Abstract The estimation of rainfall through infrared or passive microwave techniques is now well established. Comparison of the estimates from these two sources show a clear trend: Infrared techniques are good at producing long-term/large-area rainfall estimates but are poor at instantaneous/small-scale estimates, while passive microwave algorithms produce good instantaneous/small-scale estimates, but poorer long-term/large-scale estimates. This can be attributed primarily to the temporal sampling resolution of the passive microwave data. While geostationary sensors routinely provide data at 30 minute intervals, passive microwave sensors may only achieve a couple of samples per day. Although the planned Global Precipitation Mission should alleviate this problem to some degree, providing 3-hourly passive microwave imagery, there is still a need for more frequent rainfall estimates. This paper presents results of current research utilising the temporal sampling of the infrared data derived from a 30-minute global 4km data set, together with rainfall data from the Tropical Rainfall Measuring Mission Microwave Imager (TMI) and the DMSP Special Sensor Microwave/Imagers (SSM/I). Different passive microwave rainfall algorithms will be compared to assess the sensitivity of the technique to the calibration data sets. Results will also be presented relating to the optimum temporal and spatial calibration scales.