陶灵江(Lingjiang Tao)



陶灵江

指导老师:穆穆

taolingjiang@fudan.edu.cn

taolj1992@foxmail.com




研究方向

ENSO预报误差增长动力学机制,ENSO两类可预报性问题


教育背景

学士学位(2010-2014年),海洋科学,浙江海洋大学

硕士学位(2014-2017年),环境工程,中国科学院海洋研究所

博士学位(2017-2020年),地球流体力学,中国科学院大气物理研究所


研究经历

2020-至今,博士后,复旦大学


承担课题

1.四维变分资料同化方法在简单海气耦合模式中的应用及其对ENSO实时预报的改进,国家自然科学基金青年科学基金项目,417050822018.01-2020.12,参与

2.非线性强迫奇异向量-集合预报方法及其在厄尔尼诺和台风可预报性研究中的应用,国家自然科学基金,419309712020.01-2024.12,参与


发表论文

1.Tao L J, Duan W S*, Vannitsem S. Improving the forecasts of El Niño diversity: A nonlinear forcing singular vector approach. Climate Dynamics, 2020, 55: 739–754. DOI: 10.1007/s00382-020-05292-5

2.Tao L J, Duan W S*. Using a Nonlinear Forcing Singular Vector Approach to Reduce Model Error Effects in ENSO Forecasting. Weather and Forecasting, 2019, 34(5): 1321-42.

3.Li J X*, Steppeler J, Fang F X, Pain C C, Zhu J, Peng X, Dong L, Li Y Y, Tao L J, Leng W, Wang Y, Zheng J. Potential Numerical Techniques and Challenges for Atmospheric Modeling. Bulletin of the American Meteorological Society, 2019, 100(9): 239-242. DOI: 10.1175/BAMS-D-19-0031.1

4.Tao L J, Gao C, Zhang R H*. Model parameter-related optimal perturbations and their contributions to El Niño prediction errors. Climate Dynamics, 2019, 52(3-4): 1425-41.

5.Zhang, R H*, L J Tao, and C Gao, 2018: An improved simulation of the 2015 El Niño event by optimally correcting the initial conditions and model parameters in an intermediate coupled mode. Climate Dynamics, doi: 10.1007/s00382-017-3919-z

6.Tao L J, Gao C, Zhang R H*. ENSO Predictions in an Intermediate Coupled Model Influenced by Removing Initial Condition Errors in Sensitive Areas: A Target Observation Perspective. Adv Atmos Sci, 2018, 35(7): 853-67.

7.Tao L J, R H Zhang*, and C. Gao, 2017: Initial error-induced optimal perturbations in ENSO predictions, as derived from an intermediate coupled model. Adv Atmos Sci, 34, 791-803.

8.高川,王宏娜,陶灵江,张荣华*2017IOCAS ICM及其ENSO实时预测试验和改进,《海洋与湖沼》,6: 1289-1301.



#以上信息由本人提供,更新时间:2020/10/20