Spatial and Temporal Network Sampling Effects on the Correlation and Variance Structures of Rain Observations

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 ...