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Multi-Scale Window Spatiotemporal Attention Network for Subsurface Temperature Prediction and Reconstruction 期刊论文
REMOTE SENSING, 2024, 卷号: 16, 期号: 12, 页码: 18
作者:  Jiang, Jiawei;  Wang, Jun;  Liu, Yiping;  Huang, Chao;  Jiang, Qiufu;  Feng, Liqiang;  Wan, Liying;  Zhang, Xiangguang
收藏  |  浏览/下载：40/0  |  提交时间：2024/09/02
temperature structure prediction  temperature structure reconstruction  spatiotemporal window ocean  satellite observations  spatiotemporal attention mechanism  
Evaluation of machine learning method in genomic selection for growth traits of Pacific white shrimp 期刊论文
AQUACULTURE, 2024, 卷号: 581, 页码: 9
作者:  Luo, Zheng;  Yu, Yang;  Bao, Zhenning;  Li, Fuhua
收藏  |  浏览/下载：128/0  |  提交时间：2024/04/07
Growth traits  Genomic selection  Auto-machine learning  Prediction accuracy  Litopeneaus vannamei  
Graph-Based Memory Recall Recurrent Neural Network for Mid-Term Sea-Surface Height Anomaly Forecasting 期刊论文
IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING, 2024, 卷号: 17, 页码: 6642-6657
作者:  Zhou, Yuan;  Ren, Tian;  Chen, Keran;  Gao, Le;  Li, Xiaofeng
收藏  |  浏览/下载：35/0  |  提交时间：2024/06/04
Forecasting  Predictive models  Atmospheric waves  Spatiotemporal phenomena  Sea surface  Ocean waves  Data models  Sea-surface height anomaly (SSHA)  deep learning (DL)  spatiotemporal prediction  Rossby waves  
Effects of Wind Stress Uncertainty on Short-Term Prediction of the Kuroshio Extension State Transition Process 期刊论文
JOURNAL OF PHYSICAL OCEANOGRAPHY, 2023, 卷号: 53, 期号: 12, 页码: 2751-2771
作者:  Zhang, Hui;  Wang, Qiang;  Mu, Mu;  Zhang, Kun;  Geng, Yu
Adobe PDF(13027Kb)  |  收藏  |  浏览/下载：128/0  |  提交时间：2024/04/07
Wind  Forecast verification/skill  Numerical weather prediction/forecasting  Mesoscale models  
Subseasonal Prediction of Regional Antarctic Sea Ice by a Deep Learning Model 期刊论文
GEOPHYSICAL RESEARCH LETTERS, 2023, 卷号: 50, 期号: 17, 页码: 10
作者:  Wang, Yunhe;  Yuan, Xiaojun;  Ren, Yibin;  Bushuk, Mitchell;  Shu, Qi;  Li, Cuihua;  Li, Xiaofeng
收藏  |  浏览/下载：99/0  |  提交时间：2023/12/13
Antarctic  sea ice prediction  
Understanding Arctic Sea Ice Thickness Predictability by a Markov Model 期刊论文
JOURNAL OF CLIMATE, 2023, 卷号: 36, 期号: 15, 页码: 4879-4897
作者:  Wang, Yunhe;  Yuan, Xiaojun;  Bi, Haibo;  Ren, Yibin;  Liang, Yu;  Li, Cuihua;  Li, Xiaofeng
收藏  |  浏览/下载：187/0  |  提交时间：2023/12/13
Arctic  Sea ice  Climate prediction  Ice thickness  
A Transformer-Based Deep Learning Model for Successful Predictions of the 2021 Second-Year La Nina Condition 期刊论文
GEOPHYSICAL RESEARCH LETTERS, 2023, 卷号: 50, 期号: 12, 页码: 10
作者:  Gao, Chuan;  Zhou, Lu;  Zhang, Rong-Hua
收藏  |  浏览/下载：236/0  |  提交时间：2023/11/30
the 2021 second-year cooling condition  a transformer-based deep learning model  3D multivariate prediction  subsurface thermal effect  comparison with dynamical models  
A multi-model prediction system for ENSO 期刊论文
SCIENCE CHINA-EARTH SCIENCES, 2023, 页码: 10
作者:  Liu, Ting;  Gao, Yanqiu;  Song, Xunshu;  Gao, Chuan;  Tao, Lingjiang;  Tang, Youmin;  Duan, Wansuo;  Zhang, Rong-Hua;  Chen, Dake
收藏  |  浏览/下载：183/0  |  提交时间：2023/12/13
MME  ENSO  Prediction  
Predicting the Daily Sea Ice Concentration on a Subseasonal Scale of the Pan-Arctic During the Melting Season by a Deep Learning Model 期刊论文
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 2023, 卷号: 61, 页码: 15
作者:  Ren, Yibin;  Li, Xiaofeng
收藏  |  浏览/下载：53/0  |  提交时间：2023/12/13
Predictive models  Atmospheric modeling  Sea ice  Numerical models  Arctic  Ocean temperature  Data models  Deep learning  Pan-Arctic  physically constrained loss function  sea ice concentration (SIC) prediction  subseasonal scale  
High-throughput prediction and characterization of antimicrobial peptides from multi-omics datasets of Chinese tubular cone snail (Conus betulinus) 期刊论文
FRONTIERS IN MARINE SCIENCE, 2022, 卷号: 9, 页码: 13
作者:  Li, Ruihan;  Huang, Yu;  Peng, Chao;  Gao, Zijian;  Liu, Jie;  Yin, Xiaoting;  Gao, Bingmiao;  Ovchinnikova, Tatiana V.;  Qiu, Limei;  Bian, Chao;  Shi, Qiong
收藏  |  浏览/下载：67/0  |  提交时间：2023/12/13
Conus betulinus  antimicrobial peptide  multi-omics  in silico prediction  in vitro assessment