Multifractal comparison of the outputs of two optical disdrometers

Gires, Auguste; Tchiguirinskaia, Ioulia; Schertzer, D.

doi:10.1080/02626667.2015.1055270

Cited by 21 publications

(32 citation statements)

References 42 publications

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“…More details can be found in Ellis et al (2006) or in the device documentation (CampbellScientific-Ltd, 2012). Actually authors found possible to improve the oblateness correction of the PWS100 rationale (Gires 5 et al, 2017a) and used it in previous studies (Gires et al, 2015).…”

mentioning

confidence: 99%

“…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).…”

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confidence: 99%

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Two months of disdrometer data in the Paris area

Gires¹,

Tchiguirinskaia²,

Schertzer³

2018

Preprint

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confidence: 99%

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confidence: 99%

Two months of disdrometer data in the Paris area

Gires¹,

Tchiguirinskaia²,

Schertzer³

2018

Preprint

View full text Add to dashboard Cite

show abstract

“…More details can be found in Ellis et al (2006) or in the device documentation (Campbell-Scientific-Ltd, 2012). Actually the authors could improve the oblateness correction of the PWS100 rationale (Gires et al, 2017) and used it in previous studies (Gires et al, 2015).…”

Section: Brief Description Of the Devices' Functioningmentioning

confidence: 99%

“…1). They are part of the TARANIS observatory (exTreme and multi-scAle RAiNdrop parIS observatory, Gires et al, 2015) of the Fresnel Platform of École des Ponts ParisTech (https://hmco.enpc.fr/portfolio-archive/ fresnel-platform/, last access: 18 May 2018). They are located not far from the edge of the roof.…”

Section: Measurement Periodmentioning

confidence: 99%

“…Using external information and assumptions about raindrop scattering properties, it is also possible to estimate, from the DSD, equivalent local pointwise radar quantities and hence study and possibly help improve radar rainfall retrieval algorithms. 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).…”

Section: Introductionmentioning

confidence: 99%

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Two months of disdrometer data in the Paris area

Gires

Tchiguirinskaia

Schertzer

2018

Earth Syst. Sci. Data

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Abstract. The Hydrology, Meteorology, and Complexity laboratory of École des Ponts ParisTech (hmco.enpc.fr) has made a data set of optical disdrometer measurements available that come from a campaign involving three collocated devices from two different manufacturers, relying on different underlying technologies (one Campbell Scientific PWS100 and two OTT Parsivel 2 instruments). The campaign took place in JanuaryFebruary 2016 in the Paris area (France). Disdrometers provide access to information on the size and velocity of drops falling through the sampling area of the devices of roughly a few tens of cm 2 . It enables the drop size distribution to be estimated and rainfall microphysics, kinetic energy, or radar quantities, for example, to be studied further. Raw data, i.e. basically a matrix containing a number of drops according to classes of size and velocity, along with more aggregated ones, such as the rain rate or drop size distribution with filtering, are available.Link to the data set: https://zenodo.org/record/1240168 (DOI: https://doi.org/10.5281/zenodo.1240168).

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

Gires

Tchiguirinskaia

Schertzer

2018

Atmospheric Research

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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.

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Multifractal comparison of the outputs of two optical disdrometers

Cited by 21 publications

References 42 publications

Two months of disdrometer data in the Paris area

Two months of disdrometer data in the Paris area

Two months of disdrometer data in the Paris area

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

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