基于矩的系综评估和校准方法
Moment based methods for ensemble assessment and calibration
摘要：
我们描述了各种基于矩的合奏解释模型的建设概率温度预报合奏。我们将这些方法应用于一年的中期集合预报，并进行了样本内和样本外检验。我们的主要结论是，来自集合平均使用回归的概率预测是一样好的集合平均和集合传播使用更复杂的校准算法的基础上。对此的解释似乎是，预测不确定性的可变性的可预测部分只是总预测不确定性的一小部分。建议集合温度预报的用户，在获得进一步的证据之前，忽略集合传播，仅根据集合平均值建立概率预报。
英文摘要：
We describe various moment-based ensemble interpretation models for the construction of probabilistic temperature forecasts from ensembles. We apply the methods to one year of medium range ensemble forecasts and perform in and out of sample testing. Our main conclusion is that probabilistic forecasts derived from the ensemble mean using regression are just as good as those based on the ensemble mean and the ensemble spread using a more complex calibration algorithm. The explanation for this seems to be that the predictable component of the variability of the forecast uncertainty is only a small fraction of the total forecast uncertainty. Users of ensemble temperature forecasts are advised, until further evidence becomes available, to ignore the ensemble spread and build probabilistic forecasts based on the ensemble mean alone.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
