Abstract:
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Artificial intelligence is poised to become a central component for most technological endeavors, and weather prediction is no exception. Deep learning has emerged as a powerful mechanism for constructing software by example, which enable the construction functions too complex, subtle, or unintuitive to be designed by hand. These new tools may be applied to accelerate or augment all aspects of weather forecasting and climate prediction including: data collection, error correction, data thinning, assimilation, modeling, prediction, communication, anomaly detection, and data analysis. In this talk, I will give an overview of the subject, together with concrete examples of ongoing deep learning projects in climate and weather in which I am actively involved.
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