Cloud macrostructure and radiation

Cloud radiative properties are sensitive not only to drop size and other parameters of cloud micro-structure, but also cloud shape, spacing, other parameters of cloud macro-structure. Field programs such as DoE/ARM and DoE/UAV are now underway to improve the measurement and modelling of physical and radiative properties of clouds under a variety of meteorological conditions. A parallel effort is underway to improve cloud remote sensing from current GOES and AVHRR platforms as well as the upcoming NASA suite of EOS instrument which will provide higher spectral and/or spatial resolution, such as MODIS and ETM+/LATI. Corrections to plane-parallel theory to account for cloud inhomogeneity take different forms for the 100 km scale of climate model grids, and for the 1 km scale of typical satellite pixels. A first approximation for grid-scale fluxes is obtained by a linear weighting of clear and cloudy fractions, using an "effective cloud thickness" which depends on the within-cloud variability. A key finding for improving pixel-scale retrievals is that there is a characteristic "radiative smoothing" scale of several hundred meters. This scale has been observed as a change in the spatial spectrum of Landsat cloud radiances, and was also recently found with the help of Spinhirne's lidar at NASA-Goddard, by searching for returns from directions nonparallel to the incident beam. "Offbeam" Lidar returns are now being used to estimate the cloud "Green's function" which will be applied to improving simple IPA estimates of cloud radiative properties. This and other measurements of 3D transfer in clouds, coupled with 3D Monte Carlo transfer methods, are beginning to provide a better understanding of the dependence of radiation on cloud inhomogeneity, and to suggest new retrieval and parameterization algorithms which take account of cloud inhomogeneity.