Predictability of Week-3–4 Average Temperature and Precipitation over the Contiguous United States
Predictability of Week-3–4 Average Temperature and Precipitation over the Contiguous United States
复制标题
美国本土第 3-4 周平均气温和降水的可预测性
DOI：
10.1175/jcli-d-16-0567.1
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发表时间：
2017
影响因子：
4.9
通讯作者：
K. Pegion
中科院分区：
文献类型：
--
作者：
T. DelSole;L. Trenary;M. Tippett;K. Pegion
摘要本文论证了业务预报模式可以巧妙地预报美国邻近地区3-4周的平均气温和降水量。这种技术在网格点水平（约1° × 1°）上得到了证明，方法是将温度和降水异常分解为一组正交模式，这些模式可以通过长度尺度的测量进行排序，然后表明许多结果分量是可预测的，并且可以通过具有统计学显著性的技术在观测中进行预测。预测性和技能的统计学意义进行评估使用排列测试，占序列相关性。技能是基于相关性测量而不是基于均方误差测量来检测的，这表明幅度校正对于技能是必要的。可预测性的统计特征进一步澄清，找到线性组合的组件，最大限度地提高可预测性。这里分析的预测模型是...
AbstractThis paper demonstrates that an operational forecast model can skillfully predict week-3–4 averages of temperature and precipitation over the contiguous United States. This skill is demonstrated at the gridpoint level (about 1° × 1°) by decomposing temperature and precipitation anomalies in terms of an orthogonal set of patterns that can be ordered by a measure of length scale and then showing that many of the resulting components are predictable and can be predicted in observations with statistically significant skill. The statistical significance of predictability and skill are assessed using a permutation test that accounts for serial correlation. Skill is detected based on correlation measures but not based on mean square error measures, indicating that an amplitude correction is necessary for skill. The statistical characteristics of predictability are further clarified by finding linear combinations of components that maximize predictability. The forecast model analyzed here is version 2 of ...