Predictability of sea surface temperature anomalies at the eastern pole of the Indian Ocean Dipole—using a convolutional neural network model

In this study, we train a convolutional neural network (CNN) model using a selection of Coupled Model Intercomparison Project (CMIP) phase 5 and 6 models to investigate the predictability of the sea surface temperature (SST) variability off the Sumatra-Java coast in the tropical southeast Indian Oce...

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Detaylı Bibliyografya
Asıl Yazarlar:	Ming Feng, Fabio Boschetti, Fenghua Ling, Xuebin Zhang, Jason R. Hartog, Mahmood Akhtar, Li Shi, Brint Gardner, Jing-Jia Luo, Alistair J. Hobday
Materyal Türü:	Artigo
Dil:	Inglês
Baskı/Yayın Bilgisi:	Frontiers Media S.A. 2022-08-01
Seri Bilgileri:	Frontiers in Climate
Konular:	sea surface temperature (SST) eastern Indian Ocean prediction model machine learning (ML) convolutional neural network Indian Ocean Dipole (IOD)
Online Erişim:	https://www.frontiersin.org/articles/10.3389/fclim.2022.925068/full
Etiketler:	Etiketle Etiket eklenmemiş, İlk siz ekleyin!

Predictability of sea surface temperature anomalies at the eastern pole of the Indian Ocean Dipole—using a convolutional neural network model

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