scholarly journals Estimation of Rainfall Drop Size Distributions from Dual-Frequency Wind Profiler Spectra Using Deconvolution and a Nonlinear Least Squares Fitting Technique

2002 ◽  
Vol 19 (6) ◽  
pp. 864-874 ◽  
Author(s):  
Robert Schafer ◽  
Susan Avery ◽  
Peter May ◽  
Deepak Rajopadhyaya ◽  
Christopher Williams
Keyword(s):  
Least Squares ◽  
Drop Size ◽  
Nonlinear Least Squares ◽  
Wind Profiler ◽  
Size Distributions ◽  
Dual Frequency ◽  
Least Squares Fitting ◽  
Drop Size Distributions

scholarly journals Variability of Drop Size Distributions: Noise and Noise Filtering in Disdrometric Data

2005 ◽  
Vol 44 (5) ◽  
pp. 634-652 ◽  
Author(s):  
Gyu Won Lee ◽  
Isztar Zawadzki
Keyword(s):  
Least Squares ◽  
Drop Size ◽  
Size Distributions ◽  
Least Squares Regression ◽  
Fractional Difference ◽  
Regression Methods ◽  
Sampling Volume ◽  
Filtering Technique ◽  
Drop Size Distributions ◽  
Result Analysis

Abstract Disdrometric measurements are affected by the spurious variability due to drop sorting, small sampling volume, and instrumental noise. As a result, analysis methods that use least squares regression to derive rainfall rate–radar reflectivity (R–Z) relationships or studies of drop size distributions can lead to erroneous conclusions. This paper explores the importance of this variability and develops a new approach, referred to as the sequential intensity filtering technique (SIFT), that minimizes the effect of the spurious variability on disdrometric data. A simple correction for drop sorting in stratiform rain illustrates that it generates a significant amount of spurious variability and is prominent in small drops. SIFT filters out this spurious variability while maintaining the physical variability, as evidenced by stable R–Z relationships that are independent of averaging size and by a drastic decrease of the scatter in R–Z plots. The presence of scatter causes various regression methods to yield different best-fitted R–Z equations, depending on whether the errors on R or Z are minimized. The weighted total least squares (WTLS) solves this problem by taking into account errors in both R and Z and provides the appropriate coefficient and exponent of Z = aRb. For example, with a simple R versus Z least squares regression, there is an average fractional difference in a and b of Z = aRb of 17% and 14%, respectively, when compared with those derived using WTLS. With Z versus R regression, the average fractional difference in a and b is 19% and 12%, respectively. This uncertainty in the R–Z parameters may explain 40% of the “natural variability” claimed in the literature but becomes negligible after applying SIFT, regardless of the regression methods used.

scholarly journals Retrieval of Arbitrarily Shaped Raindrop Size Distributions from Wind Profiler Measurements

2005 ◽  
Vol 22 (4) ◽  
pp. 433-442 ◽  
Author(s):  
Takahisa Kobayashi ◽  
Ahoro Adachi
Keyword(s):  
Drop Size ◽  
Large Data ◽  
Doppler Spectrum ◽  
Wind Profiler ◽  
Size Distributions ◽  
Vertical Profiles ◽  
Large Variability ◽  
Median Volume ◽  
Raindrop Size ◽  
Drop Size Distributions

Abstract An efficient iterative retrieval method for arbitrarily shaped raindrop size distributions (ITRAN) is developed for Doppler spectra measured with a wind profiler. A measured Doppler spectrum is a convolution of the precipitation spectrum and the turbulent spectrum. Deconvolution of the Doppler spectra is achieved through repeated convolutions. The developed method assumes no prior shape of drop size distributions and automatically obtains raindrop size distributions; additionally, it can be applied to large data volumes. Furthermore, it is insensitive to initial values. The method was applied to both simulated and observed spectra. Derived drop size distributions agree with simulated values. Narrower turbulent spectral widths yield better results. Integral values of median volume diameter (D0), liquid water content (LWC), and radar reflectivity factor are estimated with errors of less than 10%. Accurate vertical profiles of raindrop size distributions result when this method is applied to wind profiler data. The technique performed very well with most observed spectra. Some recovered spectra departed from the corresponding measured spectra, for cases in which a clear-air peak could not be accurately reproduced because of uncertainties in the location of the minimum position between the clear-air echo and the precipitation echo. Statistical relationships between LWC and integral rainfall parameters yield interesting features. The median volume diameter is statistically independent of the LWC and is associated with the large variability of the total number of drops, NT, between events. Vertical profiles from one event show a clear inverse relationship between NT and D0

Drop-size distributions from pneumatic atomizers

AIChE Journal ◽  
1971 ◽  
Vol 17 (3) ◽  
pp. 575-584 ◽  
Author(s):  
K. Y. Kim ◽  
W. R. Marshall
Keyword(s):  
Drop Size ◽  
Size Distributions ◽  
Drop Size Distributions

scholarly journals Raindrop Size Distribution and Rain Characteristics during the 2013 Great Colorado Flood

2015 ◽  
Vol 17 (1) ◽  
pp. 53-72 ◽  
Author(s):  
Katja Friedrich ◽  
Evan A. Kalina ◽  
Joshua Aikins ◽  
Matthias Steiner ◽  
David Gochis ◽  
...  
Keyword(s):  
Drop Size ◽  
Doppler Radar ◽  
Liquid Water Content ◽  
Size Distributions ◽  
Intense Rainfall ◽  
Wind Fields ◽  
Raindrop Size Distribution ◽  
Median Volume ◽  
Drop Size Distributions ◽  
Analysis System

Abstract Drop size distributions observed by four Particle Size Velocity (PARSIVEL) disdrometers during the 2013 Great Colorado Flood are used to diagnose rain characteristics during intensive rainfall episodes. The analysis focuses on 30 h of intense rainfall in the vicinity of Boulder, Colorado, from 2200 UTC 11 September to 0400 UTC 13 September 2013. Rainfall rates R, median volume diameters D0, reflectivity Z, drop size distributions (DSDs), and gamma DSD parameters were derived and compared between the foothills and adjacent plains locations. Rainfall throughout the entire event was characterized by a large number of small- to medium-sized raindrops (diameters smaller than 1.5 mm) resulting in small values of Z (<40 dBZ), differential reflectivity Zdr (<1.3 dB), specific differential phase Kdp (<1° km−1), and D0 (<1 mm). In addition, high liquid water content was present throughout the entire event. Raindrops observed in the plains were generally larger than those in the foothills. DSDs observed in the foothills were characterized by a large concentration of small-sized drops (d < 1 mm). Heavy rainfall rates with slightly larger drops were observed during the first intense rainfall episode (0000–0800 UTC 12 September) and were associated with areas of enhanced low-level convergence and vertical velocity according to the wind fields derived from the Variational Doppler Radar Analysis System. The disdrometer-derived Z–R relationships reflect how unusual the DSDs were during the 2013 Great Colorado Flood. As a result, Z–R relations commonly used by the operational NEXRAD strongly underestimated rainfall rates by up to 43%.

scholarly journals On the Shape–Slope Relation of Drop Size Distributions in Convective Rain

2005 ◽  
Vol 44 (7) ◽  
pp. 1146-1151 ◽  
Author(s):  
Axel Seifert
Keyword(s):  
Size Distribution ◽  
Gamma Distribution ◽  
Drop Size ◽  
Empirical Relation ◽  
Size Distributions ◽  
Shape Parameters ◽  
Raindrop Size Distribution ◽  
Raindrop Size ◽  
Drop Size Distributions ◽  
Good Agreement

Abstract The relation between the slope and shape parameters of the raindrop size distribution parameterized by a gamma distribution is examined. The comparison of results of a simple rain shaft model with an empirical relation based on disdrometer measurements at the surface shows very good agreement, but a more detailed discussion reveals some difficulties—for example, deviations from the gamma shape and the overestimation of collisional breakup.

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