Automation is all you need: Faster Earth system models with AI/ML
- Geometric Data Analytics, Inc., Durham, NC (United States)
- Geometric Data Analytics, Inc., Durham, NC (United States); Duke Univ., Durham, NC (United States)
Focal Area: Data acquisition and assimilation enabled by machine learning (ML), artificial intelligence (AI) and advanced methods. Science Challenge: Tropical cyclones can in- duce extreme water cycle events through dramatic precipitation and storm surge. More reliable models of intensity will translate into better prediction of the impact of extreme events in large scale Earth systems simulations. We demonstrate and describe AI/ML methodologies for rapid assimilation of new, in situ data products.
- Research Organization:
- Artificial Intelligence for Earth System Predictability (AI4ESP) Collaboration (United States)
- Sponsoring Organization:
- USDOE Office of Science (SC), Biological and Environmental Research (BER)
- OSTI ID:
- 1769679
- Report Number(s):
- AI4ESP--1009
- Country of Publication:
- United States
- Language:
- English
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