气候变化领域集成服务门户(CCPortal): Unified deep learning model for El Niño/Southern Oscillation forecasts by incorporating seasonality in climate data

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DOI	10.1016/j.scib.2021.03.009
	Unified deep learning model for El Niño/Southern Oscillation forecasts by incorporating seasonality in climate data
	Ham Y.-G.; Kim J.-H.; Kim E.-S.; On K.-W.
发表日期	2021
ISSN	20959273
起始页码	1358
结束页码	1366
卷号	66 期号:13
英文摘要	Although deep learning has achieved a milestone in forecasting the El Niño-Southern Oscillation (ENSO), the current models are insufficient to simulate diverse characteristics of the ENSO, which depends on the calendar season. Consequently, a model was generated for specific seasons which indicates these models did not consider physical constraints between different target seasons and forecast lead times, thereby leading to arbitrary fluctuations in the predicted time series. To overcome this problem and account for ENSO seasonality, we developed an all-season convolutional neural network (A_CNN) model. The correlation skill of the ENSO index was particularly improved for forecasts of the boreal spring, which is the most challenging season to predict. Moreover, activation map values indicated a clear time evolution with increasing forecast lead time. The study findings reveal the comprehensive role of various climate precursors of ENSO events that act differently over time, thus indicating the potential of the A_CNN model as a diagnostic tool. © 2021 Science China Press
关键词	Deep learning ENSO forecasts Seasonality of the ENSO
英文关键词	Atmospheric pressure; Climate models; Climatology; Deep learning; Neural networks; Climate data; Convolutional neural network; Deep learning; El Nino southern oscillation; El nino-southern oscillation forecast; Leadtime; Learning models; Neural network modelling; Seasonality; Seasonality of the el nino-southern oscillation; Forecasting
语种	英语
来源期刊	Science Bulletin
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/207417
作者单位	Department of Oceanography, Chonnam National University, Gwangju, 61186, South Korea; Kakao Brain, Bundang-gu, Seongnam-si, Gyeonggi-do 13494, South Korea
推荐引用方式 GB/T 7714	Ham Y.-G.,Kim J.-H.,Kim E.-S.,et al. Unified deep learning model for El Niño/Southern Oscillation forecasts by incorporating seasonality in climate data[J],2021,66(13).
APA	Ham Y.-G.,Kim J.-H.,Kim E.-S.,&On K.-W..(2021).Unified deep learning model for El Niño/Southern Oscillation forecasts by incorporating seasonality in climate data.Science Bulletin,66(13).
MLA	Ham Y.-G.,et al."Unified deep learning model for El Niño/Southern Oscillation forecasts by incorporating seasonality in climate data".Science Bulletin 66.13(2021).