TempEE: Temporal-Spatial Parallel Transformer for Radar Echo Extrapolation Beyond Autoregression

Chen, S; Shu, T; Zhao, H; Zhong, G; Chen, X

TempEE: Temporal-Spatial Parallel Transformer for Radar Echo Extrapolation Beyond Autoregression

Chen, S

Shu, T Zhao, H Zhong, G Chen, X

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Publisher:: IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Publication Type:: Journal Article
Citation:: IEEE Transactions on Geoscience and Remote Sensing, 2023, 61
Issue Date:: 2023-01-01

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Field	Value	Language
dc.contributor.author	Chen, S https://orcid.org/0000-0001-9992-2264
dc.contributor.author	Shu, T
dc.contributor.author	Zhao, H
dc.contributor.author	Zhong, G
dc.contributor.author	Chen, X
dc.date.accessioned	2024-03-09T19:45:37Z
dc.date.available	2024-03-09T19:45:37Z
dc.date.issued	2023-01-01
dc.identifier.citation	IEEE Transactions on Geoscience and Remote Sensing, 2023, 61
dc.identifier.issn	0196-2892
dc.identifier.issn	1558-0644
dc.identifier.uri	http://hdl.handle.net/10453/176399
dc.description.abstract	Meteorological radar reflectivity data (i.e., radar echo) significantly influences precipitation prediction. It can facilitate accurate and expeditious forecasting of short-term heavy rainfall bypassing the need for complex numerical weather prediction (NWP) models. In comparison to conventional models, deep-learning (DL)-based radar echo extrapolation algorithms exhibit higher effectiveness and efficiency. Nevertheless, the development of a reliable and generalized echo extrapolation algorithm is impeded by three primary challenges: cumulative error spreading, imprecise representation of sparsely distributed echoes, and inaccurate description of nonstationary motion processes. To tackle these challenges, this article proposes a novel radar echo extrapolation algorithm called temporal-spatial parallel transformer, referred to as TempEE. TempEE avoids using autoregression and instead employs a one-step forward strategy to prevent the cumulative error from spreading during the extrapolation process. Additionally, we propose the incorporation of a multilevel temporal-spatial attention mechanism to improve the algorithm's capability of capturing both global and local information while emphasizing task-related regions, including sparse echo representations, in an efficient manner. Furthermore, the algorithm extracts spatio-temporal representations from continuous echo images using a parallel encoder to model the nonstationary motion process for echo extrapolation. The superiority of our TempEE has been demonstrated in the context of the classic radar echo extrapolation task, utilizing a real-world dataset. Extensive experiments have further validated the efficacy and indispensability of various components within TempEE.
dc.language	English
dc.publisher	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
dc.relation.ispartof	IEEE Transactions on Geoscience and Remote Sensing
dc.relation.isbasedon	10.1109/TGRS.2023.3311510
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0404 Geophysics, 0906 Electrical and Electronic Engineering, 0909 Geomatic Engineering
dc.subject.classification	Geological & Geomatics Engineering
dc.subject.classification	37 Earth sciences
dc.subject.classification	40 Engineering
dc.title	TempEE: Temporal-Spatial Parallel Transformer for Radar Echo Extrapolation Beyond Autoregression
dc.type	Journal Article
utslib.citation.volume	61
utslib.for	0404 Geophysics
utslib.for	0906 Electrical and Electronic Engineering
utslib.for	0909 Geomatic Engineering
pubs.organisational-group	University of Technology Sydney
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
utslib.copyright.status	closed_access	*
dc.date.updated	2024-03-09T19:45:33Z
pubs.publication-status	Published
pubs.volume	61

Abstract:

Meteorological radar reflectivity data (i.e., radar echo) significantly influences precipitation prediction. It can facilitate accurate and expeditious forecasting of short-term heavy rainfall bypassing the need for complex numerical weather prediction (NWP) models. In comparison to conventional models, deep-learning (DL)-based radar echo extrapolation algorithms exhibit higher effectiveness and efficiency. Nevertheless, the development of a reliable and generalized echo extrapolation algorithm is impeded by three primary challenges: cumulative error spreading, imprecise representation of sparsely distributed echoes, and inaccurate description of nonstationary motion processes. To tackle these challenges, this article proposes a novel radar echo extrapolation algorithm called temporal-spatial parallel transformer, referred to as TempEE. TempEE avoids using autoregression and instead employs a one-step forward strategy to prevent the cumulative error from spreading during the extrapolation process. Additionally, we propose the incorporation of a multilevel temporal-spatial attention mechanism to improve the algorithm's capability of capturing both global and local information while emphasizing task-related regions, including sparse echo representations, in an efficient manner. Furthermore, the algorithm extracts spatio-temporal representations from continuous echo images using a parallel encoder to model the nonstationary motion process for echo extrapolation. The superiority of our TempEE has been demonstrated in the context of the classic radar echo extrapolation task, utilizing a real-world dataset. Extensive experiments have further validated the efficacy and indispensability of various components within TempEE.

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http://hdl.handle.net/10453/176399