4.7 Article

Outlook for Exploiting Artificial Intelligence in the Earth and Environmental Sciences

Journal

BULLETIN OF THE AMERICAN METEOROLOGICAL SOCIETY
Volume 102, Issue 5, Pages E1016-E1032

Publisher

AMER METEOROLOGICAL SOC
DOI: 10.1175/BAMS-D-20-0031.1

Keywords

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

Funding

  1. NOAA
  2. National Science Foundation
  3. NOAA NGGPS [NA18NWS4680048]
  4. ONR [N00014-19-1-2522]
  5. NOAA NESDIS
  6. NOAA through CICS [NA14NES4320003, NA19NES4320002]
  7. CISESS at the University of Maryland/ESSIC

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A workshop was held to gather input from various experts in different fields on the prospects of using artificial intelligence in Earth science, and recommendations were made for both scientists and decision-makers to address challenges in adopting AI in all Earth science fields.
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.

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