The most common method to evaluate the quality of cloud parameter retrievals from satellite data is comparison with time series of ground-based measurements. For highly variable cloud parameters, such as liquid water path (LWP), however, a direct comparison of spatial and temporal distributions has limited value. It is questionable which period in the time series is representative of an area in the satellite image. A necessary but not sufficient boundary condition for statistical analysis is that the variability of both subsets is similar. In this paper we study the variability in terms of scaling properties of both spatial distributions and timeseries of LWP and focus on the relation between them. The time series are obtained from ground-based microwave radiometer measurements at 1 Hz. The spatial distributions are derived from Advanced Very High Resolution Radiometer 0.6 μm radiances. It is shown that the scaling properties of both distributions are similar for a stratocumulus field, which exhibits scale invariance, and for a cumulus field for which scales of preference were identified. A double logarithmic representation was appropriate to obtain the spectral exponent. A log-linear representation was appropriate to identify a scale of preference. In the cumulus case it was possible to calculate a feasible conversion factor to map variability in time to variability in space. This enables an estimation of the spatial variability at very high resolutions. ¿ 2000 American Geophysical Union