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Effects of Observation Errors on the Statistics for Ensemble Spread and Reliability

机译:观测误差对整体传播和可靠性统计的影响

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The effects of observation errors on rank histograms and reliability diagrams are investigated using a perfect model approach. The three-variable Lorenz-63 model was used to simulate an idealized ensemble prediction system (EPS) with 50 perturbed ensemble members and one control forecast. Observation errors at verification time were introduced by adding normally distributed noise to the true state at verification time. Besides these simulations, a theoretical analysis was also performed. One of the major findings was that rank histograms are very sensitive to the presence of observation errors, leading to overpopulated upper- and lowermost ranks. This sensitivity was shown to grow for larger ensemble sizes. Reliability diagrams were far less sensitive in this respect. The resulting u-shaped rank histograms can easily be misinterpreted as indicating too little spread in the ensemble prediction system. To account for this effect when real observations are used to assess an ensemble prediction system, normally distributed noise based on the verifying observation error can be added to the ensemble members before the statistics are calculated. The method has been tested for the ECMWF ensemble forecasts of ocean waves and forecasts of the geopotential at 500 hPa. The EPS waves were compared with buoy observations from the Global Telecommunication System (GTS) for a period of almost 3 yr. When the buoy observations were taken as the true value, more than 25% of the observations appeared in the two extreme ranks for the day 3 forecast range. This number was reduced to less than 10% when observation errors were added to the ensemble members. Ensemble forecasts of the 500-hPa geopotential were verified against the ECMWF analysis. When analysis errors were neglected, the maximum number of outliers was more than 10% for most areas except for Europe, where the analysis errors are relatively smaller. Introducing noise to the ensemble members, based on estimates of analysis errors, reduced the number of outliers, particularly in the short range, where a peak around day 1 more or less vanished.
机译:使用完美模型方法研究了观察误差对等级直方图和可靠性图的影响。使用三变量Lorenz-63模型模拟具有50个扰动的集合成员和一个控制预测的理想集合预测系统(EPS)。通过将正态分布的噪声添加到验证时的真实状态,来引入验证时的观察误差。除了这些模拟之外,还进行了理论分析。主要发现之一是等级直方图对观察误差的存在非常敏感,从而导致最高和最低等级的人口过多。对于较大的合奏,此灵敏度已显示增加。在这方面,可靠性图的敏感性要差得多。所产生的u形秩直方图很容易被误解为表示集成预测系统中的散布太小。为了在使用实际观测值评估集合预测系统时解决此影响,可以在计算统计信息之前将基于验证观测误差的正态分布噪声添加到集合成员中。该方法已针对ECMWF海浪整体预报和500 hPa的地势预报进行了测试。将EPS波与全球电信系统(GTS)的浮标观测值进行了近3年的比较。当将浮标观测值作为真实值时,超过25%的观测值出现在第3天预测范围的两个极端排名中。当将观测误差添加到合奏成员时,此数字减少到不足10%。通过ECMWF分析验证了500 hPa地势的整体预报。当忽略分析误差时,除欧洲以外,大多数地区的异常值最大数目都超过10%,欧洲的分析误差相对较小。根据对分析误差的估计,向合奏成员引入噪音,减少了异常值的数量,尤其是在短距离内,在此范围内,第1天左右的峰值或多或少消失了。

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