Integrative data-driven approaches for characterization & prediction of aerosol-cloud processes
- Sandia National Laboratories (SNL), Albuquerque, NM, and Livermore, CA (United States)
- Univ. of Missouri, Columbia, MO (United States)
Focal Area: Advanced methods to glean insights from complex data (Focal Area 3). Science Challenge: Fusing and interpreting the vast amount of data from disjoint sources for the purpose of elevating our understanding of aerosol-cloud interaction presents an enormous challenge and opportunity and is necessary to improve uncertainty quantification and predictions, especially for extreme events.
- 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:
- 1769729
- Report Number(s):
- AI4ESP--1120
- Country of Publication:
- United States
- Language:
- English
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