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Upper-Ocean Lateral Heat Transports in the Nino3.4 Region and Their Connection with ENSO 期刊论文
JOURNAL OF METEOROLOGICAL RESEARCH, 2022, 卷号: 36, 期号: 2, 页码: 360-373
作者:  Zhao, He;  Wu, Tongwen;  Li, Laurent Z. X.;  Wu, Fanghua;  Jie, Weihua;  Liu, Xiangwen;  Zhang, Ronghua;  Xie, Chengjun
Adobe PDF(894Kb)  |  收藏  |  浏览/下载：257/0  |  提交时间：2022/07/18
heat budget  lateral heat transport  surface heat flux  Nino3.4 region  El Nino-Southern Oscillation (EN-SO)  
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：178/0  |  提交时间：2022/04/12
ENSO prediction  the principal oscillation pattern (POP) analyses  neural network  a hybrid approach  
Interannual Salinity Variability Associated With the Central Pacific and Eastern Pacific El Ninos in the Tropical Pacific 期刊论文
JOURNAL OF GEOPHYSICAL RESEARCH-OCEANS, 2020, 卷号: 125, 期号: 10, 页码: 19
作者:  Zhi, Hai;  Zhang, Rong-Hua;  Lin, Pengfei;  Yu, Peng;  Zhou, Guanghui;  Shi, Shiwei
Adobe PDF(13185Kb)  |  收藏  |  浏览/下载：340/0  |  提交时间：2021/04/21
the eastern and central Pacific El Ninos  mixed layer salinity variability  salinity budget  the relationship between SST and MLS  
Purely satellite data-driven deep learning forecast of complicated tropical instability waves 期刊论文
SCIENCE ADVANCES, 2020, 卷号: 6, 期号: 29, 页码: 9
作者:  Zheng, Gang;  Li, Xiaofeng;  Zhang, Rong-Hua;  Liu, Bin
Adobe PDF(7758Kb)  |  收藏  |  浏览/下载：97/0  |  提交时间：2020/09/25
A review of progress in coupled ocean-atmosphere model developments for ENSO studies in China 期刊论文
JOURNAL OF OCEANOLOGY AND LIMNOLOGY, 2020, 卷号: 38, 期号: 4, 页码: 930-961
作者:  Zhang Rong-Hua;  Yu Yongqiang;  Song Zhenya;  Ren Hong-Li;  Tang Youmin;  Qiao Fangli;  Wu Tongwen;  Gao Chuan;  Hu Junye;  Tian Feng;  Zhu Yuchao;  Chen Lin;  Liu Hailong;  Lin Pengfei;  Wu Fanghua;  Wang Lin
Adobe PDF(3839Kb)  |  收藏  |  浏览/下载：242/0  |  提交时间：2020/09/25
El Nino-Southern Oscillation (ENSO)  coupled ocean-atmosphere models  simulations and predictions  model biases and uncertainties  
Interannual Salinity Variability in the Tropical Pacific in CMIP5 Simulations 期刊论文
ADVANCES IN ATMOSPHERIC SCIENCES, 2019, 卷号: 36, 期号: 4, 页码: 378-396
作者:  Zhi, Hai;  Zhang, Rong-Hua;  Lin, Pengfei;  Yu, Peng
Adobe PDF(5481Kb)  |  收藏  |  浏览/下载：401/0  |  提交时间：2019/08/28
mixed-layer salinity  salt budget  interannual variability  tropical Pacific  model simulation  
Model parameter-related optimal perturbations and their contributions to El Nino prediction errors 期刊论文
CLIMATE DYNAMICS, 2019, 卷号: 52, 期号: 3-4, 页码: 1425-1441
作者:  Tao, Ling-Jiang;  Gao, Chuan;  Zhang, Rong-Hua
Adobe PDF(6712Kb)  |  收藏  |  浏览/下载：227/0  |  提交时间：2019/05/15
Intermediate coupled model  CNOP approach  Model parameters  El Nino predictability  
Progress in ENSO prediction and predictability study 期刊论文
NATIONAL SCIENCE REVIEW, 2018, 卷号: 5, 期号: 6, 页码: 826-839
作者:  Tang, Youmin;  Zhang, Rong-Hua;  Liu, Ting;  Duan, Wansuo;  Yang, Dejian;  Zheng, Fei;  Ren, Hongli;  Lian, Tao;  Gao, Chuan;  Chen, Dake;  Mu, Mu
Adobe PDF(1925Kb)  |  收藏  |  浏览/下载：474/0  |  提交时间：2019/08/27
ENSO prediction and predictability  coupled model  ensemble prediction  optimal error growth  probabilistic prediction  
An improved simulation of the 2015 El Nio event by optimally correcting the initial conditions and model parameters in an intermediate coupled model 期刊论文
CLIMATE DYNAMICS, 2018, 卷号: 51, 期号: 1-2, 页码: 269-282
作者:  Zhang, Rong-Hua;  Tao, Ling-Jiang;  Gao, Chuan
Adobe PDF(3230Kb)  |  收藏  |  浏览/下载：354/0  |  提交时间：2019/08/21
The 2015 El Nino event  ICM  The CNOP-based approach  Optimal bias corrections to ICs and MPs  
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：326/0  |  提交时间：2019/08/21
intermediate coupled model  ENSO modeling  4D-Var data assimilation system  optimization of model parameter and initial condition