Observational Capabilities to Capture Water Cycle Event Dynamics and Impacts in the Age of AI
- Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
This whitepaper is responsive to focal area (1) Data acquisition and assimilation enabled by machine learning (ML), Artificial Intelligence (AI), and advanced methods. Here we describe how Earth observations specific to water cycle disturbances can be collected in parallel with and integrated into future model development, and make use of the latest technologies other than AI/ ML such as 5G/satellite, edge computing, big data technologies, and cloud computing.
- Research Organization:
- AI4ESP
- Sponsoring Organization:
- USDOE Office of Science (SC), Biological and Environmental Research (BER)
- OSTI ID:
- 1769755
- Report Number(s):
- AI4ESP1136
- Country of Publication:
- United States
- Language:
- English
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