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A better understanding of drivers and proc … A better understanding of drivers and processes will improve the prediction of extreme weather events and will support process-based representation of weather and climate extremes in climate model simulations. The increasing availability of observational, simulation, and user-generated (e.g., social media or crowdsourced) datasets, along with the rapid progress of computing technologies, has provided us the unprecedented opportunity to enhance the understanding and predictability of extreme weather events. My research centers on developing spatiotemporal methodologies for the analysis and prediction of extreme weather events, such as dust storms, hurricanes, and extreme heat. In this talk, I will demonstrate several case studies from my research to 1) understand the spatiotemporal dynamics of extreme weather events, 2) explore the relationship of these events with other physical and social factors, and 3) integrate heterogeneous data to enhance the predictability, response, and mitigation of extreme weather events. and mitigation of extreme weather events. +
mqy5198@psu.edu +
CSDMS 2025: Exploring Earth's Surface with Models, Data & AI +
Manzhu +
Penn State University +
Invited oral presentation +
Pennsylvania +
Spatiotemporal methodologies for the analysis and prediction of extreme weather events +
wyzsJfxV0m4 +
United States +
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00:36:39, 4 December 2024 +
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22:34:12, 11 June 2025 +
Terrestrial Working Group +, Coastal Working Group +, Marine Working Group +, Education and Knowledge Transfer (EKT) Working Group +, Cyberinformatics and Numerics Working Group +, Hydrology Focus Research Group +, Chesapeake Focus Research Group +, Critical Zone Focus Research Group +, Human Dimensions Focus Research Group +, Geodynamics Focus Research Group +, Ecosystem Dynamics Focus Research Group +, Coastal Vulnerability Initiative +, Continental Margin Initiative +, Artificial Intelligence & Machine Learning Initiative +, Modeling Platform Interoperability Initiative + and River Network Modeling Initiative +
