A Comparison of Different Station Data on Revealing the Characteristics of Extreme Hourly Precipitation Over Complex Terrain: The Case of Zhejiang, China

Wu, Meng‐Wen; Dong, Meiying; Chen, Feng; Yu, Zhuoping; Luo, Yiqi

doi:10.1029/2023ea002925

Cited by 4 publications

(2 citation statements)

References 40 publications

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“…The seasonal and diurnal probability of different types of rainstorms is varied (Wu et al., 2023). All these rainfall event types mainly concentrate during the flood season (Figure 8a).…”

Section: Resultsmentioning

confidence: 99%

The Spatiotemporal Clustering of Short‐Duration Rainstorms in Shanghai City Using a Sub‐Hourly Gauge Network

Lei,

Gao,

Liu

et al. 2024

Earth and Space Science

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Impacted by both the large‐scale climate change and the local hydrometeorological atmosphere, the spatiotemporal structure and properties of short‐duration extreme rainfall events in urban areas remain a complex issue. This study explores the spatiotemporal pattern variability of short‐duration rainstorm events using an exceptional sub‐hourly gauge network in Shanghai, China. A catalog of 207 “independent” rainstorms is extracted. The hierarchical clustering method is used to examine the spatial and temporal features including rainfall duration, intensity, and coverage for each district with different degrees of urbanization in the 2014–2018 period. The results point to a pronounced spatial heterogeneity with heavier rainfall magnitude in the north. In the urban core area, intensive short‐duration rainstorms hit more frequently than in other districts. Rainstorms can be divided into three types from the spatiotemporal process: Type delayed‐slow, Type advanced‐fast, and Type advanced‐slow. The delayed‐peak with a higher slope occurs more in the west and the advanced‐peak more along the coastal line. The other type of advanced‐peak storm with a reverse trend of intensity and coverage concentrates in June–August of the year and the 16:00–20:00 of the day. While 65.6% of the delayed‐peak storms only occur in one district, more than half of the advanced‐peak appear without other types at the same time. These findings show the importance of observation‐based analysis in understanding the spatiotemporal pattern of short‐duration storms and provide a reference for storm design of sub‐regions in urban areas.

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“…The seasonal and diurnal probability of different types of rainstorms is varied (Wu et al., 2023). All these rainfall event types mainly concentrate during the flood season (Figure 8a).…”

Section: Resultsmentioning

confidence: 99%

The Spatiotemporal Clustering of Short‐Duration Rainstorms in Shanghai City Using a Sub‐Hourly Gauge Network

Lei,

Gao,

Liu

et al. 2024

Earth and Space Science

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“…The complex terrain of Zhejiang Province is oriented from northeast to southwest direction. The mountains are concentrated in the central to southern of the province, with peak elevations of about 2000 m. The intensity and spatial distribution of the precipitation over the area are significantly influenced by complicated orography distribution [26][27][28], and gravity wave drag is one of the essential terrain-induced physical processes. Therefore, it is meaningful to study the atmospheric wind pattern influenced by the terrain-induced gravity waves, particularly for the numerical simulation of the wind field of the low level of the troposphere.…”

Section: Description Of Typhoon In-fa (2021)mentioning

confidence: 99%

Improved Gravity Wave Drag to Enhance Precipitation Simulation: A Case Study of Typhoon In-Fa

Liu,

Yu,

et al. 2023

Atmosphere

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Traditional gravity wave drag parameterizations produce wind stresses that are insensitive to changing horizontal resolution in numerical weather prediction (NWP), partly due to the idealized elliptical assumption. This study employs the modified subgrid-scale orography scheme based on the Fourier transform into gravity wave drag scheme of the China Meteorological Administration Global Forecast System (CMA-GFS) to assess its impacts on simulating precipitation during the slow-moving period of Typhoon In-Fa after its landfall in Zhejiang Province, China. The simulation with the updated scheme can effectively reduce the accumulated precipitation bias of the control one and improve the simulation of precipitation distribution and intensity, especially in the hourly precipitation simulation. The improved scheme primarily influences the wind field of the low-level troposphere and also changes the convergence of the integrated water vapor transport and ascending motions related to the reduced precipitation biases. The modified scheme enhances the tendencies of the horizontal winds caused by the varying horizontal resolutions in the model, strengthening the sensitivity of the gravity wave drag across the horizontal scales. Results from medium-range forecasts indicate the modified scheme benefits the statistics scores of precipitation over China and also reduces root-mean-square errors of 2 m temperature and 10 m winds.

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Characteristics of extreme hourly precipitation induced by tropical cyclones in Zhejiang, China: A comparative analysis based on two different datasets

Mengwen,

Meiying,

Feng

et al. 2024

Atmospheric Research

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A Comparison of Different Station Data on Revealing the Characteristics of Extreme Hourly Precipitation Over Complex Terrain: The Case of Zhejiang, China

Abstract: Under global warming (IPCC, 2013), evidence is building that the increased atmospheric temperature can lead to more extreme rainfall over short time scales (up to a few hours), which is supported by both observations and climate model simulations (

Cited by 4 publications

References 40 publications

The Spatiotemporal Clustering of Short‐Duration Rainstorms in Shanghai City Using a Sub‐Hourly Gauge Network

The Spatiotemporal Clustering of Short‐Duration Rainstorms in Shanghai City Using a Sub‐Hourly Gauge Network

Improved Gravity Wave Drag to Enhance Precipitation Simulation: A Case Study of Typhoon In-Fa

Characteristics of extreme hourly precipitation induced by tropical cyclones in Zhejiang, China: A comparative analysis based on two different datasets

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