The cloud properties algorithm used for the GCOM-C/SGLI is named the Comprehensive Analysis System for Cloud Optical Measurement (CAPCOM). The algorithm is an enhanced version of the solar reflectance method algorithm initially used for the NOAA AVHRR data analysis.17 It is adjusted to the specific spectral channel characteristics and response functions of the GCOM-C/SGLI. During the prelaunch phase of this satellite mission, areal tests are conducted on various algorithms (among which, the cloud properties’ algorithm), to evaluate their performance. Based on these tests, the capacity of the algorithms to produce quality data that fulfill the satellite target accuracies is assessed. The input data for the implementation of this algorithm are radiances and geometry data of the ADEOS-II/GLI satellite (precursor of the GCOM-C/SGLI). The theoretical basis of the algorithm2 and other related information are summarized below. The algorithm uses radiances of three channels: a nonabsorption band (1.05 μm), an absorption band (2.21 μm preferentially used here compared with the 1.65 μm also available), and a thermal band (10.8 μm). It simultaneously retrieves the COT at 0.5-μm wavelength and the CLER using the combination of 1.05 and 2.21-μm wavelengths, as these bands primarily depend on the COT and the effective particle radius, respectively. The other input data needed for the retrievals are a cloud detection mask, ancillary data consisting of the surface albedo at the 1.05 μm and bands, the surface temperature, the atmospheric temperature, water vapor, and height profiles (from the Japan Meteorological Agency re-analysis data). The retrieval process starts with the determination of the CTT. This parameter is used to locate the position of the cloud prior to the application of atmospheric and surface corrections (using ancillary data from thermal and water vapor profiles and surface albedo) on nonabsorption and absorption channels. These correction data are then used for the final retrieval of the other parameters, among which are the COT, CLER, and CLWP. One of the specificities of this algorithm is the use for the entire retrieval process of three bands belonging to the same sensor component, i.e., the SGLI-IRS (this is supposed to help to avoid errors related to pixel-pixel mis-registration between channels of different sensor components), in contrast to MODIS where the channels used belong to at least two sensor components. As in most of the algorithms, such as those of the GCOM-C/SGLI or MODIS, geophysical properties (COT and CLER) retrievals are typically solved by comparing the measured reflectances with entries in a lookup table (LUT) and by searching for the combination of COT and CLER that gives the best fit.7 The first property to be retrieved in the GCOM-C/SGLI algorithm is the CTT. It is obtained through the infrared window method using atmospherically corrected brightness temperatures at 10.8 μm. For all the cloud properties examined in this article, four LUTs are used under a water cloud scheme for the retrievals: cloud-reflected radiance in bands 1.05 and 1.65 μm, transmissivities and reflectivity in bands 1.05 and 2.2 μm, respectively, and band 10.8-μm transmissivity. The grid system of the LUTs is composed of the equivalent water vapor amount of above cloud, the cloud layer, and below cloud, and then the cloud top height and cloud geometrical thickness, the satellite zenith, solar zenith, relative azimuth angles, and the simulated COT and CLER. To build these LUTs simulating the satellite signal, an efficient radiative transfer scheme18–20 is used. A log-normal size distribution, function of the particle radius with a mode radius related to the effective particle radius as , a log-standard deviation of the size distribution for marine stratocumulus clouds (), and a Lambert surface (underlying surface) are assumed for the computations. Based on the radiative transfer theory for parallel-plane layers with an underlying Lambert surface, the unexpected radiation components, such as the solar radiation reflected by the ground surface and the thermal radiation emitted from the cloud layer and the ground surface, are removed from the satellite-received radiance in order to decouple the radiation component reflected by the cloud layer. The solar radiation reflected by the ground is removed through the use of the ground albedo data. When the removed radiation significantly dominates over the signal, the algorithm’s main iteration may not converge. This mainly occurs at optically thin clouds. When that is the case, the analysis does not go further and is consequently cancelled. Multiple reflections between the ground surface and the upper layer are taken into consideration, though their effect may be negligible, especially for optically thin clouds and ground surfaces with low reflectance. But, with optically thick clouds and large values of ground albedo, the effect is relatively large at visible wavelengths.