In this study, we use four years (2003-2006) of Tropical Rainfall Measuring Mission (TRMM) Ground Validation (GV) data from Kwajalein, Republic of the Marshall Islands (KWAJ) and Melbourne, Florida (MELB), to assess the relative performance of satellite precipitation estimates from seven polar-orbiting satellites: SSMI (F13, F14 and F15), AMSU-B (N15, N16 and N17), and AMSR-E. The comparisons are based on instantaneous estimates, all averaged to 0.25 degrees. Using GV as a reference, we will show both the similarities of the various satellite estimates, as well as some fundamental differences, due to inherent algorithmic and instrumental factors. Although considerable effort has been made to produce the highest quality and most accurate rain rate data from the GV observations, the authors make no claim as to the absolute accuracy of the GV rain data, but rather offer GV as a reference, from which the relative performance of the satellite estimates can be made. Our analysis is performed by comparing probability distributions, scatterplots, statistical summaries and two-dimensional Heidke Skill Score plots. We will show that the AMSR-E estimates have the highest skills in observing rain that is also observed by GV, as well as the highest correlations with GV data, but that it's bias is higher than the other estimates. We suggest that these biases are easily correctable at the algorithm level, after which AMSR-E would appear to be the superior estimator of surface rain rate over the SSM/I and AMSU-B platforms.