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）陳柯，洪鵬飛，韓威*👉🏽，李澤宇👍🏻，王皓，王金成🧵，陳昊，張誌清，謝振超. 2021. 基於GRAPES四維變分的靜止軌道微波觀測系統模擬試驗研究. 氣象學報🌋，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）尹若瑩,韓威*,高誌球,王根. 2019. 基於FY-4A衛星探測區域模式背景誤差和觀測誤差估計的長波紅外通道選擇研究[J]. 氣象學報, 77(5):898-910, doi:10.11676/qxxb2019.051

其它情況

長期致力於我國自主研發的數值天氣預報系統GRAPES，解決了全球業務同化多項核心技術難題🙅🏻，為我國全球同化預報系統GRAPES業務化做出了突出貢獻🧑🏼‍⚕️；在衛星資料同化領域取得多項創新成果，並在中國氣象局業務數值預報系統GRAPES和ECMWF數值預報系統中得到業務應用。曾三次應歐洲氣象衛星組織邀請到歐洲中期天氣預報中心（ECMWF）訪問工作，兩次應邀到美國開展合作研究。提出並在業務資料同化系統中實現了有約束觀測偏差訂正原創性方法（CBC,2014👋；CVarBC，2016）🦴，系統解決了國際上衛星資料同化領域困擾多年的觀測偏差訂正向模式 “偏差漂移”難題；提出了大氣化學衛星紅外高光譜輻射率資料直接同化中“錨定通道”的方法，首次在業務數值預報系統中成功實現了紅外資料臭氧通道輻射率資料的直接同化(2010)🌰，解決了臭氧分析中極區冬半年衛星觀測應用缺失問題🌍，應用於ECMWF IFS 系統，顯著改善了對流層上層臭氧分析的質量🪟；國際上率先實現了靜止軌道紅外高光譜大氣探測儀FY-4A GIIRS觀測同化（2018），提高了臺風🔰、暴雨等災害性天氣預報精度，在業務工程應用中發揮了靜止衛星高光譜大氣探測儀高時間分辨率的應用優勢🧔🏽，確認了世界氣象組織對靜止高光譜探測儀的預期價值🎓；發展了紅外高光譜大氣探測儀在軌參數快速最優估計技術（2021）🤳，發現了衛星儀器關鍵參數在復雜空間環境下的熱形變規律，提高了光譜定標精度🟣。

  #以上信息由本人提供，更新時間：2024/10/10