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Evaluation of Selected Numerical Weather Prediction Models for a Case of Widespread Rainfall over Central and Southern Nigeria

机译:尼日利亚中南部地区大范围降雨的部分数值天气预报模型的评估

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Precipitation forecasts from four Numerical Weather Prediction (NWP) models are evaluated for a case of widespread rainfall event over Central and Southern Nigeria on the 21st of March 2015. The four models evaluated are the European Centre for Medium Range Weather Forecast (ECMWF) with a resolution of 25 km, The UKMET model 20 km, NCEP Global Forecast System (GFS) 50 km and the Weather Research and Forecast Model (WRF) with 10 km resolution. Precipitation forecasts are compared with observed precipitation at station and gridded observation points for different rainfall amount thresholds using the Method of Objective-based Diagnostic Evaluation (MODE), Grid statistics and Point Statistics. The global models ECMWF, UKMET and GFS underestimated the rainfall amount when compared to the WRF regional model. The global models recorded a critical success index (CSI) of less than 10% while the regional WRF model recorded a critical success index of 40% for rainfall amount greater than 25 mm. MODE analysis showed that the WRF model also recorded a 93% relationship between observed and forecast precipitation events of 21st March 2015 over Nigeria when compared with the ECMWF, UKMET and GFS models which showed 88%, 88% and 87% relationship respectively. Our findings suggest that dynamically downscaling a global model using the WRF model added value and gave a better skill of precipitation forecast for the event under study.
机译:针对在2015年3月21日尼日利亚中部和南部发生的广泛降雨事件,对四种数值天气预报(NWP)模型的降水预报进行了评估。所评估的四种模型是欧洲中型天气预报中心(ECMWF),分辨率为25 km,UKMET模型为20 km,NCEP全球预报系统(GFS)为50 km,气象研究和预报模型(WRF)为10 km。使用基于目标的诊断评估(MODE),网格统计和点统计的方法,将降水预测与站点和网格观测点针对不同降雨量阈值的观测降水进行比较。与WRF区域模型相比,ECMWF,UKMET和GFS的全球模型低估了降雨量。全球模型记录的关键成功指数(CSI)小于10%,而区域WRF模型记录的降雨量大于25毫米的关键成功指数为40%。 MODE分析显示,与ECMWF,UKMET和GFS模型分别显示88%,88%和87%的关系的WMW模型相比,2015年3月21日尼日利亚的观测和预报降水事件之间也存在93%的关系。我们的发现表明,使用WRF模型动态缩小全球模型的规模可增加价值,并为正在研究的事件提供更好的降水预报技能。

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