AI-enabled MODEX and edge-computing over 5G for improving the predictability of water cycle extremes
- Argonne National Lab. (ANL), Argonne, IL (United States)
- Univ. of Illinois, Chicago, IL (United States)
Focal Area(s): The paper addresses focal area 1: data acquisition/assimilation enabled by AI and advanced methods including model-driven experiments, 5G, and hardware-related efforts involving edge computing.
- 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:
- 1769672
- Report Number(s):
- AI4ESP--1008
- Country of Publication:
- United States
- Language:
- English
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