违反了 PRIMARY KEY 约束 'PK_t_counter'。不能在对象 'dbo.t_counter' 中插入重复键。 语句已终止。 ECMWF集合预报系统对秦岭周边地区降水确定性预报的性能分析-Performance Analysis on Deterministic Precipitation Forecasting in Surrounding Areas of Qinling Mountains by ECMWF Ensemble Prediction System
doi:  10.3878/j.issn.1006-9585.2017.16150
ECMWF集合预报系统对秦岭周边地区降水确定性预报的性能分析

Performance Analysis on Deterministic Precipitation Forecasting in Surrounding Areas of Qinling Mountains by ECMWF Ensemble Prediction System
摘要点击 376  全文点击 287  投稿时间:2016-08-04  修订日期:2017-04-09
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基金:  陕西省自然科学基础研究计划项目2016JM4020,陕西省气象局重点科研项目2016Z-1。
中文关键词:  ECMWF  集合预报  确定性预报  技巧评分  泰勒图
英文关键词:  ECMWF  Ensemble forecasting  Certain forecasting  Skill score  Taylor diagram
           
作者中文名作者英文名单位
张宏芳ZHANG Hongfang陕西省气象服务中心, 西安 710014
潘留杰PAN Liujie陕西省气象台, 西安 710014
卢珊LU Shan陕西省气象服务中心, 西安 710014
巨晓璇JU Xiaoxuan陕西省气象服务中心, 西安 710014
引用:张宏芳,潘留杰,卢珊,巨晓璇.2017.ECMWF集合预报系统对秦岭周边地区降水确定性预报的性能分析[J].气候与环境研究,22(5):551-562,doi:10.3878/j.issn.1006-9585.2017.16150.
Citation:ZHANG Hongfang,PAN Liujie,LU Shan,JU Xiaoxuan.2017.Performance Analysis on Deterministic Precipitation Forecasting in Surrounding Areas of Qinling Mountains by ECMWF Ensemble Prediction System[J].Climatic and Environmental Research(in Chinese),22(5):551-562,doi:10.3878/j.issn.1006-9585.2017.16150.
中文摘要:
      尽管确定性预报不是集合预报系统(EPS)的主要目的和应用方向,但其每一个成员的预报表现决定了集合预报系统的预报性能,集合平均也是实际预报业务的一个重要参考指标。为此,利用2013~2015年5~10月的欧洲中期天气预报中心(ECMWF)集合预报系统的降水预报资料,CMORPH(NOAA Climate Prediction Center Morphing Method)卫星与全国3×104余个自动气象观测站的逐小时降水量融合资料,研究ECMWF集合预报系统对秦岭周边地区逐日降水的控制预报、成员预报、集合平均的预报能力,并探索提高降水集合平均预报性能的有效方法。主要结论如下:(1)无论是集合平均还是控制预报,整体上都较好的刻画了秦岭周边地区降水的空间形态,比较而言,控制预报能够更好的表现了降水的方差变化。(2)泰勒分析表明,集合平均的降水方差随预报时效增加单调减小,控制预报的方差变化随预报时效的增长振荡较小,其相关系数略优于集合平均。(3)技巧评分表明,集合平均使小雨(降水发生频次)的预报偏差显著增加,增大了空报率;使大雨以上的降水预报偏差减小,增大了漏报率,从而使得大多数情况下,集合平均TS(Threat Score)、ETS(Equitable Threat Score)评分低于控制及扰动成员预报。分析认为这主要是由于降水这一要素的偏态分布特性引起的。(4)集合平均的显著贡献在于能够较好的指示可能发生降水的空间位置。通过阈值限定,调整预报偏差,减少(增大)其对小雨(暴雨)的预报频率,能够使集合平均的TS、ETS评分大幅度提升,预报技巧显著优于成员预报和控制预报。目前,预报偏差Bias订正方法已成功应用于陕西省精细化格点预报系统中。
Abstract:
      Although deterministic forecasting is not the main purpose and application of ensemble prediction system (EPS), the forecasting performance of each individual member determines the capability of the entire EPS and the ensemble mean is also an important reference index for the actual forecasting application. Therefore, using the EPS precipitation forecast data from 2013 to 2015 (from May to October every year) from the European Centre for Medium Range Weather Forecast (ECMWF) and hourly precipitation data from CMORPH (NOAA Climate Prediction Center Morphing Method) in combination with observations collected at more than thirty thousands of automatic weather stations in China, the performance of control forecast, member forecast, and ensemble mean of ECMWF ensemble prediction system on daily precipitation in the surrounding areas of Qinling Mountains are analyzed. The effective method to improve the performance of the ensemble mean of precipitation forecast is explored. Major conclusions are as follows. (1) The spatial pattern of precipitation in the surrounding areas of Qinling Mountains is well described by the ensemble mean and the control forecast. Comparatively, the control forecast can better represent the variance of precipitation. (2) Taylor analysis shows that the precipitation variance of the ensemble mean decreases monotonously with increases in the valid period of forecast, while the variance of control forecast shows less oscillations than that of the ensemble mean and the correlation coefficient improves slightly. (3) The forecast skill scores indicate that the ensemble mean yields significantly large bias (precipitation frequency) in light rain forecast, which indicates large false alarm rate for light precipitation; meanwhile, ensemble mean decreases the bias (precipitation frequency) in heavy rain forecast, suggesting that the missing rate for heavy precipitation forecast is high. As a result, TS and ETS scores of the ensemble mean tend to be lower than those of the control forecast and disturbed member prediction, which is attributed to the skewness distribution of precipitation. (4) The significant contribution of the ensemble mean lies in its ability to well predict the spatial location of possible precipitation. By limiting the threshold, adjusting the forecast bias, decreasing (increasing) the forecast frequency on light (heavy) rain, TS, and ETS scores of the ensemble mean can be improved obviously and the ensemble forecast skill would be superior to that of the member forecast and control forecast. At present, the forecast bias correction method has been successfully applied to the fine-resolution forecast system in Shaanxi.
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