Statistical and dynamical long-range atmospheric forecasts: experimental comparison and hybridization
Statistical and dynamical long-range atmospheric forecasts: experimental comparison and hybridization
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统计和动态长期大气预报：实验比较和混合
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发表时间：
1996
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通讯作者：
R. Vautard
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作者：
J. Sarda;G. Plaut;C. Pires;R. Vautard
我们将一个以时空主成分作为预报因子的统计预报模式与在法国气象局进行的一系列实验长期(长达44天)动力预报进行了直接比较，该预报模式使用的是法国业务预报模式“Emeraude”的简化物理版本。这种比较是通过使用相同的技术措施，在相同的预测日期和提前期预测相同的高空气量，即月平均50千帕的位势高度，从而实现的。一个令人失望的结果是，由于使用的预测案例数量很少(40个)，大多数技能差异并不显著。此外，由于趋势、年代际变化和系统性误差的共同作用，技能比较被固有的偏差所掩盖。考虑到这些问题，我们使用了非常保守的显著性检验程序。仔细检查一下这项技能，就会得出结论，从长远来看，统计模型的表现要好于动态模型。特别相关的问题是，经验预测和动态预测中包含的有价值的信息是否不同。如果是这样，预测和/或预测算法的适当组合可能会导致技能的提高。这是我们的第二个目的：我们在这里提出了两种混合方法，客观地结合了动态和统计预测信息的内容。将这些混合模型的技巧与两个原始模型的技巧进行了比较。在较长的交付期内，这两种混合方案中的一种被证明比动态模型要熟练得多。DOI：10.1034/j.1600-0870.1996.t01-3-00003.x
We perform a direct comparison between a statistical forecast model using space-time principal components as predictors and a series of experimental long-range (up to 44 days) dynamical forecasts performed at Meteo-France using a simplified-physics version of the former french operational forecast model “Emeraude”. The comparison is made possible by forecasting the same upper air quantity, the monthly averaged 50 kPa geopotential height, at the same forecast dates and lead times, using the same skill measure. A disappointing result is that most of the skill differences are nonsignificant, due to the small number of forecast cases used (40). Moreover, the skill comparisons are obscured by inherent biases due to the combination of trends, interdecadal variability and systematic errors. We use a very conservative significance testing procedure taking into account these problems. A careful examination of the skill leads to the conclusion that the statistical model performs better in the long run than the dynamical one. Of particular relevance is the question whether the valuable information contained in the empirical and the dynamical forecasts differ. If such is the case an appropriate combination of both forecasts, and/or forecasting algorithms could lead to an improvement of the skill. This is our second purpose: we propose here two hybrid procedures which combine objectively the dynamical and statistical predictive information contents. The skill of these hybrid models is compared to that of the two original models. One of the two hybrid schemes is shown to be significantly more skilful, at long lead times, than the dynamical model. DOI: 10.1034/j.1600-0870.1996.t01-3-00003.x