Comparison of ensemble-MOS methods using GFS reforecasts
Comparison of ensemble-MOS methods using GFS reforecasts
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DOI：
10.1175/mwr3402.1
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发表时间：
2007-06-01
影响因子：
3.2
通讯作者：
Hamill, Thomas M.
中科院分区：
文献类型：
--
作者：
Wilks, Daniel S.;Hamill, Thomas M.
三个最近提出的和有前途的方法后处理集合预报的基础上，他们的历史误差特征（即，系综模型输出统计方法）进行了比较，使用一个多十年的再预报数据集。逻辑回归和非齐次高斯回归一般是首选的每日温度，中期（6-10和8-14天）的温度和降水预报。然而，中期集合预报的更好的清晰度有时会产生最好的Brier分数，即使它们的校准有点糟糕。相对于使用短（1或2年）训练样本，使用这些重新预测提供的长（15或25年）训练样本可以将这些概率预测的准确性和技能提高到大约相当于提前1天获得收益的水平。
Three recently proposed and promising methods for postprocessing ensemble forecasts based on their historical error characteristics (i.e., ensemble-model output statistics methods) are compared using a multidecadal reforecast dataset. Logistic regressions and nonhomogeneous Gaussian regressions are generally preferred for daily temperature, and for medium-range (6-10 and 8-14 day) temperature and precipitation forecasts. However, the better sharpness of medium-range ensemble-dressing forecasts sometimes yields the best Brier scores even though their calibration is somewhat worse. Using the long (15 or 25 yr) training samples that are available with these reforecasts improves the accuracy and skill of these probabilistic forecasts to levels that are approximately equivalent to gains of 1 day of lead time, relative to using short (1 or 2 yr) training samples.