Hello!
My name is Maryam Abdi-Oskouei. I’m an associate research scientist at NASA Global Modeling and data Assimilation (GMAO) lab. For the past decade, I’ve been working on improving weather and air quality prediction using various atmospheric models, data assmilation techniques, and in-situ and remote sensing observations.
Experience
Research Scientist
Global Modeling and Data Assimilation (GMAO), UCAR | Remote, USA
2025 - Present
- Constraining NO₂ emissions using satellite-based data assimilation experiments that integrate TEMPO hourly observations with NASA’s GEOS-CF atmospheric chemical transport model
- Developing and implementing satellite data assimilation workflows for emissions quantification in collaboration with scientists and software engineers across NASA GMAO and JCSDA
- Creating technical documentation and tutorials to communicate data assimilation methods and the JEDI framework to scientific audiences (HowToJEDI)
Computational Research Scientist
Joint Center for Satellite Data Assimilation (JCSDA), UCAR | Boulder, CO
2022 - 2025
- Developed satellite data assimilation methods to integrate remote sensing observations (TEMPO, TROPOMI, PANDORA) with NASA’s GEOS-CF model for improved emissions quantification and air quality forecasting (ArXiv preprint)
- Built end-to-end workflows for processing and incorporating satellite remote sensing data into atmospheric models, reducing uncertainties in emission estimates and predictions
- Applied advanced statistical and data assimilation techniques (3DVar, 4DVar, 4DEnVar, H-TLM) to optimize emission estimates and solve inverse modeling problems for atmospheric composition
- Developed technical documentation and training materials to support community adoption of satellite-based emissions quantification methods
Software Engineer
JCSDA and Mesoscale and Microscale Meteorology (MMM) lab, UCAR | Boulder, CO
2019 - 2022
- Developed cloud-based CI/CD pipelines and automated testing infrastructure to support operational satellite data processing and atmospheric modeling workflows using AWS
- Containerized atmospheric modeling and data assimilation workflows using Docker and Singularity, enabling reproducible scientific analyses across HPC and cloud environments
- Designed and delivered technical workshops to international scientific audiences, accelerating community adoption of satellite data assimilation systems
Graduate Research Assistant
Center for Global and Regional Environmental Research (CGRER), University of Iowa | Iowa City, IA
2013 - 2019
- Developed and applied inverse modeling methods to quantify ethane emissions from oil and gas operations using WRF-Chem atmospheric model and remote sensing measurements
- Validated and improved model performance for air quality predictions through rigorous comparison with field campaign observations and satellite data
- Built automated Python frameworks for operational atmospheric forecasting and real-time model validation during LMOS and ORACLES field campaigns
Visiting Graduate Assistant
NSF-National Center for Atmospheric Research (NSF-NCAR) | Boulder, CO
2017 & 2018
- Evaluated atmospheric model sensitivity to physical parameterizations and their impact on ethane emission estimates using WRF-Chem
- Validated model performance against FRAPPÉ and DISCOVER-AQ field campaign measurements for emissions-relevant species
CICS Summer Intern
Geophysical and Fluid Dynamics Laboratory (GFDL), NOAA | Princeton, NJ
2016
- Evaluated methane representation and emissions processes in the GFDL climate chemistry model
Graduate Research Assistant
Missouri University of Science & Technology | Rolla, MO
2011 - 2013
- Developed data-driven statistical methods to quantify operational factors affecting energy system efficiency in industrial equipment
Education
Ph.D. in Atmospheric Science
University of Iowa | 2013 - 2019
M.S. Degree
Missouri University of Science & Technology | 2011 - 2013
Skills
Emissions Science & Remote Sensing
- Expertise in inverse modeling and data assimilation methods (3DVar, 4DVar, 4DEnVar, adjoint-based, ensemble-based) for quantifying atmospheric emissions from remote sensing observations
- Proficient in processing and integrating multi-source satellite data (TEMPO, TROPOMI, PANDORA) with atmospheric models for emissions estimation and validation
- Experienced in geospatial data processing, statistical inference, and uncertainty quantification for emissions estimation across multiple temporal and spatial scales using satellite retrievals and field campaign observations
- Solid understanding of atmospheric chemistry and transport modeling for greenhouse gases and air quality species (CH₄, NO₂, ethane, aerosols)
Atmospheric Modeling & Statistical Methods
- Advanced experience developing and running atmospheric chemical transport models (WRF-Chem, GEOS-CF, MPAS) for emissions quantification and forecasting applications
- Skilled in statistical inference, uncertainty quantification, and rigorous model validation using empirical observations from field campaigns and satellite retrievals
- Familiar with machine learning techniques (regression, classification, ensemble methods, neural networks) applied to atmospheric and geophysical datasets
- Proficient in scientific Python ecosystem (NumPy, Xarray, SciPy, Pandas, PyTorch) for data analysis, statistical modeling, and visualization
Collaboration & Communication
- Proven ability to work collaboratively in cross-functional teams with scientists, software engineers, and domain experts to deliver research projects and operational systems
- Strong communication skills presenting technical results to diverse audiences through peer-reviewed publications, conference presentations, workshops, and technical documentation
- Experienced in keeping abreast of scientific literature and integrating new methods into research workflows
Software Development & Engineering
- Skilled in Python, C++, and shell scripting for scientific application development and data processing pipelines
- Experienced with high-performance computing (HPC) systems and cloud platforms (AWS) for large-scale atmospheric modeling and data analysis
- Proficient in modern software engineering practices including Git workflows, CI/CD pipelines, containerization (Docker/Singularity), and collaborative development
Selected Publications & Projects
JEDI Framework Integration with GEOS-CF
2024 | ArXiv Preprint
Developed and implemented the JEDI framework with NASA’s GEOS-CF forecasting system, contributing to improvements in weather and air quality forecasting accuracy.
View Paper
HowToJEDI: Data Assimilation Tutorials
Ongoing | Educational Resource
Hands-on tutorials teaching data assimilation concepts and the JEDI framework to the atmospheric science community.
Visit Site
Inverse Modeling for Oil & Gas Emissions
PhD Research | University of Iowa
Developed inverse modeling methods to constrain ethane emissions from oil and gas operations using WRF-Chem and multi-platform observations.
Technical Expertise
Atmospheric Science:
- Data Assimilation (3DVar, 4DVar, 4DEnVar, H-TLM)
- Inverse Modeling & Emissions Quantification
- Satellite Remote Sensing (TEMPO, TROPOMI, PANDORA, MODIS, VIIRS)
- Atmospheric Models: WRF-Chem, GEOS-CF, MPAS
- Field Campaign Validation (FRAPPÉ, DISCOVER-AQ, LMOS, ORACLES)
Programming & Tools:
- Python (NumPy, Xarray, Pandas, SciPy, PyTorch)
- C++, Fortran, Shell Scripting
- HPC & Cloud Computing (AWS)
- Git, CI/CD, Docker, Singularity
- Geospatial Data Processing
Key Competencies:
- Greenhouse gas emissions (CH₄, NO₂, ethane, CO₂)
- Uncertainty quantification & statistical inference
- Geospatial analysis across multiple scales
- Cross-functional team collaboration
- Scientific communication & documentation
Last Updated: December 2025