At short wavelengths, especially C-, X-, and K-band, weather radar signals are attenuated by the precipitation along their paths. This constitutes a major source of error for radar rainfall estimation, in particular for intense precipitation. A recently developed stochastic simulator of range profiles of raindrop size distributions (DSD) provides a controlled experiment framework to investigate the accuracy and robustness of attenuation correction algorithms. The work presented here focuses on the quantification of the influence of uncertainties concerning radar calibration, the parameterization of power law relations between the integral variables (radar reflectivity Z and specific attenuation k), and total path integrated attenuation (PIA) estimates at X-band. The analysis concerns single frequency, incoherent and nonpolarimetric radar systems. Two attenuation correction algorithms, based on a forward and a backward implementation respectively, are studied. From DSD range profiles, the corresponding profiles of integral radar variables are derived. Using a Monte Carlo approach, the accuracy and robustness of the two algorithms are quantified for the different sources of error previously mentioned. This framework of realistic DSD variability provides a robust way to confirm that, under realistic assumptions concerning the PIA estimation uncertainty, the forward algorithm outperforms the backward algorithm for PIA values below 10 dB.