冯杰（Jie Feng）
冯杰 青年研究员/博士生导师 021-31248850 个人主页：https://jiefeng-fd.github.io/ 研究兴趣/领域 资料同化- (1) 台风资料同化和预报; (2) 背景误差方差估计; (3) 局地化的粒子滤波（particle filter）; (4) 多尺度背景协方差局地化 集合预报- (1)初始集合扰动生成; (2) 分析误差估计; (3) 集合中心化的新算法; (4) 集合敏感性分析; (5)结合AI的集合预报 非线性误差增长和可预报性- (1) 大气可预报性; (2) 非线性局部Lyapunov指数和向量方法的发展和应用; (3) 混沌吸引子动力; (4) 多尺度误差增长 教育背景 2010-2015，中国科学院大气物理研究所，博士学位，气象学专业 2006-2010，南京信息工程大学大气科学学院，学士学位，气象学专业 研究经历 2020.12至今，中国复旦大学大气与海洋科学系，研究员 2020.7-2020.10，美国俄克拉荷马大学（University of Oklahoma）气象系，项目研究员 2017.7-2020.7，美国俄克拉荷马大学（University of Oklahoma）气象系，博士后 2015.8-2017.6，美国国家海洋大气局全球系统实验室（NOAA/GSL），博士后 承担课题 主持 2024.01-2027.12，国家自然科学基金面上项目，“同化分析场误差的客观定量估计及其对改进集合预报初始扰动的作用”（主持） 2022-2025，国家自然科学基金青年项目，“多空间尺度上集合预报扰动对控制预报误差的采样性能的评估及建模分析”（主持） 2022-2023，中国科学院大气物理研究所开放课题，“风云四号卫星红外高光谱加密观测对台风同化和预测的影响” （主持） 2021，复旦大学原创科研个性化支持项目，“风云四号卫星高光谱加密观测在高分辨率模式中的同化及其对台风分析和预报的影响”（主持） 2018-2019，A new measure of ensemble central tendency, 美国国家大气研究中心National Center for Atmospheric Research（主持） 参与 2015-2017，Estimation of analysis and forecast error variance，美国国家科学院National Academy of Sciences（参与） 2016-2017，A fast statistical tool for observation system experiments，美国国家海洋大气局 National Oceanic and Atmospheric Administration（参与） 2017-2020，Advance the assimilation of radar and other convective and mesoscale observations，美国俄克拉荷马大学University of Oklahoma（参与） 2014-2018，非线性局部Lyapunov向量方法在集合预报中的应用，中国国家自然科学基金委员会面上项目（参与） 教学经历 《可预报性，资料同化和集合预报》， 研究生（主讲） 《数值天气预报》， 本科生（参与） 《大气科学模拟和预测研究进展》，研究生（参与） 召集会议 2024.6.23-6.28, 第21届亚太地球科学学会年会AOGS，专题AS86 “Ensemble Modeling and Prediction of High-impact, Multi-scale Weather to Decadal Events”召集人 https://www.asiaoceania.org/aogs2024/public.asp?page=home.asp 2023.7.29-8.5, 第20届亚太地球科学学会年会AOGS，专题AS38 “Ensemble Modeling and Prediction of High-impact, Multi-scale Weather to Decadal Events”召集人 https://www.asiaoceania.org/aogs2022/public.asp?page=sessions_and_conveners.asp. 2022.8.1-8.5, 第19届亚太地球科学学会年会，专题AS21“Ensemble Modeling of High-impact, Multi-scale Weather to Decadal Phenomena”召集人 https://www.asiaoceania.org/aogs2022/ public.asp?page=sessions_and_conveners.asp. 2021.7.9-7.11, 第七届青年地学论坛，专题11.2“数值模式与资料同化”召集人，贵阳，中国 http://www.qndxlt.com/theme.html 学术兼职 2024.2至今，欧洲地球科学联合会EGU杂志Nonlinear Processes in Geophysics（NPG）的编辑 2024.1至今，Remote Sensing杂志专刊《Remote Sensing Applications for Synoptic and Mesoscale Dynamics and Forecast》编辑https://www.mdpi.com/journal/geomatics/special_issues/1H9D0AOYEL 2022.10, 担任国家气象中心主办，世界气象组织南京区域培训中心承办的面向“一带一路”灾害性天气预报业务技术培训班授课专家，课程题目为“Ensemble Forecasting of High-impact Weather and Climate Events”。 2021.9-2023.12，Remote Sensing杂志专刊《Remote Sensing for the Improvement of High-Impact Weather Analyses and Forecasts》编辑 https://www.mdpi.com/journal/remotesensing/special_issues/weather_analysis 2016年至今，美国气象学会会员 2015年至今，多家SCI期刊审稿人：Geoscientific Model Development, Journal of Advances in Modeling Earth Systems, Journal of Geophysical Research, Monthly Weather Review, Atmospheric Research, Advances in Atmospheric Sciences, Weather and Forecasting, Quarterly Journal of Royal Meteorological Society, Climate Dynamics, Atmosphere, , 等 获奖情况 2024年9月，地球系统数值模拟教学团队成员，入选为全国气象教学团队 2024年8月，台风团队成员，获评复旦大学“钟扬式”科研团队称号 2024年5月，获评复旦大学大气与海洋科学系本科毕业生“我心目中的好老师”称号 2022年，Journal of Meteorological Research 优秀审稿人 2021年，上海领军人才（青年） 发表论文 （本人名称加粗，通讯作者加*号） 2024 Feng, J., Z. Toth*, J. Zhang, and M. Pena, 2024: Ensemble forecasting: A foray of dynamics into the realm of statistics. Quart. J. Roy. Meteor. Soc., DOI: 10.1002/qj.4745 Feng, J., F. Judt, J. Zhang, and X. G. Wang, 2024: Influence of region-dependent error growth on the predictability of track and intensity of Typhoon Chan-hom (2020) in high-resolution HWRF ensembles. Atmospheric Research. DOI: 10.1016/j.atmosres.2024.107536. Liu, L. Y., J. Feng*, L. Ma, Y. R. Yang, X. T. Wu, and C. Wang, 2024: Ensemble-based sensitivity analysis of track forecasts of typhoon In-fa (2021) without and with model errors in the ECMWF, NCEP, and CMA ensemble prediction systems. Atmospheric Research. https://doi.org/10.1016/j.atmosres.2024.107596. Hou, Z. L., J. P. Li, Y. N. Diao, Y. Z. Zhang, Q. J. Zhong, J. Feng, X. Qi, 2024:Asymmetric Influences of ENSO Phases on the Predictability of North Pacific Sea Surface Temperature. Geophysical Research Letters, https://doi.org/10.1029/2023GL108091 2023 Qin, X., M. Mu, F. Zhou, B. Chen, J.Feng, 2023: Applications of Conditional Nonlinear Optimal Perturbations to Targeting Observation of Tropical Cyclones. 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_20 Feng, J., J. Wang*, G. Dai, F. Zhou, W. Duan, 2023: Spatiotemporal estimation of analysis errors in the operational global data assimilation system at the China Meteorological Administration using a modified SAFE method. Quart. J. Roy. Meteor. Soc., 149:230DOI: 10.1002/qj.4507 2022 Feng, J., X. Qin*, C. Wu, P. Zhang, L. Yang, X. S. Shen, W. Han, Y. Z. Liu, 2022: Improving typhoon predictions by assimilating the retrieval of atmospheric temperature profiles from the FengYun-4A's Geostationary Interferometric Infrared Sounder (GIIRS), Atmospheric Research, 280, 106391, ISSN 0169-8095, https://doi.org/10.1016/j.atmosres.2022.106391. Liu, D., C. Huang, J. Feng*, 2022: Influence of Assimilating Wind Profiling Radar Observations in Distinct Dynamic Instability Regions on the Analysis and Forecast of an Extreme Rainstorm Event in Southern China. Remote Sens. 14, 3478. Hou, Z., Li, J., Ding, R., and Feng, J., 2022: Investigating decadal variations of the seasonal predictability limit of sea surface temperature in the tropical Pacific. Clim Dyn.https://doi.org/10.1007/s00382-022-06179-3 Jankov, I.*, Z. Toth, and J. Feng, 2022: Initial-Value vs. Model-Induced Forecast Error: A New Perspective. Meteorology, 1(4), 377-393, https://doi.org/10.3390/meteorology1040024. (Editor’s Choice, https://www.mdpi.com/journal/meteorology/editors_choice) 2021 Zhang, J., J. Feng*, H. Li, Y. J. Zhu, X. F. Zhi, and F. Zhang, 2021: Unified ensemble mean forecasting of tropical cyclones based on the feature-oriented mean method, Wea. Forecasting, 36(6), 1945–1959. DOI: 10.1175/WAF-D-21-0062.1. Li, X., J. Feng, R. Q. Ding, J. P. Li, 2021: Application of Backward Nonlinear Local Lyapunov Exponent Method to Assessing the Relative Impacts of Initial Condition and Model Errors on Local Backward Predictability. Adv. Atmos. Sci., 38(9), 1486−1496. https://doi.org/10.1007/s00376-021-0434-2. Feng, J. and X. G. Wang, 2021: Impact of increasing horizontal and vertical resolution of the hurricane WRF model on the analysis and prediction of Hurricane Patricia (2015). Mon. Wea. Rev., 149(2), 419–441. DOI: 10.1175/MWR-D-20-0144.1 2020 Feng, J., J. Zhang, Z. Toth, M. Pena, and S. Ravela, 2020: A New Measure of Ensemble Central Tendency. Wea. Forecasting, 35(3), 879–889. Feng, J., X. G. Wang, and J. Poterjoy, 2020: A comparison of two local moment matching nonlinear filters: local particle filter (LPF) and local nonlinear ensemble transform filter (LNETF). Mon. Wea. Rev., 148(11), 4377–4395. https://doi.org/10.1175/MWR-D-19-0368.1. Feng, J.*, Z. Toth, and M. Pena, 2020: Partition of Analysis and Forecast Error Variance into Growing and Decaying Components. Quart. J. Roy. Meteor. Soc., 146(728), 1302-1321. 2019 Feng, J. and X. G. Wang, 2019: Impact of assimilating upper-level dropsonde observations collected during the TCI field campaign on the prediction of intensity and structure of Hurricane Patricia (2015), Mon. Wea. Rev., 147, 3069–3089. Feng, J., J. P. Li, J. Zhang, D. Q. Liu, and R. Q. Ding, 2019: The relationship between deterministic and ensemble mean forecast errors revealed by global and local attractor radii. Adv. Atmos. Sci., 36(3), 271–278. 2018 Feng, J., R. Q. Ding, J. P. Li, and Z. Toth, 2018: Comparison of nonlinear local Lyapunov vectors and bred vectors in estimating the spatial distribution of error growth. J. Atmos. Sci., 75, 1073–1087. Hou, Z., Li, J., Ding, R., Karamperidou, C., Duan, W., Liu, T., & Feng, J., 2018. Asymmetry of the predictability limit of the warm ENSO phase. Geophysical Research Letters, 45. Zhong, Q., L. Zhang, J. Li, R. Ding, and J. Feng, 2018: Estimating the predictability limit of tropical cyclone tracks over the western North Pacific using observational data. Adv. Atmos. Sci., 35(12): 1491-1504. Li, J. P., J. Feng, and R. Q. Ding 2018: Attractor Radius and Global Attractor Radius and their Application to the Quantification of Predictability Limits. Clim. Dyn., 51, 2359–2374, https://doi.org/10.1007/s00382-017-4017-y. Hou, Z., J. P. Li, R. Q. Ding and J. Feng, 2018: The application of nonlinear local Lyapunov vectors to the Zebiak–Cane model and their performance in ensemble prediction. Clim. Dyn., 51, 283–304. 2017 Feng, J.*, Z. Toth, and M. Peña, 2017: Spatial Extended Estimates of Analysis and Short-Range Forecast Error Variances. Tellus A, 69:1, 1325301. Huai, X., J. P. Li, R. Q. Ding, J. Feng and D. Q. Liu, 2017: Quantifying local predictability of the Lorenz system using the nonlinear local Lyapunov exponent, Atmospheric and Oceanic Science Letters, 10:5, 372-378. 2016 Feng, J., R. Q. Ding, J. P. Li and D. Q. Liu, 2016: Comparison of nonlinear local Lyapunov vectors with bred vectors, random perturbations and ensemble transform Kalman filter strategies in a barotropic model. Adv. Atmos. Sci., 33(9), 1036–1046. Ding, R. Q., J. P. Li, F. Zheng, J. Feng and D. Q. Liu, 2016: Estimating the limit of decadal-scale climate predictability using observational data. Clim. Dyn., 46(5), 1563–1580. 2015 Liu, D. Q., J. Feng, J. P. Li and J. C. Wang, 2015: The impacts of time-step size and spatial resolution on the prediction skill of the GRAPES-MESO forecast system. Chinese Journal of Atmos. Sci., 39(6), 1165–1178. Liu, D. Q., R. Q. Ding, J. P. Li and J. Feng, 2015: Preliminary application of the nonlinear local Lyapunov exponent to target observation. Chinese Journal of Atmos. Sci., 39(2), 329−337. 2014 Feng, J., R. Q. Ding, D. Q. Liu and J. P. Li, 2014: The Application of Nonlinear Local Lyapunov Vectors to Ensemble Predictions in the Lorenz Systems. J. Atmos. Sci., 71, 3554–3567. #以上信息由本人提供，更新时间：2024/09/29 |
