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Climate Prediction and Nonlinear Analysis of Climate Data

PI: Song Cai
Institution: University of British Colombia
Abstract:
Our Group is devoted to the study of short-term climate variability and prediction, and to the development of neural networks and other machine learning methods (such as Kernel Methods) for nonlinear multivariate and time series analysis.

Our goal is to understand climate variability, that subtle, nonlinear interplay between atmosphere, ocean and land. To accomplish this goal, we are deeply involved in the development and application of neural networks and other methods from the field of machine learning, a branch of computational intelligence. On the pragmatic side, we aim to develop models for climate prediction at the seasonal and intraseasonal time scales.
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