IOCAS-IR

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U-Net Models for Representing Wind Stress Anomalies over the Tropical Pacific and Their Integrations with an Intermediate Coupled Model for ENSO Studies 期刊论文
ADVANCES IN ATMOSPHERIC SCIENCES, 2024, 页码: 14
作者:  Du, Shuangying;  Zhang, Rong-Hua
收藏  |  浏览/下载:16/0  |  提交时间:2024/06/04
U-Net models  wind stress anomalies  ICM  integration of AI and physical components  
Salinity effect-induced ENSO amplitude modulation in association with the interdecadal Pacific Oscillation 期刊论文
JOURNAL OF OCEANOLOGY AND LIMNOLOGY, 2024, 页码: 18
作者:  Zhi, Hai;  Wang, Xiaokun;  Zhang, Rong-Hua;  Lin, Pengfei;  Qi, Jifeng
收藏  |  浏览/下载:17/0  |  提交时间:2024/06/04
El Nino-Southern Oscillation (ENSO) amplitude  Interdecadal Pacific Oscillation (IPO)  ocean salinity variability  tropical Pacific  upper-ocean stratification  
Different El Ni?o Flavors and Associated Atmospheric Teleconnections as Simulated in a Hybrid Coupled Model 期刊论文
ADVANCES IN ATMOSPHERIC SCIENCES, 2024, 页码: 17
作者:  Hu, Junya;  Wang, Hongna;  Gao, Chuan;  Zhang, Rong-Hua
收藏  |  浏览/下载:50/0  |  提交时间:2024/04/07
hybrid coupled model  tropical Pacific Ocean  global atmosphere  Eastern/Central-Pacific El Nino  atmospheric teleconnections  
CMIP6 models simulation of the connection between North/South Pacific Meridional Mode and ENSO 期刊论文
JOURNAL OF OCEANOLOGY AND LIMNOLOGY, 2024, 页码: 15
作者:  Lu, Yunlong;  Feng, Junqiao;  Hu, Dunxin
收藏  |  浏览/下载:41/0  |  提交时间:2024/04/07
North and South Pacific Meridional Modes (NPMM and SPMM)  El Nino-Southern Oscillation (ENSO)  Pacific decadal oscillation (PDO)  Coupled Model Intercomparison Project Phase 6 (CMIP6)  
DeepSeaNet: A Bio-Detection Network Enabling Species Identification in the Deep Sea Imagery 期刊论文
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 2024, 卷号: 62, 页码: 13
作者:  Liu, Aiyue;  Liu, Yuhai;  Xu, Kuidong;  Zhao, Feng;  Zhou, Yuan;  Li, Xiaofeng
Adobe PDF(2856Kb)  |  收藏  |  浏览/下载:30/1  |  提交时间:2024/06/04
Data augmentation  deep-sea remotely operated vehicle (ROV) data  new species indication  real-time object detection network  seamount fine-grained dataset