Urbanization Impacts on Tropical Cyclone Rainfall Extremes‐Inferences From Observations and Convection‐Permitting Model Experiments Over South China

Hu, Chenxi; Tam, Chi‐Yung; Loi, Chi Lok; Cheung, Kevin K. W.; Li, Yubin; Yang, Zong‐Liang; Au‐Yeung, Yee Man; Fang, Xiaoyi; Niyogi, Dev

doi:10.1029/2023jd038813

Cited by 4 publications

(1 citation statement)

References 86 publications

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“…One of the ways for typhoons to cause disasters is the large amount of water content they carry and bring towards the land during landfall [1] (Figure A1), which may trigger heavy rainfall on a large scale [2]. For example, Super Typhoon Doksuri (2023) affected fourteen provinces (autonomous regions and municipalities) during landfall in eastern China, causing serious damage and flooding with torrential rains in many places including many inland regions to the north [3].…”

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confidence: 99%

Contribution from the Western Pacific Subtropical High Index to a Deep Learning Typhoon Rainfall Forecast Model

Fang,

Cheung,

Yang

2024

Remote Sensing

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In this study, a tropical cyclone or typhoon rainfall forecast model based on Random Forest is developed to forecast the daily rainfall at 133 weather stations in China. The input factors to the model training process include rainfall observations during 1960–2018, typhoon information (position and intensity), station information (position and altitude), and properties of the western Pacific subtropical high. Model evaluation shows that besides the distance between a station and cyclone, the subtropical high properties are ranked very high in the model’s feature importance, especially the subtropical ridgeline, and intensity. These aspects of the subtropical high influence the location and timing of typhoon landfall. The forecast model has a correlation coefficient of about 0.73, an Index of Agreement of nearly 0.8, and a mean bias of 1.28 mm based on the training dataset. Biases are consistently low, with both positive and negative signs, for target stations in the outer rainband (up to 1000 km, beyond which the model does not forecast) of typhoons. The range of biases is much larger for target stations in the inner-core (0–200 km) region. In this region, the model mostly overestimates (underestimates) the small (large) rain rates. Cases study of Typhoon Doksuri and Talim in 2023, as independent cases, shows the high performance of the model in forecasting the peak rain rates and timing of their occurrence of the two impactful typhoons.

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Section: Introductionmentioning

confidence: 99%

Contribution from the Western Pacific Subtropical High Index to a Deep Learning Typhoon Rainfall Forecast Model

Fang,

Cheung,

Yang

2024

Remote Sensing

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Socioeconomic impacts on damage risk from typhoons in mega-urban regions in China: A case study using Typhoons Mangkhut and Lekima

Zheng,

Huang,

et al. 2024

International Journal of Disaster Risk Reduction

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Impacts of Urban Heat and Surface Roughness on Landfalling Tropical Cyclone Intensity: A Case Study Based on TC Victor (1997) in Coastal South China

Hu,

Tam,

et al. 2024

JGR Atmospheres

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This study investigates the impacts of urban‐induced anthropogenic heat (AH) and surface roughness on Tropical Cyclone (TC) Victor (1997) using the Weather Research and Forecasting Model. TC Victor originated in the South China Sea and made landfall over the Greater Bay Area (GBA) megacity. The storm was characterized by slow movement, a weakly organized structure, and a small size. Three parallel experiments were conducted in the following setups: “Nourban,” where urban areas in the GBA were replaced by croplands, “AH0” (“AH300”), in which the diurnal maximum AH was set to 0 (300 W/m2) in urban locations. The urbanization effect during the landfall period results in a notable reduction in both the Power Dissipation Index and Integrated Kinetic Energy of the storm. A Lagrangian particle dispersion model further demonstrates that from 33 to 17 hr prelandfall, the entrainment of AH‐affected warm airflow into the TC's circulation leads to a decrease in both the convective available potential energy and surface latent heat flux in the western quadrant of the TC, thus weakening the cyclone's intensity. Furthermore, from 12 hr prelandfall to 8 hr postlandfall, there is an intense influx of AH‐affected airflow into the TC's primary circulation, resulting in a decrease in relative humidity and a stronger asymmetrical structure, declining the TC intensity. Concurrently, urban surface roughness contributes to a decrease in postlandfall storm intensity through frictional energy dissipation. This study underscores the critical role of AH and urban roughness in modulating TC intensity for storms with specific characteristics, emphasizing the need for deeper insights into urban‐TC dynamics.

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Urbanization Impacts on Tropical Cyclone Rainfall Extremes‐Inferences From Observations and Convection‐Permitting Model Experiments Over South China

Cited by 4 publications

References 86 publications

Contribution from the Western Pacific Subtropical High Index to a Deep Learning Typhoon Rainfall Forecast Model

Contribution from the Western Pacific Subtropical High Index to a Deep Learning Typhoon Rainfall Forecast Model

Socioeconomic impacts on damage risk from typhoons in mega-urban regions in China: A case study using Typhoons Mangkhut and Lekima

Impacts of Urban Heat and Surface Roughness on Landfalling Tropical Cyclone Intensity: A Case Study Based on TC Victor (1997) in Coastal South China

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