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ClimSim-Online: A Large Multi-scale Dataset and Framework for Hybrid ML-physics Climate Emulation

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Yu, Sungduk, Hu, Zeyuan, Subramaniam, Akshay, Hannah, Walter, Peng, Liran, Lin, Jerry, Bhouri, Mohamed Aziz, Gupta, Ritwik, Lütjens, Björn, Will, Justus C., Behrens, Gunnar, Busecke, Julius J. M., Loose, Nora, Stern, Charles I., Beucler, Tom, Harrop, Bryce, Heuer, Helge, Hillman, Benjamin R., Jenney, Andrea, Liu, Nana, White, Alistair, Zheng, Tian, Kuang, Zhiming, Ahmed, Fiaz, Barnes, Elizabeth, Brenowitz, Noah D., Bretherton, Christopher, Eyring, Veronika, Ferretti, Savannah, Lutsko, Nicholas, Gentine, Pierre, Mandt, Stephan, Neelin, J. David, Yu, Rose, Zanna, Laure, Urban, Nathan, Yuval, Janni, Abernathey, Ryan, Baldi, Pierre, Chuang, Wayne, Huang, Yu, Iglesias-Suarez, Fernando, Jantre, Sanket, Ma, Po-Lun, Shamekh, Sara, Zhang, Guang, Pritchard, Michael
Natura:	Preprint
Pubblicazione:	2023
Soggetti:	Machine Learning Atmospheric and Oceanic Physics
Accesso online:	Acceder al recurso
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