Skip to main content
U.S. flag

An official website of the United States government

Dot Gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

HTTPS

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

The History and Practice of Ai In The Environmental Sciences

Abstract

Artificial intelligence (AI) and machine learning (ML) have become important tools for environmental scientists and engineers, both in research and in applications. Although these methods have become quite popular in recent years, they are not new. The use of AI methods began in the 1950s and environmental scientists were adopting them by the 1980s. Although an “AI Winter” temporarily slowed the growth, a more recent resurgence has brought it back with gusto. This paper tells the story of the evolution of AI in the field through the lens of the AMS Committee on Artificial Intelligence Applications to Environmental Science. The environmental sciences possess a host of problems amenable to advancement by intelligent techniques. We review a few of the early applications along with the ML methods of the time and how their progression has impacted these sciences. While AI methods have changed from expert systems in the eighties to neural networks and other data-driven methods, and more recently deep learning, the environmental problems tackled have remained similar. We discuss the types of applications that have shown some of the biggest advances due to AI usage and how they have evolved over the past decades, including topics in weather forecasting, probabilistic prediction, climate estimation, optimization problems, image processing, and improving forecasting models. We finish with a look at where AI as employed in environmental science appears to be headed and some thoughts on how it might be best blended with physical / dynamical modeling approaches to further advance our science.

Article / Publication Data
Active/Online
YES
Available Metadata
DOI ↗
Early Online Release
January 13, 2022
Fiscal Year
Peer Reviewed
YES
Publication Name
Bulletin of The American Meteorological Society
Published On
May 25, 2022
Publisher Name
American Meteorological Society
Print Volume
103
Issue
5
Submitted On
June 30, 2021
URL ↗

Authors

Authors who have authored or contributed to this publication.

  • Sue Ellen Haupt - lead Ncar
    National Center for Atmospheric Research
    1850 Table Mesa Drive, Boulder, Colorado
  • William R. Moninger - seventh Gsl
    Cooperative Institute for Research in Environmental Sciences, University of Colorado Boulder
    NOAA/Global Systems Laboratory