Quantification of the radar reflectivity sampling error in non-stationary rain using paired disdrometers

Knowledge of the raindrop size distribution (DSD) is essential for understanding the physics of precipitation and for interpreting remotely sensed observations of rain. Disdrometer measurements of DSDs are affected by uncertainties due to the limited sampling volumes or areas of the sensors. Determining this sampling error directly from disdrometer observations is of primary importance for the practical application of DSD analyses. Gage et al. ( 2004) proposed an estimator of the sampling error affecting the radar reflectivity estimates based on pairs of collocated disdrometers. We provide an interpretation of this estimator and assess its accuracy through controlled experiments using a Monte Carlo framework. Our simulation model of the disdrometer sampling process closely mimics the observations reported by Gage et al. ( 2004). Using this model, we demonstrate that the estimator proposed by Gage et al. ( 2004) provides a reliable quantification of the reflectivity sampling error. However, we also show that its accuracy depends on the ratio between the length of the disdrometer time series involved and the characteristic time scale of the rainfall.