程丛兰,陈敏,陈明轩,高峰,宋林烨,秦睿,杨璐,王勇. 2019. 临近预报的两种高时空分辨率定量降水预报融合算法的对比试验[J]. 气象学报, 77(4):701-714, doi:10.11676/qxxb2019.017
临近预报的两种高时空分辨率定量降水预报融合算法的对比试验
Comparative experiments on two high spatiotemporal resolution blending algorithms for quantitative precipitation nowcasting
投稿时间:2018-04-04  修订日期:2018-08-06
DOI:10.11676/qxxb2019.017
中文关键词:  临近预报  定量降水预报  融合预报  数值模式预报  雷达外推预报  定量降水估测
英文关键词:Nowcasting  Quantitative precipitation prediction  Blending prediction  Numerical weather prediction (NWP)  Radar extrapolation prediction  Quantitative precipitation estimation
基金项目:国家重点研发计划项目(2017YFC1502104)、国家自然科学基金项目(41575050、41705091、41105024)、北京市科技计划课题(Z171100004417008)。
作者单位
程丛兰 北京城市气象研究院, 北京, 100089 
陈敏 北京城市气象研究院, 北京, 100089 
陈明轩 北京城市气象研究院, 北京, 100089 
高峰 北京城市气象研究院, 北京, 100089 
宋林烨 北京城市气象研究院, 北京, 100089 
秦睿 北京城市气象研究院, 北京, 100089 
杨璐 北京城市气象研究院, 北京, 100089 
王勇 奥地利国家气象局, 维也纳, 1190 
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中文摘要:
      长期以来,雷达回波外推技术是0—2 h临近预报系统主要采用的方法,但其实际有效预报时间≤1 h,而中尺度数值模式预报则受平衡约束时间的限制,最初2 h的降水预报无效。为解决上述两种预报的缺陷,目前国际上流行采用外推预报与数值模式预报融合的技术,形成统一的0—6 h格点化的高分辨率无缝隙定量降水临近预报系统。对目前流行的两种融合算法(INCA (Integrated Nowcasting and Comprehensive Analysis System)算法及RAPIDS (Rainstorm Analysis and Prediction Integrated Data-processing System)算法)进行了分析和对比试验,以期为业务应用提供借鉴。RAPIDS算法的核心是用自动气象站雨量融合雷达估测得到的定量降水对模式预报的降水强度和位相进行修正;INCA算法则是用数值模式预报的风场修正外推技术的降水移动矢量。两种方法在0—6 h预报时效内,外推预报的权重均逐渐减小,模式预报的权重逐渐增大,从而实现外推预报和模式预报的平滑过渡。试验结果表明,两种方法对降水雨带和降水强度的预报均优于单一的外推预报或模式预报。集二者的优势研发最优的高时、空分辨率降水预报无缝隙融合算法,将有助于进一步提升高分辨率定量降水0—6 h无缝隙预报水平。
英文摘要:
      For a long time, the radar echo extrapolation method is the main technique applied for the 0-2 h nowcasting system. However, its actual effective lead time is only ≤ 1 h. The mesoscale numerical model is limited by the spin-up time and the first two hours of precipitation prediction is invalid. In order to solve the deficiencies in the above two types of prediction methods, the most popular technique that blends the extrapolated prediction and the numerical model prediction is applied worldwide to develop uniform 0-6 h lattice high resolution seamless quantitative precipitation prediction system. In this paper, two high spatiotemporal resolution blending algorithms for quantitative precipitation prediction are compared to provide references for their operational applications. In the RAPIDS algorithm, the precipitation intensity and phase of the model prediction are corrected based on quantitative precipitation analysis of the automatic station rainfall merged with radar precipitation estimation. In the INCA algorithm, the extrapolation of the precipitation movement vector is corrected by the wind field of the numerical model. In the two methods, the weight of the extrapolated prediction is gradually reduced and the weight of the model prediction is gradually increased within the lead time of 0-6 h, achieving a smooth transition between the extrapolated prediction and the model prediction. The experimental results show that the two methods are superior to a single extrapolation prediction or model prediction for rain band and precipitation intensity forecast. The development of an optimal seamless blending algorithm for precipitation prediction with high spatial and temporal resolution, which combines the advantages of the two methods, will help to further improve the high resolution quantitative 0-6 h seamless precipitation prediction.
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