2）Yang, Y., Han, Wei*, Sun, H., Xie, H., & Gao, Z. (2024). Reconstruction of 3D DPR observations using GMI radiances. Geophysical Research Letters, 51, e2023GL106846. https://doi.org/10.1029/2023GL106846

3）Li, Z. & Han, W.* (2024) Impact of HY-2B SMR radiance assimilation on CMA global medium-range weather forecasts. Quarterly Journal of the Royal Meteorological Society, 150(759), 937–957.  https://doi.org/10.1002/qj.4630

4）Wang Gen, Han Wei*, Yuan Song, Wang Jing, Yin Ruoying,Ye Song, Xie Feng, 2024: Retrieval of High-Frequency Temperature Profiles by FY-4A/GIIRS Based on Generalized Ensemble Learning [J].Journal of the Meteorological Society of Japan, 102, https://doi.org/10.2151/jmsj.2024-011.

5）Xu, X., Sun, X., Han, W.*, Zhong, X., Chen, L., Li, H., 2024. Fuxi-DA: A Generalized Deep Learning Data Assimilation Framework for Assimilating Satellite Observations (No. arXiv:2404.08522). arXiv. https://doi.org/10.48550/arXiv.2404.08522

6）Xie, H., Bi, L.*, and Han, W.*: ZJU-AERO V0.5: An Accurate and Efficient Radar Operator Designed for CMA-GFS/MESO with Capability of Simulating Non-spherical Hydrometeors, Geosci. Model Dev. Discuss. [preprint], https://doi.org/10.5194/gmd-2023-225, in review, 2024.

7）Han, W. et al. (2023). Assimilation of Geostationary Hyperspectral Infrared Sounders (GeoHIS): Progresses and Perspectives. In: Park, S.K. (eds) Numerical Weather Prediction: East Asian Perspectives. Springer Atmospheric Sciences. Springer, Cham. https://doi.org/10.1007/978-3-031-40567-9_8

8）Xie, H., Han, W.* & Bi, L.*(2023) Assimilating FY3D-MWRI 23.8 GHz observations in the CMA-GFS 4DVAR system based on a pseudo All-Sky data assimilation method. Quarterly Journal of the Royal Meteorological Society, 149(756), 3014–3043. https://doi.org/10.1002/qj.4544

9）Xiao, H., Han, W.*, Zhang, P., & Bai, Y. (2023). Assimilation of data from the MWHS-II onboard the first early morning satellite FY-3E into the CMA global 4D-Var system. Meteorological Applications, 30(3), e2133. https://doi.org/10.1002/met.2133

10）Ma, Z., Han, W.*, Zhao, C., Zhang, X., Yang, Y., Wang, H., Cao, Y., Li, Z., Chen, J., Jiang, Q., Sun, J., Shen, X., 2022. A case study of evaluating the GRAPES_Meso V5.0 forecasting performance utilizing observations from South China Sea Experiment 2020 of the “Petrel Project.” Atmospheric Research 280, 106437. https://doi.org/10.1016/j.atmosres.2022.106437

11）Chen, K., Fan, X., Han, W.*, Xiao, H., 2022. A Remapping Technique of FY-3D MWRI Based on a Convolutional Neural Network for the Reduction of Representativeness Error. IEEE Transactions on Geoscience and Remote Sensing 60, 1–11.  https://doi.org/10.1109/TGRS.2021.3138395

12）陈柯，洪鹏飞👨‍🔬，韩威*，李泽宇，王皓，王金成💇🏿‍♀️，陈昊♣️🔐，张志清，谢振超. 2021. 基于GRAPES四维变分的静止轨道微波观测系统模拟试验研究. 气象学报，79（5）👴🏼：769-785 doi: 10.11676/qxxb2021.048

13）Yin, R., Han, W.*, Gao, Z., Li, J., 2021. Impact of High Temporal Resolution FY-4A Geostationary Interferometric Infrared Sounder (GIIRS) Radiance Measurements on Typhoon Forecasts: Maria (2018) Case With GRAPES Global 4D-Var Assimilation System. Geophysical Research Letters 48, e2021GL093672. https://doi.org/10.1029/2021GL093672

14）Chen, H., Han, W. *, Wang, H., Pan, C., An, D., Gu, S., Zhang, P., 2021. Why and How Does the Actual Spectral Response Matter for Microwave Radiance Assimilation? Geophysical Research Letters 48, e2020GL092306. https://doi.org/10.1029/2020GL092306

15）Wang, G., Han, W.*, Lu, S., 2021. Precipitation retrieval by the L1-norm regularization: Typhoon Hagibis case. Quarterly Journal of the Royal Meteorological Society 147, 773–785. https://doi.org/10.1002/qj.3945

16）Yin, J., Han, W.*, Gao, Z., Chen, H., 2021. Assimilation of Doppler radar radial wind data in the GRAPES mesoscale model with observation error covariances tuning. Quarterly Journal of the Royal Meteorological Society 147, 2087–2102. https://doi.org/10.1002/qj.4036

17）Xiao, H., Han, W.*, Wang, H. et al. Impact of FY-3D MWRI Radiance Assimilation in GRAPES 4DVar on Forecasts of Typhoon Shanshan. J Meteorol Res 34, 836–850 (2020). https://doi.org/10.1007/s13351-020-9122-x

18）Yin Ruoying, Han Wei*, Gao Zhiqiu, Di Di. The evaluation of FY4A's Geostationary Interferometric Infrared Sounder (GIIRS) long-wave temperature sounding channels using the GRAPES global 4D-Var. Q J R Meteorol Soc. 2020; 146:1459–1476. https://doi.org/10.1002/qj.3746

19）Xie, H., Bi, L., Han, W.*, Wang, J., 2020. Vertical Inhomogeneity Effect of Frozen Hydrometeor Habits in All-Sky Passive Microwave Simulations. Journal of Geophysical Research: Atmospheres 125, e2020JD032817. https://doi.org/10.1029/2020JD032817

20）尹若莹,韩威*,高志球,王根. 2019. 基于FY-4A卫星探测区域模式背景误差和观测误差估计的长波红外通道选择研究[J]. 气象学报, 77(5):898-910, doi:10.11676/qxxb2019.051

其它情况

长期致力于我国自主研发的数值天气预报系统GRAPES👨‍💼，解决了全球业务同化多项核心技术难题，为我国全球同化预报系统GRAPES业务化做出了突出贡献；在卫星资料同化领域取得多项创新成果，并在中国气象局业务数值预报系统GRAPES和ECMWF数值预报系统中得到业务应用。曾三次应欧洲气象卫星组织邀请到欧洲中期天气预报中心（ECMWF）访问工作🫡，两次应邀到美国开展合作研究。提出并在业务资料同化系统中实现了有约束观测偏差订正原创性方法（CBC,2014🧑🏼‍🔬🪔；CVarBC，2016），系统解决了国际上卫星资料同化领域困扰多年的观测偏差订正向模式 “偏差漂移”难题；提出了大气化学卫星红外高光谱辐射率资料直接同化中“锚定通道”的方法，首次在业务数值预报系统中成功实现了红外资料臭氧通道辐射率资料的直接同化(2010)，解决了臭氧分析中极区冬半年卫星观测应用缺失问题🤵‍♂️🦸‍♀️，应用于ECMWF IFS 系统，显著改善了对流层上层臭氧分析的质量⛹️‍♂️🪣；国际上率先实现了静止轨道红外高光谱大气探测仪FY-4A GIIRS观测同化（2018），提高了台风🤸🏿‍♂️、暴雨等灾害性天气预报精度👨‍🔬，在业务工程应用中发挥了静止卫星高光谱大气探测仪高时间分辨率的应用优势，确认了世界气象组织对静止高光谱探测仪的预期价值；发展了红外高光谱大气探测仪在轨参数快速最优估计技术（2021）🧅，发现了卫星仪器关键参数在复杂空间环境下的热形变规律，提高了光谱定标精度。

  #以上信息由本人提供，更新时间🙏🏻：2024/10/10