A 1024-Member Ensemble Data Assimilation with 3.5-Km Mesh Global Weather Simulations-Reference-Cited by-同舟云学术

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A 1024-Member Ensemble Data Assimilation with 3.5-Km Mesh Global Weather Simulations

Published:2020-11 Issue: Volume: Page:
ISSN:
Container-title:SC20: International Conference for High Performance Computing, Networking, Storage and Analysis
language:
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Author:

Yashiro Hisashi,Terasaki Koji,Kawai Yuta,Kudo Shuhei,Miyoshi Takemasa,Imamura Toshiyuki,Minami Kazuo,Inoue Hikaru,Nishiki Tatsuo,Saji Takayuki,Satoh Masaki,Tomita Hirofumi

Funder

RIKEN

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9355221/9355202/09355206.pdf?arnumber=9355206

Cited by 19 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. High Throughput Training of Deep Surrogates from Large Ensemble Runs;Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis;2023-11-11

2. Rapid simulations of atmospheric data assimilation of hourly-scale phenomena with modern neural networks;Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis;2023-11-11

3. Continuous data assimilation of large eddy simulation by lattice Boltzmann method and local ensemble transform Kalman filter (LBM-LETKF);Fluid Dynamics Research;2023-11-09

4. Overcoming computational challenges to realize meter- to submeter-scale resolution in cloud simulations using the super-droplet method;Geoscientific Model Development;2023-11-02

5. Accelerating ray tracing engine of BLENDER on the new Sunway architecture;Engineering Reports;2023-10-31

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