Dr. Marwa Majdi
Atmospheric Science | AI | Weather Impacts | Data Skills Pathways | Student Research
Atmospheric Science | AI | Weather Impacts | Data Skills Pathways | Student Research
Dr. Majdi’s research combines observations, machine learning, and numerical modeling to improve fog detection, visibility estimation, cloud ceiling analysis, and short-term weather guidance for aviation and uncrewed aircraft systems. Her work also includes cloud microphysics, aerosol impacts, and data-rich student research training.
Scientific and practical relevance across weather, aviation, and training.
Improving low-visibility detection and short-term weather guidance for aviation and operations.
Building interpretable AI tools for practical weather analysis and forecasting.
Connecting students with real datasets, coding, analysis, and research communication.
Main scientific directions across the research program.
Using camera imagery and weather data to detect fog and estimate visibility.
Using machine learning to improve short-term weather guidance and hazard prediction.
Studying cloud processes, weather modification, and hail suppression evaluation.
Improving cloud ceiling and visibility support for uncrewed aircraft systems.
Studying aerosols, wildfire impacts, and air quality through modeling and analysis.
Designing data-rich research experiences that build practical data skills.
Examples of current directions that define the research program.
Developing machine learning methods for camera-based fog detection and visibility estimation.
Building near-real-time tools for visibility nowcasting in aviation and UAS operations.
Building AI-ready visibility and cloud-ceiling datasets from cameras and METAR observations.
Using machine learning to emulate WRF forecasts for short-term thunderstorm guidance.
Developing interpretable methods to quantify hail seeding effectiveness.
Embedding students in data-rich projects that strengthen coding, analysis, and research communication.
Scientific and technical approaches used across the research program.
Camera imagery, meteorological observations, METAR data, cloud probes, and field measurements.
WRF outputs, forecast verification, environmental indices, and large eddy simulations.
Machine learning, deep learning, data fusion, database development, and scientific programming.
Invited talks, course-facing research overviews, and proposal-development presentations.
First Research Session of Future Aerospace Strategic Thinking (FAST)
Selected materials for SpSt 570 and proposal-development instruction.