王强

欢迎有志于从事上述方向研究的同学和博士后与我联系（wangq@hhu.edu.cn）

主讲课程：

Qian, J., Wang, Q*., Liang, P., Peng, S., Wang, H., and Wu, Y. 2024: Deep learning-based ensemble forecast and predictability analysis of the Kuroshio intrusion into the South China Sea, Journal of Physical Oceanography, DOI: 10.1175/JPO-D-23-0175.1.

Peng, S., and Wang, Q*., 2024: Fast enhancement of the stratification in the Indian Ocean over the past 20 years. Journal of Climate, 37, 2231-2245.

Wang, Q., and Stefano Pierini, 2023: Causal forcing analysis on the low-frequency variations of eddy kinetic energy in the Kuroshio Extension region. Journal of Climate, 36, 3749-3763.

Wang, Q., and Li, X. 2023: Interannual variability and mechanism of ocean stratification over the Kuroshio Extension region in the warm season. Climate Dynamics, 61, 3481–3497.

Zhang, H., Wang, Q*., Mu, M., Zhang, K., and Geng, Y. 2023: Effects of Wind Stress Uncertainty on Short-Term Prediction of the Kuroshio Extension State Transition Process. Journal of Physical Oceanography, 53, 2751-2771.

Zhang, K., Wang, Q., Yin, B., Yang, D., and Yang L., 2023: Contribution of Deep Vertical Velocity to Deficiency of Sverdrup Transport in the Low-Latitude North Pacific. Journal of Physical Oceanography, 53, 2651-2668.

Chen, H., Wang, Q*. and Zhang, R. 2023: Sensitivity of El Niño diversity prediction to parameters in an intermediate coupled model. Climate Dynamics, 61, 2485–2502.

Qian, J., Wang, Q*., Wu, Y., Zhu, X.-H., and Shi, Y. 2023: Causality-based deep learning forecast of the Kuroshio volume transport in the East China Sea. Earth and Space Science, 10, e2022EA002722.

Geng, Y., Ren, HL. and Wang, Q. 2023: Seasonal modulation of mixed-layer temperature anomaly in Kuroshio–Oyashio confluence region by bimodal Kuroshio extension. Climate Dynamics, 60:3051–3063.

Ren, Q. J., M. Mu, G. D. Sun, and Wang, Q., 2023: A new sensitivity analysis approach using conditional nonlinear optimal perturbations and its preliminary application. Adv. Atmos. Sci., 40(2), 285−304.

Li, Y., Tang, Y., Wang, S., Toumi, R., Song, X., and Wang, Q., 2023: Recent increases in tropical cyclone rapid intensification events in global offshore regions. Nat Commun., 14, 5167.

Li, Y., Tang, Y., Li, X., Song, X., and Wang, Q.,2023:Recent increase in the potential threat of western North Pacific tropical cyclones. npj Clim Atmos Sci., 6, 53. https://doi.org/10.1038/s41612-023-00379-2.

Wang, Q., and Tang, Y., 2022: The interannual variability of eddy kinetic energy in the Kuroshio large meander region and its relationship to the Kuroshio latitudinal position at 140°E. Journal of Geophysical Research: Oceans, 127, e2021JC017915.

Zhang, H., Wang, Q*., Mu, M., and Liu, X. 2022: Local energetics mechanism for the short-term shift between Kuroshio Extension bimodality. Journal of Geophysical Research: Oceans, 127, e2022JC018794.

Liu, X., Wang, Q*., and Zhang, H. 2022: Optimal precursor triggering Kuroshio large meander decay obtained in a regional ocean model. Journalof Geophysical Research: Oceans, 127, e2021JC018397.

Liu X., Wang, Q.*, and Mu M., 2022: Identifying the sensitive areas in targeted observation for predicting the Kuroshio large meander path in a regional ocean model. Acta Oceanologica Sinica, 41(2), 3–14.

Zhou, L., Zhang, K., Wang, Q., and Mu Mu, 2022: Optimally growing initial error for predicting the sudden shift in the Antarctic Circumpolar Current transport and its application to targeted observation. Ocean Dynamics, 72, 785-800

Zhang K, Wang, Q., and Yin, B. 2022: Decadal sea surface height modes in the low-latitude northwestern Pacific and their contribution to the North Equatorial Current transport variation. J Oceanogr., 78, 381-395.

Mu M., Zhang K., and Wang, Q., 2022: Recent Progress in Applications of the Conditional Nonlinear Optimal Perturbation Approach to Atmosphere-Ocean Sciences. Chin. Ann. Math. Ser. B 43(6), 1033-1048.

Zhou, L., Wang, Q*., Mu, M., and Zhang, K. 2021: Optimal precursors triggering sudden shifts in the Antarctic circumpolar current transport through Drake Passage. Journal of Geophysical Research: Oceans, 126, e2021JC017899.

Liu, J., Tang, Y., Wu, Y., Li, T., Wang, Q., and Chen, D. 2021: Forecasting the Indian Ocean Dipole with deep learning techniques. Geophysical Research Letters, 48, e2021GL094407.

Wang, Q., Mu M., 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 M., and Sun G., 2020: A useful approach to sensitivity and predictability studies in geophysical fluid dynamics: conditional non-linear optimal perturbation, National Science Review, 7, 214-223.

Zhang, K., Mu M., and Wang, Q., 2020: Increasingly important role of numerical modeling in oceanic observation design strategy: A review. Science China Earth Sciences, 63(11): 1678–1690.

Geng, Y., Wang, Q*., 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.

Wang, Q., Stefano Pierini and Tang Y., 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.

Peng Liang, Mu Mu, Wang, Q*., 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.

Zhang K., Mu M., Wang, Q*., Yin B., and Liu, S. 2019: CNOP-Based Adaptive Observation Network Designed for Improving Upstream Kuroshio Transport Prediction, Journal of Geophysical Research: Oceans, 124, 4350-4364.

Yuan S., M. Li, Wang, Q., Zhang K., Zhang H., and Mu B., 2019: Optimal precursors of double-gyre regime transitions with an adjoint-free method. Journal of Oceanology and Limnology, 37 (4), 1137-1153.

Geng Y., Wang, Q*., and Mu M., 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 Wang, Q*., 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., Wang, Q*., 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.

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, and 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 M., 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.

Zhang, K., M. Mu, and Wang, Q*., 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, Wang, Q*., 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., Wang, Q*., and Mu M., 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., Wang, Q*., Mu M., 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., Wang, Q*., and Mu M., 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.

Wang, Q., and Mu M., 2015: A new application of conditional nonlinear optimal perturbation approach to boundary condition uncertainty, J. Geophys. Res. Oceans, 120, 7979-7996

Zhang, P., Wang, Q*., 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.

Wang, Q., and M. Mu, 2014: Responses of the ocean planktonic ecosystem to finite-amplitude perturbations, J. Geophys. Res., 119, 8454-8471.

Mu, M., Wang, Q*., W. Duan, and 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 Wang, Q., 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 Wang, Q., 2014: Mean properties of mesoscale eddies in the Kuroshio recirculation region. Chin. J. Oceanol. Limnol., 32, 681-702.

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. Oceans, 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.Oceans, 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.

Mu, M., W. Duan, Wang, Q., and R. Zhang, 2010: An extension of conditional nonlinear optimal perturbation approach and its applications, Nonlin. Processes Geophys., 17, 211-220.

陈成吉，王强*，2023：日本南部黑潮与黑潮延伸体路径状态关联性的定量分析，海洋科学，47（4），1-8.

张坤, 穆穆, 王强，2021：数值模式在海洋观测设计中的重要作用:回顾与展望.中国科学:地球科学,51(5), 653–665.

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.

孙国栋，穆穆，段晚锁，王强，彭飞，2016：条件非线性最优扰动（CNOP）：简介与数值求解，气象科技进展，6（6），6-14.

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