Rainfall Microphysics and Radar Properties: Analysis Methods for Drop Size Spectra

Ulbrich, Carlton W.; Atlas, David

doi:10.1175/1520-0450(1998)037<0912:rmarpa>2.0.co;2

Cited by 218 publications

(184 citation statements)

References 17 publications

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“…For a two-moment scheme with a fixed μ, it was found that the microphysical properties of cloud and precipitation are sensitive to the μ values, since the shape parameter plays an important role in determining sedimentation and microphysical growth rates (Milbrandt and Yau 2005). Variations of μ during precipitation events have been reported in previous studies (e.g., Ulbrich 1983; Ulbrich and Atlas 1998;Testud et al 2001;Zhang et al 2001;Islam et al 2012). Considering that the three parameters of the gamma distribution are not independent of each other, an empirical relationship between N0 and μ (Ulbrich 1983) or between μ and Λ (Zhang et al 2001) has been proposed to retrieve a gamma distribution.…”

Section: Shape-slope Relationmentioning

confidence: 60%

“…The three parameters (N0, μ and Λ) in (5) were estimated from the second, fourth, and sixth moments of the observed distributions using the moment method as described by Ulbrich and Atlas (1998) and Zhang et al (2003). In addition, the truncated-moment method (Vivekanandan et al 2004) is also used to test the effects of the fitting method.…”

Section: B Raindrop Size Distributionmentioning

confidence: 99%

“…In addition, the truncated-moment method (Vivekanandan et al 2004) is also used to test the effects of the fitting method. Detailed descriptions of these two methods have been addressed in previous studies (e.g., Ulbrich and Atlas 1998;Zhang et al 2003;Vivekanandan et al 2004) and are not presented herein.…”

Section: B Raindrop Size Distributionmentioning

confidence: 99%

See 2 more Smart Citations

Statistical Characteristics of Raindrop Size Distribution in the Meiyu Season Observed in Eastern China

Chen

Yang

2013

Journal of the Meteorological Society of Japan

149

191

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Characteristics of raindrop size distribution (DSD) during the Meiyu season are studied using ground-based disdrometer measurements carried out in eastern China (Nanjing) from 2009 to 2011. The observational spectra are divided into convective and stratiform types. The results show that the histograms of the logarithm of the generalized intercept parameter (log10NW) and mass-weighted mean diameter of raindrops (Dm) are negatively and positively skewed, respectively, for both convective and stratiform rain. The absolute value of the skewness coefficient is higher for convective rain than for stratiform rain, in particular for the log10NW distribution. The mean log10NW and Dm values are 3.80 and 1.71 mm for convective rain and 3.45 and 1.30 mm for stratiform rain, respectively. The shape (μ)̶slope (Λ) relationship of the gamma distribution and the radar reflectivity (Z)̶rain rate (R) relationship are also derived for convective rain. The Z̶R relationship is found to be Z = 368R 1.21 . The interpretation of the statistical parameters obtained in this study and possible mechanisms that yield difference and similarity in comparison with those in previous studies are discussed.

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Section: Shape-slope Relationmentioning

confidence: 60%

Section: B Raindrop Size Distributionmentioning

confidence: 99%

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Statistical Characteristics of Raindrop Size Distribution in the Meiyu Season Observed in Eastern China

Chen

Yang

2013

Journal of the Meteorological Society of Japan

149

191

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“…The integrated water content of all PSDs was normalized to W=1 g m -3 to emphasize the importance of the shape of the PSD in terms of its mean and dispersion on the reflectivity and extinction. For these PSDs, the equivalent intercept Nw can be calculated from Dm and W (Testud et al, 2001): Figure 4.>> While analytic expressions relating Z, Dm, W, and R can be derived from the gamma distribution (Ulbrich and Atlas, 1998), statistical relationships can also be derived directly from disdrometer measurements without any assumptions of the PSD shape. In Figure 6, scatter plots of kext, W, R, Dm, and Nw versus Z at Ku-band are shown for rain PSDs measured during the IFLoodS field experiment (Ryu et al, 2016).…”

Section: [[H1]] the Particle Size Distributionmentioning

confidence: 99%

Remote Sensing of Precipitation from Airborne and Spaceborne Radar

Munchak

2018

Remote Sensing of Aerosols, Clouds, and Precipitation

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Weather radar measurements from airborne or satellite platforms can be an effective remote sensing tool for examining the three-dimensional structures of clouds and precipitation. This chapter describes some fundamental properties of radar measurements and their dependence on the particle size distribution (PSD) and radar frequency. The inverse problem of solving for the vertical profile of PSD from a profile of measured reflectivity is stated as an optimal estimation problem for single-and multi-frequency measurements. Phenomena that can change the measured reflectivity Zm from its intrinsic value Ze, namely attenuation, non-uniform beam filling, and multiple scattering, are described and mitigation of these effects in the context of the optimal estimation framework is discussed. Finally, some techniques involving the use of passive microwave measurements to further constrain the retrieval of the PSD are presented. [[H1]] IntroductionThe ability of weather radar to measure the location and intensity of precipitation was rapidly realized in the late 1940s following World War II. However, ground-based radars are limited in their ability to directly detect precipitation close to the ground far from the radar site due to ground clutter, refraction of the radar beam, and the curvature of the earth. Beam blockage by terrain also poses problems for radar coverage in mountainous areas. Coverage over oceans and other remote areas, where maintaining a ground radar would be difficult and costly, is also impractical, yet the precipitation that falls in these regions has important impacts on the global https://ntrs.nasa.gov/search.jsp?R=20180000191 2018-05-12T19:37:19+00:00Z atmospheric circulations via latent heating (e.g., Hoskins and Karoly, 1981, Hartmann et al., 1984, Matthews et al., 2004 and can have a profound influence on weather patterns thousands of kilometers away. Likewise, knowledge of precipitation over land, particularly in the form of snow, is a crucial component of the mass balance equation for glaciers and ice sheets, which must be properly characterized for realistic climate simulations (Shepherd et al., 2012).Airborne radar systems can provide high sensitivity and finely resolved vertical profiles to characterize precipitation microphysics for the benefit of model parameterizations and process understanding (e.g., Reinhardt et al, 2010, Heymsfield et al., 2013, Rauber et al., 2016.However, in order to achieve true global coverage, it had been proposed from nearly the beginning of the space age to put a weather radar in space (Kreigler and Kawitz, 1960), and efforts to do so began in earnest in the late 1970s and 1980s with the planning of the Tropical Rainfall Measuring Mission satellite (TRMM; Simpson et al., 1987, Okamoto et al., 1988 increased accuracy, sensitivity, and extension to higher latitudes. Both the TRMM PR and GPM DPR were intended not only to estimate precipitation directly from the radar data but also to construct a database of precipitation profiles to unify precipitation retri...

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“…Figure 1 shows a rain event in Singapore on the November 12th, 1994. The rain event is classified into three different rain types, convective, stratiform and transition, by using the method proposed in [20]. The classification is done by using both the rain rate, R, and the median volume diameter, D 0 , variations.…”

Section: Data Selection and Measurementmentioning

confidence: 99%

Shape Slope Parameter Distribution Modelling of Electromagnetic Scattering by Rain Drops

Kumar¹,

Lee²,

Ong³

2010

PIER B

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Abstract-Gamma model parameters using 2nd, 3rd and 4th moments are calculated from the drop size data of Singapore. The gamma model is simplified into two parameter model by finding a relation between the shape and slope parameters, µ and Λ. Due to the poor correlation found between µ and Λ, the drop size data is filtered based on their rain rates before a good correlation between the two parameters can be found. The µ-Λ relations are then fitted for the different ranges of rain rate filtering. Scatter plots of µ and Λ are plotted with constant median volume diameter (D 0 ) lines. The µ-Λ relations for the different rain types for the tropical island of Singapore are proposed and compared with the µ-Λ relations from three other countries of different climatic zones. T-Matrix calculations are performed to find the polarimetric variables at S-band by using the gamma DSD calculated from the Singapore's drop size data. The calculated differential reflectivity and horizontal reflectivity are used along with the best µ-Λ relations to find the gamma model parameters. The retrieved rain rate using polarimetric variables is compared with the distrometer's measured rain rate. Results show a good agreement between the retrieved rain rate and the measured rain rate. Therefore, the proposed shape slope relationship is found to be suitable for rain rate retrieval.

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Rainfall Microphysics and Radar Properties: Analysis Methods for Drop Size Spectra

Cited by 218 publications

References 17 publications

Statistical Characteristics of Raindrop Size Distribution in the Meiyu Season Observed in Eastern China

Statistical Characteristics of Raindrop Size Distribution in the Meiyu Season Observed in Eastern China

Remote Sensing of Precipitation from Airborne and Spaceborne Radar

Shape Slope Parameter Distribution Modelling of Electromagnetic Scattering by Rain Drops

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