基本情况

罗昊，男，中山大学大气科学学院副教授，硕士生导师

联系方式

通讯地址：广东省珠海市中山大学大气科学学院，邮编：519082

E-mail:  luohao25@mail.sysu.edu.cn

教育经历

2009年09月至2013年06月，成都信息工程大学大气科学学院，理学学士

2013年09月至2019年06月，中国科学院大气物理研究所，理学博士

工作经历

2019年09月至2020年12月，中山大学，南海研究院，博士后

2021年01月至2023年10月，中山大学，大气科学学院，博士后

2023年11月至今，中山大学，大气科学学院，副教授

科研方向

（1）海冰-海洋资料同化和数值预测；（2）大气-海冰-海洋相互作用

发表论文

Chen, J., Han, B., Yang, Q., Luo, H., Xian, Z., Zhang, Y., Li, X., & Zhang, X. (2023). Assimilation of additional radiosonde observation helps improve the prediction of typhoon-related rainfall in the Pearl River Delta. J. Hydrometeorol., 24(11), 2007-2022. https://doi.org/10.1175/JHM-D-23-0024.1

Liang, K., Wang, J., Luo, H., & Yang, Q. (2023). The Role of Atmospheric Rivers in Antarctic Sea Ice Variations. Geophys. Res. Lett., 50(8), e2022GL102588. https://doi.org/10.1029/2022GL102588

Luo, H., Yang, Q., Mazloff, M., & Chen, D. (2023). A Balanced Atmospheric Ensemble Forcing for Sea Ice Modeling in Southern Ocean. Geophys. Res. Lett., 50(5), e2022GL101139. https://doi.org/10.1029/2022GL101139

Luo, H., Yang, Q., Mazloff, M., Nerger, L., & Chen, D. (2023). The Impacts of Optimizing Model-Dependent Parameters on the Antarctic Sea Ice Data Assimilation. Geophys. Res. Lett., 50(22), e2023GL105690. https://doi.org/10.1029/2023GL105690

Min, C., Yang, Q., Luo, H., Chen, D., Krumpen, T., Mamnun, N., Liu, X., & Nerger, L. (2023). Improving Arctic Sea-Ice Thickness Estimates with the Assimilation of CryoSat-2 Summer Observations. Ocean-Land-Atmos. Res., 2, 0025. https://doi.org/10.34133/olar.0025

Min, C., Zhou, X., Luo, H., Yang, Y., Wang, Y., Zhang, J., & Yang, Q. (2023). Toward Quantifying the Increasing Accessibility of the Arctic Northeast Passage in the Past Four Decades. Adv. Atmos. Sci. https://doi.org/10.1007/s00376-022-2040-3

Wang, J., Luo, H., Yu, L., Li, X., Holland, P. R., & Yang, Q. (2023). The Impacts of Combined SAM and ENSO on Seasonal Antarctic Sea Ice Changes. J. Clim., 36(11), 3553-3569. https://doi.org/10.1175/JCLI-D-22-0679.1

Yang, Q., Xiu, Y., Luo, H., Wang, J., Landy, J. C., Bushuk, M., Wang, Y., Liu, J., & Chen, D. (2023). Better synoptic and subseasonal sea ice thickness predictions are urgently required: a lesson learned from the YOPP data validation. Environ. Res. Lett., 18(7). https://doi.org/10.1088/1748-9326/acdcaa

Yang, Y., Min, C., Luo, H., Kauker, F., Ricker, R., & Yang, Q. (2023). The evolution of the Fram Strait sea ice volume export decomposed by age: estimating with parameter-optimized sea ice-ocean model outputs. Environ. Res. Lett., 18(1). https://doi.org/10.1088/1748-9326/acaf3b

Zhang, Q., Luo, H., Min, C., Xiu, Y., Shi, Q., & Yang, Q. (2023). Evaluation of Arctic Sea Ice Thickness from a Parameter-Optimized Arctic Sea Ice–Ocean Model. Remote Sens., 15(10), 2537. https://doi.org/10.3390/rs15102537

Liao, S., Luo, H., Wang, J., Shi, Q., Zhang, J., & Yang, Q. (2022). An evaluation of Antarctic sea-ice thickness from the Global Ice-Ocean Modeling and Assimilation System based on in situ and satellite observations. The Cryosphere, 16(5), 1807-1819. https://doi.org/10.5194/tc-16-1807-2022

Wang, J., Luo, H., Yang, Q., Liu, J., Yu, L., Shi, Q., & Han, B. (2022). An Unprecedented Record Low Antarctic Sea-ice Extent during Austral Summer 2022. Adv. Atmos. Sci., 39(10), 1591-1597. https://doi.org/10.1007/s00376-022-2087-1

Xiu, Y., Luo, H., Yang, Q., Tietsche, S., Day, J., & Chen, D. (2022). The Challenge of Arctic Sea Ice Thickness Prediction by ECMWF on Subseasonal Time Scales. Geophys. Res. Lett., 49(8), e2021GL097476. https://doi.org/10.1029/2021GL097476

Dong, X., Zheng, F., Lin, R., Yang, H., Zhu, J., Du, M., & Luo, H. (2021). A reasonable mean dynamic topography state on improving the ability of assimilating the altimetry observations into a coupled climate system model: an example with CAS‐ESM‐C. J. Geophys. Res.: Oceans, 126(2). https://doi.org/10.1029/2020JC016760

Luo, H., Yang, Q., Mu, L., Tian-Kunze, X., Nerger, L., Mazloff, M., Kaleschke, L., & Chen, D. (2021). DASSO: a data assimilation system for the Southern Ocean that utilizes both sea-ice concentration and thickness observations. J. Glaciol., 67(266), 1235-1240. https://doi.org/10.1017/jog.2021.57

Zhang, W., Dong, X., Liu, Z., Lin, R., & Luo, H. (2021). Influence of Decadal Ocean Signals on Meteorological Conditions Associated With the Winter Haze Over Eastern China [Original Research]. Front. Environ. Sci., 9, 727180. https://doi.org/10.3389/fenvs.2021.727180

王今菲, 杨清华, 于乐江, 宋米荣, 罗昊, 施骞, 李雪薇, 闵超, & 刘骥平. (2021). 南极海冰变化及其气候效应研究述评. 海洋学报, 43(7), 11-22. https://doi.org/10.12284/hyxb2021151

Luo, H., Zheng, F., Keenlyside, N., & Zhu, J. (2020). Ocean–atmosphere coupled Pacific Decadal variability simulated by a climate model. Clim. Dyn., 54(11), 4759-4773. https://doi.org/10.1007/s00382-020-05248-9

Zheng, F., Wang, H., Luo, H., & Yi, S. (2020). Decadal change in ENSO related seasonal precipitation over southern China under influences of ENSO and its combination mode. Clim. Dyn., 54(3), 1973-1986. https://doi.org/10.1007/s00382-019-05096-2

Yi, S., Zheng, F., & Luo, H. (2019). ENSO combination mode and its influence on seasonal precipitation over southern China simulated by ECHAM5/MPI-OM. Atmos. Ocean. Sci. Lett., 12(3), 184-191. https://doi.org/10.1080/16742834.2019.1589366

Cheng, L., Luo, H., Boyer, T., Cowley, R., Abraham, J., Gouretski, V., Reseghetti, F., & Zhu, J. (2018). How well can we correct systematic errors in historical XBT data? J. Atmos. Ocean. Technol., 35(5), 1103-1125. https://doi.org/10.1175/jtech-d-17-0122.1

Luo, H., Zheng, F., & Zhu, J. (2017). Evaluation of oceanic surface observation for reproducing the upper ocean structure in ECHAM5/MPI-OM. J. Geophys. Res.: Oceans, 122(12), 9695-9711. https://doi.org/10.1002/2017JC013413