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FY-4B GHI资料在北京一次对流下山增强过程中的应用分析

Application and analysis of FY-4B GHI data in analyzing a convective downslope intensification process in Beijing

  • 摘要: FY-4B快速成像仪(Geostationary High-speed Imager,GHI)区域扫描能力已提升至空间分辨率250 m、时间分辨率1 min,其探测资料可用于实时监测对流演变。2022年6月12日东北冷涡背景下北京平原地区出现了一次对流下山增强引发风雹灾害的过程,采用时空匹配后的GHI可见光云图、云顶亮温等卫星数据与多普勒天气雷达数据,通过对比对流触发与合并的卫星与雷达特征,分析FY-4B GHI捕捉此次过程对流发展前兆信号的能力以及积云线形成的原因。结果表明:FY-4B GHI高分辨率资料可显著提升对强对流天气前兆信号的识别时效,为改进对流下山过程的预报预警提供新的观测依据。其中,在对流触发监测中,GHI可见光云图提前约30 min识别出山区对流触发的前兆信号—积云线,并完整监测积云线上浅对流演变为深对流的全过程。在对流下山合并过程中,GHI基于云顶亮温识别的回波合并时间较雷达识别的回波合并时间提前18 min,展现出对系统整合的早期捕捉能力。为进一步揭示积云线的成因,结合新一代快速更新多尺度资料分析和预报系统的临近数值预报子系统(RMAPS-NOW)分析发现,北京陡峭地形处低层弱的辐合上升是积云线形成的重要动力因子。

     

    Abstract: The FY-4B Geostationary High-speed Imager (GHI), featuring a maximum temporal resolution of 1 minute and a spatial resolution of 250 m, enables real-time monitoring of convective evolution. A convection downslope intensification event that caused severe wind and hail disasters in the Beijing plain on June 12, 2022, under the influence of a Northeast China cold vortex, was investigated. Utilizing spatiotemporally matched GHI visible imagery, Black Body Temperature (TBB) data, and Doppler weather radar data, this study compared the capabilities of satellite and radar in monitoring CI and convective merger features and the reason of the formation of the cloud line with a particular focus on the ability of GHI to detect precursor signals. The results show that for CI monitoring, GHI visible imagery detected precursor signals over mountainous regions-cumulus cloud lines-approximately 30 minutes earlier than radar, and fully captured the transition from shallow to deep convection along these lines. During the downslope merging stage, the cloud merger time identified by GHI based on TBB was 18 minutes earlier than the echo merger time detected by radar,showcasing its early detection capability for system integration. A comprehensive analysis of GHI observations and data from the Rapid-refresh Multi-scale Analysis and Prediction System-nowcasting revealed that weak low-level convergent uplift over Beijing’s steep terrain was identified as a key dynamical factor for cumulus cloud line formation. To further reveal the formation mechanism of the cumulus cloud line, a comprehensive analysis of GHI observations combined with data from the Rapid-refresh Multi-scale Analysis and Prediction System–Nowcasting (RMAPS-NOW) revealed that weak low-level convergent uplift over Beijing’s steep terrain is a key dynamical factor in the formation of the cloud line.

     

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