## 8 Discussion  
The present study is focused on exploring the direct ground based remote sensing methods for detection and quantification of aerosol-cloud interaction. As discussed in detail, even though sophisticated multispectral imagers onboard polar (e.g., MODIS) and geostationary (e.g., GOES) satellites are available, the inability to simultaneously retrieve aerosols and cloud properties makes such techniques extremely difficult. Furthermore, we found that the performance of the GOES-VISST algorithm retrievals when compared to ground based retrievals [12] resulted in a very strong bias and poor correlation of effective radius (R\({}_{\text{eff}}\)) for \(\theta_{\text{sun}}>50\) degrees. The fact that cross-validations between MODIS and GOES were unbiased (Figure 4(b and d)) indicates that these issues are persistent across all platforms. This can be qualitatively explained by noting that for large observing angles, the effective optical depth increases and the photons interact mainly with the cloud top, which is usually has a higher R\({}_{\text{eff}}\) than the column averaged value. Motivated by these prevailing difficulties, this paper emphasizes the need for exploiting the capability of surface remote sensing which is crucial in avoiding the ambiguity of mesoscale phenomenon on aerosol/cloud interaction by suitable simultaneous probing of the interactions on short time scales (\(<\)1 min).  
In this context, we studied existing retrieval approaches [12, 13] that have been applied to the MFRSR-MWR combined system and developed a simple LUT based iteration approach adaptable under different atmospheric backgrounds for real-time monitoring of cloud retrievals. While the COD retrievals from our approach showed consistency with excellent correlation (r\({}^{2}\)\(\sim\) 0.99), the spread or inconsistency in the retrieved R\({}_{\text{eff}}\) was commensurate with LWP errors that can occur due to an inability to synchronize our LWP averaging procedure to those used internally by the [PERSON] and Harrison [12] approach.  
Once the consistency of our algorithm was demonstrated, a detailed sensitivity analysis for cloud droplet effective radius retrieval was carried out and illustrated using \"reference\" background conditions from the ARM SGP site to quantify the errors due to incorrect atmosphere/aerosol assumptions below the cloud layer, imprecise surface albedo as well as LWP and diffuse transmission uncertainties. In particular, we find that the convolved variability of both processes (atmosphere and surface albedo) was sufficiently small (\(\sim\)8%) to resolve possible ACI index trends.  
Unlike many studies that attempt to use the aerosol loading at the surface [8], we investigated the difficulties that may result when the cloud layer is sufficiently high. In particular, we re-explore an interesting case at SGP site following [PERSON] _et al._[18], wherein the existing Nephelometer measurements showed slightly positive correlations in contradiction to expected ACI trends. In particular, using Raman lidar extinction as proxy for aerosol concentration, we found that while the effective radius and surface extinction were basically uncorrelated with no trend, a general trend was observed by considering the aerosol extinction 150 m below cloud base. In this case, the regression slope resulted in an ACI \(\sim\)0.23, which is realistic and comparable to other cases [59].