Improvement of Airborne Retrievals of Cloud Droplet Number Concentration of Trade Wind Cumulus Using a Synergetic Approach.

The cloud droplet number concentration N is a major variable in numerical weather prediction (NWP) and global climate models (GCMs). In combination with the liquid water content LWC it determines the cloud droplet effective radius r_eff, which is used to calculate the radiative effects of clouds. Especially, the solar cloud top reflectivity R of shallow trade wind cumulus is highly sensitive with respect to N and LWC. In-situ measurements of N are limited by the sampled cloud volume, not covering the significant natural variability. Satellite retrievals of N suffer from large uncertainties and strictly assume adiabatic vertical profiles of cloud properties. Therefore, it is suggested to use the synergy of passive and active airborne remote sensing to reduce the uncertainty of N retrievals and to bridge the gap between in-situ cloud sampling and global averaging. For this purpose, spectral solar radiation measurements above shallow trade-wind cumulus were combined with passive microwave and active radar and lidar observations carried out during the second Next Generation Remote Sensing for Validation Studies (NARVAL-II) campaign with the High Altitude and Long Range Research Aircraft (HALO) in August 2016. The common approach to retrieve N is further developed by including combined measurements and retrievals of cloud optical thickness τ, liquid water path LWP, r_eff, as well as cloud base and top altitude. Three approaches are tested and applied to synthetic measurements and two cloud cases of NARVAL-II. Using the new techniques, errors in N due to the adiabatic assumption have been reduced significantly. [ABSTRACT FROM AUTHOR]