Most of the satellite imagers contributing to global cloud records, such as the Moderate Resolution Imaging Spectroradiometer (MODIS) and the Visible Infrared Imaging Radiometer Suite (VIIRS), combine infrared channels along with visible and near-infrared channels to derive cloud properties. A future imager, the Ocean Color Instrument (OCI) of the Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) mission will also generate global cloud properties but it will not have infrared channels. Instead, OCI will have two channels in the 2 μm window (vs. single channels for MODIS and VIIRS) where water absorption is relatively strong and particle size retrievals are most sensitive.
In this work, we quantitatively assess the probability of correct cloud thermodynamic phase discrimination using shortwave-only channels specific to the MODIS, VIIRS, and future PACE/OCI imagers and evaluate whether two channels near 2 μm provide more information about phase than a single channel. To achieve our goal, we apply the GEneralized Nonlinear Retrieval Analysis (GENRA) approach, which utilizes the Shannon information content and maximum posteriori estimate as diagnostics to quantify the information given a measurement with its associated uncertainty and a set of simulated solutions from cloud radiation models with their own set of associated uncertainties. Then, by enhancing the GENRA capabilities to mutual and conditional information content metrics, we quantify how, and to what degree, cloud radiation models show a given reflectance measurement providing information about both cloud optical thickness and effective particle size. The combination of Shannon, conditional and mutual information contents offer future directions for quantifying the information in a measurement from single or multiple platforms and for visualizing dependencies between physical parameters such as cloud or aerosol optical properties.