Skip to main content
OSTI.GOVU.S. Department of Energy Office of Scientific and Technical Information
U.S. Department of Energy
Office of Scientific and Technical Information
 

Machine learning to extend and understand the sources and limits of water cycle predictability on subseasonal-to-decadal timescales in the Earth system

Technical Report ·
DOI:https://doi.org/10.2172/1769744· OSTI ID:1769744
 [1];  [1];  [1];  [1];  [2];  [1];  [1];  [3];  [1];  [1];  [1];  [1];  [1];  [2];  [2];  [4];  [1];  [1];  [5];  [1]
  1. National Center for Atmospheric Research (NCAR), Boulder, CO (United States)
  2. Colorado State Univ., Fort Collins, CO (United States)
  3. Univ. of California, San Diego, CA (United States). Scripps Inst. of Oceanography
  4. George Mason Univ., Fairfax, VA (United States)
  5. Univ. of Colorado, Boulder, CO (United States)
+ Show Author Affiliations
This white paper provides initial insight on how artificial intelligence (AI) and machine learning (ML), including interpretability and explainable AI (XAI) methods, can be leveraged to glean insight from complex data for a paradigm-changing improvement in Earth system predictability on subseasonal-to-seasonal (S2S) and seasonal-to-decadal (S2D) timescales. The application of AI to extend and improve predictability, in combination with causal inference and uncertainty quantification, could lead to a transformative understanding of the integrative water cycle and associated extremes.
Research Organization:
National Center for Atmospheric Research (NCAR), Boulder, CO (United States); Colorado State Univ., Fort Collins, CO (United States); Univ. of California, San Diego, CA (United States). Scripps Inst. of Oceanography; Colorado State Univ., Fort Collins, CO (United States); George Mason Univ., Fairfax, VA (United States); Univ. of Colorado, Boulder, CO (United States)
Sponsoring Organization:
USDOE Office of Science (SC), Biological and Environmental Research (BER)
OSTI ID:
1769744
Report Number(s):
AI4ESP1032
Country of Publication:
United States
Language:
English

Similar Records

Initialized Earth system prediction from subseasonal to decadal timescales
Journal Article · Mon May 03 00:00:00 EDT 2021 · Nature Reviews Earth and Environment · OSTI ID:1817682
Initialized Earth System prediction from subseasonal to decadal timescales
Journal Article · Tue Apr 13 00:00:00 EDT 2021 · Nature Reviews Earth & Environment · OSTI ID:1785982

Related Subjects

54 ENVIRONMENTAL SCIENCES
58 GEOSCIENCES
97 MATHEMATICS AND COMPUTING