Identifying and interpreting extreme rainfall events using image classification

Barnes, Andrew Paul; McCullen, Nick; Kjeldsen, Thomas Rodding

Abstract

This study presents the first attempt to identify extreme rainfall events based on surrounding sea-level pressure anomalies, using neural network-based classification. Sensitivity analysis was also performed to identify the spatial importance of sea-level pressure anomalies. Three classification models were generated: the first classifies the patterns between extreme and regular rainfall events in the North West of England, the second classifies the patterns between extreme and regular rainfall events in the South East of England, and the third classifies between the patterns of extreme events in the North West and South East of England. All classifiers obtain accuracies between 60 and 65%, with precision and recall metrics showing that extreme events are easier to identify than regular events. Finally, a sensitivity analysis is performed to identify the spatial importance of the patterns across the North Atlantic, highlighting that for all three classifiers the local anomaly sea-level pressure patterns around the British Isles are key to determining the difference between extreme and regular rainfall events. In contrast, the pattern across the mid and western North Atlantic shows no contribution to the overall classifications.

• Journal Article Type: Article
• Acceptance Date: Aug 11, 2021
• Online Publication Date: Aug 27, 2021
• Publication Date: Nov 1, 2021
• Deposit Date: Apr 12, 2022
• Publicly Available Date: Apr 12, 2022
• Journal: Journal of Hydroinformatics
• Print ISSN: 1464-7141
• Publisher: IWA Publishing
• Peer Reviewed: Peer Reviewed
• Volume: 23
• Issue: 6
• Pages: 1214-1223
• DOI: https://doi.org/10.2166/hydro.2021.030
• Keywords: Atmospheric Science; Geotechnical Engineering and Engineering Geology; Civil and Structural Engineering; Water Science and Technology
• Public URL: https://uwe-repository.worktribe.com/output/9319531