基于TIGGE资料的地面气温延伸期多模式集成预报
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国家科技部科技支撑计划项目(2009BAC51B03);公益性行业(气象)科研专项(GYHY200906009);江苏高校优势学科建设工程资助项目(PAPD)


Multi-model ensemble forecasts of surface air temperature in the extended range using the TIGGE dataset
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    摘要:

    基于TIGGE资料中心提供的CMC、ECMWF、UKMO及NCEP四个集合预报中心2008年7月1日-9月30日北半球中纬度地区地面气温10~15 d延伸期集合预报产品,首先采用Talagrand分布及离散度-误差关系评估了单个预报系统的预报性能,然后分别利用多模式集成平均(Ensemble Mean,EMN)、消除偏差集成平均(Bias-Removed Ensemble Mean,BREM)及多模式超级集合(Multi-model Superensemble,SUP)对地面气温进行多模式集成预报试验。由于逐日的延伸期预报准确率相对较低,因此人们更关注延伸期预报对天气过程的预报准确率。对各个集合预报系统的逐日预报资料以及逐日"观测"资料做滑动平均,并对处理后的资料进行多模式集成,最后对超级集合预报的训练期长度进行调试,以获得最佳训练期长度。结果表明,四个集合预报系统的离散度相对于均方根误差都偏小,ECMWF预报效果最好,NCEP次之,UKMO预报效果最差。EMN、BREM及SUP三种多模式集成方法的预报效果均优于单个系统且SUP对预报效果的改善最明显。滑动平均后,预报误差进一步降低,且滑动步长越长,误差越小。对于SUP的训练期,逐日预报和3 d滑动平均10~12 d预报最佳训练期长度为75 d;13~15 d预报最佳训练期长度为35 d;5 d及7 d滑动平均其训练期长度在各个时效均以35 d为宜。

    Abstract:

    Based on the 10-15 days extended range ensemble forecasts of CMC,ECMWF,UKMO and NCEP in the TIGGE dataset,the multi-model ensemble forecasts of surface air temperature in the Northern Hemisphere middle latitudes during the period from 1 July to 30 September 2008 have been conducted by using EMN(Ensemble Mean),BREM(Bias-Removed Ensemble Mean) and SUP(Multi-model Superensemble),respectively.Meanwhile,the Talagrand distribution,ensemble spread,and RMS error are utilized to evaluate the forecast skills of each single forecast system.As is known that the precision of daily forecasts in extended range is relatively low,people pay more attention to the prediction precision of weather process in extended range.The daily data of each single forecast system and the daily observed data are processed by the moving average method.The processed data are also conducted with multi-model ensemble means.Finally,the training period of SUP is tested.Results show that four single forecast system’s ensemble spread are low compared with RMS error.ECMWF is the best single system,NCEP follows and UKMO is the worst.The forecast skills of EMN,BREM and SUP are all higher than single system and SUP is the best ensemble method.Moving average methods further improve the forecast skill of surface air temperature and the longer of moving step is,the better of the prediction results are.For the training period of SUP about daily and 3-day moving average,75 days are the best training period for 10-12 d forecasts and 35 days are the best for 13-15 d forecasts.For 5-day and 7-day moving average,the best training period is 35 days at all forecasting time.

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  • 收稿日期:2012-02-29
  • 最后修改日期:2012-09-03
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