Dr. Marwa Majdi logo

Dr. Marwa Majdi

Atmospheric Science | AI | Weather Impacts

University of North Dakota

Atmospheric science research powered by observations, modeling, and artificial intelligence.

I am an Assistant Research Professor in Atmospheric Sciences at the University of North Dakota. My work focuses on fog detection, visibility estimation, cloud and aerosol processes, AI-driven forecasting, and operational weather applications for aviation and UAS environments.

Explore Research Selected Publications Download CV
Portrait of Dr. Marwa Majdi

Highlights

  • Assistant Research Professor in Atmospheric Sciences at UND.
  • Research spanning fog detection, visibility prediction, weather AI, and atmospheric microphysics.
  • Active proposal development, funded project management, publications, invited talks, and student mentoring.

About

Dr. Marwa Majdi is an Assistant Research Professor in Atmospheric Sciences at the University of North Dakota. Her current research program combines camera observations, meteorological data, numerical modeling, and machine learning to improve fog detection, visibility estimation, and short-term weather prediction. Her background also includes aerosol chemistry, wildfire impacts on air quality, and cloud microphysics. She is also active in research-embedded undergraduate training through the Data Skills Pathway and related mentoring efforts.

Quick Links

Research Themes Most Important News Featured Products & Resources Selected Publications Contact & Profiles

Research Themes

The homepage should communicate a few clear scientific themes instead of only listing documents. These themes make your expertise easier to understand for collaborators, reviewers, students, and funding agencies.

Fog Detection & Visibility Estimation

Developing AI-enabled methods to detect fog and estimate visibility using camera imagery, meteorological observations, and forecast-model information.

AI for Weather Forecasting

Building machine learning frameworks to improve short-term prediction of weather hazards, cloud ceiling, visibility reduction, and convective environments.

Cloud Microphysics, Aerosols & Applied Weather

Studying cloud processes, aerosols, low-visibility environments, and operational weather applications relevant to aviation, UAS, and environmental decision-making.

Most Important News

  • : .
  • : .
  • Recognition: recipient of the NASA IMPACTS Group Achievement Award in December 2024 and the UND Early Career Award in December 2023.

Current Momentum

  • Managed or co-managed four funded projects supporting camera and model data pipelines and UAS-relevant operations.
  • Current program includes submitted and in-preparation papers on AeroVis, fog and visibility ML, wind and turbulence prediction, cloud seeding analysis, and camera–METAR database development.
  • Recent invited and conference presentations include AGU, AMS, FAST Research Sessions, and the Bridging AI and Human Decision-Making series.

Featured Products & Resources

  • AeroVis: software system for operational visibility nowcasting to support UAS operations.
  • Weather Camera Database: development and management of Camera.atmos.und.edu for camera-based atmospheric analysis and data access.
  • Camera–METAR Visibility and Ceiling Database: a data product being developed for future AI applications.
  • Data Skills Pathway: a research-embedded undergraduate training effort focused on authentic data work, mentoring, and pathway development.

What Else to Add

  • Links to ORCID, Google Scholar, CV, and professional profiles.
  • A featured publications section limited to your 3 strongest papers.
  • A current projects section with 1-line summaries and sponsor names.
  • A small media or datasets section for public-facing scientific outputs.
  • A student mentoring section showing graduate, undergraduate, and pathway impact.

Selected Publications

  • Chrit, M., & Majdi, M. Operational Wind and Turbulence Nowcasting Capability for Advanced Air Mobility. Neural Computing and Applications, 2024.
  • Majdi, M., Sartelet, K., Turquety, S., & Kim, Y. Impact of the Mixing State on the Aerosol Optical Properties During Severe Wildfires Over the Euro-Mediterranean Region. Atmospheric Environment, 2019.
  • Majdi, M., Sartelet, K., Lanzafame, G.M., et al. Precursors and Formation of Secondary Organic Aerosols from Wildfires in the Euro-Mediterranean Region. Atmospheric Chemistry and Physics, 2019.

Recent and In-Progress Work

  • Regime-Specific ML Framework for Fog Detection and Visibility Prediction Using Camera-Derived Features.
  • Toward Trustworthy Wind and Turbulence Predictions for Advanced Air Mobility.
  • Cloud Microphysical Observations of Seeded Clouds during the SARPEC campaign.
  • Building a Visibility and Ceiling Database from Publicly Available Camera and METAR Data for Future AI Applications.

Contact

Department of Atmospheric Sciences, University of North Dakota

Email: marwa.majdi@und.edu

Email ORCID Google Scholar CV