2007.9-2011.6   中国科学院大气物理研究所    水文气象学专业   联合培养博士

2006.9-2011.6   北京交通大学  　　　　　　　计算数学专业　　 理学博士

2002.9-2006.6   安阳师范学院  　　　　　　　应用数学专业  　　理学学士

2017.1-至今　北京师范大学陆地表层系统科学与可持续发展研究院　教学科研2011.8-2016.12　北京师范大学全球变化与地球系统科学研究院　　　　教学科研

本科生课程：全球水文学（参与）

研究生课程：大复杂动力系统模型的不确定分析方法：参数筛选

　　　　　　流域生态与环境遥感（参与）

　　　　　　陆面水文过程与建模（参与）

　　　　　　全球水与能量循环（参与）

科技部第二次青藏综合科学考察项目：“安全屏障功能与优化体系”; 2019-2024; 专题五第三子课题参与

国家自然科学基金重点项目：“中国陆表气候观测数据的渐变型不均一性”; 2020-2024；子课题主持

中科院大气所LASG开放基金: “陆面模式参数优化及其对蒸散发估算的应用研究”; 2019-2020; 主持

国家青年科学基金项目: “拟三维地下水模型与通用陆面模式(CoLM)的耦合研究”; 2014-2016; 主持

北京市优秀人才培养资助项目:“基于北京天气系统的WRF模式参数化方案组合优化研究”; 2013-2014; 主持

中央高校基本科研基金：“拟三维地下水模型与陆面过程模式的区域耦合研究”; 2013-2014; 主持

中科院大气所LASG开放基金: “陆面模式CLM4.0的参数敏感性分析研究”; 2013-2014; 主持

1. Li, M., Di, Z.*, Duan, Q. (2021). Effect of sensitivity analysis on parameter optimization: case study based onstreamflow simulations using the SWAT model in China. Journal of Hydrology, 2021, 603, 126896
2. Wang, X., Di, Z.*, Li, M., et al. (2021). Satellite-derived variation in burned area in China from 2001 to 2018 and its response to climatic factors. Remote Sensing, 2021, 13, 1287.
3. Di, Z., Ye, A., Duan, Q., et al. (2021). Assessment of parametric sensitivity analysis methods based on a quasi two- dimensional groudwater model. Joural of Environmental Informatics, 37(1): 62-78.
4. Di, Z., Duan, Q., Shen, C., et al. (2020). Improving WRF typhoon precipitation and intensity simulation using a surrogate-based automatic parameter optimization method. Atmosphere, 11: 89.
5. Zhang, C., Di, Z.*, Duan, Q., et al. (2020). Improved land evapotranspiration simulation of the Community Land Model using a surrogate-based automatic parameter optimization method. Water, 12:943.
6. Di, Z., Ao, J., Duan, Q., et al. (2019). Improving WRF model turbine-height wind-speed forecasting using a surrogate- based automatic optimization method. Atmospheric Research, 226(9): 1-16.
7. Di, Z., Gong, W., Gan, Y., et al. (2019). Combinatorial optimization for WRF physical parameterization schemes: A case study of three-day typhoon simulations over the Northwest Pacific Ocean. Atmosphere,10(5): 233.
8. Di, Z., Duan, Q., Wang, C., Ye, A. ,&Gong, W. (2018). Assessing the applicability of WRF optimal parameters under the different precipitation simulations in the Greater Beijing Area. Clim. Dyn. 50: 1927-1948.
9. Di, Z., Duan, Q., Gong, W.,et al.(2017). Parametric sensitivity analysis of precipitation and temperature based on multi-uncertainty quantification methods in the Weather Research and Forecasting model. Sci. China Earth Sci. 60(5): 876-898.
10. Duan, Q., Di, Z., Quan, J., et al.(2017). Automatic model calibration - a new way to improve numerical weather forecasting. B. Am. Meteorol. Soc. 98(5): 959-970.
11. Quan, J., Di, Z., Duan, Q.,et al.(2016). An evaluation of parametric sensitivities of different meteorologicalvariables simulated by the WRF model. Q. J. Roy.Meteor.Soc.142(700): 2925-2934.
12. Di, Z., Duan, Q., Gong, W., et al.(2015). Assessing WRF model parameter sensitivity: A case study with 5 daysummer precipitation forecasting in the Greater Beijing Area. Geophys.Res. Lett. 42(2):579-587.
13. Di, Z., Luo, Z., Xie, Z., et al.(2012). An optimizing implicit difference scheme based on proper orthogonaldecomposition for the two-dimensional unsaturated soil water flow equation. Int. J. Numer. Meth. Fl. 68(10):1324-1340.
14. 狄振华, 谢正辉, 陈亚宁. (2021).  塔里木河下游长期输水条件下河流剖面地下水埋深估算. 干旱区地理, 44(3): 659-669.