首页|Real-time predictions of the 2023-2024 climate conditions in the tropical Pacific using a purely data-driven Transformer model
Real-time predictions of the 2023-2024 climate conditions in the tropical Pacific using a purely data-driven Transformer model
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Real-time predictions of the 2023-2024 climate conditions in the tropical Pacific using a purely data-driven Transformer model
Following triple La Niña events during 2020-2022,the future evolution of climate conditions over the tropical Pacific has been a focused interest in ENSO-related communities.Observations and modeling studies indicate that an El Niño event is occurring in 2023;however,large uncertainties remain in terms of its detailed evolution,and the factors affecting its resultant amplitude remain to be understood.Here,a novel deep learning-based Transformer model is adopted to make real-time predictions for the 2023-2024 climate conditions in the tropical Pacific.Several key fields vital to the El Niño and Southern Oscillation(ENSO)in the tropical Pacific are collectively and simultaneously utilized in model training and in making pre-dictions;therefore,this purely data-driven model is configured in both training and predicting procedures such that the coupled ocean-atmosphere interactions are adequately represented.Also similar to dynamic models,the prediction procedure is executed in a rolling manner to allow ocean-atmosphere anomaly exchanges month by month;the related key fields during multi-month time intervals(TIs)prior to prediction target months are taken as input predictors,serving as initial conditions to precondition the future evolution more effectively.Real-time predictions indicate that the climate conditions in the tropical Pacific are surely to develop into an El Nino state in late 2023.Furthermore,sensitivity experiments are conducted to examine how prediction skills are affected by the input predictor specifications,including TIs during which information on initial conditions is retained for making predictions.A comparison with other dynamic coupled models is also made to demonstrate the prediction performance for the 2023-2024 El Nino event.
Transformer model3D-GeoformerCoupling representationThe 2023-2024 El NinoReal-time predictionPerformance and evaluation
Rong-Hua ZHANG、Lu ZHOU、Chuan GAO、Lingjiang TAO
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School of Marine Sciences,Nanjing University of Information Science and Technology,Nanjing 210044,China
Laoshan Laboratory,Qingdao 266237,China
Key Laboratory of Ocean Observation and Forecasting,Key Laboratory of Ocean Circulation and Waves,Institute of Oceanology,Chinese Academy of Sciences,Qingdao 266071,China
University of Chinese Academy of Sciences,Beijing 101408,China
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Transformer model 3D-Geoformer Coupling representation The 2023-2024 El Nino Real-time prediction Performance and evaluation
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