Variability of Drop Size Distributions: Time-Scale Dependence of the Variability and Its Effects on Rain Estimation

Abstract

A systematic and intensive analysis is performed on 5 yr of reliable disdrometric data (over 20 000 one-minute drop size distributions, DSDs) to investigate the variability of DSDs in the Montreal, Quebec, Canada, area. The scale dependence (climatological scale, day to day, within a day, between physical processes, and within a physical process) of the DSD variability and its effect on rainfall intensity R estimation from radar reflectivity Z are explored in terms of bias and random errors. Detail error distributions are also provided. The use of a climatological R–Z relationship for rainfall—affected by all of the DSDs’ variability—leads on average to a random error of 41% in instantaneous rain-rate estimation. This error decreases with integration time, but the decrease becomes less pronounced for integration times longer than 2 h. Daily accumulations computed with the climatological R–Z relationship have a bias of 28% because of the day-to-day DSD variability. However, when daily R–Z relationships are used, a random error of 32% in instantaneous rain rate is still present because of the DSD variability within a day. This illustrates that most of the variability of DSDs has its origin within a storm or between storms within a day. Physical processes leading to the formation of DSDs are then classified according to the vertical structure of radar data as measured by a UHF profiler collocated with the disdrometer. The DSD variability among different physical processes is larger than the day-to-day variability. A bias of 41% in rain accumulations is due to the DSD variability between physical processes. Accurate rain-rate estimation (∼7%) can be achieved only after the proper underlying physical process is identified and the associated R–Z relationship is used.

Quantitative precipitation estimation based on high-resolution numerical weather prediction and data assimilation with WRF – a performance test

“…Germann et al, 2009;Rossa et al, 2009), the investigation of the usefulness of additional observations on the spacetime structure of rainfall (e.g. Lee and Zawadzki, 2005;Lee et al, 2007;Berenguer and Zawadzki, 2008;Niu et al, 2010;Tapiador et al, 2010) and a detailed understanding of radar signals and their interaction with hydrometeors to retrieve their microphysical properties (e.g. Dotzek and Beheng, 2001;Brandes et al, 2004aBrandes et al, , 2004bPeters et al, 2005;Cao et al, 2008).…”

REAL—Ensemble radar precipitation estimation for hydrology in a mountainous region

“…However, there are many sources of error which require both careful system design and sophisticated data processing (Austin, 1987;Joss and Waldvogel, 1990;Yuter, 2002). We have to deal with beam shielding and the vertical profile of reflectivity (Joss and Waldvogel, 1990;Kitchen, 1995;Joss and Lee, 1995;Pellarin et al, 2002;German and Joss, 2002;Bellon et al, 2007), beam smoothing and post-detection integration (Zawadzki, 1982), variability in raindrop size distributions and related uncertainty in the relation between reflectivity and rain rate (Joss and Gori, 1978;Lee and Zawadzki, 2005), attenuation by water on the radome (Germann, 1999), attenuation in heavy rain and hail (Delrou et al, 2000), and overestimation in hail (Austin, 1987). Illingworth (2004) discusses to what extent polarimetric measurements can improve the accuracy of radar precipitation estimates, Friedrich et al (2007) investigate the sensitivity of polarimetric rainfall estimation to partial beam shielding, Gabella et al (2001) promote weighted multiple regression for radar-rain-gauge merging, Mittermaier et al (2004) use mesoscale model winds to correct wind-drift errors, and Wesson and Pegram (2006) propose using geostatistics for proper interpolation and extrapolation.…”

Effect of R-Z Relationships Derived from Disdrometer Data on Radar Rainfall Estimation during the Heavy Rain Event on 5 July 2005

“…This transformation process causes the significant error (Fujiwara, 1965;Joss and Waldvogel, 1970;Zawadzki, 1984;Lee and Zawadzki, 2005b;Lee et al, 2007;Berenguer and Zawadzki, 2008;Berenguer and Zawadzki, 2009;Tokay et al, 2009). The use of a single climatological R-Z relationship can lead to 41% error in Montreal, Quebec, Canada (Lee and Zawadzki, 2005b) and however can be significantly reduced to 7% with proper R-Z relationships that are classified with proper microphysical processes.…”

“…Here, the climatological R-Z relationship is derived from the 5-yr disdrometric data set (Lee and Zawadzki, 2005b). The climatological relationship will overestimate rainfall intensity up to 45 dBZ and underestimate when Z>45 dBZ.…”