长江
构造盆地
环境科学
多样性(政治)
水文学(农业)
自然地理学
流域
气候学
地理
地质学
中国
地貌学
地图学
考古
社会学
岩土工程
人类学
作者
Yadan Xue,Suxiang Yao,Qian Huang
摘要
Abstract Extreme hourly rainfall (EHR), which frequently occurs in the middle and lower reaches of the Yangtze River Basin (MLYB) in China, is characterized by its high disaster potential and widespread impacts. A thorough investigation into their formation mechanisms is crucial for enhancing the accuracy of weather forecasts and improving disaster prevention capabilities. Based on the hourly precipitation data from the CMORPH data set and the ERA5 data set, this study adopts a moving coordinate system to reconstruct the thermo‐dynamic fields associated with rainfall centers, systematically revealing the diversity of formation mechanisms behind EHR in the MLYB during the summers of 2013–2022. The findings reveal three distinct categories of EHR in the MLYB based on their dominant formation mechanisms: frontogenesis‐driven, jet‐driven, and frontal‐instability‐driven EHR, with the jet‐driven EHR being the most frequent. Frontogenesis‐driven EHR is maintained by wind‐field deformation: stretching and shearing of the flow intensify temperature gradients, which in turn strengthen the frontal zone. In jet‐driven EHR, intense coupling between upper‐ and lower‐level jets, along with pronounced super‐geostrophic characteristics, generates robust dynamic lifting. Frontal‐instability‐driven EHR is characterized by substantial convective available potential energy accumulation concurrent with frontal system influences. Frontogenesis‐driven rainfall occurs with comparable frequency in both the morning and afternoon. Jet‐driven EHR predominantly occurs in the morning while frontal‐instability‐driven EHR is concentrated in the afternoon. This temporal distribution exhibits a significant relationship with their respective dominant formation mechanisms. The findings of this study can enhance the understanding of the formation mechanisms of EHR and provide valuable insights for improving the forecasting of EHR in the MLYB.
科研通智能强力驱动
Strongly Powered by AbleSci AI