Identification of synoptic patterns for extreme rainfall events associated with landfalling typhoons in China during 1960–2020

Zhao, Dajun; Xu, Hongxiong; Yu, Yubin; Chen, Lianshou

doi:10.1016/j.accre.2022.07.002

Cited by 13 publications

(6 citation statements)

References 38 publications

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“…The correlation coefficient and IA values are considered high compared with other studies using machine learning on precipitation estimates, e.g., [41]. Given the large range of daily rain rates associated with typhoons (up to hundreds of mm), the error in our model is also considered small [42,43].…”

Section: Model Evaluationmentioning

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: Model Evaluationmentioning

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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“…While precipitation is a common hazard factor of both TC and rainstorm disasters, it remains unclear whether there are distinct differences in the influence of precipitation between them (Zhao et al 2022a(Zhao et al , 2022b. In this study, meteorological station data in Guangdong Province were utilized to analyze the characteristics of TC and non-TC precipitation in Guangdong Province.…”

Section: Introductionmentioning

confidence: 99%

Assessing hotspot for tropical cyclone compound rainstorms from a disaster perspective: a case study in Guangdong, China

Zhao,

Li,

Zhao

et al. 2024

Environ. Res. Commun.

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In Guangdong Province, China, known as a hotspot for tropical cyclone (TC) and rainstorm disasters, investigating the role of hazard factors for both TC and rainstorm disasters is of great scientific and operational importance. Daily observational data from meteorological stations along with rainstorm/TC disaster loss data at the county level in Guangdong during 2001–2020 were analyzed to compare the characteristics of rainstorm and TC disasters. During the study period, although non-TC rainstorm rainfall in Guangdong was generally greater than TC rainstorm rainfall, TC disaster losses were double those of rainstorm disasters. Non-TC rainstorms and their losses predominantly occurred during the first flood season, whereas TC disaster primarily occurred in the second flood season. Although regional-average TC rainstorm rainfall was similar to non-TC rainstorm rainfall during the second flood season in Guangdong, it was greater than non-TC rainstorm rainfall in western Guangdong. Additionally, the coastal areas were frequently affected by TC strong winds, resulting in greater TC disaster losses than rainstorm disaster losses. The maximum daily precipitation was significantly correlated with rainstorm disaster losses. In areas influenced by both TC strong winds and rainstorms, TC disaster losses exhibited a significant positive correlation with the daily maximum wind speed, and the combined effect of strong winds and rainstorms led to greater disaster losses than with TC rainstorms alone. These findings emphasize the effects of TC strong wind and storm surges, besides those of heavy rainfall, in coastal areas of China when assessing the risks related to TC disasters.

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“…TC precipitation is a major hazard and more positively correlated with DELs than wind speed in China (Wen et al., 2018; D. Zhao et al., 2021). Indeed, the six highest records of 24‐hr accumulated rainfall in China were all caused by TCs (L. Chen et al., 2010; Zhao, Gao, et al., 2022; Zhao, Xu, et al., 2022). The annual TC precipitation volume over China has shown a significant downward trend (Ren et al., 2006).…”

Section: Introductionmentioning

confidence: 99%

Changes in Tropical Cyclone Disasters Over China During 2001–2020

Zhao

et al. 2023

Earth and Space Science

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Tropical cyclones (TCs) constitute one of the biggest threats to human life and property, and generate a number of different hazards such as storm surges, flooding, extreme winds, tornadoes, and lightning, leading to an annual average of 43 deaths and US$78 million in losses from 1970 to 2019 (WMO, 2021). TC disasters losses refers to the socioeconomic impact of TC disasters, such as fatality, crop failure, and direct economic losses (DELs). Since 1970, China has been one of the countries most frequently affected by TCs (HRD, 2021). The DELs and fatalities induced by TCs in China accounted for 17% and 10% of the total losses induced by meteorological disasters during 2001 , respectively (Y. Li et al., 2021. The southern part of China is most frequently affected by TCs, with increasing trends in economic losses for most areas (Wen et al., 2018). Guangdong is the province most frequently influenced by TCs in this region, and suffers the greatest losses among all provinces in China (Y. Wang et al., 2016;Yao et al., 2020). TC precipitation is a major hazard and more positively correlated with DELs than wind speed in China (Wen et al., 2018;. Indeed, the six highest records of 24-hr accumulated rainfall in China were all caused by TCs (L. Chen et al., 2010;Zhao, Gao, et al., 2022;. The annual TC precipitation volume over China has shown a significant downward trend (Ren et al., 2006). However, both the frequency and intensity of TC extreme rainfall have shown slight increasing trends (Y. Li & Zhao, 2022). The spatial distribution of annual TC precipitation and its trend is uneven over China. Specifically, TCs affect southeastern

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Identification of synoptic patterns for extreme rainfall events associated with landfalling typhoons in China during 1960–2020

Cited by 13 publications

References 38 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

Assessing hotspot for tropical cyclone compound rainstorms from a disaster perspective: a case study in Guangdong, China

Changes in Tropical Cyclone Disasters Over China During 2001–2020

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