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    基于雷达组网拼图的定量降水反演II:方案改进及综合评估

    Quantitative precipitation inversion algorithm based on the multi-radar mosaic II:scheme improvement and evaluation

    • 摘要: 为提高雷达定量降水反演精度,结合多普勒天气雷达组网拼图资料与地面加密自动站降水观测资料,在利用最优化法建立辽宁本地化动态Z-I关系基础上,进一步优化雷达定量降水反演方案,分别开展了分雷达回波强度等级与分地理区域优化降水Z-I关系研究。结果表明:两种优化方案的定量降水反演评估指标均有所改善,整体而言,优化方案有效降低了雷达定量降水反演误差,降水反演能力得到进一步提高。2013年辽宁抚顺“8.16”典型强降水个例定量降水反演结果显示,两种优化方案反演降水的空间分布形势与地面降水实况场更加接近,尤其弥补了单一动态Z-I关系法对20.0 mm·h-1以上量级强降水落区范围以及40.0 mm·h-1以上量级超强降水中心反演不足的问题,采用优化方案后的各项评估指标均大幅提升,明显改善了雷达定量降水反演的不确定性,降水反演效果具有更强的稳定性和可靠性,为灾害性短时强降水天气事件短临预报预警提供了定量参考。

       

      Abstract: In order to improve the accuracy of radar quantitative precipitation retrievals,combining Doppler multi-radar mosaic data and the gauge-observed hourly rainfall intensity data,the radar quantitative precipitation retrieval scheme was optimized in Liaoning province based on the established Z-I relationship using the optimization method.The results show that the evaluation indexes of the two optimization schemes for quantitative precipitation retrieval are improved.Overall,the two optimization schemes reduce the errors of radar quantitative precipitation retrieval effectively.The capability of precipitation retrieval is further improved.The precipitation retrieval results for Fushun “8.16” typical strong precipitation indicate that the spatial distribution of the radar quantitative precipitation retrieval using the two optimization retrieval schemes showed high accuracy compared to the surface precipitation data.In particular,the problems that the precipitation zone above 20.0 mm·h-1 is smaller than actually happening and the magnitude of the short-time strong precipitation above 40.0 mm·h-1 is underestimated are largely corrected.After using the optimization scheme,all the evaluation indexes are significantly improved and the uncertainties and dispersions in the radar quantitative precipitation retrieval are smaller than before.Precipitation retrieval results have better stability and reliability,which provide a quantitative reference for short-term forecasting and early warning of heavy rainfall events.

       

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