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Merhala Thurai and V. N. Bringi

1. Introduction Drop size distribution (DSD) data from two collocated disdrometers, a Droplet Measurement Technologies, Inc., Meteorological Particle Spectrometer (MPS) and a 2D-video disdrometer (2DVD), from measurement campaigns in Greeley, Colorado, and Huntsville, Alabama, have been reported in Thurai et al. (2017a) . The DSD spectra in that study, obtained by combining data from the high-resolution (50 μ m) MPS and the 170- μ m-resolution 2DVD, showed 1) a

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Olivier P. Prat, Ana P. Barros, and Firat Y. Testik

1. Introduction The transient evolution of the drop size distribution (DSD) under the influence of microphysical processes such as coalescence, breakup, evaporation, condensation, raindrop clustering, and mixing effects has been the object of extensive research over the past decades (e.g., Testik and Barros 2007 ). While drop–drop collisions leading to coalescence or drop breakup are considered to be the main driving mechanism behind the transient evolution of the DSD, there are yet a

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Dmitri N. Moisseev and V. Chandrasekar

1. Introduction A functional representation of the drop size distribution (DSD) is often used to describe naturally occurring raindrop size distributions. Ulbrich (1983) proposed to use gamma distribution to express rain DSD in the following form: Since the radar retrieval of rain rate is an underdetermined problem there is a need to reduce the number of unknown parameters. There are two main approaches to address this issue ( Haddad et al. 1997 ). One might consider neglecting the

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Paul L. Smith, Donna V. Kliche, and Roger W. Johnson

expressed in the form where n ( D ) is the number concentration of drops of diameter D per unit size interval, with DSD parameters n 1 (proportional to concentration), μ (distribution shape), and λ (size, or scale). It is customary to portray such DSDs on semilogarithmic scales, with log[ n ( D )] plotted against D . For purposes of these simulations, (1) is more conveniently expressed in terms of the total drop number concentration N T as By introducing the mass-weighted mean diameter D

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Donna V. Kliche, Paul L. Smith, and Roger W. Johnson

the gamma distribution, because it can give a more appropriate description of the natural variations of the observed RSDs; in addition, the exponential distribution is a special case. A gamma RSD can be expressed by with D ≥ 0, where n 1 is related to the raindrop concentration, μ is the dimensionless shape parameter, and λ is the size (scale) parameter. For a gamma distribution, positive (negative) values of the shape parameter μ indicate a concave down (up) shape of the drop spectrum

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B. Dolan, B. Fuchs, S. A. Rutledge, E. A. Barnes, and E. J. Thompson

1. Introduction Understanding the variability of drop size distributions (DSDs) around the globe is important for remote sensing of precipitation, retrieving distributions of latent heating, and parameterizing microphysical processes in numerical models. Remote sensing retrievals routinely make assumptions about DSDs to relate observations to physical quantities ( Munchak et al. 2012 ). For example, the DSD fundamentally determines the relationship between radar reflectivity Z and rainfall R

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Gyu Won Lee, Alan W. Seed, and Isztar Zawadzki

1. Introduction The transformation of remote sensing measurements of clouds and precipitation into quantities of interest requires the knowledge of drop size distribution (DSD). As a simplification, we often assume some deterministic relationship between a measurable quantity, such as radar reflectivity factor Z , and a quantity of interest, such as rain rate R . We assume that the parameters of this relationship can be stratified into a finite number of categories related to different

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A. R. Jameson, M. L. Larsen, and A. B. Kostinski

1. Introduction Ever since the first reports of the frequency distribution of drops of various sizes [i.e., drop size distributions (DSDs)] ( Laws and Parsons 1943 ; Marshall and Palmer 1948 ; Best 1950 ), there has been a growing appreciation of their variability particularly with regard to different meteorological conditions. For decades, a great deal of effort has been spent characterizing DSDs of all kinds, so much so that there are now hundreds and possibly thousands of expressions

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Chao-Hsin Chen, Ching-Lun Su, Jui-Hsiang Chen, and Yen-Hsyang Chu

al. 2009 ). This unique property facilitates the effective separation of clear-air turbulence and precipitation echoes in the Doppler spectral domain of the VHF radar returns. It has already been known that, in addition to drop size distribution (DSD) contribution, the Doppler spectral width of the precipitation echoes contains extra broadenings caused by clear-air turbulence and cross-radar-beam wind ( Wakasugi et al. 1986 ). The former is called turbulence broadening, and the latter is called

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M. Thurai, D. Hudak, and V. N. Bringi

(specifically at C band, frequency ∼5.5 GHz) for estimating the rain drop size distribution (DSD) from dual-polarized radar measurements, the estimation procedure utilizing ρ co as well as other polarimetric parameters. The DSD estimation procedure proposed here does not require an a priori model, for example, gamma DSD assumption. One set of three parameters used to describe arbitrary DSD forms is N w , D m , and σ m . The quantity N w is proportional to the ratio of water content ( W ) to D 4 m

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