The cloud droplet number concentration (N_d) is of central interest to improve the understanding of cloud physics and for quantifying the effective radiative forcing by aerosol-cloud interactions. Current standard satellite retrievals do not operationally provide N_d, but it can be inferred from retrievals of cloud optical depth (τ_c) cloud droplet effective radius (r_e) and cloud top temperature. This review summarizes issues with this approach and quantifies uncertainties. A total relative uncertainty of 78% is inferred for pixel-level retrievals for relatively homogeneous, optically thick and unobscured stratiform clouds with favorable viewing geometry. The uncertainty is even greater if these conditions are not met. For averages over 1° ×1° regions the uncertainty is reduced to 54% assuming random errors for instrument uncertainties. In contrast, the few evaluation studies against reference in situ observations suggest much better accuracy with little variability in the bias. More such studies are required for a better error characterization. N_d uncertainty is dominated by errors in r_e, and therefore, improvements in r_e retrievals would greatly improve the quality of the N_d retrievals. Recommendations are made for how this might be achieved. Some existing N_d data sets are compared and discussed, and best practices for the use of N_d data from current passive instruments (e.g., filtering criteria) are recommended. Emerging alternative N_d estimates are also considered. First, new ideas to use additional information from existing and upcoming spaceborne instruments are discussed, and second, approaches using high-quality ground-based observations are examined.

Ground-based remote sensing of water clouds, using radar and lidar, has proven to be very succesfull in determining the microphysical properties of clouds. We discuss the methodologies and underlying assumptions. We conclude with a case study and show how ground-based sky radiation measurements can be used for validation.