Paper Info
Title
Application of Deep Convolutional Neural Networks for Detecting Extreme Weather in Climate Datasets.
Abstract
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
Year
Venue
Field
2016
arXiv: Computer Vision and Pattern Recognition
Climate change,Weather front,Convolutional neural network,Computer science,Extreme weather,Climate pattern,Artificial intelligence,Deep learning,Tropical cyclone,Machine learning,Bayesian probability
DocType
Volume
Citations 
Journal
abs/1605.01156
15
PageRank 
References 
Authors
0.70
15
9
Name
Order
Citations
PageRank
Yunjie Liu1152.05
Evan Racah2545.35
Prabhat345634.79
Joaquin Correa4151.38
Amir Khosrowshahi5161.04
David Lavers6150.70
Kenneth Kunkel7150.70
Michael Wehner8150.70
William D. Collins9243.05