Opportunities and challenges for digital twins in atmospheric and climate sciences proceedings of a workshop--in brief

The digital twin is an emerging technology that builds on the convergence of computer science, mathematics, engineering, and the life sciences. Digital twins have the potential to revolutionize atmospheric and climate sciences in particular, as they could be used, for example, to create global-scale...

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Bibliographic Details
Main Author: Casola, Linda Clare
Corporate Authors: National Academies of Sciences, Engineering, and Medicine (U.S.) Division on Engineering and Physical Sciences, Opportunities and Challenges for Digital Twins in Atmospheric and Climate Sciences (Workshop) (2023, Online)
Format: eBook
Language:English
Published: Washington, DC National Academies Press 2023, September 2023
Series:Proceedings of a workshop--in brief
Online Access:
Collection: National Center for Biotechnology Information - Collection details see MPG.ReNa
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520 |a The digital twin is an emerging technology that builds on the convergence of computer science, mathematics, engineering, and the life sciences. Digital twins have the potential to revolutionize atmospheric and climate sciences in particular, as they could be used, for example, to create global-scale interactive models of Earth to predict future weather and climate conditions over longer timescales. On February 1-2, 2023, the National Academies of Sciences, Engineering, and Medicine hosted a public, virtual workshop to discuss characterizations of digital twins within the context of atmospheric, climate, and sustainability sciences and to identify methods for their development and use. Workshop panelists presented varied definitions and taxonomies of digital twins and highlighted key challenges as well as opportunities to translate promising practices to other fields. The second in a three-part series, this evidence-gathering workshop will inform a National Academies consensus study on research gaps and future directions to advance the mathematical, statistical, and computational foundations of digital twins in applications across science, medicine, engineering, and society