Abstract
Abstract. In situ measurements of cloud droplet number concentration N are limited by the sampled cloud volume. Satellite retrievals of N suffer from inherent uncertainties, spatial averaging, and retrieval problems arising from the commonly assumed strictly adiabatic vertical profiles of cloud properties. To improve retrievals of N it is suggested in this paper to use a synergetic combination of passive and active airborne remote sensing measurement, 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 technique to retrieve N is refined by including combined measurements and retrievals of cloud optical thickness τ, liquid water path (LWP), cloud droplet effective radius reff, and cloud base and top altitude. Three approaches are tested and applied to synthetic measurements and two cloud scenarios observed during NARVAL-II. Using the new combined retrieval technique, errors in N due to the adiabatic assumption have been reduced significantly.
Highlights
Clouds influence the Earth’s radiative energy budget by reflecting, absorbing, and emitting solar and terrestrial radiation. These effects are typically quantified by the cloud radiative forcing (CRF), which is defined by the difference between the net radiation in cloudy and cloud-free conditions
An important source of uncertainty of these models is caused by an insufficient representation of the first aerosol effect (Bony and Dufresne, 2005), which describes the correlation of the cloud droplet number concentration N and the cloud optical thickness τ or cloud-top reflectivity R, commonly known as the Twomey effect (Twomey, 1977)
Airborne remote sensing techniques are able to bridge the scale gap between in situ and satellite measurements, as they allow for the sampling of individual clouds under specific conditions and to cover a sufficiently large area to quantify the natural variability of N, reff, and liquid water content (LWC)
Summary
Clouds influence the Earth’s radiative energy budget by reflecting, absorbing, and emitting solar and terrestrial radiation. Based on passive remote sensing in the solar and terrestrial wavelength range, N is estimated by combining the results of bispectral retrievals of τ and reff with cloud-top temperature TCT by Brenguier et al (2000), Quaas et al (2006), and Zeng et al (2014) They assumed a vertically constant LWC and N throughout the cloud profile, which is at least for LWC not a realistic scenario. Airborne remote sensing techniques are able to bridge the scale gap between in situ and satellite measurements, as they allow for the sampling of individual clouds under specific conditions and to cover a sufficiently large area to quantify the natural variability of N , reff, and LWC. Resulting values of N are correlated with measured R and separated for different thermodynamic conditions (binned LWP) to demonstrate the possibility to obtain parameterizations for the Twomey effect
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