Deriving Accurate Surface Meteorological States at Arbitrary Locations via Observation-Guided Continuous Neural Field Modeling-Reference-Cited by-同舟云学术

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Deriving Accurate Surface Meteorological States at Arbitrary Locations via Observation-Guided Continuous Neural Field Modeling

Published:2024 Issue: Volume:62 Page:1-20
ISSN:0196-2892
Container-title:IEEE Transactions on Geoscience and Remote Sensing
language:
Short-container-title:IEEE Trans. Geosci. Remote Sensing

Author:

Liu Zili¹^ORCID,Chen Hao²,Bai Lei²,Li Wenyuan³,Chen Keyan¹^ORCID,Wang Zhengyi²,Ouyang Wanli²,Zou Zhengxia²^ORCID,Shi Zhenwei¹^ORCID

Affiliation:

1. Image Processing Center, School of Astronautics, and the State Key Laboratory of Virtual Reality Technology and Systems, Beihang University, Beijing, China

2. Shanghai Artificial Intelligence Laboratory, Shanghai, China

3. Department of Geography, The University of Hong Kong, Hong Kong, China

Funder

National Natural Science Foundation of China

National Key Research and Development Program of China

Beijing Natural Science Foundation

Fundamental Research Funds for the Central Universities

Youth Innovation Team of China Meteorological Administration

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx8/36/10354519/10643214.pdf?arnumber=10643214

Reference74 articles.

1. Deep Learning-Based Weather Prediction: A Survey

2. AI foundation models for weather and climate: Applications, design, and implementation;Karthik Mukkavilli;arXiv:2309.10808,2023

3. Foundation models for weather and climate data understanding: A comprehensive survey;Chen;arXiv:2312.03014,2023

4. Accurate medium-range global weather forecasting with 3D neural networks

5. Learning skillful medium-range global weather forecasting

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