Best Practices for Postprocessing Ensemble Climate Forecasts. Part I: Selecting Appropriate Recalibration Methods
Best Practices for Postprocessing Ensemble Climate Forecasts. Part I: Selecting Appropriate Recalibration Methods
复制标题
后处理集合气候预报的最佳实践。
DOI：
10.1175/jcli-d-15-0868.1
复制
发表时间：
2016
影响因子：
4.9
通讯作者：
S. Mason
中科院分区：
文献类型：
--
作者：
Philip G. Sansom;C. Ferro;D. Stephenson;L. Goddard;S. Mason
摘要本研究描述了一种选择最佳统计重新校准方法和后报设计的系统方法，用于在季节到十年时间尺度上产生可靠的概率预报。一个新的重新校准方法，包括调整无条件和条件偏差的平均值和方差的预测分布和线性时间依赖性的平均偏差。可以通过限制参数来系统地改变重新校准的复杂性。简单的重新校准方法可能优于更复杂的方法，因为训练数据有限。提出了一种新的交叉验证方法，它允许使用有限的数据比较多种重新校准方法和不同的训练周期。第一部分考虑了不同的重新校准复杂性和训练周期长度对预测技能的影响。这些因素之间的相互作用进行了分析，从CanCM4模式的年平均近地表温度的网格箱预报。重新校准
AbstractThis study describes a systematic approach to selecting optimal statistical recalibration methods and hindcast designs for producing reliable probability forecasts on seasonal-to-decadal time scales. A new recalibration method is introduced that includes adjustments for both unconditional and conditional biases in the mean and variance of the forecast distribution and linear time-dependent bias in the mean. The complexity of the recalibration can be systematically varied by restricting the parameters. Simple recalibration methods may outperform more complex ones given limited training data. A new cross-validation methodology is proposed that allows the comparison of multiple recalibration methods and varying training periods using limited data.Part I considers the effect on forecast skill of varying the recalibration complexity and training period length. The interaction between these factors is analyzed for gridbox forecasts of annual mean near-surface temperature from the CanCM4 model. Recalibra...
影响因子：
4.6
作者：
Goddard, L.;Kumar, A.;Delworth, T.
通讯作者：
Delworth, T.
影响因子：
5.7
作者：
Roman Schefzik;T. Thorarinsdottir;T. Gneiting
通讯作者：
Roman Schefzik;T. Thorarinsdottir;T. Gneiting