Homogenization of Geostationary Infrared Imager Channels for Cold Cloud Studies Using Megha-Tropiques/ScaRaB

Abstract

Infrared (IR) observations from the fleet of multiagencies meteorological geostationary satellites have a great potential to support scientific and operational investigations at a quasi-global scale. In particular, such a data record, defined as the GEOring data set, is well suited to document the tropical convective systems life cycles by applying cloud tracking algorithms. Yet, this GEOring data set is far from being homogeneous, preventing the realization of its potential. A number of sources of inhomogeneities are identified ranging from spatiotemporal resolutions to spectral characteristics of the IR channels and calibration methodologies. While previous efforts have attempted to correct such issues, the adjustment of the cold part of the IR spectrum remains unfit for cold cloud studies. Here, a processing method is introduced to minimize the inhomogeneities against a reference observational data set from the Scanner for Radiation Budget (ScaRaB) instrument onboard the Megha-Tropiques satellite. The method relies on the collocations between the geostationary observations and the reference. The techniques exhibit significant sensitivity to the selection of the relevant pairs of observations requiring a dedicated filtering of the data. A second effort is then proposed to account for the limb-darkening effect and a method is developed to correct the brightness temperature (BT) dependence on the geostationary viewing zenith angle (VZA). Overall, results show a residual after the processing of 0 K between any of the geostationary data and the ScaRaB reference. The final calibrated and limb-adjusted IR observations are then homogeneous for cold BT lower than 240 K with a standard deviation lower than 1.5 K throughout the GEOring.