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首页> 外文期刊>Hydrology and Earth System Sciences >Impact of multiple radar reflectivity data assimilation on the numerical simulation of a flash flood event during the HyMeX campaign
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Impact of multiple radar reflectivity data assimilation on the numerical simulation of a flash flood event during the HyMeX campaign

机译:多雷达反射率数据同化对Hymex运动期间闪光事件数值模拟的影响

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An analysis to evaluate the impact of multiple radar reflectivity data with a three-dimensional variational (3-D-Var) assimilation system on a heavy precipitation event is presented. The main goal is to build a regionally tuned numerical prediction model and a decision-support system for environmental civil protection services and demonstrate it in the central Italian regions, distinguishing which type of observations, conventional and not (or a combination of them), is more effective in improving the accuracy of the forecasted rainfall. In that respect, during the first special observation period (SOP1) of HyMeX (Hydrological cycle in the Mediterranean Experiment) campaign several intensive observing periods (IOPs) were launched and nine of which occurred in Italy. Among them, IOP4 is chosen for this study because of its low predictability regarding the exact location and amount of precipitation. This event hit central Italy on 14 September 2012 producing heavy precipitation and causing several cases of damage to buildings, infrastructure, and roads. Reflectivity data taken from three C-band Doppler radars running operationally during the event are assimilated using the 3-D-Var technique to improve high-resolution initial conditions. In order to evaluate the impact of the assimilation procedure at different horizontal resolutions and to assess the impact of assimilating reflectivity data from multiple radars, several experiments using the Weather Research and Forecasting (WRF) model are performed. Finally, traditional verification scores such as accuracy, equitable threat score, false alarm ratio, and frequency bias - interpreted by analysing their uncertainty through bootstrap confidence intervals (CIs) - are used to objectively compare the experiments, using rain gauge data as a benchmark.
机译:提出了一种分析,以评价用三维变分(3-D-VAR)同化系统在重沉淀事件上的多雷达反射率数据的影响。主要目标是建立一个区域调整的数字预测模型和用于环境民用保护服务的决策支持系统,并在中央意大利地区证明它,区分哪种观察类型,常规和不(或它们的组合)是更有效地提高预测降雨的准确性。在这方面,在Hymex的第一个特殊观察期(SOP1)期间(地中海实验中的水文循环),在意大利发起了几个强化观察期(IOPS),其中九个发生了九个。其中,由于其关于精确位置和降水量的低可预测性,因此选择了IOP4。这一活动于2012年9月14日袭击了意大利中部,产生了重度降水,并导致建筑物,基础设施和道路造成的几个损坏。使用3-D-VAR技术在操作期间运行的三个C波段多普勒雷达的反射率数据通过三维VAR技术进行同化,以改善高分辨率初始条件。为了评估同化程序在不同水平分辨率下的影响,并评估来自多个雷达的同化反射率数据的影响,进行了使用天气研究和预测(WRF)模型的几个实验。最后,传统的验证分数如准确性,公平威胁评分,误报例和频率偏置 - 通过自举自由置信间隔(CIS)分析它们的不确定性来解释 - 用于客观地比较实验,使用雨量仪数据作为基准。

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