The University of British Columbia

• Machine learning methods and their applications to the environmental sciences
• Seasonal climate and extreme weather prediction
• Atmosphere-ocean climate dynamics

Machine learning (ML), a major branch of computational intelligence (i.e. artificial intelligence), has a huge impact on our everyday lives through its ability to recognize complicated, nonlinear signals in large datasets. When we post a letter, the post office uses ML technology to understand our handwriting. Online vendors such as Amazon and Netflix suggest books and movies of interest using ML. Internet providers detect spam and credit card companies detect fraudulent transactions via ML.

The question that has intrigued us for the last two decades has been: How would machine learning impact the environmental sciences?

A prominent example of climate variability is the famous El Niño-La Niña phenomenon, an irregular fluctuation of the climate system which produces anomalous warming in the equatorial Pacific during El Niño and cooling during La Niña, with notable influence on the Canada winter climate. El Niño/La Niña episodes can be forecasted with reasonable accuracy 3-12 months in advance. Our group has built models for El Niño/La Niña prediction using artificial neural networks and other ML methods. We have developed neural network models for nonlinear principal component analysis, nonlinear canonical correlation analysis, and nonlinear singular spectrum analysis (our codes are freely downloadable and have users from over 60 countries). We have identified nonlinear atmospheric teleconnection patterns associated with the El Niño/La Niña, the Arctic Oscillation, the quasi-biennial oscillation and the Madden-Julian oscillation.

Our most recent research efforts have been directed to the following three areas:

• With general circulation models having spatial resolution too coarse to reveal climate variability at local scales, ML methods are being developed to downscale the model output to finer spatial scales, especially for precipitation and streamflow.
• Machine learning methods are ideal for extracting information from satellite data. Crop yield prediction models are being developed by applying ML methods to vegetation indices derived from satellite data.
• While ML methods are able to extract nonlinear signals missed by linear statistical methods, they are computationally much more expensive. We are studying new ML methods which are several orders of magnitude faster than the standard ML methods.

My graduate-level book "Machine Learning Methods in the Environmental Sciences: Neural Networks and Kernels" was published by Cambridge Univ. Press in 2009.

Our climate forecasts are updated monthly on our web site: http://www.ocgy.ubc.ca/projects/clim.pred/.

Selected Publications