## 1 Introduction
The negative correlation may result from the mechanism that an increase of CDNC with subsequent reduction in cloud droplet size can enhance evaporation just below cloud base. In this scenario, the cloud decouples from the boundary layer in warmer locations, decreasing the supply of water to the cloud from the surface, thereby reducing the cloud liquid water. While such statistical studies have provided some useful information, poor temporal statistics make it particularly difficult to establish underlying causal mechanisms. One possible approach maybe the use of suitable multispectral imagers on geostationary platforms (e.g., GOES) for simultaneous cloud/aerosol retrieval. One clear advantage is that characterization of the cloud diurnal cycle is potentially feasible with geostationary satellite instruments. In particular, the availability of additional spectral channels at 1.6, 3.9, and 12 or 13.3 \(\upmu\)m in addition to the original VIS (0.65 \(\upmu\)m) and IR (10.8 \(\upmu\)m) channels have led to the operational retrieval of optical depth, effective radius and LWP and motivates the possibility of following the cloud through its lifecycle as it is transported in polluted areas [7]. However, even in this case, a large number of issues degrading the observations can be expected. For example, the poor spatial resolution of the satellite observations \(\sim\)4 km are significantly above the optimal length scales attributed to the aerosol/cloud processed under investigation. In particular, extensive investigations [8,9] based on the analysis of ground based/_in situ_ observations on the appropriate scales indicate the need for spatial sampling of the processes \(<\)1 km. Based on realistic advection velocities of 5 \(\mathrm{m}\cdot\mathrm{s}^{-1}\), temporal sampling averaging should be constrained to \(\sim\)3 min which is reachable with our MFRSR/MWR/lidar approach. In addition, an equally difficult issue is to retrieve the true aerosol loading near the cloud base. In particular, it is quite evident that column measurements of aerosol optical depth do not provide the vertical information needed to quantify the pollution level at the cloud base. Furthermore, the relationship between optical depth and particle number depends on the microphysical distribution models and does not discriminate between aerosol types below cloud base that impact cloud micro-physics and aerosol particles located higher than cloud top that only interact with the radiative transfer above clouds. Therefore, it is clearly important to develop short-term measurements with high temporal resolution to eliminate many of the previously cited ambiguities. In particular, under such an approach, simultaneous measurements of clouds and aerosol loading below cloud base along with important vertical updraft data are achievable. Most importantly, within the measurement cycle, the exact nature of the cloud within the cloud lifecycle including general meso-scale processes is less important for these short-term measurements. Various efforts have been made to develop retrieval algorithms to infer COD and R\({}_{\text{eff}}\) from ground based passive radiometric measurements. In one approach, a Microwave Radiometer (MWR) is combined with Millimeter-wavelength Cloud Radar (MMCR) to infer droplet diameters. In this case, the radar reflectivity and the MWR LWP provide the information to retrieve R\({}_{\text{eff}}\)[10]. However, the cost of the MMCR can be quite substantial. On the other hand, significant success in combining COD from the Multi-filter Rotating Shadowband Radiometer (MFRSR) together with LWP from MWR is a reasonable cost-effective solution [11; 12; 13]. One drawback in this method is obviously the limitation that observations must be made during daytime. However, significant aerosol-cloud interaction signals should exist during daytime especially under conditions of strong convective heating which enhances vertical uptake [14].