Improving the Deep Convective Cloud Invariant Target Methodology to Radiometrically Scale Himawari-8 AHI to VIIRS

The NASA CERES project provides the climate community with global regional flux and cloud products designed for long-term climate monitoring and validation. CERES utilizes geostationary (GEO) imager-derived broadband fluxes in order to approximate the fluxes in between CERES observations. The GEO imagers are inter-calibrated with MODIS and VIIRS to place the GEO imagers on the same radiometric scale as well as to remove any calibration drifts. This is accomplished using all-sky tropical ocean VIIRS and GEO time and ray-matched radiance pairs to provide monthly GEO calibration gain adjustments. Tropical deep convective clouds (DCC) are used as invariant targets as DCC are among the brightest and most Lambertian Earth targets near the tropopause. DCC targets can also be used to intercalibrate VIIRS and GEO imagers by utilizing DCC ray-matched pairs as well as collectively characterizing the DCC invariant (DCC-IT) targets. The focus of this effort is to improve the DCC-IT methodology. The DCC-IT methodology is a large ensemble statistical approach that relies on the monthly consistency of probability density functions (PDF) of DCC pixel radiances. The VIIRS and GEO PDFs should also be similar and allow the GEOs to be radiometrically scaled with VIIRS. Several PDF statistics, BRDFs, and DCC pixel identification thresholds are tested to determine the optimal DCC-IT inter-calibration methodology. This study focuses on VIIRS and Himawari-8 AHI, where the DCC-IT inter-calibration methodology is validated against the DCC ray-matched calibration.