Abstract
AbstractPromising 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.
Highlights
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
We extend and update the perspective of Boukabara et al (2019b) to include current activities, and expected future trends, based on presentations and discussion from 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 in College Park, Maryland
The panel discussions entitled: “How can scientists and engineers embrace AI technology to flows in the value chain that exploits these observations thru data assimilation, environmental numerical model, extreme weather monitoring and prediction, and postprocessing of forecasts, and onward to other applications and products across the public and private sectors that use environmental intelligence for decision making
Summary
The increase of data volume comes from higher-resolution satellites and sensors, from a growing list of new sensors (traditional as well as SmallSats and CubeSats; Stephens et al 2020), and from an explosion of new observing systems that are beneficial byproducts of the Internet of Things (IoT; e.g., Madaus and Mass 2017) and unmanned systems These data sources should help provide more accurate and detailed forecasts but their exploitation is expected to be a major challenge to any future computing infrastructure, not least in the area of data transfer and storage. The panel discussions entitled: “How can scientists and engineers embrace AI technology to flows in the value chain that exploits these observations thru data assimilation, environmental numerical model, extreme weather monitoring and prediction, and (bottom) postprocessing of forecasts, and onward to other applications and products across the public and private sectors that use environmental intelligence for decision making. The five colored blocks and large-type labels correspond to sections in this paper and the small-type entries correspond to topics related to the different sections
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