对尼日利亚中部和南部大范围降雨的若干数值天气预报模式的评价

Chibuzo N Agogbuo, M. Nwagbara, E. Bekele, A. Olusegun
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引用次数: 3

摘要

对2015年3月21日尼日利亚中南部大范围降雨事件的四个数值天气预报模式的降水预报进行了评估。评估的四个模式分别是欧洲中期天气预报中心(ECMWF),分辨率为25公里,UKMET模式为20公里,NCEP全球预报系统(GFS)为50公里,天气研究与预报模式(WRF)为10公里。采用基于目标的诊断评价方法(MODE)、网格统计和点统计方法,对不同降雨量阈值的降水预报与站点和网格观测点的观测降水进行比较。与WRF区域模式相比,全球模式ECMWF、UKMET和GFS低估了降雨量。对于大于25 mm的降雨量,全球模式的临界成功指数(CSI)低于10%,而区域WRF模式的临界成功指数为40%。MODE分析显示,WRF模式记录的2015年3月21日尼日利亚降水事件与预报事件的相关性为93%,而ECMWF、UKMET和GFS模式的相关性分别为88%、88%和87%。我们的研究结果表明,使用WRF模式的动态降尺度全球模式增加了研究事件的降水预报价值,并提供了更好的降水预报技能。
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Evaluation of Selected Numerical Weather Prediction Models for a Case of Widespread Rainfall over Central and Southern Nigeria
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.
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