ExtremeWeather: A large-scale climate dataset for semi-supervised detection, localization, and understanding of extreme weather events
Racah, Evan and Beckham, Christopher and Maharaj, Tegan and Kahou, Samira and Prabhat, Mr. and Pal, Chris



Support
Academic Torrents!

Disable your
ad-blocker!

h5data (25 files)
climo_1979.h566.13GB
climo_1980.h566.11GB
climo_1981.h565.93GB
climo_1982.h566.13GB
climo_1984.h566.15GB
climo_1985.h566.14GB
climo_1986.h566.12GB
climo_1987.h566.13GB
climo_1988.h566.15GB
climo_1989.h566.11GB
climo_1990.h566.13GB
climo_1991.h566.14GB
climo_1992.h566.15GB
climo_1993.h566.15GB
climo_1994.h566.13GB
climo_1995.h566.11GB
climo_1996.h566.10GB
climo_1997.h566.12GB
climo_1998.h566.15GB
climo_2000.h566.15GB
climo_2001.h566.13GB
climo_2002.h566.12GB
climo_2003.h566.11GB
climo_2004.h566.13GB
climo_2005.h565.94GB
Type: Dataset
Tags:

Bibtex:
@incollection{nips2017_6932,
title= {ExtremeWeather: A large-scale climate dataset for semi-supervised detection, localization, and understanding of extreme weather events},
author= {Racah, Evan and Beckham, Christopher and Maharaj, Tegan and Kahou, Samira and Prabhat, Mr. and Pal, Chris},
booktitle= {Advances in Neural Information Processing Systems 30},
editor= {I. Guyon and U. V. Luxburg and S. Bengio and H. Wallach and R. Fergus and S. Vishwanathan and R. Garnett},
pages= {3405--3416},
year= {2017},
publisher= {Curran Associates, Inc.},
url= {http://papers.nips.cc/paper/6932-extremeweather-a-large-scale-climate-dataset-for-semi-supervised-detection-localization-and-understanding-of-extreme-weather-events.pdf},
abstract= {The 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.

## Citation
Racah, Evan, et al. "ExtremeWeather: A large-scale climate dataset for semi-supervised detection, localization, and understanding of extreme weather events." Advances in Neural Information Processing Systems. 2017.

## Pictures
https://extremeweatherdataset.github.io/variables.jpg},
keywords= {},
terms= {},
license= {Unrestricted Use},
superseded= {}
}