Rainfall and sampling uncertainties : a rain gauge perspective
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 rain...
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sg-ntu-dr.10356-946232020-09-26T21:37:50Z Rainfall and sampling uncertainties : a rain gauge perspective Villarini, Gabriele. Mandapaka, Pradeep V. Krajewski, Witold F. Moore, Robert J. DRNTU::Science::Physics::Meteorology and climatology 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. Published version 2012-05-29T08:58:56Z 2019-12-06T18:59:20Z 2012-05-29T08:58:56Z 2019-12-06T18:59:20Z 2008 2008 Journal Article Villarini, G., Mandapaka, P. V., Krajewski, W. F., & Moore, R. J. (2008). Rainfall and Sampling Uncertainties: A Rain Gauge Perspective. Journal of Geophysical Research, 113. https://hdl.handle.net/10356/94623 http://hdl.handle.net/10220/8179 10.1029/2007JD009214 en Journal of geophysical research © 2008 by the American Geophysical Union. This paper was published in Journal of Geophysical Research-Atmospheres and is made available as an electronic reprint (preprint) with permission of American Geophysical Union. The paper can be found at DOI: [http://dx.doi.org/10.1029/2007JD009214]. One print or electronic copy may be made for personal use only. Systematic or multiple reproduction, distribution to multiple locations via electronic or other means, duplication of any material in this paper for a fee or for commercial purposes, or modification of the content of the paper is prohibited and is subject to penalties under law. 12 p. application/pdf |
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DRNTU::Science::Physics::Meteorology and climatology Villarini, Gabriele. Mandapaka, Pradeep V. Krajewski, Witold F. Moore, Robert J. Rainfall and sampling uncertainties : a rain gauge perspective |
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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. |
format |
Article |
author |
Villarini, Gabriele. Mandapaka, Pradeep V. Krajewski, Witold F. Moore, Robert J. |
author_facet |
Villarini, Gabriele. Mandapaka, Pradeep V. Krajewski, Witold F. Moore, Robert J. |
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Villarini, Gabriele. |
title |
Rainfall and sampling uncertainties : a rain gauge perspective |
title_short |
Rainfall and sampling uncertainties : a rain gauge perspective |
title_full |
Rainfall and sampling uncertainties : a rain gauge perspective |
title_fullStr |
Rainfall and sampling uncertainties : a rain gauge perspective |
title_full_unstemmed |
Rainfall and sampling uncertainties : a rain gauge perspective |
title_sort |
rainfall and sampling uncertainties : a rain gauge perspective |
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2012 |
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https://hdl.handle.net/10356/94623 http://hdl.handle.net/10220/8179 |
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1681059512743624704 |