中文版 | English

在结果中检索

IOCAS-IR

浏览/检索结果: 共5条，第1-5条 帮助

限定条件                        
已选(0)清除 条数/页：   排序方式：
Intraseasonal Variability of the North Equatorial Current Bifurcation Off the Philippines 期刊论文
JOURNAL OF GEOPHYSICAL RESEARCH-OCEANS, 2021, 卷号: 126, 期号: 11, 页码: 16
作者:  Zhou, Hui;  Liu, Xueqi;  Li, Ruixiang;  Wang, Yonggang;  Yang, Guang
Adobe PDF(5032Kb)  |  收藏  |  浏览/下载：171/0  |  提交时间：2022/02/18
western tropical Pacific Ocean  NEC bifurcation  intraseasonal variability  mesoscale eddy  MJO  cyclonic eddy  
Indian Ocean warming as a potential trigger for super phytoplankton blooms in the eastern equatorial Pacific from El Nino to La Nina transitions 期刊论文
ENVIRONMENTAL RESEARCH LETTERS, 2021, 卷号: 16, 期号: 5, 页码: 11
作者:  Tian, Feng;  Zhang, Rong-Hua;  Wang, Xiujun
Adobe PDF(2045Kb)  |  收藏  |  浏览/下载：260/0  |  提交时间：2021/06/04
El Niñ  o–  Southern Oscillation  phytoplankton blooms  Indian Ocean warming  ocean chlorophyll  
Coupling ocean-atmosphere intensity determines ocean chlorophyll-induced SST change in the tropical Pacific 期刊论文
CLIMATE DYNAMICS, 2021, 页码: 21
作者:  Tian, Feng;  Zhang, Rong-Hua;  Wang, Xiujun
Adobe PDF(3219Kb)  |  收藏  |  浏览/下载：267/0  |  提交时间：2021/04/14
A three-dimensional gravest empirical mode determined from hydrographic observations in the western equatorial Pacific Ocean 期刊论文
JOURNAL OF MARINE SYSTEMS, 2021, 卷号: 214, 页码: 19
作者:  Liu, Hengchang;  Zhou, Hui;  Yang, Wenlong;  Liu, Xueqi;  Li, Yao;  Yang, Ya;  Chen, Xu;  Li, Xiaofeng
Adobe PDF(23320Kb)  |  收藏  |  浏览/下载：276/0  |  提交时间：2021/04/21
Nascent NECC  Volume transport  PIES  Non-stationary gravest empirical mode  El Nino  
A Machine-Learning Model for Forecasting Internal Wave Propagation in the Andaman Sea 期刊论文
IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING, 2021, 卷号: 14, 页码: 3095-3106
作者:  Zhang, Xudong;  Li, Xiaofeng;  Zheng, Quanan
Adobe PDF(6735Kb)  |  收藏  |  浏览/下载：281/0  |  提交时间：2021/05/11
Predictive models  Training  Satellites  Machine learning  Oceans  MODIS  Data models  Andaman sea  internal wave (IW) forecast  machine learning