Deep Learning Based Cloud Microphysics Parameterization Scheme for Numerical Weather Prediction with 1DD-CNN-Reference-Cited by-同舟云学术

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Deep Learning Based Cloud Microphysics Parameterization Scheme for Numerical Weather Prediction with 1DD-CNN

Published:2024-05-24 Issue: Volume:8 Page:47-52
ISSN:
Container-title:2024 7th International Conference on Artificial Intelligence and Big Data (ICAIBD)
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Author:

Shu Ting¹,Zhou Xiyuan²,Xie Yuanfu¹,Zhao Huan³,Chen Xunlai⁴,Wang Rui⁴

Affiliation:

1. Shenzhen Institute of Meteorological Innovation,Shenzhen,China

2. The Chinese University of Hong Kong, Shenzhen,Shenzhen,China

3. The Hong Kong Polytechnic University,Hong Kong,China

4. Shenzhen Meteorological Bureau / Shenzhen Key laboratory of severe weather in South China,Shenzhen,China

Funder

National Natural Science Foundation of China

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx8/10604456/10604435/10604645.pdf?arnumber=10604645

Reference14 articles.

1. Numerical Weather Prediction

2. New RAMS cloud microphysics parameterization part I: the single-moment scheme

3. A New Double-Moment Microphysics Parameterization for Application in Cloud and Climate Models. Part I: Description

4. Polynyas and leads: An overview of physical processes and environment

5. The Impact of Parameterized Subgrid-Scale Orographic Forcing on Systematic Errors in a Global NWP Model

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