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Journal Article

Citation

Li P, Guo Z, Furtado K, Chen H, Li J, Milton S, Field PR, Zhou T. Q. J. Roy. Meteorol. Soc. 2019; 145(724): 3300-3319.

Copyright

(Copyright © 2019, John Wiley and Sons)

DOI

10.1002/qj.3621

PMID

unavailable

Abstract

During the period from June 30th to July 6th, 2016, a heavy rainfall event affected the middle and lower reaches of the Yangtze River valley in eastern China. The event was characterized by high-intensity, long-duration (lasted more than 6 days) precipitation and huge amounts (over 600.0 mm) of rainfall. The rainfall moved eastward from the Sichuan basin to the middle Yangtze River valley during the first 2 days, then Mei-yu front formed and circulations became more "quasi-stationary". During the second-phase, successive heavy rainfall systems occurred repeatedly over the same areas along the front, leading to widespread and catastrophic flooding. In this study, limited-area convection-permitting models (CPMs) covering all of eastern China, and global-model simulations from the Met Office Unified Model are compared to investigate the added values of CPMs on the veracity of short-range predictions of the heavy rainfall event. The results show that all the models can successfully simulate the accumulated amount and the evolution of this heavy rainfall event. However, the global model produces too much light rainfall (10.0 mm/day), fails to simulate the small-scale features of both atmospheric circulations and precipitation, and tends to generate steady heavy rainfall over mountainous region. Afternoon precipitation is also excessively suppressed in global model. By comparison, the CPMs add some value in reproducing the spatial distribution of precipitation, the smaller-scale disturbances within the rain-bands, the diurnal cycle of precipitation and also reduce the spurious topographical rainfall, although there is a tendency for heavy rainfall to be too intense in CPMs.


Language: en

Keywords

convection permitting model; heavy rainfall; Mei-yu front; prediction; topographical rainfall

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