A Practical Online Incremental Learning Framework for Precipitation Nowcasting-Reference-Cited by-同舟云学术

A Practical Online Incremental Learning Framework for Precipitation Nowcasting

Published:2024 Issue: Volume:62 Page:1-14
ISSN:0196-2892
Container-title:IEEE Transactions on Geoscience and Remote Sensing
language:
Short-container-title:IEEE Trans. Geosci. Remote Sensing

Author:

Luo Chuyao¹^ORCID,Zhang Zheng²^ORCID,Lin Huiwei²^ORCID,Zhang Baoquan²^ORCID,Li Xutao²^ORCID,Zhang Tingting³^ORCID,Ye Yunming²^ORCID

Affiliation:

1. Department of Electronics and Information Engineering, Harbin Institute of Technology, Shenzhen, China

2. Department of Computer Science, Harbin Institute of Technology, Shenzhen, China

3. School of Electronics and Information Engineering and Guangdong Provincial Key Laboratory of Aerospace Communication and Networking Technology, Harbin Institute of Technology, Shenzhen, China

Funder

National Key R&D Program of China

Shenzhen Science and Technology Program

National Natural Science Foundation of China

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Earth and Planetary Sciences,Electrical and Electronic Engineering

Link

http://xplorestaging.ieee.org/ielx7/36/10354519/10309842.pdf?arnumber=10309842

Reference55 articles.

1. Motion-Guided Global–Local Aggregation Transformer Network for Precipitation Nowcasting

2. Convective Precipitation Nowcasting Using U-Net Model

3. PrecipLSTM: A Meteorological Spatiotemporal LSTM for Precipitation Nowcasting

4. MM-RNN: A Multimodal RNN for Precipitation Nowcasting

5. Skilful precipitation nowcasting using deep generative models of radar

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