(Peer-Reviewed) Move a Tropical Cyclone with 4D-Var and Vortex Dynamical Initialization in WRF Model
WANG Ting 王挺 ¹, PENG Yue-hua 彭跃华 ² ³, ZHANG Bang-lin 张邦林 ⁴, LEUNG Jeremy Cheuk-Hin 梁卓轩 ⁴, SHI Wei-lai 施伟来 ⁵
¹ Mail Box 5111, Beijing 100094 China
中国 北京 5111信箱
² Dalian Naval Academy, Dalian, Liaoning 116018 China
中国 辽宁 大连 大连舰艇学院
³ Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029 China
中国 北京 中国科学院大气物理研究所
⁴ Guangzhou Institute of Tropical and Marine Meteorology/ Guangdong Provincial Key Laboratory of Regional Numerical Weather Prediction, China Meteorological Administration, Guangzhou 510000 China
中国 广州 中国气象局广州热带海洋气象研究所/ 广东省区域数值天气预报重点实验室
⁵ College of Meteorology and Oceanography, National University of Defense Technology, Changsha 410073 China
中国 长沙 国防科技大学气象海洋学院
Abstract
Previous studies showed that 4D-Var technique used for data assimilation could be modified for weather control. This study demonstrates the ability of 4D-Var to influence the future path of a tropical cyclone by calculating perturbations in WRF simulation. Given the background error covariance matrix, the initial field is improved by the vortex dynamic initialization technique.
Our results show that 4D-Var can be applied to control the trajectory of simulated tropical cyclones by producing "optimal" perturbations. In the numerical simulation experiment of Typhoon Mitag in 2019, after this kind of weather control similar to data assimilation, the tropical cyclone moved obviously, and the damaging wind over the coastline weakened. The prediction results after the initial field modified by 4D-Var have a great change, and the position of the tropical cyclone moved about 0.5° southeastward after assimilation, which misses the southeast coast of China.
Moreover, the damaging wind is also weakened. Since the 4D-Var is premised on the assumption that the model is perfect and does not consider the model error, then the research plan to consider model error and introduce new methods is discussed in the paper.
Separation and identification of mixed signal for distributed acoustic sensor using deep learning
Huaxin Gu, Jingming Zhang, Xingwei Chen, Feihong Yu, Deyu Xu, Shuaiqi Liu, Weihao Lin, Xiaobing Shi, Zixing Huang, Xiongji Yang, Qingchang Hu, Liyang Shao
Opto-Electronic Advances
2025-11-25
