Spatial and Temporal Network Sampling Effects on the Correlation and Variance Structures of Rain Observations
Spatial and Temporal Network Sampling Effects on the Correlation and Variance Structures of Rain Observations
复制标题
时空网络采样对降雨观测相关性和方差结构的影响
DOI：
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发表时间：
2017
期刊：
影响因子：
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通讯作者：
A. R. Jameson
中科院分区：
文献类型：
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作者：
A. R. Jameson
摘要网络观测受合并测量的时间间隔的长度以及网络大小的影响。当观测间隔较小时，只有网络规模很重要。然后，网络充当高通滤波器，其扭曲空间相关函数ρr，并因此扭曲方差谱。对于指数递减的ρr，给出了一种将观测到的空间相关性恢复到其原始内在值的方法。然而，当观测间隔变大时，平流增强了来自较长波长的贡献，导致ρr和相关的方差谱的失真。然而，目前还没有已知的方法来修正这种影响，这意味着为了正确地测量空间相关性，观测间隔应该保持尽可能小。最后指出，与网络测量相比，遥感仪器具有更好的应用前景。
AbstractNetwork observations are affected by the length of the temporal interval over which measurements are combined as well as by the size of the network. When the observation interval is small, only network size matters. Networks then act as high-pass filters that distort both the spatial correlation function ρr and, consequently, the variance spectrum. For an exponentially decreasing ρr, a method is presented for returning the observed spatial correlation to its original, intrinsic value. This can be accomplished for other forms of ρr. When the observation interval becomes large, however, advection enhances the contributions from longer wavelengths, leading to a distortion of ρr and the associated variance spectrum. However, there is no known way to correct for this effect, which means that the observation interval should be kept as small as possible in order to measure the spatial correlation correctly. Finally, it is shown that, in contrast to network measurements, remote sensing instruments act as ...