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首页> 外文期刊>Monthly Weather Review >Multiple-Radar Data Assimilation and Short-Range Quantitative Precipitation Forecasting of a Squall Line Observed during IHOP_2002
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Multiple-Radar Data Assimilation and Short-Range Quantitative Precipitation Forecasting of a Squall Line Observed during IHOP_2002

机译：在IHOP_2002期间观测到的Line线的多雷达数据同化和短距离定量降水预测

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The impact of multiple-Doppler radar data assimilation on quantitative precipitation forecasting (QPF) is examined in this study. The newly developed Weather Research and Forecasting (WRF) model Advanced Research WRF (ARW) and its three-dimensional variational data assimilation system (WRF 3DVAR) are used. In this study, multiple-Doppler radar data assimilation is applied in WRF 3DVAR cycling mode to initialize a squall-line convective system on 13 June 2002 during the International H_2O Project (IHOP_2002) and the ARW QPF skills are evaluated for the case. Numerical experiments demonstrate that WRF 3DVAR can successfully assimilate Doppler radial velocity and reflectivity from multiple radar sites and extract useful information from the radar data to initiate the squall-line convective system. Assimilation of both radial velocity and reflectivity results in sound analyses that show adjustments in both the dynamical and thermodynamical fields that are consistent with the WRF 3DVAR balance constraint and background error correlation. The cycling of the Doppler radar data from the 12 radar sites at 2100 UTC12 June and 0000 UTC 13 June produces a more detailed mesoscale structure of the squall-line convection in the model initial conditions at 0000 UTC 13 June. Evaluations of the ARW QPF skills with initialization via Doppler radar data assimilation demonstrate that the more radar data in the temporal and spatial dimensions are assimilated, the more positive is the impact on the QPF skill. Assimilation of both radial velocity and reflectivity has more positive impact on the QPF skill than does assimilation of either radial velocity or reflectivity only. The improvement of the QPF skill with multiple-radar data assimilation is more clearly observed in heavy rainfall than in light rainfall. In addition to the improvement of the QPF skill, the simulated structure of the squall line is also enhanced by the multiple-Doppler radar data assimilation in the WRF 3DVAR cycling experiment. The vertical airflow pattern shows typical characteristics of squall-line convection. The cold pool and its related squall-line convection triggering process are better initiated in the WRF 3DVAR analysis and simulated in the ARW forecast when multiple-Doppler radar data are assimilated.
机译：本研究研究了多普勒雷达数据同化对定量降水预报（QPF）的影响。使用新开发的天气研究和预报（WRF）模型Advanced Research WRF（ARW）及其三维变分数据同化系统（WRF 3DVAR）。在这项研究中，在2002年6月13日国际H_2O项目（IHOP_2002）期间，在WRF 3DVAR循环模式中应用了多普勒雷达数据同化来初始化a线对流系统，并针对这种情况对ARW QPF技能进行了评估。数值实验表明，WRF 3DVAR可以成功地吸收来自多个雷达站点的多普勒径向速度和反射率，并从雷达数据中提取有用的信息以启动qua线对流系统。径向速度和反射率的同化会导致声音分析，声音分析显示动态和热力学字段中的调整与WRF 3DVAR平衡约束和背景误差相关性一致。来自6月2100 UTC 12月和6月0000 UTC的12个雷达站点的多普勒雷达数据的循环产生了6月0000 UTC模型初始条件下s线对流的中尺度结构。通过多普勒雷达数据同化对ARW QPF技能进行的初始化评估表明，在时间和空间维度上吸收的雷达数据越多，对QPF技能的影响就越大。相比仅吸收径向速度或反射率，径向速度和反射率的同化对QPF技能的影响更大。与多雨数据相比，多雨数据同化QPF技术的提高更为明显。除了提高QPF技能外，在WRF 3DVAR循环实验中通过多普勒雷达数据同化还增强了线的模拟结构。垂直气流模式显示线对流的典型特征。当吸收多普勒雷达数据时，在WRF 3DVAR分析中更好地启动冷池及其相关的qua流对流触发过程，并在ARW预测中对其进行模拟。

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