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首页> 外文期刊>Journal of earth system science >Assimilation of Doppler weather radar observations in a mesoscale model for the prediction of rainfall associated with mesoscale convective systems
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Assimilation of Doppler weather radar observations in a mesoscale model for the prediction of rainfall associated with mesoscale convective systems

机译：中尺度模型中多普勒天气雷达观测的同化，以预测与中尺度对流系统有关的降雨

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Obtaining an accurate initial state is recognized as one of the biggest challenges in accurate model prediction of convective events. This work is the first attempt in utilizing the India Meteorological Department (IMD) Doppler radar data in a numerical model for the prediction of mesoscale convective complexes around Chennai and Kolkata. Three strong convective events both over Chennai and Kolkata have been considered for the present study. The simulation experiments have been carried out using fifth-generation Pennsylvania State Universitya€“National Center for Atmospheric Research (PSUa€“NCAR) mesoscale model (MM5) version 3.5.6. The variational data assimilation approach is one of the most promising tools available for directly assimilating the mesoscale observations in order to improve the initial state. The horizontal wind derived from the DWR has been used alongwith other conventional and non-conventional data in the assimilation system. The preliminary results from the three dimensional variational (3DVAR) experiments are encouraging. The simulated rainfall has also been compared with that derived from the Tropical Rainfall Measuring Mission (TRMM) satellite. The encouraging result from this study can be the basis for further investigation of the direct assimilation of radar reflectivity data in 3DVAR system. The present study indicates that Doppler radar data assimilation improves the initial field and enhances the Quantitative Precipitation Forecasting (QPF) skill.
机译：在对流事件的精确模型预测中，获得准确的初始状态被认为是最大的挑战之一。这项工作是在数值模型中利用印度气象局（IMD）多普勒雷达数据预测金奈和加尔各答周围中尺度对流复合体的首次尝试。本研究考虑了钦奈和加尔各答上空发生的三场强对流事件。使用第五代宾夕法尼亚州立大学国家大气研究中心（PSUa NCAR）中尺度模型（MM5）版本3.5.6进行了模拟实验。变异数据同化方法是可用于直接同化中尺度观测值以改善初始状态的最有前途的工具之一。从DWR导出的水平风已与同化系统中的其他常规和非常规数据一起使用。三维变分（3DVAR）实验的初步结果令人鼓舞。还比较了模拟降雨与热带雨量测量任务（TRMM）卫星的模拟降雨。这项研究的令人鼓舞的结果可以作为进一步研究3DVAR系统中雷达反射率数据直接同化的基础。本研究表明，多普勒雷达数据同化可改善初始场并增强定量降水预报（QPF）技能。

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