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.

2025 Impact Factor: 4.56

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.
  • Perspectives: These short articles can be based on the authors’ experiences, vision, or knowledge of a given field. They can be forward-looking thought pieces or more speculative, summarizing a current problem and providing informed opinions about a proposed solution and calling for new integrative research, and/or highlighting entry points for emerging approaches and techniques. Perspectives differ from regular articles in their forward-looking focus, which can include opinions. This differs from a typical research article that focuses on the authors’ current work. Up to 5000 words but shorter contributions are encouraged. Perspectives require a proposal to the Chief Editor or may be solicited by the journal Editors.

Editors and Staff Contacts

Chief Editor

Amy McGovern, University of Oklahoma

Associate Chief Editor

Mark Veillette, NVIDIA

Editors

John T. Allen, Central Michigan University

Alexandra Anderson-Frey, University of Washington

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

Scott M. Collis, Argonne National Laboratory

Jason Furtado, University of Oklahoma

Ruoying He, North Carolina State University

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)

Yonggang Liu, University of South Florida

Corey Potvin, NOAA/OAR/National Severe Storms Laboratory

Julian Quinting, University of Cologne

Haruko Murakami Wainwright, Massachusetts Institute of Technology

Associate Editors

Nachiketa Acharya, CIRES, University of Colorado Boulder and NOAA Physical Sciences Laboratory
Sam Allen, Karlsruhe Institute of Technology
Marina Astitha, University of Connecticut
Blanka Balogh, CNRM, Météo-France, CNRS, Université de Toulouse
Randy J. Chase, Tomorrow.io
Mariana C. A. Clare, European Centre for Medium-Range Weather Forecasts (ECMWF)
Julie Demuth, National Center for Atmospheric Research
Akila de Silva, San Francisco State University
Gregory Dusek, NOAA National Ocean Service
Kimberly L. Elmore, University of Oklahoma
D. Aaron Evans, tomorrow.io
Alison R. Gray, University of Washington
Siddhant Gupta, Argonne National Laboratory
Alex M. Haberlie, Northern Illinois University
Katherine Haynes, Cooperative Institute for Research in the Atmosphere (CIRA), Colorado State University
Aaron Hill, University of Oklahoma
Michael Howland, Massachusetts Institute of Technology
Zhanxiang Hua, University of Oklahoma
Susan A. Jasko, Independent Scholar
Dani Jones, Cooperative Institute for Great Lakes Research (CIGLR), University of Michigan
Fahim H. Khan, California Polytechnic State University, San Luis Obispo
Sarah A. King, U.S. Naval Research Laboratory
Sebastian Lerch, Marburg University, Germany
Redouane Lguensat, Institut Pierre-Simon Laplace (IPSL)
Eric D. Loken, Cooperative Institute for Severe and High-Impact Weather Research and Operations
Dan Lu, Oak Ridge National Laboratory
Maria M. Madsen, Salient Predictions
Mashkoor Malik, NOAA
Antonios Mamalakis, University of Virginia
Brandon McClung, Air Force Institute of Technology
Chuyen Nguyen, U.S. Naval Research Laboratory
Stephanie M. Ortland, Cooperative Institute for Research in the Atmosphere (CIRA), Colorado State University
Joseph Ripberger, University of Oklahoma
Daniel Rothenberg, Brightband
David Ryglicki, The Demex Group, LLC
Jakob Schloer, European Centre for Medium-Range Weather Forecasts (ECMWF)
John Schreck, NSF National Center for Atmospheric Research
Israel Silber, Pacific Northwest National Laboratory
Christopher J. Slocum, NOAA/NESDIS Center for Satellite Applications and Research
Maike Sonnewald, University of California Davis, NOAA/Geophysical Fluid Dynamics Laboratory, and University of Washington
Joanna Staneva, Helmholtz-Zentrum Hereon, Germany
Jingjing Tian, Pacific Northwest National Laboratory
Andre J. van der Westhuysen, The Nielsen Company, LLC
Lijing Wang, University of Connecticut
Kirien Whan, The Royal Netherlands Meteorological Institute
Charles H. White, NOAA/NESDIS Center for Satellite Applications and Research
Anthony Wimmers, Cooperative Institute for Meteorological Satellite Studies (CIMSS), University of Wisconsin — Madison
Yan Xie, University of Oklahoma
Tiantian Yang, The School for Environment and Sustainability (SEAS), University of Michigan - Ann Arbor
Kiley L. Yeakel, MIT Lincoln Laboratory
Hungjui Yu, Cooperative Institute for Research in the Atmosphere (CIRA), Colorado State University
Michaël Zamo, Centre national de recherches météorologiques, France

Peer Review Support Staff

Colleen Gaffney, Assistant to Amy McGovern, Scott M. Collis, and Christina Kumler
Aylin Arruda, Assistant to Yonggang Liu, Corey Potvin and Mark Veillette
Cristina Barletta, Assistant to Ruoying He
Hayley Charney, Assistant to John T. Allen
Erin Gumbel, Assistant to William F. Campbell and Ryan Lagerquist
Tom Justice, Assistant to Jason Furtado and Haruko Murakami Wainwright
Robbie Matlock, Assistant to Julian Quinting
Andrea Schein, Assistant to Alexandra Anderson-Frey

Production Contacts

Please see the AMS Publications contacts page.

6 2025 Journal Impact Factors by Clarivate Analytics; Meteorology and Atmospheric Sciences and Computer Science, Artificial Intelligence categories.