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

机译：季节性预测的偏置调整和集合重新校准方法：使用C3S数据集进行全面的互通

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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：这项工作介绍了对季节性预测校准的不同替代方案的全面相应，从简单的偏差调整（BA）-e.g。定量映射 - 更复杂的集合重新校准（RC）方法-e.g。非同质高斯回归，基于气候模型与相应观察之间的时间对应，以产生可靠的预测。尽可能致力于，我们在许多指标方面验证原始模型和校准的预测，该指标考虑了预测质量（关联，准确性，歧视和可靠性）的不同方面。我们专注于来自四个最先进的季节性预测系统的一个月的降水和温度预测，其中三个包括在Copernicus气候变化服务数据集（Ecmwf-Seas5，英国Met Office-Glosea5和Meteo France- System5）对于具有不同技能特征（欧洲和东南亚）的两种说明性地区的北冬和夏季。我们的结果表明，BA和RC方法都有效地纠正了大型原始模型偏差，这对于用户至关重要，特别是当直接使用气候模型输出运行影响模型时，或根据绝对值计算气候指标时。然而，除了特定地区和/或季节（通常具有高技能）外，仅存在边缘附加值 - 关于原始模型输出 - 超出该偏置的偏差。对于那些情况，RC方法可以优于BA系列，主要是由于可靠性的提高。最后，我们还表明，虽然成员数量的增加仅适度地影响从校准获得的结果，但较长的HindCast期导致改善预测质量，特别是对于RC方法。

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