  Detecting extreme events in large datasets is a major challenge in climate
science research. Current algorithms for extreme event detection are build upon
human expertise in defining events based on subjective thresholds of relevant
physical variables. Often, multiple competing methods produce vastly different
results on the same dataset. Accurate characterization of extreme events in
climate simulations and observational data archives is critical for
understanding the trends and potential impacts of such events in a climate
change content. This study presents the first application of Deep Learning
techniques as alternative methodology for climate extreme events detection.
Deep neural networks are able to learn high-level representations of a broad
class of patterns from labeled data. In this work, we developed deep
Convolutional Neural Network (CNN) classification system and demonstrated the
usefulness of Deep Learning technique for tackling climate pattern detection
problems. Coupled with Bayesian based hyper-parameter optimization scheme, our
deep CNN system achieves 89\%-99\% of accuracy in detecting extreme events
(Tropical Cyclones, Atmospheric Rivers and Weather Fronts
