In this study, we assessed the performance of the decaying average bias correction method in removing the systematic error in daily maximum temperature (dTmax) ensemble forecasts. We applied the technique to a short-range high-resolution limited-area ensemble prediction system of the Korea Meteorological Administration, which shows under-predictive and under-dispersive characteristics for dTmax. The bias correction was applied to the grid of the model using spatial interpolation of the decaying average bias from surrounding reference points. The method was verified by evaluating the accuracy of the ensemble mean, spread-skill relationship, and the performance of the probabilistic forecasts. The results showed that the decaying average technique minimized the systematic error in the ensemble mean and improved the performance of the probabilistic forecasts. The overall mean absolute error of the ensemble mean was lowered from 2.2 to 1.2 ^∘C and the root-mean-square error from 2.5 to 1.6 ^∘C. The continuous ranked probability score decreased from 1.9 to 1.0 ^∘C. The reliability of three dichotomous events also improved and the Brier skill scores increased. However, the bias correction only slightly affected the ensemble spread, and the system remained under-dispersive.

中文翻译：

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使用衰减平均值对每日最高温度的短距离集合预报的偏差校正

在这项研究中，我们评估了衰减平均偏差校正方法在消除每日最高温度（dTmax）集合预报中的系统误差方面的性能。我们将该技术应用于了韩国气象局的短距离高分辨率有限区域集合预报系统，该系统显示了dTmax的预测不足和扩散不足的特征。使用来自周围参考点的衰减平均偏差的空间插值，将偏差校正应用于模型的网格。通过评估整体平均的准确性，传播技巧之间的关系以及概率预测的性能来验证该方法。结果表明，衰减平均技术使集合均值中的系统误差最小，并提高了概率预测的性能。整体平均的总平均绝对误差从2.2降低到1.2^∘C和均方根误差从2.5降低到^1.6。C。连续排名概率评分从1.9降低到^1.0。C。三个二分事件的可靠性也有所提高，并且Brier技能得分也有所提高。但是，偏差校正仅稍微影响了整体扩散，并且系统仍然分散不足。