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Impact of variational assimilation technique on simulation of a heavy rainfall event over Pune, India

机译:变异同化技术对印度浦那一次强降雨事件模拟的影响

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Prediction of heavy rainfall events due to severe convective storms in terms of their spatial and temporal scales is a challenging task for an operational forecaster. The present study is about a record-breaking heavy rainfall event observed in Pune (18A degrees 31'N, 73A degrees 55'E) on October 4, 2010. The day witnessed highest 24-h accumulated precipitation of 181.3 mm and caused flash floods in the city. The WRF model-based real-time weather system, operating daily at Centre for Development of Advanced Computing using PARAM Yuva supercomputer showed the signature of this convective event 4-h before, but failed to capture the actual peak rainfall and its location with reference to the city's observational network. To investigate further, five numerical experiments were conducted to check the impact of assimilation of observations in the WRF model forecast. First, a control experiment was conducted with initialization using National Centre for Environmental Prediction (NCEP)'s Global Forecast System 0.5A degrees data, while surface observational data from NCEP Prepbufr system were assimilated in the second experiment (VARSFC). In the third experiment (VARAMV), NCEP Prepbufr atmospheric motion vectors were assimilated. Fourth experiment (VARPRO) was assimilated with conventional soundings data, and all the available NCEP Prepbufr observations were assimilated in the fifth experiment (VARALL). Model runs were compared with observations from automated weather stations (AWS), synoptic charts of Indian Meteorological Department (IMD). Comparison of 24-h accumulated rainfall with IMD AWS 24-h gridded data showed that the fifth experiment (VARALL) produced better picture of heavy rainfall, maximum up to 251 mm/day toward the southern side, 31 km away from Pune's IMD observatory. It was noticed that the effect of soundings observations experiment (VARPRO) caused heavy precipitation of 210 mm toward the southern side 49 km away from Pune. The wind analysis at 850 and 200 hPa indicated that the surface and atmospheric motion vector observations (VARAMV) helped in shifting its peak rainfall toward Pune, IMD observatory by 18 km, though VARALL over-predicted rainfall by 60 mm than the observed.
机译:就对流风暴的时空尺度而言,对由于强对流风暴造成的强降雨事件的预报对于业务预报员而言是一项艰巨的任务。本研究是关于2010年10月4日在浦那(18A度31'N,73A度55'E)观察到的创纪录的强降雨事件。这一天见证了24小时最高的181.3毫米累积降水量,并引发了山洪暴发。在城市。每天使用PARAM Yuva超级计算机在高级计算开发中心运行的基于WRF模型的实时天气系统在4小时之前就显示了对流事件的特征,但未能捕获实际的峰值降雨量及其位置。城市的观测网络。为了进一步调查,进行了五个数值实验,以检查同化对WRF模型预测的影响。首先,使用国家环境预测中心(NCEP)的全球预测系统0.5A度数据初始化进行了对照实验,而在第二个实验(VARSFC)中吸收了来自NCEP Prepbufr系统的地面观测数据。在第三个实验(VARAMV)中,将NCEP Prepbufr大气运动矢量同化。第四实验(VARPRO)与常规测深数据同化,所有可用的NCEP Prepbufr观测值在第五实验(VARALL)中同化。将模型运行与自动气象站(AWS)的观测值,印度气象局(IMD)的天气图进行了比较。 24小时累积降雨与IMD的比较AWS 24小时网格数据显示,第五个实验(VARALL)产生了更好的强降雨图像,最大降水量朝南侧达251 mm / day,距浦那的IMD天文台31公里。值得注意的是,测深试验的结果(VARPRO)导致向距浦那49公里的南侧210毫米处出现了大降水。 850和​​200 hPa的风分析表明,地面和大气运动矢量观测(VARAMV)有助于将其峰值降雨向IMD观测站Pune转移了18 km,尽管VARALL高估了60 mm的降雨量。

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