简历：
王强，威尼斯5994 教授，博士生导师，资料同化与预测研究所所长。2011年毕业于中国科学院大气物理研究所获博士学位，加拿大University of Northern British Columbia博士后（2015-2016），曾任中国科学院海洋研究所助理研究员、副研究员（2011-2019）。近年来，主要致力于理解海洋大气变异的机理及可预测性，（1）提出并发展了模式参数敏感性识别及可预测性研究的新方法，克服了现有方法的局限性，解决了现有方法难以处理的问题；（2）系统研究了海洋西边界流（黑潮）变异的机理及可预测性，揭示了影响其预报准确性的主要因子和物理机制，为更好地观测黑潮系统提供了最佳的观测位置并设计了观测网，为提高整个黑潮系统的预测水平提供了科学支撑。
已在国内外权威杂志如Journal of Climate, Journal of Geophysical Research-Oceans，Geophysical Research Letter，Climate Dynamics，Journal of Physical Oceanography，National Science Review上发表第一或通讯作者论文30余篇。相关研究工作被英国皇家学会会士Kerswell教授在流体力学领域顶级期刊Annual Review of Fluid Mechanics的综述论文中引用；同时被日本气象厅海洋预报专家Yosuke Fujii牵头国际上海洋预报领域多位专家撰写的海洋预报方面的综述论文引用。主持多项国家级科研项目，并被卫星海洋环境动力学国家重点实验室聘为青年访问“海星学者”。
海洋大气的非线性动力学、可预报性以及海洋在天气气候系统中的作用
欢迎有志于从事上述方向研究的同学与我联系（wangq@hhu.edu.cn）
Wang, Q., Mu Mu, and Stefano Pierini, 2020: The fastest growing initial error in prediction of the Kuroshio Extension state transition processes and its growth, Climate Dynamics, 54: 1953-1971.
Wang, Q., and Stefano Pierini, 2020: On the Role of the Kuroshio Extension Bimodality in Modulating the Surface Eddy Kinetic Energy Seasonal Variability, Geophysical Research Letter, 47, e2019GL086308.
Wang, Q., Mu Mu and Guodong Sun, 2020: A useful approach to sensitivity and predictability studies in geophysical fluid dynamics: conditional non-linear optimal perturbation, National Science Review, 7：214-223.
Wang, Q., Stefano Pierini and Youmin Tang, 2019: Parameter sensitivity analysis of the short-range prediction of Kuroshio extension transition processes using an optimization approach, Theoretical and Applied Climatology, 138：1481-1492
Wang, Q., Y. Tang, S. Pierini, and M. Mu, 2017: Effects of Singular-Vector-Type Initial Errors on the Short-Range Prediction of Kuroshio Extension Transition Processes, J. Climate, 30, 5961-5983.
Wang, Q., Y. Tang, H. A. Dijkstra, 2017: An Optimization Strategy for Identifying Parameter Sensitivity in Atmospheric and Oceanic Models, Monthly Weather Review, 145, 3293-3305.
Wang, Q., and Mu Mu, 2017: Application of conditional nonlinear optimal perturbation to target observations for high-impact ocean-atmospheric environmental events, S.K. Park and L. Xu (eds.), Data Assimilation for Atmospheric, Oceanic and Hydrologic Applications (Vol. III), 513-526.
Wang, Q., and Mu Mu, 2015: A new application of conditional nonlinear optimal perturbation approach to boundary condition uncertainty, J. Geophys. Res., 120, 7979-7996
Wang, Q., and M. Mu, 2014: Responses of the ocean planktonic ecosystem to finite-amplitude perturbations, J. Geophys. Res., 119, 8454-8471.
Wang, Q., M. Mu, and H. A. Dijkstra, 2013: Effects of nonlinear physical processes on optimal error growth in predictability experiments of the Kuroshio Large Meander. J. Geophys.Res., 118, 6425-6436.
Wang, Q., M. Mu, and H. A. Dijkstra, 2013: The similarity between optimal precursor and optimally growing initial error in prediction of Kuroshio large meander and its application to targeted observation. J. Geophys. Res., 118, 869-884.
Wang, Q., L. Ma, and Q. Xu, 2013: Optimal precursor of the transition from Kuroshio large meander to straight path. Chin. J. Oceanol. Limnol., 31, 1153-1161.
Wang, Q., M. Mu, and H. A. Dijkstra, 2012: Application of the conditional nonlinear optimal perturbation method to the predictability study of the Kuroshio large meander. Adv. Atmos.Sci., 29, 118-134.
Yu Geng, Q. Wang*, Mu Mu, and K. Zhang, 2020: Predictability and error growth dynamics of the Kuroshio Extension state transition process in an eddy-resolving regional ocean model. Ocean Modelling, 153, 101659.
Zhang K, Mu M, Q. Wang, 2020. Increasingly important role of numerical modeling in oceanic observation design strategy: A review. Science China Earth Sciences, 63(11): 1678-1690.
Peng Liang, Mu Mu, Q. Wang*, and Lina Yang, 2019: Optimal Precursors Triggering the Kuroshio Intrusion Into the South China Sea Obtained by the Conditional Nonlinear Optimal Perturbation Approach, Journal of Geophysical Research: Oceans, 124：3941-3962.
Kun Zhang, Mu Mu, Q. Wang*, Baoshu Yin, and Shixuan Liu, 2019: CNOP-Based Adaptive Observation Network Designed for Improving Upstream Kuroshio Transport Prediction, Journal of Geophysical Research: Oceans, 124：4350-4364.
Yuan Shijin, M. Li, Q. Wang, Zhang Kun, Zhang Huazhen, Mu Bin, 2019: Optimal precursors of double-gyre regime transitions with an adjoint-free method. Journal of Oceanology and Limnology, 37 (4): 1137-1153.
Yu Geng, Q. Wang*, and Mu Mu, 2018: Effect of the Decadal Kuroshio Extension Variability on the Seasonal Changes of the Mixed-Layer Salinity Anomalies in the Kuroshio-Oyashio Confluence Region, Journal of Geophysical Research: Oceans, 123：8849-8861.
Liu, X., M. Mu and Q. Wang*, 2018: The nonlinear optimal triggering perturbation of the Kuroshio large meander and its evolution in a regional ocean model. Journal of Physical Oceanography, 48：1771-1786.
Liu, X., Q. Wang*, and M. Mu, 2018: Optimal initial error growth in the prediction of the Kuroshio large meander based on a high-resolution regional ocean model. Advances in Atmospheric Sciences. 35(11)：1362-1371.
Zhang, K., M. Mu, and Q. Wang*, 2017: Identifying the sensitive area in adaptive observation for predicting the upstream Kuroshio transport variation in a 3-D ocean model, Sci. China. Earth. Sci., 60, 866-875.
Zhang, X., M. Mu, Q. Wang*, and S. Pierini, 2017: Optimal Precursors Triggering the Kuroshio Extension State Transition Obtained by the Conditional Nonlinear Optimal Perturbation Approach, Adv. Atmos. Sci., 34, 685-699.
Zhang, X., Q. Wang*, and Mu Mu, 2017: The impact of global warming on Kuroshio Extension and its southern recirculation using CMIP5 experiments with a high-resolution climate model MIROC4h, Theor Appl Climatol., 127, 815-827.
Zhang, K., Q. Wang*, Mu Mu, and P. Liang, 2016: Effects of optimal initial errors on predicting the seasonal reduction of the upstream Kuroshio transport, Deep-Sea Research I, 116, 220-235.
Zou, G. A., Q. Wang*, and Mu Mu, 2016: Identifying sensitive areas of adaptive observations for prediction of the Kuroshio large meander using a shallow-water model, Chin . J . Oceanol . Limnol., 34, 1122-1133.
Zhang, P., Q. Wang*, and L. Ma, 2015: Impact of nonlinear processes on formation of the Kuroshio large meander path in a barotropic inflow-outflow model. Chin. J. Oceanol. Limnol., 33, 252-261.
Mu, M., Q. Wang*, W. Duan, Z. Jiang, 2014: Application of conditional nonlinear optimal perturbation to targeted observation studies of the atmosphere and ocean, Journal of Meteorological Research, 28, 923-933.
Ma, L., and Q. Wang, 2014: Interannual variations in energy conversion and interaction between the mesoscale eddy field and mean flow in the Kuroshio south of Japan. Chin. J. Oceanol. Limnol., 32, 210-222.
Ma, L., and Q. Wang, 2014: Mean properties of mesoscale eddies in the Kuroshio recirculation region. Chin. J. Oceanol. Limnol., 32, 681-702.
Mu, M., W. Duan, Q. Wang, and R. Zhang, 2010: An extension of conditional nonlinear optimal perturbation approach and its applications, Nonlin. Processes Geophys., 17, 211-220.
张星，穆穆，王强，张坤，2018：条件非线性最优扰动方法在黑潮目标观测研究中的应用，海洋气象学报，38，1-9.
穆穆，王强，2017：非线性最优化方法在大气—海洋科学研究中的若干应用，中国科学-数学，47，1207-1222.
张坤，穆穆，王强*，2015：初始误差对双环流变异可预报性的影响，海洋科学，39，120-128.
张培军，王强*，2015：模式参数的不确定性对日本南部黑潮大弯曲路径预报的影响，海洋科学，39，101-113.
穆穆，王强*，段晚锁，姜智娜，2014：条件非线性最优扰动法在大气与海洋目标观测研究中的应用，气象学报，72，1001-1011.
徐强强, 王 强*, 马利斌, 2013: 日本南部黑潮路径发生弯曲的最优前期征兆及其发展机制, 海洋科学，37，52-61.
1. 国家自然科学基金面上项目，42076017，黑潮延伸体的第二类可预报性研究：风应力不确定性对预报的影响，2021.01-2024.12，在研，主持
2. 中国科学院战略性先导科技专项,XDA20060502，热带印度洋环流动力与季风相互作用及其影响,2018.03-2023.02,在研，专题负责人
3. 中央高校基本科研业务费项目-自由探索专项，B200201011，黑潮延伸体双模态对海洋动力环境场的影响，2020.01-2021.12，在研，主持
4. 国家自然科学基金面上项目，41576015，初始误差对黑潮延伸体年代际变异预测的影响及其机制，2016.01-2019.12，已结题，主持
5. 国家自然科学基金青年科学基金项目，41306023，模式参数误差对黑潮路径变异预报的影响，2014.01-2016.12，已结题，主持
6. 青岛海洋科学与技术国家实验室开放基金，分析海洋与气候模式中参数敏感性的新方法及其应用，2017.04-2020.08，已结题，主持
7. 国家自然科学基金重大项目，41490644，黑潮及延伸体海域海气相互作用机制及其气候效应，2015.01-2019.12，已结题，参加
8. 国家自然科学基金重点项目，41230420，可预报性研究中最优前期征兆与增长最快初始误差的相似性及其在目标观测中的应用，2013.01-2017.12，已结题，参加
9. 中国科学院战略性先导科技专项，XDA11010303，NEC和STCC的变异对黑潮上游段的影响及其可预报性，2013.07-2017.12，已结题，参加
