HakamShams/IDEE
[NeurIPS'24] Identifying Spatio-Temporal Drivers of Extreme Events
This project helps climate scientists and environmental researchers understand why extreme events happen. It analyzes spatial and temporal climate data to identify which physical variables (like temperature, pressure, or humidity anomalies) act as 'drivers' or precursors to events such as heatwaves, floods, or droughts. It takes historical climate datasets and outputs predictions of extreme events along with maps showing their underlying causes, helping you pinpoint the critical factors.
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Use this if you need to identify the hidden, time-delayed, and spatially complex relationships between various climate conditions and the occurrence of extreme weather or environmental events.
Not ideal if you are looking for real-time forecasting of specific weather events, or if your primary interest is in short-term meteorological predictions rather than understanding long-term drivers.
Stars
9
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Language
Python
License
MIT
Category
Last pushed
Sep 30, 2025
Commits (30d)
0
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