Machine learning (ML), a major branch of 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. Self-driving automobiles promise
a revolution in transportation. While most environmental scientists are
not familiar with machine learning, ML has been the fastest growing
field in the last twenty years, fueled by Apple, Alphabet/Google and
Microsoft, the three largest companies in the world by market
capitalization according to Wikipedia.
- Machine learning methods and their applications to the
- Seasonal climate and extreme weather prediction
- Atmosphere-ocean climate dynamics
The question that has intrigued me for over two decades has been: How
would machine learning impact the environmental sciences? This single
question has been the main driving force of my research program.
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
on the Canada winter climate. El Niño/La Niña episodes
can be forecast with reasonable accuracy 3-12 months in
Our group has built models for El Niño/La Niña prediction
using artificial neural networks and other ML methods.
forecasts are updated monthly on our web site http://www.ocgy.ubc.ca/projects/clim.pred/
and are included in the IRI ensemble forecasts.
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
In the last few years, our research efforts have been directed to the following areas:
My graduate-level book "Machine Learning Methods in the
Environmental Sciences: Neural Networks and Kernels" was
published by Cambridge Univ. Press in 2009.
With general circulation models having spatial resolution too
coarse to reveal climate variability at local scales, ML
methods have been developed to nonlinearly
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 have been developed by applying
ML methods to vegetation indices derived from satellite data.
ML methods have been used to improve forecasts of air quality over
ML methods have been used in data fusion, i.e. combining various gridded
products, to improve estimates of snow depth (i.e. snow water
equivalent) over British Columbia.
While ML methods such as artificial neural networks are able to extract
nonlinear signals missed by linear statistical methods, they are
computationally much more expensive. We have been developing new ML
models which are several orders of magnitude faster than the standard ML
models, especially when the models need to be updated frequently as new
data arrive continually (i.e. online learning).