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Outlook for exploiting artificial intelligence in the Earth and environmental sciences

  • Sid Ahmed Boukabara
  • , Vladimir Krasnopolsky
  • , Stephen G. Penny
  • , Jebb Q. Stewart
  • , Amy McGovern
  • , David Hall
  • , John E. Ten Hoeve
  • , Jason Hickey
  • , Hung Lung Allen Huang
  • , John K. Williams
  • , Kayo Ide
  • , Philippe Tissot
  • , Sue Ellen Haupt
  • , Kenneth S. Casey
  • , Nikunj Oza
  • , Alan J. Geer
  • , Eric S. Maddy
  • , Ross N. Hoffman
    • National Oceanic and Atmospheric Administration
    • University of Colorado Boulder
    • University of Oklahoma
    • NVIDIA
    • Alphabet Inc.
    • University of Wisconsin-Madison
    • IBM
    • University of Maryland, College Park
    • Texas A&M University-Corpus Christi
    • National Center for Atmospheric Research
    • NASA Ames Research Center
    • European Centre for Medium-Range Weather Forecasts

    Research output: Contribution to journalArticlepeer-review

    68 Scopus citations

    Abstract

    Promising new opportunities to apply artificial intelligence (AI) to the Earth and environmental sciences are identified, informed by an overview of current efforts in the community. Community input was collected at the first National Oceanic and Atmospheric Administration (NOAA) workshop on “Leveraging AI in the Exploitation of Satellite Earth Observations and Numerical Weather Prediction” held in April 2019. This workshop brought together over 400 scientists, program managers, and leaders from the public, academic, and private sectors in order to enable experts involved in the development and adaptation of AI tools and applications to meet and exchange experiences with NOAA experts. Paths are described to actualize the potential of AI to better exploit the massive volumes of environmental data from satellite and in situ sources that are critical for numerical weather prediction (NWP) and other Earth and environmental science applications. The main lessons communicated from community input via active workshop discussions and polling are reported. Finally, recommendations are presented for both scientists and decision-makers to address some of the challenges facing the adoption of AI across all Earth science.

    Original languageEnglish
    Pages (from-to)E1016-E1023
    JournalBulletin of the American Meteorological Society
    Volume102
    Issue number5
    DOIs
    StatePublished - 2021

    Keywords

    • Artificial intelligence
    • Data mining
    • Machine learning
    • Numerical weather prediction/ forecasting
    • Remote sensing
    • Satellite observations

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