ECMWF全风速场集合预报结果的偏差订正与预报不一致性分析
投稿时间:2018-03-26  修订日期:2018-05-10  点此下载全文
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任晨辰 南京信大气象科技有限公司, 江苏 南京 210044
南京信息工程大学 气象灾害教育部重点实验室/气候与环境变化国际合作联合实验室/气象灾害预报预警与评估协同创新中心, 江苏 南京 210044 
 
段明铿 南京信息工程大学 气象灾害教育部重点实验室/气候与环境变化国际合作联合实验室/气象灾害预报预警与评估协同创新中心, 江苏 南京 210044
中国气象科学研究院 灾害天气国家重点实验室, 北京 100081 
mingkeng@sina.com 
基金项目:国家自然科学基金资助项目(91437218;41675056);灾害天气国家重点实验室开放课题(2014LASW-A01)
中文摘要:采用卡尔曼滤波类型自适应误差订正法和滑动自适应权重法,对2012年夏季ECMWF 10 m全风速场集合预报结果进行偏差订正,对订正前后的预报结果进行评估,并通过Jumpiness指数对预报结果订正前后的预报不一致性特征进行分析。结果表明,卡尔曼滤波类型自适应误差订正法能有效降低集合预报的均方根误差,且当起报时刻为00时对中低纬度地区的订正效果更显著,当起报时刻为12时对中高纬度地区的订正效果更明显;卡尔曼滤波类型自适应误差订正法能有效改善Talagrand的U型或L型分布;由均方根误差分析结果知道,ECMWF 10 m全风速场集合预报本身存在较大的预报不一致性,经过卡尔曼滤波类型自适应误差订正后,集合预报的预报不一致性明显降低,偏差订正可有效改善集合预报的预报不一致性,且随着预报时效的延长,卡尔曼滤波误差法对预报不一致性的改善效果更加明显;从预报不一致性的发生次数特征来看,单点跳跃出现的次数最多,异号三点跳跃的次数最少;经过卡尔曼滤波类型自适应误差订正后,单点跳跃、异号两点跳跃、异号三点跳跃次数都有所下降。
中文关键词:卡尔曼滤波类型自适应误差订正法  全风速场  预报不一致性  Jumpiness指数  集合预报
 
Calibration and inconsistency analysis of ECMWF wind speed ensemble forecasts
Abstract:Based on the 10 m wind speed forecasts during the summer of 2012 from the ECMWF in the TIGGE datasets,a Kalman filter bias-correction combining with a sliding weight method has been done to calibrate the ensemble perturbed forecasts.The effect of this calibration method is examined.Then,the jumpiness index is used to analyse the results before and after calibration.Results show that:(1)In general,the calibration method can effectively reduce the RMSEs of the 10 m wind speed ensemble forecasts at different start times.When the start time is 0000 UTC,the correction results are better in the middle and low latitudes.When the start time is 1200 UTC,the correction results are better in the middle and high latitudes.(2)The calibration method has a good effect on improving the dispersion of ensemble members.The Talagrand pictures show that U-type and L-type distributions decrease after calibration.(3)Analysis of RMSE shows that the 10 m wind speed ensemble forecasts from ECMWF has great inconsistency of prediction.After calibration,the period-average forecast inconsistency indices of ensemble mean are lower than before,showing that the Kalman filter bias-correction method can reduce the forecast inconsistency of the 10 m wind speed ensemble forecasts.(4)In terms of the period-average inconsistency features of the 10 m wind speed ensemble forecasts from ECMWF,all average period-average inconsistency indices increase with the forecast range,in agreement with the practical experience that the forecasts are usually more consistent at short forecast ranges.(5)The calibration method has better effects on reducing the frequencies of flip,flip-flop and flip-flop-flip.The flip happens more frequently than the other two,and the frequency of flip-flop-flip is the lowest.
keywords:Kalman filter bias-correction method  wind speed  forecast inconsistency  Jumpiness index  ensemble prediction
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