The diurnal cycle present in many climate properties is undersampled in asynoptic data, which, through aliasing, introduces a bias into time-mean behavior derived from satellite measurements. This source of systematic error is investigated in high-resolution Global Cloud Imagery (GCI), which provides a proxy, with realistic space–time variability, for several climate properties to be observed from space. The GCI, which resolves mesoscale and diurnal variability on a global basis, is sampled asynoptically according to orbital and viewing characteristics from one and multiple platforms. Sampling error is then evaluated by comparing the resulting time-mean behavior against the true time-mean behavior in the GCI. The bias from undersampled diurnal variability is most serious in polar-orbiting measurements from an individual platform. However, it emerges even in precessing measurements, which drift through local time, because diurnal variability is still sampled too slowly to be truly resolved in such observations. A “mean diurnal cycle” can be constructed by averaging precessing measurements, provided that the ensemble of observations at individual local times is large enough (e.g., that observations are averaged over a long enough duration). The pattern of time-mean error closely resembles the pattern of error in the mean diurnal cycle. Time-mean behavior can therefore be determined only about as accurately as can the mean diurnal cycle. Determining accurate time-mean properties often requires averaging measurements from an individual platform over several months, which cannot be performed without contaminating mean behavior with seasonal variations. The sampling limitations from an individual orbiting platform are alleviated by sampling from multiple platforms, which provide observations frequently enough in space and time to determine accurate monthly mean properties.