Rahul Mahajan
Physical Scientist at NOAA: National Oceanic & Atmospheric Administration
College Park, Maryland, United States
Summary
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Rockstar🎓
Top SchoolRahul Mahajan is a Physical Scientist with 12 years of experience building and improving numerical weather and seasonal prediction systems at NOAA and NASA, specializing in data assimilation and state estimation. He applies ensemble Kalman filter, adjoint and ensemble Monte Carlo techniques to reduce forecast uncertainty and identify sensitive initial-condition regions that drive forecast errors. At NOAA he has moved beyond pure science into engineering work—implementing IAU in global workflows and enhancing CI/CD and cross-platform build automation for the widely used UFS Weather Model. His background blends deep academic training (PhD, University of Washington) with hands-on operational impact, from research postdoc work at Goddard to production forecast systems. Rahul’s contributions reveal a rare combination of theoretical rigor and practical DevOps skill, making models more reliable and reproducible for operational forecasting. Located in College Park, MD, he is comfortable navigating both research and software-engineering roles to close the loop between model development and operational use.
12 years of coding experience
15 years of employment as a software developer
Doctor of Philosophy (PhD), Doctor of Philosophy (PhD) at University of WashingtonBachelor of Engineering (B.E.), Bachelor of Engineering (B.E.) at University of Mumbai
Github Skills (18)
scripting10
fortran10
cmake10
cicd10
automation10
script10
sh10
ufs10
automations10
shell10
numerical9
weather9
build-system9
numeric9
nws9
Programming languages (18)
Github contributions (5)
NOAA-EMC/global-workflow
Mar 2019 - Jan 2023
Global Superstructure/Workflow supporting the Global Forecast System (GFS)
Role in this project:
Back-end Developer
Back-end DeveloperContributions:1 release, 2651 reviews, 127 commits in 3 years 11 months
Contributions summary:Rahul implemented IAU (Incremental Analysis Update) capabilities in the global forecast script. This involved modifying the script to incorporate IAU options, including handling initial conditions and linking increment files. Furthermore, the user modified the config files to enable the IAU features. This work directly impacted the forecast script, by enabling the inclusion of IAU in the global forecast system.
gfsworkflowsupportingforecastgdas
ufs-community/ufs-weather-model
Jul 2020 - Sep 2021
UFS Weather Model
Role in this project:DevOps Engineer & Automation Engineer
DevOps Engineer & Automation EngineerContributions:171 reviews, 7 commits, 27 PRs in 1 year 2 months
Contributions summary:Rahul primarily focused on enhancing the build and testing infrastructure of the UFS Weather Model. Their contributions include adapting CMake configurations for different compilers and platforms, enabling the building of coupled models, and setting up the CI/CD pipelines. Key changes involve modifications to build scripts (build.sh, tests/compile.sh, tests/rt.sh) and the introduction of new build options and tests, improving the overall automation and build process. The user also added support for optional components like the Data Atmosphere component.
reactnoaanwpweather-modelatmosphere
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