Can AI help us predict extreme weather?
AI models are starting to revolutionize weather forecasting.
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We’ve learned how to predict weather over the past century by understanding the science that governs Earth’s atmosphere and harnessing enough computing power to generate global forecasts. But in just the past three years, AI models from companies like Google, Huawei, and Nvidia that use historical weather data have been releasing forecasts rivaling those created through traditional forecasting methods.
This video explains the promise and challenges of these new models built on artificial intelligence rather than numerical forecasting, particularly as it relates to the ability to foresee extreme weather.
Here are the papers that describe the models mentioned in the video.
Google’s GraphCast: https://www.science.org/stoken/author-tokens/ST-1550/full
Huawei’s Pangu-Weather: https://www.nature.com/articles/s41586-023-06185-3
Nvidia’s FourCastNet: https://arxiv.org/abs/2202.11214
Here is the announcement of the ERA5 dataset, released by the European Centre for Medium-Range Weather Forecasts in 2020:
https://rmets.onlinelibrary.wiley.com/doi/full/10.1002/qj.3803
We interviewed Dr. Aaron Hill over email for this video. Hill is involved in developing responsible AI for environmental science via AI2ES:
https://www.ai2es.org
Google has also developed a weather forecasting model called Nowcasting, which is already embedded in its weather products specifically for short-term precipitation forecasts:
https://deepmind.google/discover/blog/nowcasting-the-next-hour-of-rain
If you’re interested in learning more about the history of how we developed weather forecasting, I’d recommend The Weather Machine by Andrew Blum:
https://www.amazon.com/Weather-Machine-Journey-Inside-Forecast/dp/0062368613
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Subscribe to our channel and turn on notifications (?) so you don't miss any videos: http://goo.gl/0bsAjO
This video is sponsored by Microsoft Copilot for Microsoft 365. Microsoft has no editorial influence on our videos, but their support makes videos like these possible.
We’ve learned how to predict weather over the past century by understanding the science that governs Earth’s atmosphere and harnessing enough computing power to generate global forecasts. But in just the past three years, AI models from companies like Google, Huawei, and Nvidia that use historical weather data have been releasing forecasts rivaling those created through traditional forecasting methods.
This video explains the promise and challenges of these new models built on artificial intelligence rather than numerical forecasting, particularly as it relates to the ability to foresee extreme weather.
Here are the papers that describe the models mentioned in the video.
Google’s GraphCast: https://www.science.org/stoken/author-tokens/ST-1550/full
Huawei’s Pangu-Weather: https://www.nature.com/articles/s41586-023-06185-3
Nvidia’s FourCastNet: https://arxiv.org/abs/2202.11214
Here is the announcement of the ERA5 dataset, released by the European Centre for Medium-Range Weather Forecasts in 2020:
https://rmets.onlinelibrary.wiley.com/doi/full/10.1002/qj.3803
We interviewed Dr. Aaron Hill over email for this video. Hill is involved in developing responsible AI for environmental science via AI2ES:
https://www.ai2es.org
Google has also developed a weather forecasting model called Nowcasting, which is already embedded in its weather products specifically for short-term precipitation forecasts:
https://deepmind.google/discover/blog/nowcasting-the-next-hour-of-rain
If you’re interested in learning more about the history of how we developed weather forecasting, I’d recommend The Weather Machine by Andrew Blum:
https://www.amazon.com/Weather-Machine-Journey-Inside-Forecast/dp/0062368613
Subscribe to our channel! http://goo.gl/0bsAjO
Vox.com is a news website that helps you cut through the noise and understand what's really driving the events in the headlines. Check out http://www.vox.com.
Watch our full video catalog: http://goo.gl/IZONyE
Follow Vox on Facebook: http://goo.gl/U2g06o
Or Twitter: http://goo.gl/XFrZ5H
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