Articles | Volume 25, issue 7
https://doi.org/10.5194/nhess-25-2255-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/nhess-25-2255-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Super typhoons Mangkhut (2018) and Saola (2023) during landfall: comparison and insights for wind engineering practice
Yujie Liu
Research Center for Wind Engineering and Engineering Vibration, Guangzhou University, Guangzhou, China
Research Center for Wind Engineering and Engineering Vibration, Guangzhou University, Guangzhou, China
Pakwai Chan
Hong Kong Observatory, Hong Kong SAR, China
Aiming Liu
Shenzhen Meteorological Bureau, Shenzhen, China
Qijun Gao
Department of Civil Engineering, Guangzhou University, Guangzhou, China
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EGUsphere, https://doi.org/10.5194/egusphere-2025-4668, https://doi.org/10.5194/egusphere-2025-4668, 2025
This preprint is open for discussion and under review for Atmospheric Measurement Techniques (AMT).
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A trans-scale simulation framework integrating the open-source WRF and OpenFOAM codes is proposed to numerically simulate the urban wind environment under specific weather conditions.
Biao Tong, Xiangfei Sun, Jiyang Fu, Yuncheng He, and Pakwai Chan
Atmos. Meas. Tech., 15, 1829–1848, https://doi.org/10.5194/amt-15-1829-2022, https://doi.org/10.5194/amt-15-1829-2022, 2022
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In recent years, there has been numerous research on tropical cyclone (TC) observation based on satellite cloud images (SCIs), but most methods are limited by low efficiency and subjectivity. To overcome subjectivity and improve efficiency of traditional methods, this paper uses deep learning technology to do further research on fingerprint identification of TCs. Results provide an automatic and objective method to distinguish TCs from SCIs and are convenient for subsequent research.
Lei Li, Chao Lu, Pak-Wai Chan, Zi-Juan Lan, Wen-Hai Zhang, Hong-Long Yang, and Hai-Chao Wang
Atmos. Chem. Phys. Discuss., https://doi.org/10.5194/acp-2021-579, https://doi.org/10.5194/acp-2021-579, 2021
Revised manuscript not accepted
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The COVID-19 induced lockdown provided a time-window to study the impact of emission decrease on atmospheric environment. A 350 m meteorological tower in the Pearl River Delta recorded the vertical distribution of pollutants during the lockdown period. The observation confirmed that an extreme emission reduction, can reduce the concentrations of fine particles and the peak concentration of ozone at the same time, which had been taken as difficult to realize in the past in many regions.
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Short summary
Offshore wind turbines are sensitive to tropical cyclones (TCs). Wind data from super typhoons Mangkhut and Saola, impacting south China, are vital for design and operation. Despite Saola's higher intensity, it caused less damage. Both had concentric eyewall structures, but Saola completed an eyewall replacement before landfall, becoming more compact. Mangkhut decayed but affected a wider area. Their wind characteristics provide insights for turbine maintenance and operation.
Offshore wind turbines are sensitive to tropical cyclones (TCs). Wind data from super typhoons...
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