Muszynski, Grzegorz, Prabhat, , Balewski, Jan, Kashinath, Karthik, Wehner, Michael, Kurlin, Vitaliy 
ORCID: 0000-0001-5328-5351 and SOC, IEEE COMP
(2021)
Atmospheric Blocking Pattern Recognition in Global Climate Model Simulation Data.
In: 2020 25th International Conference on Pattern Recognition (ICPR), 2021-1-10 - 2021-1-15.
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Abstract
In this paper, we address a problem of atmospheric blocking pattern recognition in global climate model simulation data. Understanding blocking events is a crucial problem to society and natural infrastructure, as they often lead to weather extremes, such as heat waves, heavy precipitation, and the unusually poor air condition. Moreover, it is very challenging to detect these events as there is no physics-based model of blocking dynamic development that could account for their spatiotemporal characteristics. Here, we propose a new two-stage hierarchical pattern recognition method for detection and localisation of atmospheric blocking events in different regions over the globe. For both the detection stage and localisation stage, we train five different architectures of a convolutional neural network (CNN) based classifier and regressor. The results show the general pattern of the atmospheric blocking detection performance increasing significantly for the deep CNN architectures. In contrast, we see the estimation error of event location decreasing significantly in the localisation problem for the shallow CNN architectures. We demonstrate that CNN architectures tend to achieve the highest accuracy for blocking event detection and the lowest estimation error of event localisation in regions of the Northern Hemisphere than in regions of the Southern Hemisphere.
| Item Type: | Conference or Workshop Item (Unspecified) |
|---|---|
| Uncontrolled Keywords: | 13 Climate Action |
| Divisions: | Faculty of Science and Engineering > School of Electrical Engineering, Electronics and Computer Science |
| Depositing User: | Symplectic Admin |
| Date Deposited: | 07 Oct 2021 08:02 |
| Last Modified: | 14 Mar 2024 17:32 |
| DOI: | 10.1109/ICPR48806.2021.9412736 |
| Related URLs: | |
| URI: | https://livrepository.liverpool.ac.uk/id/eprint/3139537 |
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