12.1 Estimation of Cloud Droplet Number Concentration of Shallow Trade-Wind Cumulus by Using Synergistic Airborne Remote Sensing.

Thursday, 12 July 2018: 10:30 AM
Regency E/F (Hyatt Regency Vancouver)
Kevin Wolf, University of Leipzig, Leipzig, Germany; and A. Ehrlich, S. Crewell, M. Jacob, M. Wirth, and M. Wendisch

Handout (1.3 MB)

The cloud droplet number concentration N is of major interest for numerical weather prediction (NWP) and global climate models (GCM). Together with the liquid water content (LWC) it determines the cloud droplet effective radius (reff) and therefore the cloud radiative properties. Especially the reflectivity of shallow trade-wind cumuli is shown to be highly sensitive with respect to N.

However, current satellite retrievals of N suffer from large uncertainties and strict assumptions about the adiabatic cloud profile. Estimated global statistics of N are averaged over different thermodynamic conditions masking the effect of N, reff and LWC on the radiative budget of the cloud. In-situ measurements of N are often limited by flight time and sampled cloud profiles, not covering the natural variability. 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 global averaging and single cloud sampling.

Spectral solar radiation measurements above shallow trade-wind cumuli were obtained in combination with passive microwave and active radar and lidar observations in August 2016 during the Next Generation Remote Sensing for Validation Studies (NARVAL-II) campaign with the High Altitude and Long Range Research Aircraft (HALO) of the German Aerospace Center (DLR).

The common approaches to estimate N is extended by including additional information from passive and active remote sensing. Therefore, combined measurements and retrievals of cloud optical thickness, liquid water path, cloud droplet effective radius, as well as cloud base and cloud top altitude retrieved from the different instruments were used. Different combinations of parameters are applied in the calculations to compare the benefit of each parameter. Two cloud cases are selected to illustrate the potential and limitations of this approach to estimate N from airborne remote sensing. A sensitivity study was performed to estimate retrieval uncertainties.

For the case study of homogeneous, non-precipitating shallow trade-wind cumuli reasonable values of N are obtained. By combining passive microwave and spectral solar radiation measurements the influence of sun-glint and scattering due to aerosol particles could be minimized. However, for heterogeneous and precipitating clouds the remaining assumptions of the N retrieval are violated. Non-realistic values of N are obtained, which indicates the limits of the method.

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