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Regional Data Assimilation with the NCMRWF Unified Model (NCUM): Impact of Doppler Weather Radar Radial Wind

机译:与NCMRWF统一模型(NCUM)的区域数据同化:多普勒天气雷达径向风的影响

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The Indian sub-continent often receives heavy rainfall events, especially during active phases of the south-west monsoon (SWM). The accurate prediction of such events requires a high-resolution model with data assimilation techniques which can assimilate high-density spatial and temporal observed data, like radar observations. This study is undertaken to evaluate the impact of assimilation of radial velocity from multiple Doppler weather radars (DWRs) for simulation of three convective rainfall events during the SWM using the high-resolution (4 km) National Centre for Medium Range Weather and Forecasting (NCMRWF) Unified Model (NCUM) with a three-dimensional variational (3DVAR) data assimilation system. Two numerical experiments are carried out viz. control simulation (CNTL) and a second experiment (RAD, assimilation of all observations used in CNTL plus radial winds from Indian DWRs). The study clearly shows that the assimilation of DWR data has a positive impact in the simulation of heavy rainfall events by the model. The root mean square error (RMSE) of the analyzed (simulated) u- and v-components of wind is reduced by 46% (25%) and 37% (19%) in the RAD experiment as compared to the CNTL experiment. The simulated wind fields at different pressure levels are stronger after assimilation of DWR observations, and it is also successfully produced the cyclonic circulation as compared to the CNTL simulation. The skew-T plots suggested that the convection is more properly represented by the RAD experiment than the CNTL. The value of stability parameters is properly simulated in the RAD experiment, while compared with the CNTL, the values are reasonably well matched with the observed values in all cases. The intensity of the rainfall is reasonably well simulated by the RAD as compared to the CNTL experiment. The statistical skill scores of rainfall with different thresholds are significantly improved in the RAD experiment for all the cases.
机译:印度子大陆经常收到大雨事件,特别是在西南季风(SWM)的活跃阶段。对这些事件的准确预测需要具有数据同化技术的高分辨率模型,其可以同化高密度空间和时间观察数据,如雷达观察。本研究旨在评估来自多普勒天气雷达(DWRS)的径向速度同化的影响,以模拟SWM期间使用高分辨率(4公里)国家中等范围天气和预测中心(NCMRWF )具有三维变分(3DVAR)数据同化系统的统一模型(NCUM)。进行了两个数值实验。控制仿真(CNTL)和第二个实验(RAD,CNTL中使用的所有观测的ASIMITATION,来自印度DWR的径向风)。该研究清楚地表明,DWR数据的同化在模型模拟大雨事件的模拟中具有积极影响。与CNTL实验相比,在RAD实验中,在风中的分析(模拟)U和V组分的均方误差(RMSE)减少了46%(25%)和37%(19%)。在DWR观察的同化后,不同压力水平的模拟风场更强,并且与CNTL模拟相比,它也成功地产生了旋风循环。 Skew-T图建议对流比CNTL更适当地代表RAD实验。在RAT实验中正确模拟稳定性参数的值,而与CNTL相比,该值与所有情况下的观察值相匹配。与CNTL实验相比,RAD的降雨强度合理地模拟。在所有情况下,在RAD实验中,具有不同阈值的降雨的统计技能评分显着改善。

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