An Experimental Study of Small-Scale Variability of Raindrop Size Distribution

Abstract

An experimental study of small-scale variability of raindrop size distributions (DSDs) has been carried out at Wallops Island, Virginia. Three Joss-Waldvogel disdrometers were operated at a distance of 0.65, 1.05, and 1.70 km in a nearly straight line. The main purpose of the study was to examine the variability of DSDs and its integral parameters of liquid water content, rainfall, and reflectivity within a 2-km array: a typical size of Cartesian radar pixel. The composite DSD of rain events showed very good agreement among the disdrometers except where there were noticeable differences in midsize and large drops in a few events. For consideration of partial beam filling where the radar pixel was not completely covered by rain, a single disdrometer reported just over 10% more rainy minutes than the rainy minutes when all three disdrometers reported rainfall. Similarly two out of three disdrometers reported 5% more rainy minutes than when all three were reporting rainfall. These percentages were based on a 1-min average, and were less for longer averaging periods. Considering only the minutes when all three disdrometers were reporting rainfall, just over one quarter of the observations showed an increase in the difference in rainfall with distance. This finding was based on a 15-min average and was even less for shorter averaging periods. The probability and cumulative distributions of a gamma-fitted DSD and integral rain parameters between the three disdrometers had a very good agreement and no major variability. This was mainly due to the high percentage of light stratiform rain and to the number of storms that traveled along the track of the disdrometers. At a fixed time step, however, both DSDs and integral rain parameters showed substantial variability. The standard deviation (SD) of rain rate was near 3 mm h 21 , while the SD of reflectivity exceeded 3 dBZ at the longest separation distance. These standard deviations were at 6-min average and were higher at shorter averaging periods. The correlations decreased with increasing separation distance. For rain rate, the correlations were higher than previous gauge-based studies. This was attributed to the differences in data processing and the difference in rainfall characteristics in different climate regions. It was also considered that the gauge sampling errors could be a factor. In this regard, gauge measurements were simulated employing existing disdrometer dataset. While a difference was noticed in cumulative distribution of rain occurrence between the simulated gauge and disdrometer observations, the correlations in simulated gauge measurements did not differ from the disdrometer measurements.

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

“…By examining the time series of disdrometer data, we determined 23 rain events (Table 1). Herein, we adopted the definition of a rain event proposed by Tokay and Bashor (2010) based on the DSD Vol. 91, No.…”

“…DSD measure-ments carried out around the world show that they vary both spatially and temporally and over a wide range (e.g., Bringi et al 2003;Uijlenhoet et al 2003;Ulbrich and Atlas 2007;Lee et al 2009;Radhakrishna et al 2009;Marzano et al 2010;Niu et al 2010;Thurai et al 2010;Tokay and Bashor 2010;). The differences in DSD characteristics have been found to be associated with precipitation types, atmospheric conditions, and geographical locations or climatic regimes (Rosenfeld and Ulbrich 2003).…”

Radar subpixel-scale rainfall variability and uncertainty: lessons learned from observations of a dense rain-gauge network

“…Two dense networks of eight gauges within a 4 km 2 grid located at the Brue catchment were used by Wood et al (2000) to estimate the errors of the individual gauge and radar compared to the "true" mean areal rainfall. This network was later used by Villarini et al (2008) to assess the errors resulting from temporal gaps in rainfall observations and the uncertainties resulting from areal-topoint estimations. Habib et al (2001a) estimated the correlation coefficient of point rainfall using a clustered network of rain gauges deployed in Florida (TEFLUN-B network).…”

“…The Walnut Gulch Experimental Watershed (WGEW), equipped with about 10 rain gauges per every TRMM Precipitation Radar pixel (∼ 5 km in diameter), was used by Amitai et al (2012) who conducted rain rate comparisons of these two resources for a semiarid climate. Several studies have explored the small-scale spatial variability of the rainfall drop size distribution (DSD) (Tapiador et al, 2010;Tokay et al, 2010). Jaffrain et al (2011) deployed 16 optical disdrometers over a 1 × 1 km area in Switzerland and determined the coefficient of variation of the total concentration of drops, the mass-weighted diameter and the rain rate over the network.…”

Remote Sensing of Precipitation from Airborne and Spaceborne Radar

“…Similarly, the GPM combined radar-radiometer algorithm subdivides a beam into components with lognormallyvarying reflectivities, but this method requires an estimate of the variability as well. Universal estimates can be used, but these can introduce regional and seasonal biases since the scale of variability of precipitation is often storm-dependent (Tokay and Bashor, 2010). A desirable alternative is to use some local context, such as the convective/stratiform classification of the profile and the horizontal variability of SRT PIA estimates at the large scale, which can be used to inform the estimate of the local variability (Kozu and Iguchi 1999).…”