The tipping-bucket rain gauge is known to underestimate rainfall at high intensities because of the rain water amount that is lost during the tipping movement of the bucket. The related biases are known as systematic mechanical errors and, since their effect increases with rain intensity, have a significant influence on the derived statistics of rainfall extremes. A correction procedure for rain intensity data sets is proposed in this paper based on the dynamic calibration of the gauge at both fine and coarse resolution, either in direct form or after proper downscaling of the available figures. The effect of systematic mechanical errors on the assessment of design rainfall for urban-scale applications is quantified based on two rain-rate data sets recorded at very different resolutions in time. A random cascade downscaling algorithm is used for the processing of coarse-resolution data so that correction can be applied at suitable time scales. The resulting depth–duration–frequency curves obtained from the original and corrected data sets are used to quantify the impact of uncorrected rain-intensity measurements on design rainfall and the related statistical parameters.

The impact of tipping-bucket raingauge measurement errors on design rainfall for urban-scale applications

MOLINI, ANNALISA;LANZA, LUCA GIOVANNI;LA BARBERA, PAOLO
2005-01-01

Abstract

The tipping-bucket rain gauge is known to underestimate rainfall at high intensities because of the rain water amount that is lost during the tipping movement of the bucket. The related biases are known as systematic mechanical errors and, since their effect increases with rain intensity, have a significant influence on the derived statistics of rainfall extremes. A correction procedure for rain intensity data sets is proposed in this paper based on the dynamic calibration of the gauge at both fine and coarse resolution, either in direct form or after proper downscaling of the available figures. The effect of systematic mechanical errors on the assessment of design rainfall for urban-scale applications is quantified based on two rain-rate data sets recorded at very different resolutions in time. A random cascade downscaling algorithm is used for the processing of coarse-resolution data so that correction can be applied at suitable time scales. The resulting depth–duration–frequency curves obtained from the original and corrected data sets are used to quantify the impact of uncorrected rain-intensity measurements on design rainfall and the related statistical parameters.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11567/206223
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