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Then detection and identification of extreme weather events in large-scaleclimate simulations is an important problem for risk management, informinggovernmental policy decisions and advancing our basic understanding of theclimate system. Recent work has shown that fully supervised convolutionalneural networks (CNNs) can yield acceptable accuracy for classifying well-knowntypes of extreme weather events when large amounts of labeled data areavailable. However, many different types of spatially localized climatepatterns are of interest including hurricanes, extra-tropical cyclones, weatherfronts, and blocking events among others. Existing labeled data for thesepatterns can be incomplete in various ways, such as covering only certain yearsor geographic areas and having false negatives. This type of climate datatherefore poses a number of interesting machine learning challenges. We presenta multichannel spatiotemporal CNN architecture for semi-supervised bounding boxprediction and exploratory data analysis. We demonstrate that our approach isable to leverage temporal information and unlabeled data to improve thelocalization of extreme weather events. Further, we explore the representationslearned by our model in order to better understand this important data. Wepresent a dataset, ExtremeWeather, to encourage machine learning research inthis area and to help facilitate further work in understanding and mitigatingthe effects of climate change. The dataset is available atextremeweatherdataset.github.io and the code is available athttps://github.com/eracah/hur-detect.

ExtremeWeather: A large-scale climate dataset for semi-supervised detection, localization, and understanding of extreme weather events