Correction of raindrop size distributions measured by Parsivel disdrometers, using a two-dimensional video disdrometer as a reference

Abstract

Abstract. The raindrop size distribution (DSD) quantifies the microstructure of rainfall and is critical to studying precipitation processes. We present a method to improve the accuracy of DSD measurements from Parsivel (particle size and velocity) disdrometers, using a two-dimensional video disdrometer (2DVD) as a reference instrument. Parsivel disdrometers bin raindrops into velocity and equivolume diameter classes, but may misestimate the number of drops per class. In our correction method, drop velocities are corrected with reference to theoretical models of terminal drop velocity. We define a filter for raw disdrometer measurements to remove particles that are unlikely to be plausible raindrops. Drop concentrations are corrected such that on average the Parsivel concentrations match those recorded by a 2DVD. The correction can be trained on and applied to data from both generations of OTT Parsivel disdrometers, and indeed any disdrometer in general. The method was applied to data collected during field campaigns in Mediterranean France for a network of first-and second-generation Parsivel disdrometers, and on a first-generation Parsivel in Payerne, Switzerland. We compared the moments of the resulting DSDs to those of a collocated 2DVD, and the resulting DSD-derived rain rates to collocated rain gauges. The correction improved the accuracy of the moments of the Parsivel DSDs, and in the majority of cases the rain rate match with collocated rain gauges was improved. In addition, the correction was shown to be similar for two different climatologies, suggesting its general applicability.

Retrieval of the raindrop size distribution from polarimetric radar data using double-moment normalisation

“…Parsivel data are subject to uncertainty due to differences across individual instruments and instrument generations (e.g. Jaffrain and Berne, 2011;Tokay et al, 2014;Thurai et al, 2011, Raupach andBerne, 2015a), and their limited sampling area introduces a bias, as reported by Tapiador et al (2017). The Iowa data were provided in diameter class definitions that differed from those of the instrument manufacturer (Petersen et al, 2014).…”

“…To discount noise, PPI records were subset to those for which Z H was greater than or equal to 10 dBZ, and the signal-to-noise ratio in horizontal polarisation was greater than or equal to 5 dB. DSD data were treated as in Raupach and Berne (2017): Parsivel DSDs were truncated to 0.2495 (0.2565) to 7 (7.21) mm for HyMeX and Payerne (Iowa) Parsivel data (Raupach and Berne, 2015a); to avoid including overestimated numbers of small drops (Peters et al, 2005), DSDs estimated by the MRR were truncated to 0.6 to 5.8 mm and MRR data were further subset to records with R ≤ 150 mm h −1 (thus removing 0.2 % of records); MRR data for altitudes greater than 2250 m were excluded because not enough points were available at those altitudes, and all DSDs were subset to time steps in which R > 0.1 mm h −1 . In each data set, more than 85 % of the DSDs sampled were classified as stratiform type by Raupach and Berne (2017).…”

Drop Size Distribution Climatology in Cévennes-Vivarais Region, France

“…The last one (S3) was the "correction" method, proposed by [42]. This method consisted of applying pre-determined correction factors, depending on the intensities and the diameters that were estimated during inter-comparison measurement campaigns, using a 2DVD (used as a reference) and Parsivels of both generations.…”

Dual-polarization radar rainfall estimation in Korea according to raindrop shapes obtained by using a 2-D video disdrometer

“…However, a number of particle outliers were measured, and these anomalous data points were due to wind turbulence, splashing, break up of drops, and mismatching between camera A and B (Raupach and Berne, 2015). These results can lead to incorrect information about the particles.…”