Use of machine deep learning for climate forecasts

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AI machine learning has attracted more and more attention for Earth Science applications in recent years, including phenomenon identification and classification, weather and climate forecasts, ocean forecast, renewable energy forecast, data assimilation and reconstruction, numerical-AI hybrid model development and postprocess (e.g., bias correction and downscaling), among many others. In this talk Professor Jing-Jia Luo, Director of the Institute for Climate and Application Research (ICAR) & Institute of AI for Meteorology at Nanjing University of Information Science and Technology, China, will present their recent progresses on using various deep learning methods for improved seasonal-to-multi-seasonal predictions of El Niño–Southern Oscillation (ENSO), the Indian Ocean Dipole (IOD), summer precipitation in China and East Africa including the bias correction and downscaling of dynamical climate model’s forecasts, Arctic sea ice cover, as well as the AI forecasts and bias correction of ocean waves. A discussion and perspective on the fusion of AI and physics will also be presented.

This live event includes a 30-minute networking event hosted on the AI for Good Neural Network. This is your opportunity to ask questions, interact with the panelists and participants and build connections with the AI for Good community.

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Start date
22 March 2023 at 15:00 CET Geneva | 07:00-08:30 PDT, San Diego | 22:00-23:30 CST, Beijing
End date
22 March 2023 at 16:30 CET Geneva | 07:00-08:30 PDT, San Diego | 22:00-23:30 CST, Beijing
Duration
90 minutes (including 30 minutes networking)
Programme stream
- Discovery - AI and Climate Science
Topics
- Environment & Climate change
UN SDGs
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Use of machine deep learning for climate forecasts