A library of AI-assisted FAIR water cycle and related disturbance datasets to enable model training, parameterization and validation

Technical Report · Thu Feb 11 23:00:00 EST 2021

DOI:https://doi.org/10.2172/1769646· OSTI ID:1769646

Crystal-Ornelas, Robert ^[1]; Varadharajan, Charuleka ^[1]; Christianson, Danielle ^[1]; Damerow, Joan ^[1]; Weierbach, Helen ^[1]; Robles, Emily ^[1]; Ramakrishnan, Lavanya ^[1]; Faybishenko, Boris ^[1]; Pastorello, Gilberto ^[1]

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1. Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)

This whitepaper is responsive to focal area Data acquisition and assimilation enabled by machine learning, AI, and advanced methods. Here we describe how FAIR (Findable, Accessible, Reusable, Interoperable) datasets related to water cycle extremes are essential for successful implementation of ML in Earth System and other models. We also describe how AI can be used to acquire and integrate water cycle data related to extreme events to create a library of FAIR datasets for training and evaluating algorithms.

• Research Organization: Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)
• Sponsoring Organization: USDOE Office of Science (SC), Biological and Environmental Research (BER)
• OSTI ID: 1769646
• Report Number(s): AI4ESP1030
• Country of Publication: United States
• Language: English

Related Subjects

58 GEOSCIENCES
96 KNOWLEDGE MANAGEMENT AND PRESERVATION
97 MATHEMATICS AND COMPUTING