Convective-gust nowcasting based on radar reflectivity and a deep learning algorithm
Abstract ∘ × ∘ and a 6 min temporal resolution. CGsNet is shown to be effective, and it has an essential advantage in learning the spatiotemporal features of CGs. In addition, quantitative evaluation experiments indicate that CGsNet exhibits higher generalization performance for CGs than the traditional nowcasting method based on numerical weather prediction models. CG-nowcasting technology can be applied to provide real-time quantitative CG forecasts.
- Location
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Deutsche Nationalbibliothek Frankfurt am Main
- Extent
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Online-Ressource
- Language
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Englisch
- Bibliographic citation
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Convective-gust nowcasting based on radar reflectivity and a deep learning algorithm ; volume:16 ; number:12 ; year:2023 ; pages:3611-3628 ; extent:18
Geoscientific model development ; 16, Heft 12 (2023), 3611-3628 (gesamt 18)
- Creator
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Xiao, Haixia
Wang, Yaqiang
Zheng, Yu
Zheng, Yuanyuan
Zhuang, Xiaoran
Wang, Hongyan
Gao, Mei
- DOI
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10.5194/gmd-16-3611-2023
- URN
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urn:nbn:de:101:1-2023070604260358596171
- Rights
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Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Last update
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14.08.2025, 11:02 AM CEST
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Associated
- Xiao, Haixia
- Wang, Yaqiang
- Zheng, Yu
- Zheng, Yuanyuan
- Zhuang, Xiaoran
- Wang, Hongyan
- Gao, Mei
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