ISSN 1000-0526
CN 11-2282/P
CN 11-2282/P
Archive > Volume 51 Issue 12 > 2025,51(12):1596-1607. DOI:10.7519/j.issn.1000-0526.2024.122601 Prev Next
Analysis of Mesoscale Characteristics and Forecast Bias of a Severe Torrential Rain in Western Hunan Province in June 2023
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Abstract:
From 29 to 30 June 2023, a localized abrupt severe torrential rain occurred in the western region of Hunan Province, but forecasters and numerical models both failed to forecast the rainfall intensity. In this study, the mesoscale characteristics and possible causes of forecast biases are analyzed based on the multiple observations data, ERA5 reanalysis data and numerical forecast products. The results show that the northwest air flow behind the upper-level trough drove the cold air to the south and merged with the southwest warm-humid air flows which were strengthened at night, which led to the occurrence of this process. The severe torrential rain was generated by a backward propagation of quasi-stationary mesoscale convective system (MCS), which was composed of multiple strongly developing γ-MCSs, manifested as an organized linear echo band. Under the favorable environmental background, the long-time maintenance of the boundary layer convergence line, the wind velocity fluctuation of the low-level jet and the vertical structure of low-level convergence and high-level divergence contributed to the triggering and organization of the convective cells. The merging, strengthening backward propagation of MCS and the train effect of convective cells were important causes for the severe torrential rain. Significant errors were made in the short-time subjective forecasts because of the forecast biases of the low-level dynamic and thermodynamic fields of the numerical models, the deficiency of forecasters’ ability to correct the model forecast, and the complex topography of western Hunan Province. Therefore, it is very crucial to use the automatic weather station data, satellite data and radar data with high spatio-temporal resolution to analyze the changes of the mesoscale environmental conditions, strengthen the short-time nowcasting and issue early warning in time.
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