Presenters-0660
From CSDMS
CSDMS 2025: Exploring Earth's Surface with Models, Data & AI
Spatiotemporal methodologies for the analysis and prediction of extreme weather events
Abstract
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.
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