Natural Variations in the Analytical Form of the Raindrop Size Distribution

Ulbrich, Carlton W.

doi:10.1175/1520-0450(1983)022<1764:nvitaf>2.0.co;2

Cited by 1,129 publications

(815 citation statements)

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“…But this problem, if at all present, will exist in both monsoon seasons. Ulbrich (1983) observed a wide distribution of raindrops with large numbers of smaller drops (small median volume diameter, D 0 ) in orographic rain. The terrain surrounding Gadanki (shown in Figure 7(a)) is complex with many small hills (altitudes <1 km).…”

Section: Discussionmentioning

confidence: 99%

Differences in raindrop size distribution from southwest monsoon to northeast monsoon at Gadanki

Rao

Radhakrishna

Nakamura

et al. 2009

Quart J Royal Meteoro Soc

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Characteristics of raindrop size distribution (DSD) are studied during the southwest (SW) and northeast (NE) monsoon seasons using 4 1/2 years of DSD measurements made at Gadanki (13.5 • N, 79.2 • E) by an impact-type disdrometer. The observed DSD is found to be distinctly different in the NE monsoon from that of the SW monsoon. The stratified DSD (based on rain rate) shows more small drops and fewer bigger drops in the NE monsoon than in the SW monsoon, particularly in the low rain rate regime. This feature is not an anomalous one, rather observed consistently in all the years. The diurnal variation of DSD (in terms of mass-weighted mean diameter, D m ) plots in both monsoons show large values of D m in the evening hours. Several possible reasons, from instrumental problems to geophysical mechanisms, for the observed DSD differences are examined. The wind field and the range of D m values indicate that the DSDs in the SW and NE monsoons are continental and oceanic in nature, respectively. Further, high temperature and intense convective activity (vigorous updraughts) in the SW monsoon modify the DSD through evaporation, drop sorting, and collision-coalescence processes.

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Section: Discussionmentioning

confidence: 99%

Differences in raindrop size distribution from southwest monsoon to northeast monsoon at Gadanki

Rao

Radhakrishna

Nakamura

et al. 2009

Quart J Royal Meteoro Soc

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“…Three mutually independent DSD parameters are used: a bulk quantity parameter R (the rain rate), and two shape parameters D" and s", the first representing essentially the massweighted mean drop diameter and the second representing essentially the relative standard deviation of diameters about this mean ("essentially" because these variables have to be modified in order to be mutually independent -Eqs. (1)-(3) below relate these parameters to C. Ulbrich's A/De-D, (see Ulbrich, 1983). This parameterization was derived from a careful analysis of over 15,000 DSD samples measured during two rainy seasons at Darwin, and during the TOGA-COARE field campaign (Haddad et al, 1997).…”

Section: Introductionmentioning

confidence: 99%

The TRMM ‘Day-1’ Radar/Radiometer Combined Rain-Profiling Algorithm

Haddad

Smith

Kummerow

et al. 1997

Journal of the Meteorological Society of Japan

186

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The Tropical Rainfall Measuring Mission (TRMM)'s 'day-1' combined radar/radiometer algorithm uses a rain-profiling approach which gives as much importance to the measurements of the TRMM satellite's precipitation radar (PR) and the TRMM microwave imager (TMI) as their respective intrinsic ambiguities warrant, which avoids any ad hoc shortcuts that might introduce large biases in the rain estimates, yet which is simple enough to be operational when TRMM is launched in 1997. The algorithm is based on the idea of estimating the rain profile using the radar reflectivities, while constraining this inversion to be consistent with the radiometer-derived estimate of the total attenuation. To perform the data fusion, the problem is expressed in terms of drop-size-distribution variables. Starting with an a priori probability density function (pdf) for these variables, a Bayesian approach is used to condition the pdf successively on the radar and the radiometer measurements. The resulting algorithm is mathematically consistent and physically reasonable. The conditional variances which it calculates serve to quantify the accuracy of its estimates: small variances indicate that the TRMM observations can indeed be explained by the models used; large variances imply that the models are not sufficiently consistent with the measurements.

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“…In the next section we will also refer to D 0 , defined as the diameter corresponding to drops having median volume. Ulbrich (1983) showed that this was well approximated by D 0 = (µ + 3.67)/ , which is also the modal diameter for a gamma rain rate distribution (with units h −1 ).…”

Section: Estimation Of the Parameters Of Gamma Dsdsmentioning

confidence: 99%