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, (0):-, 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
Comparison Experiments on Two High Spatial and Temporal Resolution Quantitative Precipitation Prediction Blending Algorithms for Nowcasting
投稿时间:2018-04-02  修订日期:2018-05-24
DOI:10.11676/qxxb2019.017
中文关键词:  临近预报,定量降水,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:Quantitative Precipitation Prediction, Blending Prediction, Numerical Weather Prediction (NWP), Extrapolation Prediction, QPF
基金项目:国家重点研发计划,国家自然科学基金
作者单位E-mail
程丛兰 中国气象局北京城市气象研究所 clcheng@ium.cn 
陈敏 中国气象局北京城市气象研究所 mchen@ium.cn 
陈明轩 中国气象局北京城市气象研究所 mxchen@ium.cn 
高峰 中国气象局北京城市气象研究所 fgao@ium.cn 
宋林烨 中国气象局北京城市气象研究所 lysong@ium.cn 
秦睿 中国气象局北京城市气象研究所 rqin@ium.cn 
杨璐 中国气象局北京城市气象研究所 lyang@ium.cn 
王勇 奥地利国家气象局 Yong.wang@zamg.ac.at 
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中文摘要:
      长期以来,雷达回波外推技术是0-2h临近预报系统主要采用的方法,但其实际有效预报时间<=1h,而中尺度数值模式预报则受平衡约束时间的限制(Spin-up),最初2h的降水预报无效。为解决上述两种预报之间的缺陷,目前国际上流行采用将外推预报与数值模式预报相融合的技术,形成统一的0-6h格点化的高分辨率无缝隙定量降水临近预报系统。本文对目前流行的两种融合算法(INCA算法及RAPIDS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0-6h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之间的平滑过渡。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0-6h无缝隙预报水平。
英文摘要:
      For a long time, radar echo extrapolation is the main technique applied for the 0-2h nowcasting system, but its actual effective lead time is only <= 1h. The mesoscale numerical model is limited by the spin-up time and the first 2 hours of precipitation prediction is invalid. In order to solve the deficiencies between the above two kinds of prediction, the most popular technique that blends the extrapolated prediction and the numerical model prediction is applied in the world and formed a uniform 0-6h lattice high resolution seamless quantitative precipitation prediction system. In this paper, two high spatial and temporal resolution quantitative precipitation prediction blending algorithms are comparison experiments to provide reference for operation applications. The core of the first algorithm (RAPIDS algorithm) is to correct the precipitation intensity and phase of the model prediction by the quantitative precipitation analysis by the automatic station rainfall merging with radar precipitation estimation. The core of the second algorithm (INCA algorithm, integrated analysis of multiple data and nowcasting developed by Austrian Central Institute for Meteorology and Geodynamics) is to correct the extrapolation precipitation movement vector by the wind field of the numerical model. The common core of the two methods is that within the lead time of 0-6 hours, the weight of the extrapolated prediction is gradually reduced, and the weight of the model prediction is gradually increased, thus 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 precipitation rain band and precipitation intensity forecast. Combining the both advantages, the development of an optimal seamless blend algorithm for precipitation prediction with high spatial and temporal resolution will help to further improve the high resolution quantitative precipitation 0-6 hours seamless prediction.
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