Performance of post-processing algorithms for rainfall intensity using measurements from tipping-bucket rain gauges

Abstract

Eight rainfall events recorded from May to September 2013 at Hong Kong International Airport (HKIA) have been selected to investigate the performance of post-processing algorithms used to calculate the rainfall intensity (RI) from tipping-bucket rain gauges (TBRGs). We assumed a drop-counter catching-type gauge as a working reference and compared rainfall intensity measurements with two calibrated TBRGs operated at a time resolution of 1 min. The two TBRGs differ in their internal mechanics, one being a traditional single-layer dual-bucket assembly, while the other has two layers of buckets. The drop-counter gauge operates at a time resolution of 10 s, while the time of tipping is recorded for the two TBRGs. The post-processing algorithms employed for the two TBRGs are based on the assumption that the tip volume is uniformly distributed over the inter-tip period. A series of data of an ideal TBRG is reconstructed using the virtual time of tipping derived from the drop-counter data. From the comparison between the ideal gauge and the measurements from the two real TBRGs, the performances of different post-processing and correction algorithms are statistically evaluated over the set of recorded rain events. The improvement obtained by adopting the inter-tip time algorithm in the calculation of the RI is confirmed. However, by comparing the performance of the real and ideal TBRGs, the beneficial effect of the inter-tip algorithm is shown to be relevant for the mid–low range (6–50 mmh−1) of rainfall intensity values (where the sampling errors prevail), while its role vanishes with increasing RI in the range where the mechanical errors prevail.