MDTNet: Multiscale Deformable Transformer Network With Fourier Space Losses Toward Fine-Scale Spatiotemporal Precipitation Nowcasting-Reference-Cited by-同舟云学术

MDTNet: Multiscale Deformable Transformer Network With Fourier Space Losses Toward Fine-Scale Spatiotemporal Precipitation Nowcasting

Published:2024 Issue: Volume:62 Page:1-17
ISSN:0196-2892
Container-title:IEEE Transactions on Geoscience and Remote Sensing
language:
Short-container-title:IEEE Trans. Geosci. Remote Sensing

Author:

Zhao Zewei¹^ORCID,Dong Xichao¹^ORCID,Wang Yupei¹^ORCID,Wang Jianping¹^ORCID,Chen Yubao²,Hu Cheng¹^ORCID

Affiliation:

1. School of Information and Electronics and the Key Laboratory of Electronic and Information Technology in Satellite Navigation, Ministry of Education, Beijing Institute of Technology, Beijing, China

2. Meteorological Observation Center of the China Meteorological Administration (CMA), Beijing, China

Funder

Special Fund for Research on National Major Research Instruments

National Science Fund for Distinguished Young Scholars

National Natural Science Foundation of China

Chongqing Technology Innovation and Application Development Project

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx8/36/10354519/10557669.pdf?arnumber=10557669

Reference70 articles.

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3. Development of a precipitation nowcasting algorithm based upon optical flow techniques

4. Optical flow models as an open benchmark for radar-based precipitation nowcasting (rainymotion v0.1)

5. Predictability of Precipitation from Continental Radar Images. Part III: Operational Nowcasting Implementation (MAPLE)

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