November 17, 2020
By leveraging enormous volumes of satellite and environmental data
The US National Oceanic and Atmospheric Administration (NOAA) and Google have agreed to work together to investigate how AI might be used to enhance the prediction of extreme weather events, such as hurricanes and tornadoes.
NOAA’s National Environmental Satellite, Data, and Information Service (NESDIS) and Google Cloud signed a three-year deal to pilot AI and machine learning projects to amplify NOAA’s environmental monitoring, weather forecasting, climate research, and technical innovation.
The research will start with the development of small-scale AI/ML systems, leading to full-scale prototypes that NOAA could operationalize across the organization.
From up high
“Strengthening NOAA’s data processing through the use of big data, artificial intelligence, machine learning, and other advanced analytical approaches is critical for maintaining and enhancing the performance of our systems in support of public safety and the economy,” said Neil Jacobs, acting NOAA administrator. “I am excited to utilize new authorities granted to NOAA to pursue cutting-edge technologies that will enhance our mission and better protect lives and property.”
NOAA and Google plan to improve the work of the agency via AI-related research and development, partnerships, and training over the course of different joint projects.
"By bringing together NOAA and Google’s expertise and talent, we can both resource and jointly explore AI/ML methods to achieve a more effective use of satellite and other environmental data,” said Mike Daniels, vice president, Global Public Sector, Google Cloud.
“Our goal is to increase scientific impact and improve the efficiency and effectiveness of environmental and satellite data by leveraging Google Cloud’s infrastructure and AI/ML know-how. All this will help improve weather forecasting, research, and unlock innovation.”
Earlier in 2020, NOAA adopted a strategy to dramatically expand the application of AI in every NOAA mission area to improve the “efficiency, effectiveness, and coordination of AI development and usage across the agency.”
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