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Bias adjustment and ensemble recalibration methods for seasonal forecasting: a comprehensive intercomparison using the C3S dataset

机译：季节性预测的偏差调整和整体重新校准方法：使用C3S数据集的全面比较

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This work presents a comprehensive intercomparison of different alternatives for the calibration of seasonal forecasts, ranging from simple bias adjustment (BA)-e.g. quantile mapping-to more sophisticated ensemble recalibration (RC) methods-e.g. non-homogeneous Gaussian regression, which build on the temporal correspondence between the climate model and the corresponding observations to generate reliable predictions. To be as critical as possible, we validate the raw model and the calibrated forecasts in terms of a number of metrics which take into account different aspects of forecast quality (association, accuracy, discrimination and reliability). We focus on one-month lead forecasts of precipitation and temperature from four state-of-the-art seasonal forecasting systems, three of them included in the Copernicus Climate Change Service dataset (ECMWF-SEAS5, UK Met Office-GloSea5 and Meteo France-System5) for boreal winter and summer over two illustrative regions with different skill characteristics (Europe and Southeast Asia). Our results indicate that both BA and RC methods effectively correct the large raw model biases, which is of paramount importance for users, particularly when directly using the climate model outputs to run impact models, or when computing climate indices depending on absolute values/thresholds. However, except for particular regions and/or seasons (typically with high skill), there is only marginal added value-with respect to the raw model outputs-beyond this bias removal. For those cases, RC methods can outperform BA ones, mostly due to an improvement in reliability. Finally, we also show that whereas an increase in the number of members only modestly affects the results obtained from calibration, longer hindcast periods lead to improved forecast quality, particularly for RC methods.
机译：这项工作为校准季节预报提供了不同选择的全面比较，范围从简单的偏差调整（BA）-例如。分位数映射-到更复杂的集成重校准（RC）方法-例如非均质高斯回归，它建立在气候模型和相应观测值之间的时间对应关系上，以生成可靠的预测。为了尽可能关键，我们根据许多指标验证原始模型和已校准的预测，这些指标考虑了预测质量的各个方面（关联，准确性，区分度和可靠性）。我们着重于四个最新的季节预报系统对降雨和温度的一个月领先预报，其中三个预报系统包括在哥白尼气候变化服务数据集中（ECMWF-SEAS5，英国气象局-GloSea5和法国气象局- System5）用于两个具有不同技能特征的说明性地区（欧洲和东南亚）的冬季和夏季的寒冬。我们的结果表明，BA方法和RC方法都可以有效地校正较大的原始模型偏差，这对用户而言至关重要，特别是在直接使用气候模型输出运行影响模型时，或者在根据绝对值/阈值计算气候指数时。但是，除了特定的区域和/或季节（通常具有较高的技能）外，除原始偏差外，相对于原始模型输出，仅存在边际附加值。对于那些情况，RC方法可以胜过BA方法，这主要是由于可靠性的提高。最后，我们还表明，尽管成员数量的增加仅会适度影响通过校准获得的结果，但是较长的后播期会提高预测质量，尤其是对于RC方法。

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