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The predictability of atmospheric and oceanic motions: Retrospect and prospects 期刊论文
SCIENCE CHINA-EARTH SCIENCES, 2017, 卷号: 60, 期号: 11, 页码: 2001-2012
作者:  Mu Mu;  Duan WanSuo;  Tang YouMin
Adobe PDF(817Kb)  |  收藏  |  浏览/下载：215/2  |  提交时间：2018/01/11
Atmosphere-ocean  Predictability  Intrinsic Predictability Limit  Ensemble Forecast  
Identifying the sensitive area in adaptive observation for predicting the upstream Kuroshio transport variation in a 3-D ocean model 期刊论文
SCIENCE CHINA-EARTH SCIENCES, 2017, 卷号: 60, 期号: 5, 页码: 866-875
作者:  Zhang Kun;  Mu Mu;  Wang Qiang
Adobe PDF(1350Kb)  |  收藏  |  浏览/下载：228/0  |  提交时间：2017/09/29
Sensitive Area  Adaptive Observation  The Upstream Kuroshio Transport  Conditional Nonlinear Optimal Perturbation (Cnop)  
Identifying sensitive areas of adaptive observations for prediction of the Kuroshio large meander using a shallow-water model 期刊论文
CHINESE JOURNAL OF OCEANOLOGY AND LIMNOLOGY, 2016, 卷号: 34, 期号: 5, 页码: 1122-1133
作者:  Zou Guang'an;  Wang Qiang;  Mu Mu
Adobe PDF(1002Kb)  |  收藏  |  浏览/下载：219/1  |  提交时间：2016/09/19
Kuroshio Large Meander  Conditional Nonlinear Optimal Perturbation (Cnop)  First Singular Vector (Fsv)  Sensitive Areas  
Influence of positive and negative Indian Ocean Dipoles on ENSO via the Indonesian Throughflow: Results from sensitivity experiments 期刊论文
ADVANCES IN ATMOSPHERIC SCIENCES, 2015, 卷号: 32, 期号: 6, 页码: 783-793
作者:  Zhou Qian;  Duan Wansuo;  Mu Mu;  Feng Rong
Adobe PDF(2994Kb)  |  收藏  |  浏览/下载：211/0  |  提交时间：2015/06/15
Iod  Pacific Ocean  Enso  Indonesian Throughflow  
Application of conditional nonlinear optimal perturbation to targeted observation studies of the atmosphere and ocean 期刊论文
JOURNAL OF METEOROLOGICAL RESEARCH, 2014, 卷号: 28, 期号: 5, 页码: 923-933
作者:  Mu Mu;  Wang Qiang;  Duan Wansuo;  Jiang Zhina;  Wang, Q (reprint author), Chinese Acad Sci, Inst Oceanol, Key Lab Ocean Circulat & Waves, Qingdao 266071, Peoples R China.
Adobe PDF(1687Kb)  |  收藏  |  浏览/下载：297/0  |  提交时间：2015/06/11
Conditional Nonlinear Optimal Perturbation  Targeted Observation  Enso  Kuroshio Path Variations  Blocking  
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：198/0  |  提交时间：2015/06/11
Enso Predictability  Spring Predictability Barrier  Initial Errors  Parameter Errors  Error Growth  
Can Adaptive Observations Improve Tropical Cyclone Intensity Forecasts? 期刊论文
ADVANCES IN ATMOSPHERIC SCIENCES, 2014, 卷号: 31, 期号: 2, 页码: 252-262
作者:  Qin Xiaohao;  Mu Mu;  Qin, XH (reprint author), Chinese Acad Sci, Inst Atmospher Phys, State Key Lab Numer Modeling Atmospher Sci & Geop, Beijing 100029, Peoples R China.
Adobe PDF(2793Kb)  |  收藏  |  浏览/下载：183/0  |  提交时间：2015/06/11
Adaptive Observation  Tropical Cyclone  Intensity Forecast  Conditional Nonlinear Optimal Perturbation  
Addressing the issue of fog and haze: A promising perspective from meteorological science and technology 期刊论文
SCIENCE CHINA-EARTH SCIENCES, 2014, 卷号: 57, 期号: 1, 页码: 1-2
作者:  Mu Mu;  Zhang RenHe
Adobe PDF(342Kb)  |  收藏  |  浏览/下载：153/0  |  提交时间：2015/06/15
Methods, current status, and prospect of targeted observation 期刊论文
SCIENCE CHINA-EARTH SCIENCES, 2013, 卷号: 56, 期号: 12, 页码: 1997-2005
作者:  Mu Mu;  Mu, M
Adobe PDF(404Kb)  |  收藏  |  浏览/下载：216/0  |  提交时间：2014/07/17
Targeted Observation  Atmosphere  Ocean  Predictability  
Application of the conditional nonlinear optimal perturbation method to the predictability study of the Kuroshio large meander 期刊论文
ADVANCES IN ATMOSPHERIC SCIENCES, 2012, 卷号: 29, 期号: 1, 页码: 118-134
作者:  Wang Qiang;  Mu Mu;  Dijkstra, Henk A.;  Mu, M (reprint author), Chinese Acad Sci, Inst Oceanol, Key Lab Ocean Circulat & Wave, Qingdao 266071, Peoples R China.
Adobe PDF(1434Kb)  |  收藏  |  浏览/下载：165/0  |  提交时间：2013/09/24
Conditional Nonlinear Optimal Perturbation  Kuroshio Large Meander  Predictability  Model Parameters