Remote sensing of droplet number concentration in warm clouds: A review of the current state of knowledge and perspectives

Journal article

The cloud droplet number concentration (Nd) 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 Nd, but it can be inferred from retrievals of cloud optical depth (τc) cloud droplet effective radius (re) 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 1o×1o 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. Nd uncertainty is dominated by errors in re and, therefore, improvements in re retrievals would greatly improve the quality of the Nd retrievals. Recommendations are made for how this might be achieved. Some existing Nd datasets are compared and discussed, and best practices for the use of Nd data from current passive instruments (e.g., filtering criteria) are recommended. Emerging alternative Nd estimates are also considered. Firstly, new ideas to use additional information from existing and upcoming spaceborne instruments are discussed, and secondly, approaches using high‐quality ground‐based observations are examined.

Authors: Grosvenor, DP; Sourdeval, O; Zuidema, P; Ackerman, A; Alexandrov, MD

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: American Geophysical Union
• Journal: Reviews of Geophysics
• Volume: 56
• Issue: 2
• Pages: 409-453
• Publication date: 2018-06-27
• Acceptance date: 2018-04-06
• DOI: 10.1029/2017RG000593
• ISSN: 1944-9208
• Keywords: passive retrievals; remote sensing; lidar; satellite; cloud droplet concentrations; radar