The wave of the Information Technology revolution is propelling us into a new era of research in atmospheric and environmental sciences. New techniques, including Artificial Intelligence/Machine Learning (AI/ML) are enabling a deeper understanding of complex atmospheric and environmental systems, as well as the interactions among weather/climate, air quality, public health, and socioeconomic factors. Meanwhile, the unprecedented growth in both the volume and dimensionality of diverse and heterogeneous data is creating new opportunities while challenging conventional research paradigms for data integration, analysis, and interpretation. These emerging techniques are therefore becoming increasingly central to addressing the scientific challenges of this new era and supporting science-based policymaking in response to evolving environmental and societal challenges.

In this session, we welcome submissions addressing the latest advances in emerging techniques applied to all aspects of atmospheric, climate, and environmental sciences, including but not limited to:
- Applications of AI/ML and other emerging techniques for:
• Advancing our understanding of the complex Earth system, especially the underlying mechanisms of weather/climate systems, atmospheric environmental systems, and their interactions
• Facilitating faster and more accurate weather/climate/air quality modeling and forecasting, especially for extreme weather events, climate change, and air pollution episodes
• Providing new insights into the mechanisms of atmospheric chemistry and physics
• Improving air pollution tracing and source attribution
• Supporting policymakers in making decisions that promote environmental sustainability (e.g., considering interactions among extreme weather, climate change, air quality, socioeconomic factors, and public health
- The adaptation and development of AI/ML and other emerging techniques through:
• Explainable AI (XAI)
• Hybrid methods (e.g., hybrid ML, physics-integrated ML)
• Multi-source heterogeneous data fusion and mining
• Transfer learning
• Advanced modeling frameworks

We believe that exchanges across research fields could help break down the limitations of disciplinary thinking and enabling technological innovations. Therefore, contributions from fields other than atmospheric, climate, and environmental sciences are also encouraged.