A General Approach to Double-Moment Normalization of Drop Size Distributions

Lee, GyuWon; Zawadzki, Isztar; Szyrmer, Wanda; Sempere‐Torres, Daniel; Uijlenhoet, R.

doi:10.1175/1520-0450(2004)043<0264:agatdn>2.0.co;2

Cited by 97 publications

(154 citation statements)

References 18 publications

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“…The double-moment normalisation method of Lee et al (2004) allows for the DSD to be expressed as a combination of two of its moments M i and M j of arbitrary orders i and j and a double-moment normalised DSD h(x) (-), where…”

Section: Double-moment Dsd Normalisationmentioning

confidence: 99%

“…is the second-normalised diameter (Lee et al, 2004). Using the normalisation, the DSD can be written as…”

Section: Double-moment Dsd Normalisationmentioning

confidence: 99%

“…For these reasons, retrieval of the DSD from radar data has been a long-standing goal. In this paper we present a new technique for DSD retrieval from polarimetric radar data, which is based on the doublemoment normalisation technique of Lee et al (2004).…”

Section: Introductionmentioning

confidence: 99%

“…It performs well compared to contemporary algorithms and disdrometer observations . In this paper we present a new method for DSD retrieval that uses the double-moment DSD normalisation of Lee et al (2004), we and compare it to SCOP-ME.…”

Section: Introductionmentioning

confidence: 99%

“…The rest of this manuscript is organised as follows: we briefly describe the double-moment DSD normalisation technique of Lee et al (2004) in Sect. 2.…”

Section: Introductionmentioning

confidence: 99%

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Retrieval of the raindrop size distribution from polarimetric radar data using double-moment normalisation

Raupach¹,

Berne²

2017

Atmos. Meas. Tech.

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Abstract.A new technique for estimating the raindrop size distribution (DSD) from polarimetric radar data is proposed. Two statistical moments of the DSD are estimated from polarimetric variables, and the DSD is reconstructed using a double-moment normalisation. The technique takes advantage of the relative invariance of the double-moment normalised DSD. The method was tested using X-band radar data and networks of disdrometers in three different climatic regions. Radar-derived estimates of the DSD compare reasonably well to observations. In the three tested domains, in terms of DSD moments, rain rate, and characteristic drop diameter, the proposed method performs similarly to and often better than a state-of-the-art DSD-retrieval technique. The approach is flexible because no specific DSD model is prescribed. In addition, a method is proposed to treat noisy radar data to improve DSD-retrieval performance with radar measurements.

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Section: Double-moment Dsd Normalisationmentioning

confidence: 99%

“…is the second-normalised diameter (Lee et al, 2004). Using the normalisation, the DSD can be written as…”

Section: Double-moment Dsd Normalisationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…The rest of this manuscript is organised as follows: we briefly describe the double-moment DSD normalisation technique of Lee et al (2004) in Sect. 2.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Retrieval of the raindrop size distribution from polarimetric radar data using double-moment normalisation

Raupach¹,

Berne²

2017

Atmos. Meas. Tech.

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Normalized particle size distribution for remote sensing application

et al. 2014

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(2014), Normalized particle size distribution for remote sensing application, J. Geophys. Res. Atmos., 119, 4204-4227, doi:10.1002 AbstractThe ice particle size distribution (PSD) is fundamental to the quantitative description of a cloud. It is also crucial in the development of remote sensing retrieval techniques using radar and/or lidar measurements. The PSD allows one to link characteristics of individual particles (area, mass, and scattering properties) to characteristics of an ensemble of particles in a sampling volume (e.g., visible extinction ( ), ice water content (IWC), and radar reflectivity (Z)). The aim of this study is to describe a normalization technique to represent the PSD. We update an earlier study by including recent in situ measurements covering a large variety of ice clouds spanning temperatures ranging between −80• C and 0 • C. This new data set also includes direct measurements of IWC. We demonstrate that it is possible to scale the PSD in size space by the volume-weighted diameter D m and in the concentration space by the intercept parameter N * 0 and obtain the intrinsic shape of the PSD. Therefore, by combining N * 0 , D m , and a modified gamma function representing the normalized PSD shape, we are able to approximate key cloud variables (such as IWC) as well as cloud properties which can be remotely observed (such as Z) with an absolute mean relative error smaller than 20%. The underlying idea is to be able to retrieve the PSD using two independent measurements. We also propose parameterizations for ice cloud key parameters derived from the normalized PSD. We also investigate the effects of uncertainty present in the ice crystal mass-size relationships on the parameterizations and the normalized PSD approach.

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Johnson SB as general functional form for raindrop size distribution

Cugerone

Michele

2015

Water Resources Research

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Drop size distribution represents the statistical synthesis of rainfall dynamics at particle size scale. Gamma and Lognormal distributions have been widely used in the literature to approximate the drop diameter variability, contrarily to the natural upper boundary of the variable, with almost always sitespecific studies and without the support of statistical goodness-of-fit tests. In this work, we present an extensive statistical investigation of raindrop size distribution based on eight data sets, well distributed on the Earth's surface, which have been analyzed by using skewness-kurtosis plane, AIC and BIC indices and Kolmogorov-Smirnov test. Here for the first time, the Johnson SB is proposed as general functional form to describe the drop diameter variability specifically at 1 min time scale. Additional analyses demonstrate that the model is well suitable even for larger time intervals (!1 min).

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A General Approach to Double-Moment Normalization of Drop Size Distributions

Cited by 97 publications

References 18 publications

Retrieval of the raindrop size distribution from polarimetric radar data using double-moment normalisation

Retrieval of the raindrop size distribution from polarimetric radar data using double-moment normalisation

Normalized particle size distribution for remote sensing application

Johnson SB as general functional form for raindrop size distribution

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