Primary Convener:
Jiaxi Hu, State University of New York, University at Albany

Convener:
Hui Su, Hong Kong University of Science and Technology
Xiaoming Shi, The Hong Kong University of Science and Technology
Yixin Wen, University of Florida

Chair:
Jiaxi Hu, State University of New York, University at Albany
Hui Su, The Hong Kong University of Science and Technology
Xiaoming Shi, The Hong Kong University of Science and Technology
Yixin Wen, University of Florida

Advanced artificial intelligence (AI) and machine learning (ML) models are rapidly transforming the field of meteorology, particularly in the domains of high-impact weather prediction and analysis. This session welcomes in-depth exploration of the state-of-the-art AI/ML methodologies applied to enhance the accuracy and timeliness of forecasts for severe weather events such as hurricanes, tornadoes, extreme rainfall, snow blizzard, wildfire, etc. AI-based projection of those events’ responses to climate change is also welcomed. The session will discuss how AI/ML techniques facilitate the extraction of intricate patterns and relationships within the data that traditional models may overlook or misrepresent, leading to improved predictive capabilities. Furthermore, we highlight innovative approaches to leverage AI's ability to interpolate sparse data and assimilate multimodal data, thereby better representing fine-grained atmospheric dynamic processes. This includes but is not limited to novel AI/ML model/algorithm development for severe weather prediction, numerical model enhancement using AI/ML methods, and climate change projection.

Index Terms
3305 Climate change and variability
3314 Convective processes
1817 Extreme events
1821 Floods

Suggested Itineraries:
Disasters‚ Calamities and Extreme Events
Machine Learning and AI
Open Science and Open Data

Neighborhoods:
3. Earth Covering