Calibrated probabilistic forecasting using ensemble model output statistics and minimum CRPS estimation
Calibrated probabilistic forecasting using ensemble model output statistics and minimum CRPS estimation
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DOI：
10.1175/mwr2904.1
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发表时间：
2005-05-01
影响因子：
3.2
通讯作者：
Goldman, T
中科院分区：
文献类型：
--
作者：
Gneiting, T;Raftery, AE;Goldman, T
集合预报系统通常表现出正的传播误差相关性，但它们容易受到预报偏差和分散误差的影响，因此无法校准。这项工作提出使用集合模型输出统计(EMOS)，这是一种易于实现的后处理技术，既解决了预测偏差和欠分散问题，又考虑了传播技能关系。该技术基于多元线性回归，类似于传统上用于确定性预测的超集合方法。EMOS技术产生的概率预报采用连续天气变量的高斯预测概率密度函数(PDF)的形式，并可应用于网格模式输出。EMOS预测平均值是集合成员预测的经偏差校正的加权平均，其系数可以用成员模型对集合的相对贡献来解释，并提供具有高度竞争性的确定性风格的预测。EMOS预测方差是系综方差的线性函数。为了对EMOS系数进行拟合，引入了最小连续排序概率得分(CRPS)估计方法。该技术为训练数据找到优化CRP的系数值。利用华盛顿大学的中尺度集合，将EMOS技术应用于2000年春季对北美太平洋西北部海平面气压和地表温度的48小时预报。与偏差校正集合相比，确定性模式的海平面气压预报的均方根误差减小了9%，平均绝对误差减小了7%。EMOS预测PDF清晰，而且比原始合奏或偏差校正合奏的校准要好得多。
Ensemble prediction systems typically show positive spread-error correlation, but they are subject to forecast bias and dispersion errors, and are therefore uncalibrated. This work proposes the use of ensemble model output statistics (EMOS), an easy-to-implement postprocessing technique that addresses both forecast bias and underdispersion and takes into account the spread-skill relationship. The technique is based on multiple linear regression and is akin to the superensemble approach that has traditionally been used for deterministic-style forecasts. The EMOS technique yields probabilistic forecasts that take the form of Gaussian predictive probability density functions (PDFs) for continuous weather variables and can be applied to gridded model output. The EMOS predictive mean is a bias-corrected weighted average of the ensemble member forecasts, with coefficients that can be interpreted in terms of the relative contributions of the member models to the ensemble, and provides a highly competitive deterministic-style forecast. The EMOS predictive variance is a linear function of the ensemble variance. For fitting the EMOS coefficients, the method of minimum continuous ranked probability score (CRPS) estimation is introduced. This technique finds the coefficient values that optimize the CRPS for the training data. The EMOS technique was applied to 48-h forecasts of sea level pressure and surface temperature over the North American Pacific Northwest in spring 2000, using the University of Washington mesoscale ensemble. When compared to the bias-corrected ensemble, deterministic-style EMOS forecasts of sea level pressure had root-mean-square error 9% less and mean absolute error 7% less. The EMOS predictive PDFs were sharp, and much better calibrated than the raw ensemble or the bias-corrected ensemble.