Multi-scale Multi-physics Scientific Machine Learning for Water Cycle Extreme Events Identification, Labelling, Representation, and Characterization
- Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
- Univ. of Texas, Austin, TX (United States)
- Univ. of Southern California, Los Angeles, CA (United States)
- Univ. of Arizona, Tucson, AZ (United States)
Impacts of climate are usually felt through extreme events such as droughts, floods, thunderstorms, windstorms, wildfires, and so on, that are intimately tied to the water cycle. Predicting the frequency and severity of extreme events under climate change remains a significant challenge; meanwhile, the mechanisms and impacts of these extremes are far from well understood. There are several major science challenges: (1) Lack of labelled extreme events data and missing standards in defining extremes; (2) Computational demand of high-resolution ensemble climate modeling; (3) Modeling the multiscale multi-physics hierarchical structure of compound extremes; (4) Lack of understanding of mechanisms of extreme events; (5) Large uncertainty in extreme events impacts on infrastructure; (6) Subjective assessment of weather-related risk from seasonal to multi-decadal time scales and lack of metrics for risk assessment and mitigation control.
- Research Organization:
- Pacific Northwest National Laboratory (PNNL), Richland, WA (United States); Univ. of Texas, Austin, TX (United States); Univ. of Southern California, Los Angeles, CA (United States); Univ. of Arizona, Tucson, AZ (United States)
- Sponsoring Organization:
- USDOE Office of Science (SC), Biological and Environmental Research (BER)
- OSTI ID:
- 1769751
- Report Number(s):
- AI4ESP1062
- Country of Publication:
- United States
- Language:
- English
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