Deep learning for detecting extreme weather patterns

Mudigonda, Mayur, Ram, Prabhat, Kashinath, Karthik, Racah, Evan, Mahesh, Ankur, Liu, Yunjie, Beckham, Christopher, Biard, Jim, Kurth, Thorsten, Kim, Sookyung, Kahou, Samira, Maharaj, Tegan, Loring, Burlen, Pal, Christopher, O'Brien, Travis, Kunkel, Kenneth E., Wehner, Michael F. and Collins, William D. ORCID: https://orcid.org/0000-0002-4463-9848 (2021) Deep learning for detecting extreme weather patterns. In: Deep Learning for the Earth Sciences. Wiley, pp. 163-185. ISBN 9781119646143

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Abstract

Identifying, detecting, and localizing extreme weather events is a crucial first step in understanding how they may vary under different climate change scenarios. Pattern recognition tasks such as classification, object detection, and segmentation (i.e. pixel-level classification) have remained challenging problems in the weather and climate sciences. Deep learning has shown remarkable success in similar problems in computer vision, robotics, and other domains. In this chapter we take a look at various deep learning models which attempt to solve identification, detection, localization and segmentation as applied to climate data. We conclude with open challenges for the field.

Item Type:	Book Section
Additional Information:	Publisher Copyright: © 2021 John Wiley & Sons Ltd. All rights reserved.
Uncontrolled Keywords:	atmospheric rivers,deep learning,extreme weather patterns,semi-supervised approach,tropical cyclones,weather front,general engineering,general earth and planetary sciences,sdg 13 - climate action ,/dk/atira/pure/subjectarea/asjc/2200/2200
Faculty \ School:	Faculty of Science > School of Environmental Sciences Faculty of Science > Tyndall Centre for Climate Change Research University of East Anglia Research Groups/Centres > Faculty of Science > Research Centres > Tyndall Centre for Climate Change Research
Related URLs:	https://www.scopus.com/pages/publication...
Depositing User:	LivePure Connector
Date Deposited:	06 Jul 2026 15:17
Last Modified:	30 Sep 2026 11:01
URI:	https://ueaeprints.uea.ac.uk/id/eprint/103676
DOI:	10.1002/9781119646181.ch12

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