1. Atmospheric Science

• Boundary-Layer Meteorology – evolution, dynamics, and inherent processes of the atmospheric boundary layer.

• Atmospheric Measurement Techniques – methods for air quality monitoring, upper atmosphere studies, and related observations.

• Upper Atmosphere Monitoring – processes at higher altitudes and their links to weather and climate.

• Climate Variability and Change – historical patterns, variability modes, and long-term shifts in climate systems.

2. AI & Data Science Applications

• Artificial Intelligence and Weather – use of machine learning and deep learning for forecasting and nowcasting.

• Climate Data Reconstruction – improving incomplete or uncertain datasets for reliable climate assessments.

• Data Science for Climate Applications – computational tools and algorithms for analysis of complex climate and environmental datasets.

3. Climate Impacts & Society

• Socio-Economic Impacts of Climate Extremes – assessing how weather and climate extremes affect communities, infrastructure, and development.

• Social Dimensions of AI Uptake – exploring governance, adoption, and local relevance of AI in environmental and climate contexts.