globalchange  > 气候变化与战略
DOI: 10.1016/j.scib.2021.03.009
论文题名:
Unified deep learning model for El Niño/Southern Oscillation forecasts by incorporating seasonality in climate data
作者: Ham Y.-G.; Kim J.-H.; Kim E.-S.; On K.-W.
刊名: Science Bulletin
ISSN: 20959273
出版年: 2021
卷: 66, 期:13
起始页码: 1358
结束页码: 1366
语种: 英语
中文关键词: 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
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
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资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/170261
Appears in Collections:气候变化与战略

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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

Recommended Citation:
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]. Science Bulletin,2021-01-01,66(13)
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