Pseudo-radar algorithms with two extremely wet months of disdrometer data in the Paris area

Abstract

Disdrometer data collected during the two extremely wet months of May and June 2016 at the Ecole des Ponts ParisTech are used to get insights on radar algorithms. The rain rate and pseudo-radar quantities (horizontal and vertical reflectivity, specific differential phase shift) are all estimated over several durations with the help of drop size distributions DSD) collected at 30 s time steps. The pseudo-radar quantities are defined with simplifying hypotheses, in particular on the DSD homogeneity. First it appears that the parameters of the standard radar relations R Z h − , dp K R − and dr h Z Z R − − for these pseudo-radar quantities exhibit strong variability between events and even within an event. Second an innovative methodology that relies on checking the ability of a given algorithm to reproduce the good scale invariant multifractal behaviour (on scales 30 s-few h) observed on rainfall time series is implemented. In this framework, the classical hybrid model (R Z h − for low rain rates and dp K R − for great ones) performs best, as well as the local estimates of the radar relations' parameters. However, we emphasise that due to the hypotheses on which they rely these observations cannot be straightforwardly extended to real radar quantities.

Two months of disdrometer data in the Paris area

“…This approach has been implemented by numerous authors (Jaffrain and Berne, 2012a;Leinonen et al, 2012;Ryzhkov et al , 2005;Verrier et al, 2013;Gires et al, 2015). It should be mentioned that it relies on strong unrealistic hypothesis, notably the homogeneity of the DSD within a radar bin (see Gires et al, 2017b for a discussion of the limitations of this approach). The DSD also enables to compute the kinetic energy of rainfall which is critical to the understanding and modelling of soil erosion (see van Dijk et al, 2002, for a review).…”

Approximate multifractal correlation and products of universal multifractal fields, with application to rainfall data

“…Data are collected with 30 s time steps. Data will only be briefly presented in this paper and interested readers are referred to Gires et al (2018b), who discusses available database in detail along with some data samples for a similar measurement campaign.…”

“…It should be noted that the disdrometer provides data binned per class of equivolumic diameter and fall velocity, from which a discrete DSD is computed and then used to evaluate the integrals of Eqs. (18) and (19) (see Gires et al, 2018b, for more details).…”

Two months of disdrometer data in the Paris area

“…This approach has been implemented by numerous authors (Jaffrain and Berne, 2012a;Leinonen et al, 2012;Ryzhkov et al, 2005;Verrier et al, 2013;Gires et al, 2015). It should be mentioned that it relies on a strong unrealistic hypothesis, notably the homogeneity of the DSD within a radar bin (see Gires et al, 2018, for a discussion of the limitations of this approach). The DSD also enables the kinetic energy of rainfall to be computed, which is critical to the understanding and modelling of soil erosion (see van Dijk et al, 2002, for a review).…”