Based on the extreme forecast index (EFI) of ECMWF, the “observed” EFI (OEFI) of observation is defined and the EFI is calibrated. Then the EFI equivalent percentile (EFIEP) and EFI equivalent quantile (EFIEQ) are d...Based on the extreme forecast index (EFI) of ECMWF, the “observed” EFI (OEFI) of observation is defined and the EFI is calibrated. Then the EFI equivalent percentile (EFIEP) and EFI equivalent quantile (EFIEQ) are designed to forecast the daily extreme precipitation quantitatively. The formulation indicates that the EFIEP is correlated not only to the EFI but also to the proportion of no precipitation. This characteristic is prominent as two areas with nearly same EFIs but different proportions of no precipitation. Cases study shows that the EFIEP can forecast reliable percentile of daily precipitation and 100% percentiles are forecasted for over max extreme events. The EFIEQ is a considerable tool for quantitative precipitation forecast (QPF). Compared to the probabilistic forecast of ensemble prediction system (EPS), it is quantitative and synthesizes the advantage of extreme precipitation location forecast of EPS. Using the observations of 2311 stations of China in 2016 to verify the EFIEP and EFIEQ, the results show that the forecast biases are around 1. The threat scores (TS) for 20 years return period events are about 0.21 and 0.07 for 36 and 180 hours lead times respectively. The equivalent threat scores (ETS) are all larger than 0 and nearly equal to the TS. The TS for heavy rainfall are 0.23 and 0.07 for 36 and 180 lead times respectively. The scores are better than those of high resolution deterministic model (HRDet) and show significant forecast skills for quantitative forecast of extreme daily precipitation.展开更多
研究山西极端暴雨发生规律对开展预报预警、灾害防御具有重要意义。本文利用常规观测资料和欧洲中期天气预报中心(European Centre for Medium-Range Weather Forecasts,ECMWF)第五代大气再分析资料(ERA5),采用标准化距平作为异常度,运...研究山西极端暴雨发生规律对开展预报预警、灾害防御具有重要意义。本文利用常规观测资料和欧洲中期天气预报中心(European Centre for Medium-Range Weather Forecasts,ECMWF)第五代大气再分析资料(ERA5),采用标准化距平作为异常度,运用环流分析和物理量诊断等方法,研究1981—2018年6—9月山西17次极端暴雨的气候特征、环流影响系统和水汽异常特征。结果表明:山西极端暴雨主要出现在7—8月,暴雨区主要位于中南部,2010年以来极端暴雨明显多发;影响系统主要是700 h Pa低涡和台风系统,有偏南和偏东两支水汽通道。极端暴雨过程中,低层水汽含量明显偏高,从暴雨区平均比湿的过程最大值看,大部分过程850 h Pa超过14.2 g·kg^(-1),700 h Pa则可超过9.8 g·kg^(-1)、对应暴雨区平均异常度达1.6以上;水汽的极端性在低层水汽通量辐合中心表现突出,17次极端暴雨700、850 h Pa暴雨区水汽通量辐合中心过程最大值的异常度均值分别达-8、-6,其中台风减弱低压影响下的极端暴雨850 h Pa水汽通量辐合中心最大异常度达-12。根据以上环流和水汽特征建立极端暴雨概念模型,并给出极端暴雨低层水汽含量和水汽通量辐合强度预报参考指标。展开更多
基于中国T213集合预报系统资料,根据Anderson-Darling检验原理,研究基于集合预报与模式历史预报累积概率密度(简称模式气候)分布函数连续差异特征的极端温度天气预报方法,建立极端温度天气预报指数(Extreme Temperature Forecast Ind...基于中国T213集合预报系统资料,根据Anderson-Darling检验原理,研究基于集合预报与模式历史预报累积概率密度(简称模式气候)分布函数连续差异特征的极端温度天气预报方法,建立极端温度天气预报指数(Extreme Temperature Forecast Index,简称EFI)的数学模型。利用S指数评分方法确定发布极端温度预警信号的阈值,得出:1月的发布极端高温的预警信号的阈值为0.7或0.8,发布极端低温的预警信号的阈值为-0.7或-0.8。基于EFI指数以及该阈值,对2013年1月中国极端温度天气进行预报试验,得出:极端天气预报指数对极端温度天气具有较好的识别能力,可提前3~7 d发出极端温度预警信号,随着预报时效的延长,预报技巧逐渐降低。展开更多
评估分析了欧洲中期天气预报中心(European Centre for Medium-range Weather Forecasts,ECMWF)细网格模式(以下简称EC-thin)在长三角地区汛期(5—9月)的暴雨预报评分及ECMWF降水极端天气预报指数(EFI)对暴雨预警的指示作用。研究发现:(...评估分析了欧洲中期天气预报中心(European Centre for Medium-range Weather Forecasts,ECMWF)细网格模式(以下简称EC-thin)在长三角地区汛期(5—9月)的暴雨预报评分及ECMWF降水极端天气预报指数(EFI)对暴雨预警的指示作用。研究发现:(1) EC-thin降水和降水EFI对暴雨预报的ETS评分随着预报时效的延长而明显降低,在短时效内,细网格模式降水预报占优,超过60 h后,降水EFI的评分相对更好。(2)对EC-thin降水而言,在不同的预报时效采用不同的降水阈值来预报暴雨,可望达到最佳的评分效果。短期时效内该阈值随着预报时效的延长,大致从55 mm逐渐下降到35 mm。(3)对于降水EFI而言,12—36 h内EFI为0.65~0.7时,暴雨预报ETS评分最高。随着预报时效的延长逐渐下降,60—84 h内EFI为0.55~0.6时,暴雨预报ETS评分最高。(4)在不同预报时效内,采用合理的方式和阈值综合考虑EC-thin降水和降水EFI,可望得到更高的暴雨预报评分。展开更多
文摘Based on the extreme forecast index (EFI) of ECMWF, the “observed” EFI (OEFI) of observation is defined and the EFI is calibrated. Then the EFI equivalent percentile (EFIEP) and EFI equivalent quantile (EFIEQ) are designed to forecast the daily extreme precipitation quantitatively. The formulation indicates that the EFIEP is correlated not only to the EFI but also to the proportion of no precipitation. This characteristic is prominent as two areas with nearly same EFIs but different proportions of no precipitation. Cases study shows that the EFIEP can forecast reliable percentile of daily precipitation and 100% percentiles are forecasted for over max extreme events. The EFIEQ is a considerable tool for quantitative precipitation forecast (QPF). Compared to the probabilistic forecast of ensemble prediction system (EPS), it is quantitative and synthesizes the advantage of extreme precipitation location forecast of EPS. Using the observations of 2311 stations of China in 2016 to verify the EFIEP and EFIEQ, the results show that the forecast biases are around 1. The threat scores (TS) for 20 years return period events are about 0.21 and 0.07 for 36 and 180 hours lead times respectively. The equivalent threat scores (ETS) are all larger than 0 and nearly equal to the TS. The TS for heavy rainfall are 0.23 and 0.07 for 36 and 180 lead times respectively. The scores are better than those of high resolution deterministic model (HRDet) and show significant forecast skills for quantitative forecast of extreme daily precipitation.
文摘研究山西极端暴雨发生规律对开展预报预警、灾害防御具有重要意义。本文利用常规观测资料和欧洲中期天气预报中心(European Centre for Medium-Range Weather Forecasts,ECMWF)第五代大气再分析资料(ERA5),采用标准化距平作为异常度,运用环流分析和物理量诊断等方法,研究1981—2018年6—9月山西17次极端暴雨的气候特征、环流影响系统和水汽异常特征。结果表明:山西极端暴雨主要出现在7—8月,暴雨区主要位于中南部,2010年以来极端暴雨明显多发;影响系统主要是700 h Pa低涡和台风系统,有偏南和偏东两支水汽通道。极端暴雨过程中,低层水汽含量明显偏高,从暴雨区平均比湿的过程最大值看,大部分过程850 h Pa超过14.2 g·kg^(-1),700 h Pa则可超过9.8 g·kg^(-1)、对应暴雨区平均异常度达1.6以上;水汽的极端性在低层水汽通量辐合中心表现突出,17次极端暴雨700、850 h Pa暴雨区水汽通量辐合中心过程最大值的异常度均值分别达-8、-6,其中台风减弱低压影响下的极端暴雨850 h Pa水汽通量辐合中心最大异常度达-12。根据以上环流和水汽特征建立极端暴雨概念模型,并给出极端暴雨低层水汽含量和水汽通量辐合强度预报参考指标。
文摘基于中国T213集合预报系统资料,根据Anderson-Darling检验原理,研究基于集合预报与模式历史预报累积概率密度(简称模式气候)分布函数连续差异特征的极端温度天气预报方法,建立极端温度天气预报指数(Extreme Temperature Forecast Index,简称EFI)的数学模型。利用S指数评分方法确定发布极端温度预警信号的阈值,得出:1月的发布极端高温的预警信号的阈值为0.7或0.8,发布极端低温的预警信号的阈值为-0.7或-0.8。基于EFI指数以及该阈值,对2013年1月中国极端温度天气进行预报试验,得出:极端天气预报指数对极端温度天气具有较好的识别能力,可提前3~7 d发出极端温度预警信号,随着预报时效的延长,预报技巧逐渐降低。
文摘通过个例总结和大样本分析的方法,本文分析和总结了ECMWF集合预报系统(EPS)中的极端温度和降水预报产品。以上产品主要为08—08时的平均气温、最高气温、最低气温和降水量四个要素的极端天气预报指数(extreme forecast index,EFI)和SOT("shift of tail"index)。研究显示,气温EFI和SOT预报效果接近,降水SOT优于EFI。运用过去3年的资料,以TS评分最大为标准,分别确定了不同时效、不同百分位的极端高低温和极端强降水事件在我国的预报阈值,及其对应的各检验参数。对于1%(99%)百分位的极端低温(高温)事件,平均气温EFI和SOT的阈值分别在-0.85(0.75)和0.38(0),最高和最低气温的阈值与平均气温的阈值接近。对于95%和99%的极端强降水事件,EFI的阈值分别在0.45和0.7左右,SOT的阈值分别在-0.6和0.4左右。整体上呈现时效越长阈值越小,预报效果越差;事件越极端,阈值越大的特点。且此时的bias接近或略大于1,表明预报的发生频率与实况比较接近,具有较好的应用价值。气温EFI和SOT的预报效果和阈值存在明显的季节差异,夏季预报较好,阈值较大,冬季预报较差,阈值较小。降水的季节差异不明显。EFI和SOT的预报效果和阈值在空间分布上也存在一定的差异,且不同的产品空间分布差异不同。