## 2 Assessment of Satellite-Based Remote Sensing for Cloud Property Retrievals
### Comparisons between Different Satellite Retrievals  
To obtain cloud property measurements (COD and R\({}_{\mathrm{eff}}\)) from satellites, the need for multi-wavelength imager measurements is critical. Over the past 10 years, significant improvements in multispectral satellite sensors onboard polar and geostationary orbiting satellites and the radiative transfer modeling of clouds have provided a mechanism for the retrieval of relevant cloud microphysical properties as well as estimates of aerosol column properties. In order to improve the climatology of satellite-derived cloud properties over the ARM SGP domain (32N-42N, 91W-105W), the Visible Infrared Solar-infrared Split Window Technique (VISST algorithm), developed by [PERSON] _et al._[24], is used. Although the latest version of the VISST algorithms is fairly complex, the essence of the VISST relies mainly on the thermal (12.0 \(\upmu\)m and 10.8 \(\upmu\)m) channels to determine cloud temperature and phase, the visible (0.65 \(\upmu\)m) reflectance to retrieve COD, and the solar-infrared (3.9 \(\upmu\)m) radiance to derive cloud particle size. To visualize this VISST algorithm performance, we show in Figure 1, the TOA reflectance measurement space as a function of COD and R\({}_{\mathrm{eff}}\). Clearly, the retrieval sensitivity for the cloud effective radius is nearly independent of the visible COD retrieval. Therefore, a reasonably accurate calibration of the 3.9 \(\upmu\)m should result in quite good retrievals of the R\({}_{\mathrm{eff}}\) as long as the cloud parameterization is reasonably accurate. The GOES radiances are calibrated with collocated measurements from the TRMM (Tropical Rainfall Measuring Mission Visible Infrared Scanner) [25]. Based on various size distributions of water droplets and hexagonal ice crystal columns, radiances are modeled in VISST to simulate liquid and ice clouds, respectively [26]. The resultant VISST algorithm provides cloud optical depth, phase, effective particle size, ice or liquid water path, effective radiating temperature, and effective cloud height (determined from the effective cloud temperature using a vertical profile of temperature for a particular location). The pixel level data from GOES-WEST (G10/G11) & GOES-EAST (G12/G13) over ARM SGP domain were obtained in netcdf format from the archives of the ARM site. In a fairly similar multispectral approach, the MODIS (Moderate Resolution Imaging Spectroradiometer) Level 2, Collection 5 retrievals [27] are provided at 1 km \(\times\) 1 km spatial resolution at nadir by combining infrared and visible techniques to determine physical and radiative properties. Using the MODIS visible (0.645 \(\upmu\)m) and near-infrared (1.64, 2.13, and 3.75 \(\upmu\)m) spectral bands, the daytime shortwave cloud retrieval algorithm over land surfaces will provide the cloud optical thickness and effective particle radius. To retrieve cloud optical thickness and effective particle radius, a radiative transfer model is first used to compute the reflected intensity field (radiance). This radiance, I\({}_{\lambda}\)(0,-\(\upmu\),\(\phi\)), is normalized in terms of the incident solar flux, F\({}_{0}\)(\(\lambda\)), such that the reflection function, R\({}_{\lambda}\)(\(\tau\),R\({}_{\mathrm{eff}}\);\(\upmu\),\(\upmu\)\({}_{0}\),\(\phi\)), is given by,  
\[R_{\lambda}(\tau,R_{\mathrm{eff}};\mu,\mu_{0},\phi)=\frac{\pi\mu_{\lambda}(0,- \mu,\phi)}{\mu_{0}F_{0}(\lambda)} \tag{2}\]  
where \(\tau\) is the total optical thickness of the atmosphere (or cloud), R\({}_{\mathrm{eff}}\) the effective particle radius as defined in Hansen and Travis [28]. The differences in reflected solar radiation between the 0.645 \(\upmu\)m and 1.64 \(\upmu\)m bands contain information regarding cloud particle phase due to distinct differences in bulk absorption characteristics between water and ice at the longer wavelength.