Error investigation of rain retrievals from disdrometer data using triple colocation

Abstract

Assessing the uncertainty of precipitation measurements is a challenging problem because precipitation estimates are inevitably influenced by various errors and environmental conditions. A way to characterize the error structure of coincident measurements is to use the triple colocation (TC) statistical method. Unlike more typical approaches, where measures are compared in pairs and one of the two is assumed error-free, TC has the enviable advantage to succeed in characterizing the uncertainties of co-located measurements being compared to each other, without requiring the knowledge of the true value which is often unknown. However, TC requires to have at least three co-located measuring systems and the compliance with several initial assumptions. In this work, for the first time, TC is applied to in-situ measurements of rain precipitation acquired by three co-located devices: a weighing rain gauge, a laser disdrometer and a bidimensional video disdrometer. Both parametric and nonparametric formulations of TC are implemented to derive the rainfall product precision associated with the three devices. While the parametric TC technique requires tighter constraints and explicit assumptions which may be violated causing some artifacts, the nonparametric formulation is more flexible and requires less strict constrains. For this reason, a comparison between the two TC formulations is also presented to investigate the impact of TC constrains and their possible violations. The results are obtained using a statistically robust dataset spanning a 1.5 year period collected in Switzerland and presented in terms of traditional metrics. According to triple colocation analysis, the two disdrometers outperform the classical weighing rain gauge and they have similar measurement error structure regardless of the integration time intervals.