ISSN 1000-0526
CN 11-2282/P
CN 11-2282/P
Archive > Volume 46 Issue 8 > 2020,46(8):1039-1052. DOI:10.7519/j.issn.1000-0526.2020.08.004 Prev Next
Study on Forecast Errors and Predictability of a Warm-Sector Rainstorm in South China
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Abstract:
For warm-sector rainstorms, forecast errors often appear in the short-term forecast due to the complexity of environmental conditions, the difficulty in capturing the trigger mechanisms and the poor prediction ability of the numerical models. From 19 to 20 April 2016, a warm-sector rainstorm event occurred in Guangxi, China, but forecasters and numerical models both failed to forecast the rainfall intensity. Based on numerical forecast products, observation data of automatic weather stations, conventional surface and upper-air observation data, Doppler radar and FY-2G data, the forecast errors of this heavy rainfall event were analyzed. The results show that the development of the low- and mid-level jet and the southwest warm low pressure provided the environment with high temperature, high humidity and high energy in the area between the northern part Vietnam and the central and southern part of Guangxi, the topographic convergence and vortex triggered the convection and the mesoscale convergence lines effectively organized the convection. In addition, radar echoes were characterized by low centroid and high rainfall efficiency. The reasons for the failure are that forecasters and numerical models lacked the forecasting ability of the short-term warm-sector rainstorms, and could not accurately grasp the trigger mechanism of the convection. By analyzing the convective cloud clusters in the upstream zone, the evolution of the surface mesoscale convergence lines and the topographic effects and other trigger conditions, forecasters could have made qualitative nowcasting of warm-sector rainstorms and issued early warnings to make up for the ability shortage of short-term forecasting. Therefore, strengthening the understanding of the mechanism of warm-sector rainstorms and making fine analysis are effective ways to improve the forecast ability.
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