Artificial Intelligence for the Earth Systems

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Scope

Artificial Intelligence for the Earth Systems (AIES) (ISSN: 2769-7525) publishes research on the development and application of methods in Artificial Intelligence (AI), Machine Learning (ML), data science, and statistics that is relevant to meteorology, atmospheric science, hydrology, climate science, and ocean sciences. Topics include development of AI/ML, statistical, and hybrid methods and their application; development and application of methods to further the physical understanding of earth system processes from AI/ML models such as explainable and physics-based AI; the use of AI/ML to emulate components of numerical weather and climate models; incorporation of AI/ML into observation and remote sensing platforms; the use of AI/ML for data assimilation and uncertainty quantification; and societal applications of AI/ML for AIES disciplines, including ethical and responsible use of AI/ML and educational research on AI/ML.

Artificial Intelligence for the Earth Systems is fully open access.

Submission Types

  • Articles: Up to 7500 words, including the body text, acknowledgments, and appendixes. The word limit does not include the title page, abstract, references, captions, tables, and figures. If a submission exceeds the word limit, the author must provide a justification for the length of the manuscript and request the Chief Editor’s approval of the overage. This request may be uploaded in a document with the "Cover Letter" item type or entered in the comment field in the submission system.
  • Reviews: Synthesis of previously published literature that may address successes, failures, and limitations. Requires Review Proposal. For more information, see Review Articles.
  • Comment and Reply Exchange: Comments are written in response to a published article and should be submitted within 2 years of the publication date of the original article (although the editor can waive this limit in extenuating circumstances). The author of the original article has the opportunity to write a Reply. These exchanges are published together. 
  • Corrigenda: The corrigendum article type is available for authors to address errors discovered in already published articles. For more information, see Corrigenda.

  • Lessons Learned: Short papers on insights regarding the efficacy of AI methods that apply to and are deemed significant for an entire class of earth system applications. Such insights could be derived from research results for which such methods were successful or unsuccessful, or from a meta-analysis or perspective based on existing research results. Up to 3,000 words, including the body text, acknowledgments, and appendixes. The word limit does not include the title page, abstract, references, captions, tables, and figures. No more than 3 figures/tables.

Editors and Staff Contacts

Chief Editor

Amy McGovern, University of Oklahoma

Editors

John T. Allen, Central Michigan University

William F. Campbell, U.S. Naval Research Laboratory

Scott M. Collis, Argonne National Laboratory

Imme Ebert-Uphoff, Colorado State University

David John Gagne II, National Center for Atmospheric Research

Ruoying He, North Carolina State University

Michael Scheuerer, Norwegian Computing Center, NR

Mark Veillette, MIT Lincoln Labs

Haruko Murakami Wainwright, Massachusetts Institute of Technology

Associate Editors

Alexandra Anderson-Frey, University of Washington
Randy J. Chase, University of Oklahoma
Julie Demuth, National Center for Atmospheric Research
Gregory Dusek, NOAA National Ocean Service
Montgomery Flora, Cooperative Institute for Severe and High-Impact Weather Research and Operations
Tim Gallaudet, Ocean STL Consulting LLC
Alex M. Haberlie, Northern Illinois University
Aaron Hill, Colorado State University
Susan A. Jasko, Center for Advanced Public Safety, University of Alabama
Sarah A. King, U.S. Naval Research Laboratory
Christina Kumler, CIRES, University of Colorado Boulder and NOAA Global Systems Laboratory
Ryan Lagerquist, Cooperative Institute for Research in the Atmosphere (CIRA) and NOAA Global Systems Laboratory (GSL)
Sebastian Lerch, Karlsruhe Institute of Technology
Eric D. Loken, Cooperative Institute for Severe and High-Impact Weather Research and Operations
Dan Lu, Oak Ridge National Laboratory
Mashkoor Malik, NOAA
Antonios Mamalakis, Colorado State University
Maria J. Molina, National Center for Atmospheric Research
Chuyen Nguyen, U.S. Naval Research Laboratory
Benjamin L. Richards, NOAA Fisheries
Jamese Sims, NOAA
Christopher J. Slocum, NOAA/NESDIS Center for Satellite Applications and Research
Maike Sonnewald, Princeton University, NOAA/Geophysical Fluid Dynamics Laboratory, and University of Washington
Jebb Q. Stewart, NOAA Global System Laboratory
Andre J. van der Westhuysen, IMSG at NOAA/NWS/NCEP/Environmental Modeling Center
Campbell D. Watson, IBM Research
Kirien Whan, The Royal Netherlands Meteorological Institute
Anthony Wimmers, Cooperative Institute for Meteorological Satellite Studies (CIMSS), University of Wisconsin — Madison
Michaël Zamo, Centre national de recherches météorologiques, France

Peer Review Support Staff

Andrea Herbst, Assistant to Amy McGovern, Imme Ebert-Uphoff, Mark Veillette
Cristina Barletta, Assistant to Ruoying He
Tiffany Bischoff, Assistant to Scott M. Collis
Hayley Charney, Assistant to John T. Allen and Michael Scheuerer
Felicia Gullotta, Assistant to David John Gagne II
Erin Gumbel, Assistant to William F. Campbell
Christine Ziebarth, Assistant to Haruko Murakami Wainwright

Production Staff

Mike Friedman, Senior Manager for Publishing Operations
Katie Quirk, Author Submission Support