The most commonly used polarimetric radar measurements in rainfall estimation are reflectivity at one polarization (ZH), differential reflectivity (ZDR), and specific differential propagation phase (KDP). The reflectivity measurement requires knowledge of absolute power and is prone to calibration errors. Differential reflectivity is a relative power measurement and is not affected by absolute calibration errors. However, all the algorithms that use differential reflectivity also include reflectivity and therefore are prone to absolute calibration errors. Algorithms to estimate rainfall from specific differential propagation phase are immune to absolute calibration error and attenuation effects <Zrnic and Ryzhkov, 1996>. However, specific differential propagation phase is very noisy at low rain rates. In addition, specific differential propagation phase is estimated as the slope of differential propagation phase measurements over a path. Consequently, there is a trade-off between accuracy and resolution of KDP. Thus there are advantages and disadvantages of each multiparameter radar measurement that translate into the error structure of algorithms involving multiparameter radar measurements. This paper presents a quantitative evaluation of the performance of five different polarimetric radar rainfall algorithms. The performance of the five algorithms is evaluated in the presence of physical variability in rainfall, radar measurement errors, and systematic calibration error. ¿ 2000 American Geophysical Union