  Then detection and identification of extreme weather events in large-scale
climate simulations is an important problem for risk management, informing
governmental policy decisions and advancing our basic understanding of the
climate system. Recent work has shown that fully supervised convolutional
neural networks (CNNs) can yield acceptable accuracy for classifying well-known
types of extreme weather events when large amounts of labeled data are
available. However, many different types of spatially localized climate
patterns are of interest including hurricanes, extra-tropical cyclones, weather
fronts, and blocking events among others. Existing labeled data for these
patterns can be incomplete in various ways, such as covering only certain years
or geographic areas and having false negatives. This type of climate data
therefore poses a number of interesting machine learning challenges. We present
a multichannel spatiotemporal CNN architecture for semi-supervised bounding box
prediction and exploratory data analysis. We demonstrate that our approach is
able to leverage temporal information and unlabeled data to improve the
localization of extreme weather events. Further, we explore the representations
learned by our model in order to better understand this important data. We
present a dataset, ExtremeWeather, to encourage machine learning research in
this area and to help facilitate further work in understanding and mitigating
the effects of climate change. The dataset is available at
extremeweatherdataset.github.io and the code is available at
https://github.com/eracah/hur-detect.
