Rainfall and sampling uncertainties: A rain gauge perspective
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Rainfall and sampling uncertainties: A rain gauge perspective
Rainfall and sampling uncertainties: A rain gauge perspective

Author: Gabriele Villarini, Pradeep V. Mandapaka, Witold F. Krajewski, and Robert J. Moore | Size: 733 KB | Format: PDF | Quality: Unspecified | Publisher: Wiley | Year: 2008 | pages: 12

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Rain gauge networks provide rainfall measurements with a high degree of accuracy at
specific locations but, in most cases, the instruments are too sparsely distributed to
accurately capture the high spatial and temporal variability of precipitation systems. Radar
and satellite remote sensing of rainfall has become a viable approach to address this
problem effectively. However, among other sources of uncertainties, the remote-sensing
based rainfall products are unavoidably affected by sampling errors that need to be
evaluated and characterized. Using a large data set (more than six years) of rainfall
measurements from a dense network of 50 rain gauges deployed over an area of about
135 km2 in the Brue catchment (south-western England), this study sheds some light on
the temporal and spatial sampling uncertainties: the former are defined as the errors
resulting from temporal gaps in rainfall observations, while the latter as the uncertainties
due to the approximation of an areal estimate using point measurements. It is shown that
the temporal sampling uncertainties increase with the sampling interval according to a
scaling law and decrease with increasing averaging area with no strong dependence on
local orography. On the other hand, the spatial sampling uncertainties tend to decrease for
increasing accumulation time, with no strong dependence on location of the gauge
within the pixel or on the gauge elevation. For the evaluation of high resolution satellite
rainfall products, a simple rule is proposed for the number of rain gauges required to
estimate areal rainfall with a prescribed accuracy. Additionally, a description is given of
the characteristics of the rainfall process in the area in terms of spatial correlation

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