Spectral and spatial localization of background‐error correlations for data assimilation
Spectral and spatial localization of background‐error correlations for data assimilation
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DOI：
10.1002/qj.50
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发表时间：
2007-04
影响因子：
8.9
通讯作者：
M. Buehner;M. Charron
中科院分区：
文献类型：
--
作者：
M. Buehner;M. Charron
在这项研究中，背景误差相关性在谱域和空间域的局部化进行了研究。虽然空间局部化已经成为减少背景误差相关采样误差的标准方法，但光谱相关的局部化还没有得到充分的探索。结果表明，光谱局部化导致了相关函数在格点空间中的空间平滑。在数值天气预报(NWP)的数据同化应用中，经常使用光谱空间中对角线的相关，从而产生全球齐次相关。最近，由小波函数的展开定义的空间中对角线的相关性已被用于同时在谱空间和网格点空间中以特定方式隐式局部化相关性。在这项研究中，通过在空间域和谱域通过不同数量的相关性的显式局部化，来评估它们减少采样误差的互补能力。
In this study, the localization of background‐error correlations in both the spectral and the spatial domains is examined. While spatial localization has become a standard approach for reducing the sampling error of background‐error correlations, localization of spectral correlations has not yet been fully explored. It is shown that spectral localization results in a spatial smoothing of the correlation functions in grid‐point space. The use of correlations that are diagonal in spectral space, resulting in globally homogeneous correlations, has been frequently employed with data assimilation applications for numerical weather prediction (NWP). More recently, correlations that are diagonal in the space defined by an expansion of wavelet functions have been used to implicitly localize the correlations in a particular way in both spectral and grid‐point spaces simultaneously. In this study, the explicit localization of correlations by varying amounts in both the spatial and the spectral domains is applied, to evaluate their complementary ability to reduce sampling error.