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中国海洋大学海洋与大气学院导师教师师资介绍简介-侯兆禄

本站小编 Free考研考试/2020-11-26

侯兆禄



讲师
中国海洋大学海洋与大气学院
青岛市松岭路238号,266100
E-mail:hzl@ouc.edu.cn


教育经历:
2010/09-2014/06 南京大学,大气科学,学士
2014/09-2019/06 中国科学院大气物理研究所,气象学,博士
工作经历:
2019/07至今 中国海洋大学,海洋与大气学院,讲师
主要项目:
[1]2021/01-2023/12,国家自然科学基金青年项目,ENSO动力系统的多维非线性误差增长模态及其在集合预报中的应用,主持
[2]2020/11-2022/12,中国博士后科学基金面上项目,ENSO系统季节可预报性的年代际变化研究,主持
[3]2019/10-2023/09,山东省重大基础研究项目,热带与中高纬对山东极端天气气候事件的协同影响及其机理,参与


代表文章:
[1]Hou, Z., Li, J., Ding, R., Feng, J., & Duan, W. (2018). The application of nonlinear local Lyapunov vectors to the Zebiak–Cane model and their performance in ensemble prediction. Climate Dynamics, 51(1-2), 283-304.
[2]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(15), 7646-7653.
[3]Xu, Z., Hou, Z., Han, Y., & Guo, W. (2016). A diagram for evaluating multiple aspects of model performance in simulating vector fields. Geoscientific Model Development, 9(12), 4365-4380.
[4]Ding, R., Tseng, Y. H., Li, J., Sun, C., Xie, F., & Hou, Z. (2019). Relative contributions of North and South Pacific sea surface temperature anomalies to ENSO. Journal of Geophysical Research: Atmospheres, 124(12), 6222-6237.
[5]Liu, T., Tang, Y., Yang, D., Cheng, Y., Song, X., Hou, Z., Shen Z., Gao Y., Wu Y., Li X., & Zhang, B. (2019). The relationship among probabilistic, deterministic and potential skills in predicting the ENSO for the past 161 years. Climate Dynamics, 53(11), 6947-6960.
[6]Zhou, X., Wang, W., Ding, R., Li, J., Hou, Z., & Xie, W. (2019). An Investigation of the Differences between the North American Dipole and North Atlantic Oscillation. Atmosphere, 10(2), 58.
[7]Hou, Z., Zuo, B., Zhang, S., Huang, F., Ding, R., Duan, W., & Li, J. (2020). Model forecast error correction based on the Local Dynamical Analog method: an example application to the ENSO forecast by an Intermediate Coupled Model. Geophysical Research Letters, 47(19), e2020GL088986.
[8]Zuo, B., Hou, Z., Zheng, F., Sheng, L., Gao, Y., & Li, J. (2020). Robustness Assessment of the RSD t‐Test for Detecting Trend Turning in a Time Series.Earth and Space Science,7(5), e2019EA001042.
[9]Sun, R., Li, J., Feng, J., Hou, Z., & Zhang, Y. (2020). Contrasting impacts of two types of El Ni?o on the yields of early rice in Southern China.Agronomy Journal, 112(2), 1084-1100.
[10]Wen T., Chen Q., Li J., Ding R., Tseng Y., & Hou Z. (2020). Influence of the North Pacific Victoria mode on the Madden-Julian Oscillation. Frontiers in Earth Science, section Atmospheric Science, accepted
[11]Zou, Q., Ding, R., Li, J., Tseng, Y. H., Hou, Z., Wen, T., & Ji, K. (2020). Is the North Pacific Victoria mode a predictor of winter rainfall over South China?. Journal of Climate, 33(20), 8833-8847.


荣誉与奖励
中国科学院大学“三好学生标兵”
教学活动
本科:动力气象学,计算方法


研究兴趣:
海洋-大气相互作用;非线性气候动力学与可预报性;机器学习

Zhaolu Hou

Lecturer,
College of Oceanic and Atmospheric Sciences
Ocean University of China
238 Songling Rd, Qingdao 266100, China
Email: hzl@ouc.edu.cn
Education
09/2010-06/2014, B.S. in Atmosphere Science, Nanjing University
09/2014-06/2019, Ph.D. in Meteorology, Institute of Atmospheric Physics, Chinese Academy of Sciences
EMPLOYMENT
07/2019 to present, Lecturer, Ocean University of China
Major PROJECTS
2021/01-2023/12, NSFC project, “ENSO dynamic system’s multi-nonlinear error growth modes and their application in ensemble prediction” (**), 240K, PI
2020/11-2022/12, China Postdoctoral Science Foundation, “Interdecadal variability of ENSO seasonal predictability” (2020M680094), 120K, PI
PUBLICATIONS
[1]Hou, Z., Li, J., Ding, R., Feng, J., & Duan, W. (2018). The application of nonlinear local Lyapunov vectors to the Zebiak–Cane model and their performance in ensemble prediction. Climate Dynamics, 51(1-2), 283-304.
[2]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(15), 7646-7653.
[3]Xu, Z., Hou, Z., Han, Y., & Guo, W. (2016). A diagram for evaluating multiple aspects of model performance in simulating vector fields. Geoscientific Model Development, 9(12), 4365-4380.
[4]Ding, R., Tseng, Y. H., Li, J., Sun, C., Xie, F., & Hou, Z.(2019). Relative contributions of North and South Pacific sea surface temperature anomalies to ENSO. Journal of Geophysical Research: Atmospheres, 124(12), 6222-6237.
[5]Liu, T., Tang, Y., Yang, D., Cheng, Y., Song, X., Hou, Z., Shen Z., Gao Y., Wu Y., Li X., & Zhang, B. (2019). The relationship among probabilistic, deterministic and potential skills in predicting the ENSO for the past 161 years. Climate Dynamics, 53(11), 6947-6960.
[6]Zhou, X., Wang, W., Ding, R., Li, J.,Hou, Z., & Xie, W. (2019). An Investigation of the Differences between the North American Dipole and North Atlantic Oscillation. Atmosphere, 10(2), 58.
[7]Hou, Z., Zuo, B., Zhang, S., Huang, F., Ding, R., Duan, W., & Li, J. (2020). Model forecast error correction based on the Local Dynamical Analog method: an example application to the ENSO forecast by an Intermediate Coupled Model. Geophysical Research Letters, 47(19), e2020GL088986.
[8]Zuo, B., Hou, Z., Zheng, F., Sheng, L., Gao, Y., & Li, J. (2020). Robustness Assessment of the RSD t‐Test for Detecting Trend Turning in a Time Series.Earth and Space Science, 7(5), e2019EA001042.
[9]Sun, R., Li, J., Feng, J., Hou, Z., & Zhang, Y. (2020). Contrasting impacts of two types of El Ni?o on the yields of early rice in Southern China.Agronomy Journal, 112(2), 1084-1100.
[10]Wen T., Chen Q., Li J., Ding R., Tseng Y., & Hou Z.(2020). Influence of the North Pacific Victoria mode on the Madden-Julian Oscillation. Frontiers in Earth Science, section Atmospheric Science, accepted
[11]Zou, Q., Ding, R., Li, J., Tseng, Y. H., Hou, Z., Wen, T., & Ji, K. (2020). Is the North Pacific Victoria mode a predictor of winter rainfall over South China?.Journal of Climate, 33(20), 8833-8847.
Courses
Undergraduate: Dynamic Meteorology;Numerical Analysis
Research Interests
Ocean-Atmosphere Interaction, Nonlinear Climatic Dynamics and Predictability, Machine Learning


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