（Email:wangq@hhu.edu.cn）
王強，伟德国际1916备用网址 教授，博士生導師，海洋觀測與預報技術研究所所長。2011年畢業于中國科學院大氣物理研究所獲博士學位，加拿大University of Northern British Columbia博士後（2015-2016），曾任中國科學院海洋研究所助理研究員、副研究員（2011-2019）。近年來，主要從事大氣海洋動力學、可預報性及智能預測研究，取得成果：（1）提出并發展了可預測性研究新方法，克服了現有方法的局限性，應用新方法提高了大氣海洋預測的準确性；（2）系統研究了海洋西邊界流變異的機理及可預測性，揭示了影響其預報準确性的主要因子和物理機制，并為其設計了海洋目标觀測網，為提高西邊界流預測能力提供了科學支撐；（3）建立了海洋西邊界流智能化集合預測系統，實現了針對西邊界流路徑和流量的高效快速預測。
已在國内外權威雜志如Journal of Climate, Journal of Geophysical Research-Oceans，Geophysical Research Letter，Journal of Physical Oceanography，Climate Dynamics，National Science Review上發表學術論文60餘篇。相關論文被多國學者在Nature子刊等大氣海洋領域高影響期刊引用，并給予高度評價。主持多項國家級科研項目，并被衛星海洋環境動力學國家重點實驗室聘為青年訪問“海星學者”。
研究興趣：
大氣海洋非線性動力學和可預報性
人工智能海洋學
歡迎有志于從事上述方向研究的同學和博士後與我聯系（wangq@hhu.edu.cn）
主講課程：
數學物理方法（本科生）
非線性海洋動力學（研究生）
漫話海洋與氣候（全校本科生公選課）
表彰獎勵：
第一屆江蘇省高校海洋類專業青年教師講課競賽特等獎
2021年度海洋科學技術獎特等獎（7/13）
江蘇省海洋學會科學技術獎一等獎（7/8）
2023年度Acta Oceanologica Sinica期刊優秀論文獎（TOP5%）
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.
陳成吉，王強*，2023：日本南部黑潮與黑潮延伸體路徑狀态關聯性的定量分析，海洋科學，47（4），1-8.
張坤, 穆穆, 王強，2021：數值模式在海洋觀測設計中的重要作用:回顧與展望.中國科學:地球科學,51(5), 653–665.
張星，穆穆，王強，張坤，2018：條件非線性最優擾動方法在黑潮目标觀測研究中的應用，海洋氣象學報，38，1-9.
穆穆，王強，2017：非線性最優化方法在大氣—海洋科學研究中的若幹應用，中國科學-數學，47: 1207-1222.
孫國棟，穆穆，段晚鎖，王強，彭飛，2016：條件非線性最優擾動（CNOP）：簡介與數值求解，氣象科技進展，6（6），6-14.
張坤，穆穆，王強*，2015：初始誤差對雙環流變異可預報性的影響，海洋科學，39，120-128.
張培軍，王強*，2015：模式參數的不确定性對日本南部黑潮大彎曲路徑預報的影響，海洋科學，39，101-113.
穆穆，王強*，段晚鎖，姜智娜，2014：條件非線性最優擾動法在大氣與海洋目标觀測研究中的應用，氣象學報，72，1001-1011.
徐強強, 王強*, 馬利斌, 2013: 日本南部黑潮路徑發生彎曲的最優前期征兆及其發展機制, 海洋科學，37，52-61.
1. 國家自然科學基金面上項目，42076017，黑潮延伸體的第二類可預報性研究：風應力不确定性對預報的影響，2021.01-2024.12，在研，主持
2. 中國科學院戰略性先導科技專項,XDA20060502，熱帶印度洋環流動力與季風相互作用及其影響,2018.03-2023.02,在研，專題負責人
3. 中央高校基本科研業務費項目-自由探索專項，B200201011，黑潮延伸體雙模态對海洋動力環境場的影響，2020.01-2021.12，在研，主持
4. 國家自然科學基金面上項目，41576015，初始誤差對黑潮延伸體年代際變異預測的影響及其機制，2016.01-2019.12，已結題，主持
5. 國家自然科學基金青年科學基金項目，41306023，模式參數誤差對黑潮路徑變異預報的影響，2014.01-2016.12，已結題，主持
6. 青島海洋科學與技術國家實驗室開放基金，分析海洋與氣候模式中參數敏感性的新方法及其應用，2017.04-2020.08，已結題，主持
7. 國家自然科學基金重大項目，41490644，黑潮及延伸體海域海氣相互作用機制及其氣候效應，2015.01-2019.12，已結題，參加
8. 國家自然科學基金重點項目，41230420，可預報性研究中最優前期征兆與增長最快初始誤差的相似性及其在目标觀測中的應用，2013.01-2017.12，已結題，參加
9. 中國科學院戰略性先導科技專項，XDA11010303，NEC和STCC的變異對黑潮上遊段的影響及其可預報性，2013.07-2017.12，已結題，參加
中國海洋學會人工智能海洋學專業委員會委員
江蘇省海洋學會海洋-氣象信息服務專業委員會委員
擔任國内外多個重要期刊如Journal of Geophysical Research: Oceans、Ocean Dynamics、Journal of Hydrology、 Nat Commun.等審稿人。
