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A Hybrid Neural Network Model for ENSO Prediction in Combination with Principal Oscillation Pattern Analyses 期刊论文
ADVANCES IN ATMOSPHERIC SCIENCES, 2022, 页码: 14
作者:  Zhou, Lu;  Zhang, Rong-Hua
Adobe PDF(2978Kb)  |  收藏  |  浏览/下载：128/0  |  提交时间：2022/04/12
ENSO prediction  the principal oscillation pattern (POP) analyses  neural network  a hybrid approach  
Structure and Evolution of Decadal Spiciness Variability in the North Pacific during 2004-20, Revealed from Argo Observations 期刊论文
ADVANCES IN ATMOSPHERIC SCIENCES, 2022, 页码: 14
作者:  Zhou, Guanghui;  Zhang, Rong-Hua
Adobe PDF(4536Kb)  |  收藏  |  浏览/下载：137/0  |  提交时间：2022/04/12
isopycnal analysis  spiciness anomalies  subduction pathway  advection role  decadal variability  North Pacific  
The Optimal Precursors for ENSO Events Depicted Using the Gradient-definition-based Method in an Intermediate Coupled Model 期刊论文
ADVANCES IN ATMOSPHERIC SCIENCES, 2019, 卷号: 36, 期号: 12, 页码: 1381-1392
作者:  Mu, Bin;  Ren, Juhui;  Yuan, Shijin;  Zhang, Rong-Hua;  Chen, Lei;  Gao, Chuan
Adobe PDF(1429Kb)  |  收藏  |  浏览/下载：166/0  |  提交时间：2020/09/21
optimal precursor  ENSO  gradient-definition-based method  conditional nonlinear optimal perturbation  intermediate coupled model  
Optimal Initial Error Growth in the Prediction of the Kuroshio Large Meander Based on a High-resolution Regional Ocean Model 期刊论文
ADVANCES IN ATMOSPHERIC SCIENCES, 2018, 卷号: 35, 期号: 11, 页码: 1362-1372
作者:  Li, Xia;  Wang, Qiang;  Mu, Mu
Adobe PDF(16199Kb)  |  收藏  |  浏览/下载：333/0  |  提交时间：2019/08/27
Kuroshio large meander  predictability  ROMS  optimal initial error growth  
ENSO Predictions in an Intermediate Coupled Model Influenced by Removing Initial Condition Errors in Sensitive Areas: A Target Observation Perspective 期刊论文
ADVANCES IN ATMOSPHERIC SCIENCES, 2018, 卷号: 35, 期号: 7, 页码: 853-867
作者:  Tao, Ling-Jiang;  Gao, Chuan;  Zhang, Rong-Hua
Adobe PDF(1354Kb)  |  收藏  |  浏览/下载：337/0  |  提交时间：2019/08/21
El Nino prediction  initial condition errors  target observations  
Idealized Experiments for Optimizing Model Parameters Using a 4D-Variational Method in an Intermediate Coupled Model of ENSO 期刊论文
ADVANCES IN ATMOSPHERIC SCIENCES, 2018, 卷号: 35, 期号: 4, 页码: 410-422
作者:  Gao, Chuan;  Zhang, Rong-Hua;  Wu, Xinrong;  Sun, Jichang
Adobe PDF(1370Kb)  |  收藏  |  浏览/下载：265/0  |  提交时间：2019/08/21
intermediate coupled model  ENSO modeling  4D-Var data assimilation system  optimization of model parameter and initial condition  
Initial error-induced optimal perturbations in ENSO predictions, as derived from an intermediate coupled model 期刊论文
ADVANCES IN ATMOSPHERIC SCIENCES, 2017, 卷号: 34, 期号: 6, 页码: 791-803
作者:  Tao, Ling-Jiang;  Zhang, Rong-Hua;  Gao, Chuan
Adobe PDF(1934Kb)  |  收藏  |  浏览/下载：261/0  |  提交时间：2017/09/29
El Nino Predictability  Initial Errors  Intermediate Coupled Model  Spring Predictability Barrier  
Optimal precursors triggering the Kuroshio Extension state transition obtained by the Conditional Nonlinear Optimal Perturbation approach 期刊论文
ADVANCES IN ATMOSPHERIC SCIENCES, 2017, 卷号: 34, 期号: 6, 页码: 685-699
作者:  Zhang, Xing;  Mu, Mu;  Wang, Qiang;  Pierini, Stefano
Adobe PDF(9596Kb)  |  收藏  |  浏览/下载：253/0  |  提交时间：2017/09/29
Kuroshio Extension  States Transition  Cnop Approach  Optimal Precursor  Ocean Modeling  
Testing a four-dimensional variational data assimilation method using an improved intermediate coupled model for ENSO analysis and prediction 期刊论文
ADVANCES IN ATMOSPHERIC SCIENCES, 2016, 卷号: 33, 期号: 7, 页码: 875-888
作者:  Gao, Chuan;  Wu, Xinrong;  Zhang, Rong-Hua
Adobe PDF(1168Kb)  |  收藏  |  浏览/下载：317/0  |  提交时间：2016/09/21
Four-dimensional Variational Data Assimilation  Intermediate Coupled Model  Twin Experiment  Enso Prediction  
Role of parameter errors in the spring predictability barrier for ENSO events in the Zebiak-Cane model 期刊论文
ADVANCES IN ATMOSPHERIC SCIENCES, 2014, 卷号: 31, 期号: 3, 页码: 647-656
作者:  Yu Liang;  Mu Mu;  Yu, Yanshan;  Mu, M (reprint author), Chinese Acad Sci, Inst Oceanol, Key Lab Ocean Circulat & Waves, Qingdao 266071, Peoples R China.
Adobe PDF(500Kb)  |  收藏  |  浏览/下载：203/0  |  提交时间：2015/06/11
Enso Predictability  Spring Predictability Barrier  Initial Errors  Parameter Errors  Error Growth