor of Philosophy (Ph.D.), Atmospheric Science, Doctor of Philosophy (Ph.D.), Atmospheric Science at Colorado State UniversityBachelor’s Degree, Mathematics, 4.0 GPA, Bachelor’s Degree, Mathematics, 4.0 GPA at Missouri State University
Github Skills (9)
prediction10
script10
bash10
numerical10
weather10
numeric10
testing10
github-ci8
githubaction-workflow8
Programming languages (5)
Github contributions (5)
ufs-community/ufs-weather-model
Dec 2021 - Nov 2022
UFS Weather Model
Role in this project:
QA Engineer / Test Automation Engineer
QA Engineer / Test Automation EngineerContributions:12 reviews, 12 commits, 40 PRs in 11 months
Contributions summary:Grant primarily contributed to the testing infrastructure of the UFS Weather Model. Their work involved modifying the `tests/rt.sh` script, including updating the baseline date, and adjusting test configurations based on machine IDs. They also added and modified variables within `default_vars.sh` and made updates related to the ccpp-physics submodule. Overall, the user's changes appear focused on maintaining and adapting the testing framework to accommodate new features and bug fixes within the model.
reactnoaanwpweather-modelatmosphere
grantfirl/ufs-weather-model
Dec 2019 - Apr 2025
UFS Weather Model
Contributions:1 review, 2 PRs, 390 pushes in 5 years 4 months
weather-modelreactufs
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